Résumé

Restitutionde l'émissivité de surface terrestre à partir de données AMSR-E et

SEVIRI/MSG2

Le climat de la Terre est déterminé principalement par la façon dont l'énergie est répartie entre les océans, la surface terrestre et l'atmosphère. Dans ces trois composantes principales, la surface de la Terre a un intérêt particulier en raison de sa relation directe avec les activités humaines.

La couverture terrestre de la Terre correspond environ à 30 % de la surface totale. Elle se compose de sols nus, de végétation, de neige, des glaciers, des eaux intérieures, des montagnes, des canopées urbaines, etc. Les processus en jeu à la surface de la Terre sont principalement dus aux échanges de chaleur, d'eau, de CO 2 entre la surface terrestre et l'atmosphère et constituent la partie principale des études sur le changement climatique. Ainsi, le changement climatique est du aux échanges d'eau et d'énergie entre la surface et l'atmosphère. L'eau, et plus concrètement l'humidité du sol, influencent fortement la répartition de l'énergie solaire incidente sur la surface de la Terre. On trouve le flux de chaleur latente, le flux de chaleur sensible et le flux de chaleur au sol. Le flux de chaleur latente (évapotranspiration), avec les précipitations, les changements d'humidité du sol et le ruissellement de surface, forment le bilan hydrique. La végétation affecte à la fois le bilan hydrique et énergétique et ce de plusieurs façons. En protégeant le sol du rayonnement solaire direct, la végétation limite l'énergie disponible par le flux de chaleur latente à partir des couches de surface. En même temps, la végétation extrait de l'eau de la Terre par ces racines pour sa propre alimentation et l'expulse ensuite dans l'atmosphère.

La couverture de la Terre est très variable sur une gamme d'échelles spatiales et temporelles. Cette grande hétérogénéité est un défi pour les études de validation des modèles globales météorologiques et climatiques. Les mesures in situ de ses caractéristiques ne sont en général pas représentatives d'une région plus large, ce qui rend nécessaire la mise au point d'un réseau dense de mesures reparties sur toute la surface terrestre. La création d'un tel réseau à l'échelle continentale n'est malheureusement pas réaliste. La seule solution pour acquérir des observations à l'échelle mondiale et sur le long terme de ces paramètres est la télédétection [START_REF] Entekhabi | An agenda for land surface hydrology research and a call for the second international hydrological decade[END_REF].

L'émissivité de la surface de la Terre (LSE) est l'un des paramètres de surface les plus importants. Elle traduit la capacité de la surface à émettre un rayonnement. La LSE est d'une importance considérable pour de nombreuses applications telles que les études sur l'érosion des sols, les estimations des quantités et des changements de la couverture végétale, la cartographie du substratum rocheux et l'exploration des ressources [START_REF] Gillespie | A temperature and emissivity separation algorithm for ASTER images[END_REF].

D'un autre côté, elle est aussi le paramètre clé pour déterminer l'humidité du sol et la température de la surface terrestre (LST) à partir d'études radiométriques [START_REF] Kerr | Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) Mission[END_REF][START_REF] Owe | A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index[END_REF][START_REF] Njoku | Soil moisture retrieval from AMSR-E[END_REF][START_REF] Verstraeten | Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests[END_REF].

Considérant la progressive prise de conscience de l'importance de la LSE, l'intérêt pour l'estimation de celle-ci à partir de données de télédétection avec le développement de nouvelles techniques est aussi croissant, rendant possible la détermination et le suivi de la LSE à grande échelle. Dans le cas d'une estimation précise de la LSE à partir de données de télédétection, de nombreuses études ont été réalisées avec des méthodes différentes [START_REF] Sobrino | A comparative study of land surface emissivity retrieval from NOAA data[END_REF][START_REF] Sobrino | A simple algorithm to estimate evapotranspiration from DAIS data: Application to the DAISEX Campaigns[END_REF]. Dans ces procédés, les méthodes de détermination de la LSE à partir des données infrarouge thermique (TIR) sont relativement matures et largement répandus. De la fin des années 1970, il y a eu de nombreuses études qui ont porté sur l'estimation de la LSE en utilisant la télédétection dans le domaine TIR. L'infrarouge thermique offre une des possibilités pour déterminer simultanément la LSE et la LST, à partir du rayonnement thermique émis par la surface de la Terre. Si les conditions atmosphériques sont favorables, il est possible de déterminer exactement LES et LST.

Comme les autres capteurs en télédétection optique, la télédétection TIR ne peut pas acquérir d'informations dans des conditions nuageuses. Par conséquent elle ne peut pas être utilisée pour produire les LSE et LST quand il y a de nuages. Une analyse de la NASA sur les produits LST, révèle que le résultat final a été affecté par les nuages pour plus de 60 % des régions étudiées, ce qui signifie que la télédétection dans l'infrarouge thermique comporte des limites importantes dans la pratique. D'un autre côté, des méthodes existent utilisant les données micro-ondes (MW) pour estimer la LST et sont très faiblement influencées par les effets atmosphériques comme les nuages, et donc peuvent compléter les résultats obtenus dans l'infrarouge thermique. La télédétection dans la bande des micro-ondes (MW) combinée avec le domaine TIR offre donc une capacité unique de détecter les paramètres de la surface terrestre dans presque toutes les conditions météorologiques v Bien que les données MW sont exploitées depuis de nombreuses années, des études ont mis l'accent sur les problèmes d'estimation de la LSE dans cette bande parce que: (1) il est difficile d'interpréter physiquement la LSE d'une surface hétérogène à faible résolution spatiale, (2) l'impact de l'humidité du sol et la rugosité de surface sur le LSE est très important,

(3) comme dans le domaine TIR , la LST et le LSE sont difficiles à séparer, les deux grandeurs étant combinées dans le signal détecté, (4) bien que les données MW soient moins perturbées par l'atmosphère et les nuages que dans le domaine TIR, les effets atmosphériques doivent cependant encore être corrigés pour obtenir la LSE de façon précise, (5) Les problèmes rencontrés sont de différents ordres :

Tout d'abord, la détermination de l'émissivité et la température de surface (LSE et LST) est un problème mathématiquement sans solution, même pour des mesures en surface.

L'équation de transfert radiatif montre que le rayonnement émis à partir de la surface dans le domaine de l'infrarouge est une fonction de sa température et de l'émissivité. Si le rayonnement est mesuré à N longueurs d'onde, il y aura toujours N+1 inconnues, soit N émissivités correspondant à chaque longueur d'onde et une température de surface. La solution de ce système d'équations sous-déterminé décrits par les radiances mesurées est la principale difficulté dans la détermination simultanée de LST et LSE. Le couplage des deux grandeurs de surface (émissivité et température) dans la radiance mesurée, oblige un calcul précis de l'émissivité pour déterminer la température et vice-versa [START_REF] Li | Feasibility of land surface temperature and emissivity determination from AVHRR data[END_REF][START_REF] Becker | Surface temperature and emissivity at various scales: Definition, measurement and related problems[END_REF][START_REF] Li | A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data[END_REF].

Deuxièmement, l'absorption et l'émission propre de l'atmosphère, et la réflexion de la surface terrestre, compliquent davantage le problème de détermination de l'émissivité et la température de surface. L'échange d'énergie entre la surface terrestre et l'atmosphère est toujours combiné dans la bande spectrale, en particulier dans la bande spectrale TIR. D'une part, l'émissivité inférieure à l'unité (l'émissivité est égale à l'unité uniquement dans le cas idéal d'un corps noir) entraîne une réduction de la radiance émise par la surface, d'autre part, l'atmosphère reflète une partie du rayonnement atmosphérique descendant propre de l'atmosphère en le renvoyant dans l'atmosphère.

L'anisotropie de la réflectivité et l'émissivité peut réduire ou accroître le rayonnement total de la surface [START_REF] Prata | Land surface temperature derived from the Advanced Very High Resolution Radiometer and the Along-Track Scanning Radiometer 1[END_REF]. En outre, le rayonnement de surface mesuré par le satellite, est atténué par l'atmosphère avant d'atteindre le capteur. Considérant le rayonnement atmosphérique montant, l'atmosphère peut aussi avoir des effets indéterminés sur le rayonnement de surface initial. De toute évidence, l'effet combiné de l'émissivité inférieure à l'unité et des effets de l'atmosphère rend difficile l'obtention des LST et LSE à partir des mesures au niveau de l'atmosphère (TOA « Top Of Atmosphere ») (Dashet al, 2005). De ce point de vue, la LSE peut être considérée comme une variable cruciale qui doit être connue avant de corriger la radiance de surface des effets atmosphériques et de récupérer la LST. En outre, elle est le point clé pour résoudre le problème de couplage entre la surface de la terre et l'atmosphère. En plus, pour la région MW, les capteurs actuels ne sont pas encore très performants, et rendent les valeurs de température de brillances incertaines. En particulier dans la région de basse fréquence, il existe un grand écart de température de brillance due à l'interférence de radiofréquence. Néanmoins, la contribution atmosphérique est faible dans cette région de basse fréquence, et donc la plupart des modèles utilisent les données de basse fréquence, ce qui augmente la fiabilité des résultats. Ignorer la correction atmosphérique est généralement une autre raison pour réduire la précision en la détermination de l'émissivité à partir de données satellite et l'ajout d'une correction de ces effets peut résoudre ce problème.

Troisièmement, en raison de la complexité de la simulation de l'émissivité de la surface qui est affecté par la végétation, la topographie, les inondations, la neige et d'autres facteurs, les méthodes pour d'obtention la LST à partir des données passives MW doivent prendre en compte une variabilité spatiale et temporelle très importante, et par conséquent, l'étude de la surface de la Terre et l'inversion des paramètres atmosphériques en utilisant les données MW est encore très limitée.

Considérant les questions et les problèmes identifiés ci-dessus et à travers une étude complète des méthodes régionales d'estimation de l'émissivité à partir de données de télédétection existants, ce travail porte sur le développement d'une méthodologie permettant de déterminer l'émissivité à l'échelle régionale à partir des données TIR et des données passives MW à bord de satellites en orbite polaire. vii Ce mémoire est composé de six chapitres.

Dans le premier chapitre, après l'introduction, l'état de l'art sur les méthodes d'estimation de la LSE régionale à partir de données satellites dans les deux régions spectrales d'étude (MW et TIR) est présenté. Trois différentes types de méthodes existent pour estimer la LSE à partir des données TIR : (1) les méthodes semi-empiriques, (2) les méthodes de séparation émissivité/température multicanal et (3) les méthodes basées sur des hypothèses physiques.

Pour les études utilisant les données MW, les principales méthodes dédiées à l'estimation du LSE sont: (1) méthodes que utilisent des hypothèses physiques, (2) méthodes empiriques et (3) méthodes qui utilisent des réseaux neuronaux.

Dans le chapitre 2 les principes fondamentaux de la physique utilisés dans la détermination de la LSE à partir de données qui proviennent d'un satellite sont rappelés. Cela comprend les concepts de base des processus, la théorie de base de la télédétection pour la région des micro-ondes et pour l'infrarouge thermique (TIR), la diffusion de surface et les propriétés radiatives, l'absorption atmosphérique et les mécanismes de diffusion.

L'équation de transfert radiatif dans un cas général, s'écrit :

Cette équation peut être réécrite en termes de température de brillance comme:

La contribution de la surface à la température de brillance totale est : Où est la température de brillance au niveau du satellite; θ v est l'angle zénithale;

est la radiance spectrale totale mesurée à niveau du sol; est la température de brillance à niveau du sol; est la transmission totale de l'atmosphère; et

( ) ( ) ( ) ( ) ( ) , , , , , v G v v v v atm s R R t R R
q l q l q l q l q l --

= + + ( ) ( ) ( ) , , ( , , ) , , , ,
B S v B G v v v atm B T B T t R
q l q l q l q l -= + ( ) ( )

( ) ( ) ( ) ( ) ( ) ( ) ( )
q l q l e q l l e q l l l r q q j l q l Le Chapitre 4 est centré sur l'estimation du LSE à partir de données micro-ondes passives.

Notre premier objectif est de construire un modèle de transfert radiatif allant de la surface à l'atmosphère. Dans cette partie le modèle AIEM (Advanced Integral Equation Model) a été utilisé pour calculer l'émissivité de la surface de la Terre. Généralement, le modèle AIEM ( )

, v s R q l - ( ) atm R l ¯( ) s R l ( ) , , , b v s
r q q j l ( ) En s'appuyant sur l'équation de transfert radiatif, une méthode a été proposée pour directement extraire l'émissivité de la surface à partir des données du capteur AMSR-E et MODIS, supposant l'atmosphère totalement caractérisée.

,
où les indices p et f représentent l'état de polarisation (horizontale ou verticale) et la fréquence.

T sp est une constante que représente la température équivalente de l'espace, (T sp = 2.7 K.).

Dans cette étude, la LSE a été estimée à partir de la T B (température de brillance à niveau du capteur), et de f et p, et la LST (T s ) estimée à partir des données MODIS et les profils atmosphériques fournis par ECMWF utilisés pour estimer les quantités atmosphériques (t f , T au,f , T ad,f ) avec le logiciel de transfert radiatif MonoRTM.

Un autre objectif de ce chapitre est d'analyser les effets atmosphériques sur des données

AMSR-E et de proposer ensuite un algorithme de correction atmosphérique à 18,7 GHz en polarisation verticale, pour la restitution de LST. Dans cette section, une base de données provenant de simulations couvrant diverses conditions atmosphériques et surfaces a été

List of Figures:

Figure 2.1 Geometry of incident and scattered radiation (Ulaby et al., 1982) ......................... Figure 2.2 Radiative transfer at an wavelength λ (μm), [1] is the emission emitted by the surface; [2] is the space equivalent emission reflected by the surface; [3] is the downwelling atmospheric emission reflected by the surface; [4] is part of direct solar irradiance reaches the surface, and is reflected by the surface; [5] is the surface emission, the reflected atmospheric downwelling radiance and the reflected direct solar irradiance upwards penetrate the atmosphere and part of them reach the sensor; [START_REF] Conclusions…… | References[END_REF] is the upwelling atmospheric emission . 4.6 Relationships between vertical and horizontal polarized emissivities at each frequency (6.925GHz,10.5GHz,18.7GHz,23.8GHz,36.5GHz and 89.0GHz) ............ 4.8 The atmospheric upwelling radiation (T au ) and the difference of upwelling and downwelling radiation (T ad -T au ) for six frequencies (6.925GHz,10.5GHz,18.7GHz,23.8GHz,36.5GHz and 89.0GHz

Chapter 1 Introduction

The Earth's natural climate is mainly determined by the way energy is distributed between the ocean, the land surface, and the atmosphere. In these three main components, the land surface is of particular interest because of its direct and local impact on human activities.

Land surface covers about 30% of the Earth's surface, it consists of soil, vegetation, snow, glaciers, inland water, mountains, and much more. Land surface process, in principal, refers to the exchanges of heat, water, CO 2 , and other trace constituents at the interface of land surface and atmospere, it is the center of climate change studies. The climate change is the coupled water and energy balance within the land-atmosphere continuum. Water, particularly surface soil moisture, strongly influences the partitioning of the incoming solar energy at land surface into latent, sensible and ground heat fluxes. Latent heat flux, together with precipitation, soil moisture change, surface and subsuface runoff makes up the water balance. Vegetation affects both water and energy balances in several ways. By shielding the soil from direct solar radiation, vegetation limits the energy available for the latent heat flux from the surface layers.

At the same time, the vegetation extracts water from the whole root zone and transpires it into the atmosphere (Chahine, 1992;[START_REF] Njoku | Soil moisture retrieval from AMSR-E[END_REF]Weng et al., 2004).

The land surface is also most variable over a broad range of temporal and spatial scales.

This large heterogeneity is a challenge for validation studies of global meteorological and climate models. In situ measurements of its characterizations are generally not representative for a wider region, making dense long-term measurement networks necessary. Creating such a network on a continental scale is, unfortunately, not feasible. The only realistic way to acquire long-term global observations of the key surface parameters is by means of remote sensing (e.g. [START_REF] Entekhabi | An agenda for land surface hydrology research and a call for the second international hydrological decade[END_REF]. Land surface emissivity (LSE) is one of the most important surface parameters, it is the relative ability of the radiation emitted by the land surface. LSE is of considerable importance for many applications such as soil development and erosion studies, estimating amounts and changes in spare vegetation cover, bedrock mapping and resource exploration [START_REF] Gillespie | A temperature and emissivity separation algorithm for ASTER images[END_REF]. In addition, it is the key parameter to retrieve soil moisture and Land Surface Temperature (LST) from radiometry (e.g. [START_REF] Kerr | Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) Mission[END_REF][START_REF] Owe | A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index[END_REF][START_REF] Njoku | Soil moisture retrieval from AMSR-E[END_REF][START_REF] Verstraeten | Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests[END_REF].

As the gradual awareness of the importance of the 1.1 Overview of LSE retrieval methods from TIR data in the past decades LSE, as an intrinsic property of natural materials, is often regarded as an indicator of material composition, especially for the silicate minerals, although it varies with viewing angle and surface roughness [START_REF] Sobrino | A comparative study of land surface emissivity retrieval from NOAA data[END_REF][START_REF] Sobrino | A simple algorithm to estimate evapotranspiration from DAIS data: Application to the DAISEX Campaigns[END_REF]. Being so important, the methodology which produces LSE from remotely sensed data has been developed for many decades.

Semi-empirical methods

This type of methods estimates the LSE from the semi-empirical classification-based look-up table or the statistical relationship between the normalised difference vegetation indexes (NDVI) derived from the VNIR bands and LSE in the TIR bands. The representative methods are the classification-based emissivity method (Snyder et al., 1998a;[START_REF] Sun | Estimation of land surface temperature from Geostationary Operational Environmental Satellite (GOES-8)[END_REF][START_REF] Peres | Emissivity maps to retrieve land surface temperature from MSG/SEVIRI[END_REF] and the NDVI-based emissivity method (Van de Griend and [START_REF] Van De Griend | On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces[END_REF][START_REF] Valor | Mapping land surface emissivity from NDVI: application to European, African and South American areas[END_REF][START_REF] Sobrino | Toward remote sensing methods for land cover dynamic monitoring. Application to Morocco[END_REF].

(1) Classification-based emissivity method (CBEM). Generally, the CBEM is based on the use of conventional land-cover classification information. The key point of this method is to properly classify the land surface and then to assign the emissivity from classification-based look-up tables. Theoretically, the CBEM can produce accurate LSE products over the area in which land covers are accurately classified and each class has well-known emissivities [START_REF] Gillespie | Portfolio Assessment: Some questions, some answers, some recommendations[END_REF]. However, Snyder et al. (1998a) pointed out several major difficulties in using CBEM, such as the determination of surface wetness, the identification of senescent vegetation and the uncertainty of the dynamics of snow and ice surface states. The superiority of CBEM are: 1) simplicity; 2) accuracy for the pixels with classes having well known emissivities; 3) accurate atmospheric correction is not required; 4) emissivity can be obtained at the same spatial resolution as that of visible and near infrared data; 5) no requirement of the TIR bands. In another hand, the limitations and disadvantages are: 1) require the a prior knowledge of emissivity database for classes, as well as the corresponding classification map; 2) depend on classification accuracy; 3) seasonal and dynamic states, such as the surface wetness, senescent vegetation and the uncertainty of dynamics of snow and ice, may degrade the accuracy; 4) less accurate for coarse resolution and less reliable for the classes with contrast emissivities, e.g., geologic substrates; 5) displays discontinuities.

(2) NDVI based emissivity method (NBEM). Within the framework of the project where a and b are constants derived from regression analysis. Although this method is a potentially powerful tool to estimate the LSE at a pixel scale directly from space because NDVI can be easily derived from the reflectance in VNIR bands, the relationship has been proven to be quite dependent on the area studied, and the coefficients a and b obtained for one site cannot be applied to other sites (Van de Griend and [START_REF] Van De Griend | On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces[END_REF]. Inspired from the work of Van de Griend and [START_REF] Van De Griend | On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces[END_REF], using the NDVI as a connection to describe the 'cavity effect', [START_REF] Valor | Mapping land surface emissivity from NDVI: application to European, African and South American areas[END_REF] proposed a method derived from the model of [START_REF] Caselles | Determination of frosts in orange groves from NOAA-9 AVHRR data[END_REF], to estimate the effective LSE for a row-distributed rough system.

Considering the complexity of method proposed by [START_REF] Valor | Mapping land surface emissivity from NDVI: application to European, African and South American areas[END_REF], [START_REF] Sobrino | Toward remote sensing methods for land cover dynamic monitoring. Application to Morocco[END_REF] further developed an operational NDVI threshold method to derive the LSE from space using the following criteria and formulae: e are the vegetation and soil emissivities in channel i, respectively. Both of them can be measured in the field [START_REF] Rubio | Emissivity measurements of several soils and vegetation types in the 8-14 um waveband: analysis of two field methods[END_REF][START_REF] Rubio | Thermal infrared emissivities of natural surfaces: Improvements on the experimental set-up and new measurements[END_REF] or obtained from an emissivity database [START_REF] Baldridge | The ASTER Spectral Library version 2.0[END_REF]). P v is the fraction of vegetation that can be derived either from the NDVI [START_REF] Valor | Mapping land surface emissivity from NDVI: application to European, African and South American areas[END_REF][START_REF] Carlson | On the relation between NDVI, fractional vegetation cover, and leaf area index[END_REF][START_REF] Sobrino | Toward remote sensing methods for land cover dynamic monitoring. Application to Morocco[END_REF] or from the variable atmospherically resistant index (VARIgreen) and spectral-mixture analysis techniques [START_REF] Sobrino | NOAA-AVHRR orbital drift correction from solar zenithal angle data[END_REF], dε ,i is the mean cavity effect and can take values of 0.02 or higher from numerical simulation [START_REF] Valor | Mapping land surface emissivity from NDVI: application to European, African and South American areas[END_REF] and NDVI v is the full vegetation NDVI. NDVI s and NDVI v can be estimated from the histogram for the entire scene [START_REF] Dash | Separating surface emissivity and temperature using two-channel spectral indices emissivity composites comparison with a vegetation method[END_REF][START_REF] Sobrino | NOAA-AVHRR orbital drift correction from solar zenithal angle data[END_REF].

, , , (1 )
i i red s i v i v s i v i s v v i i v a b
Because of its simplicity, the NDVI threshold method has already been successfully applied to various sensors. However, the main problem with this method is the lack of continuity for emissivity values at NDVI = NDVI s and NDVI = NDVI v because they are calculated using different functions [START_REF] Sobrino | NOAA-AVHRR orbital drift correction from solar zenithal angle data[END_REF]. In addition, the NDVI threshold method can only provide acceptable results in the 10-12 μm interval bands because the relationship between the emissivity and reflectivity for bare soil samples does not provide satisfactory results in the 8-9.5 μm domain for some soil types.

Multi-channel temperature/emissivity separation methods

The multi-channel temperature emissivity separation methods referred to a group of algorithms that retrieve the LSTs and LSEs from the at-surface radiances. After introducing some reasonable assumptions or constraints, these methods retrieve the LSEs directly from the emitted radiance.

(1) Emissivity spectrum feature-based methods. This type of methods determines the emissivity from the characteristics of emissivity spectra. The emissivity can be derived using several methods: by assuming that the emissivity at a channel is time-invariable, e.g., the two-temperature method which can directly estimate the spectral emissivity without any assumption about the spectral shape, but it is sensitive to the noise [START_REF] Watson | Two-temperature method for measuring emissivity[END_REF]; by assuming that emissivity has a flat spectrum at specific wavelengths, e.g., the gray-body emissivity method which is thought to be more applicable for hyperspectral TIR data [START_REF] Barducci | Temperature and emissivity retrieval from remotely sensed images using the "grey body emissivity" method[END_REF]; by assuming that the emissivity spectrum is smooth, e.g., the iterative spectrally smooth temperature emissivity separation method, which is proposed to estimate LST and LSE from hyperspectral data [START_REF] Borel | Iterative retrieval of surface emissivity and temperature for a hyperspectral sensor[END_REF]1998); or by applying a priori knowledge about the emissivity distribution range, e.g., the emissivity bounds method (Jaggiet al. 1992).

(2) Reference channel method (RCM). The RCM was first developed by [START_REF] Kahle | Middle infrared multispectral aircraft scanner data analysis for geological applications[END_REF]. Supposing the LSE of channel i have been known, the LST can be derived for each pixel from the measured radiance in reference channel with known emissivity after atmospheric correction. This LST is then used to derive emissivity values for the remaining channels.

The RCM is the simplest method for the emissivity retrieval from space, and it suffers from some limitations. First, it is difficult to find a unique emissivity value that is appropriate for all surface materials in one reference channel. [START_REF] Gillespie | Portfolio Assessment: Some questions, some answers, some recommendations[END_REF][START_REF] Li | Channel selection for soil spectrum reconstruction in 8-13 um region[END_REF]. Second, there is no emissivity spatial information in reference channel. Third, the emissivities derived for adjacent channels are significantly affected by the constant value of emissivity in this channel and appear to be very noisy [START_REF] Hook | A comparison of techniques for extracting emissivity information from thermal infrared data for geologic studies[END_REF]. The superiority of the method RCM is simplicity, and the simultaneous retrieval of surface temperature and emissivity for the remaining channels. The limitations and disadvantages are: 1) require accurate atmospheric corrections in TIR channels; 2) inappropriate to assign a unique value for all the surface materials in a specific channel; 3) accuracy of emissivity and temperature depends largely on the assigned constant value.

(3) Normalisation emissivity method (NEM). This method, which is an improvement of RCM as the channel with the maximum emissivity can be different in the NEM for different materials, was first described by [START_REF] Gillespie | Lithologic mapping of silicate rocks using TIMS. The TIMS data users' workshop[END_REF] and used by [START_REF] Realmuto | Separating the effects of temperature and emissivity: emissivity spectrum normalization[END_REF].

This method assumes a constant emissivity in all N channels for a given pixel, which enables N temperatures to be calculated for each pixel from their measured radiance provided that the atmospheric quantities involved in the RTE are known. The maximum of those N temperatures is considered to be the LST and used to derive LSEs for the other channels as is done with RCM. The superiority of this method are: 1) simplicity; 2) channel with the maximum emissivity is not specified, and may be different between pixels; 3) having better performance than RCM. In another hand, require accurate atmospheric corrections in TIR channels, and the accuracy of emissivity and temperature depends largely on the assigned maximum emissivity value are the method's limitations and disadvantages.

(4) Temperature emissivity separation (TES) method. TES is an algorithm initially developed for retrieving LST and LSE from ASTER images [START_REF] Gillespie | A temperature and emissivity separation algorithm for ASTER images[END_REF][START_REF] Abrams | ASTER: data products for the high spatial resolution imager on NASA's EOS-AM1 platform[END_REF]. This algorithm hybridizes three mature modules: NEM, spectral ratio (SR) and min-max difference (MMD). TES first uses the NEM module to estimate the initial surface temperature and the normalised emissivities from the atmospherically corrected radiances at ground level; the SR module is subsequently used to calculate the ratio of the normalised emissivities to their average; finally, on the basis of the spectral ratio, the MMD module is used to find the spectral contrast in N channels and establishes an empirical relationship between the minimum emissivity in N channels and MMD. This method is operationally applied to the ASTER TIR data for recovery of LST and LSEs.

Numerical simulations demonstrate that the TES algorithm can recover temperature within about ±1.5K, and emissivities within about ±0.015 for ASTER data if the atmospheric effects are accurately corrected [START_REF] Abrams | ASTER: data products for the high spatial resolution imager on NASA's EOS-AM1 platform[END_REF]. The superiority of this method is: 1) refines the values of the maximum emissivity used in the NEM; 2) does not need any assumptions on emissivity; 3) retrieve simultaneously LST and LSEs for any kinds of surfaces. The limitations and disadvantages are: 1) require accurate atmospheric corrections in TIR channels; 2) require at least three TIR bands in atmospheric windows;

3) accuracy depends on atmospheric compensation and the empirical relationship between the minimum emissivity and MMD; 4) uncertainty is more serious for gray bodies, for example agricultural areas.

Physically based methods (PBM)

The methods reviewed above generally assume that the atmospheric effects on the radiances measured at the TOA have been accurately corrected for or that the radiances are measured at ground level. As far as the estimation of LST and LSEs from space measurements is concerned, in addition to the unknown LST and LSEs, there are some additional unknowns from the spectral absorption and emission in the intervening atmosphere.

With N spectral measurements from space, the solution for temperature and N spectral LSEs is underdetermined. There are three of the early physically based methods that have been fairly widely used to approach this problem using various physically based assumptions or constraints. These methods include the Day/night temperature-independent spectral-indices (TISI) based method (Becker and Li, 1990a;[START_REF] Li | Feasibility of land surface temperature and emissivity determination from AVHRR data[END_REF][START_REF] Li | A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data[END_REF], the physics-based day/night operational method (Wan and Li, 1997), and the two-step physical retrieval method, which uses the principal component analysis (PCA) technique to decrease the number of unknowns [START_REF] Ma | Characteristics of energy transfer and micrometeorology in the surface layer of the atmosphere above marshland of the Tibetan Plateau area[END_REF]2002;[START_REF] Li | Physical retrieval of surface emissivity spectrum from hyperspectral infrared radiances[END_REF].

(1) Day/night TISI-based method. Becker and Li (1990a), and Li and Becker (1990) first proposed a TISI-based method to perform spectral analysis in the TIR region.

Subsequently, assuming that the TISI ij (i is the MIR channel and j is the TIR channel) in the daytime without the contribution of solar illumination is the same as the TISI ij in the night-time, [START_REF] Li | Feasibility of land surface temperature and emissivity determination from AVHRR data[END_REF] and [START_REF] Li | A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data[END_REF] further developed a day/night TISI-based method to first extract the bidirectional reflectivity in MIR channel i by eliminating the emitted radiance during the day in this channel by comparing the TISI ij in the daytime and the night time. Once the bidirectional reflectivity in an MIR channel is retrieved, the directional emissivity in that MIR channel can be estimated to be complementary to the hemispheric-directional reflectivity, which can be estimated from a bidirectional reflectivity data series using either an angular form factor [START_REF] Li | A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data[END_REF], a semi-empirical phenomenological model (Petitcolin et al., 2002) or a kernel-driven bidirectional reflectivity model (Jacob et al., 2004;Jiang and Li, 2008a;[START_REF] Lucht | Considerations in the parametric modeling of BRDF and albedo from multiangular satellite sensor observations[END_REF][START_REF] Roujean | A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data[END_REF][START_REF] Wanner | On the derivation of kernels for kernel-driven models of bidirectional reflectance[END_REF]. Finally, based on the concept of the TISI, the LSEs in the TIR channel j can be obtained from the two-channel TISI and the emissivity in the MIR channel (Jiang et al., 2006;[START_REF] Li | A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data[END_REF].

Because of its physical basis, the day/night TISI based method does not require any a priori information about the surface and can be applied to any surface, even those with strong spectral dynamics. Generally, the time-invariant TISI assumption appears to be reasonable in most situations. The TISI, which is a ratio on emissivities, will remain unchanged over several days unless rain and/or snow occur. It is worth nothing that night time dew formation may affect the assumption, especially for low-emissivity surfaces in dry areas [START_REF] Snyder | BRDF models to predict spectral reflectance and emissivity in the thermal infrared[END_REF]. Although the frequency of dew occurrence is not so high in most semi-arid and arid regions, it is worth to try checking the relative humidity value in the low boundary layer to avoid heavy dew events becoming a serious problem [START_REF] Wan | MODIS land surface temperature algorithm theoretical basis document[END_REF]. Therefore, this method is superior to the (semi-) empirical stepwise retrieval methods above, especially on bare and geologic substrates that exhibit contrast emissivities. However, several requirements may limit the usage of this algorithm in LSE retrieval from space. First of all, approximate atmospheric corrections and concurrence of both MIR and TIR data are required [START_REF] Sobrino | Toward remote sensing methods for land cover dynamic monitoring. Application to Morocco[END_REF]. Then, accurate image co-registration must be performed [START_REF] Dash | Separating surface emissivity and temperature using two-channel spectral indices emissivity composites comparison with a vegetation method[END_REF]. Additionally, the surfaces must be observed under similar observation conditions, e.g., viewing angle, during both day and night [START_REF] Dash | Separating surface emissivity and temperature using two-channel spectral indices emissivity composites comparison with a vegetation method[END_REF].

(2) Physics-based day/night operational (D/N) method. The physical method usually faces more unknowns simultaneously; in other words, the physical method needs more channels than other methods. To simultaneously retrieve LSEs and LST without an accurate a priori knowledge of emissivity information and atmospheric parameters, Wan and Li (1997) proposed a physics-based retrieval method using day/night pairs of combined MIR and TIR data. The main purpose of this method is to retrieve LST and LSEs in semiarid and arid regions where the surface emissivity varies spatially over a wide range [START_REF] Wan | MODIS land surface temperature algorithm theoretical basis document[END_REF]. The method is based on three assumptions of surface optical properties. First, the surface emissivity does not significantly change in the day/night times in several days unless rain and/or snow occur during the short period of time. Second, the angular form factor i f has very small variation (<2%) in the wavelength range of interest in MIR. And third, the Lambertian approximation of surface reflection for downwelling diffuse solar irradiance and atmospheric thermal irradiance does not introduce significant error in the 3-14 µm regions. The radiance measured can be expressed as

1 2 0 3 4 1 ( ) [ ] i i i i i S i i i i ati sli sli ati L B T R R f E R R e t e t m t t p - - ¯- = + + + + + , (1.3)
where ati R -and sli R -are the thermal path radiance resulting from the atmosphere and scattering of solar radiation, respectively, μ 0 is cosine of the solar zenith angle, and ji t , 1,..., 4 j = are band effective-transmission functions for the corresponding terms. To reduce the uncertainties in the initial atmospheric conditions, two variables are used to modify the initial atmospheric profiles. One is the air temperature at the surface level (T a )

and the other is the total atmospheric column water vapour (CWV). With two measurements (day and night) in N bands, the numbers of unknowns are N+7 (N band ε, 2 T s , 2 T a , 2 CWV and 1 f). To make the equations deterministic, the number of observations (2N) must be equal to or greater than the number of unknowns (N+7), which makes N 7.

Because 2N equations are nonlinear, a statistical regression method is used to give the initial values of N+7 unknowns. Next, a numerical algorithm, such as the least-squares-fit (χ 2 ) method, is used to find an accurate solution for N+7 unknowns from 2N measurements (2N equations) (Wan and Li, 1997). In the day/night algorithm, a look-up table of atmospheric parameters is also employed for high efficiency. More details on the MODIS D/N method can be found in Wan and Li (1997), [START_REF] Wan | New refinements and validation of the MODIS land-surface temperature/emissivity products[END_REF] and [START_REF] Wan | MODIS land surface temperature and emissivity products[END_REF].

The superiority of this method is: 1) does not require a priori accurate atmospheric profiles; 2) solution is more stable and accurate by introducing MIR channels; 3) accuracy of LSTs and LSEs is largely improved by modifying the atmospheric profiles in the retrieval; 4) retrieve accurately both LSTs and LSEs on the physical basis. The limitations and disadvantages are: 1) require multi-temporal data in several channels in the MIR and TIR atmospheric windows; 2) require accurate geometric registration; 3) approximate shapes of the atmospheric profiles need to be given a priori; 4) retrieval process is complicated and initial guess values are required.

(3) Two-step physical retrieval method (TSRM). Although MODIS is not specifically designed as a sounding instrument, it has 16 bands in the MIR and TIR regions, several of which match the corresponding bands on the high-resolution infrared radiation sounder (HIRS). MODIS can be therefore used to extract atmospheric profiles (Menzelet al., 2006).

However, due to the coupling between the atmospheric information and surface temperature by both the surface emissivity and atmospheric transmittance, the retrieval process is a difficult task. [START_REF] Ma | Characteristics of energy transfer and micrometeorology in the surface layer of the atmosphere above marshland of the Tibetan Plateau area[END_REF] made a first attempt to simultaneously retrieve LST, atmospheric temperature-humidity profiles by assuming that emissivities are constant in MIR and TIR regions respectively and the solar contribution in MIR is ignored.

Nevertheless, these assumptions may degrade the accuracy of atmospheric parameter retrievals in the troposphere [START_REF] Ma | Characteristics of energy transfer and micrometeorology in the surface layer of the atmosphere above marshland of the Tibetan Plateau area[END_REF]. Li et al. (1999c) demonstrated that surface emissivity spectra of more than 50 soil and vegetation samples measured in the laboratory could be reconstructed with an uncertainty of 0.005 by six selected bands in the 8-13 mm.

Due to the fact that emissivity spectra can be recovered by a few number of unknowns in the interest spectral region, [START_REF] Ma | Simultaneous retrieval of atmospheric profiles, land-surface temperature, and surface emissivity from Moderate-Resolution Imaging Spectroradiometer thermal infrared data: extension of a two-step physical algorithm[END_REF] proposed an extended two-step physical retrieval method to extract LSEs, LST and atmospheric temperature-humidity profiles with MODIS MIR and TIR data by taking the solar contribution into account.

The main idea of this method inherits that of atmospheric profiles retrieval. First, the atmospheric RTE is tangent-linearized with respect to the atmospheric temperature-humidity profiles, LST and LSEs. Giving an initial guess of LSEs, LST and atmospheric temperature-humidity profiles, a set of equations based on the tangent-linearized RTE can be derived with the MODIS measurements. However, this is still a nondeterministic problem because the number of unknowns is still larger than the number of equations. Consequently, the PCA technique is applied to the atmospheric temperature-humidity profiles as well as to the surface emissivity spectra so that the number of retrieved parameters is reduced and the solution of the set of equations becomes deterministic. On this basis, [START_REF] Ma | Characteristics of energy transfer and micrometeorology in the surface layer of the atmosphere above marshland of the Tibetan Plateau area[END_REF][START_REF] Ma | Simultaneous retrieval of atmospheric profiles, land-surface temperature, and surface emissivity from Moderate-Resolution Imaging Spectroradiometer thermal infrared data: extension of a two-step physical algorithm[END_REF] proposed a two-step physical retrieval method to extract emissivity, together with LST, atmospheric temperature and moisture profiles by using one after the other the Tikhonov regularisation and Newton iterative algorithms. The Tikhonov regularisation is used to stabilize the ill-posed problem and to obtain a meaningful solution, while the Newton iterative algorithm is used to further improve the solution. Thus, the name of the technique is "two steps".

There are some assumptions involved in the linearization of the RTE. These assumptions include a horizontally homogeneous atmospheric condition, a specular surface reflection to simplify the integral of the downwelling atmospheric radiation, and a constant anisotropic factor (angular form factor) in MIR region to describe the non-Lambertian distribution of the bidirectional reflectance. However, these plausible assumptions may introduce some errors in the retrieval, but play only a secondary role. As discussed by [START_REF] Ma | Simultaneous retrieval of atmospheric profiles, land-surface temperature, and surface emissivity from Moderate-Resolution Imaging Spectroradiometer thermal infrared data: extension of a two-step physical algorithm[END_REF], one possible improvement of this type of method is to improve the first-guess of the profiles and LSEs. They suggested using a model-based first guess of the MODIS retrieval, the Atmospheric Infrared Sounder (AIRS) retrievals, and the first-guess of LSE retrieved by D/N method. In addition, this method can be used to process the hyperspectral TIR data, such as AIRS and Interferometer Atmospheric Sounding Instrument (IASI). These sensors have thousands of channels in 3-14 mm. Although the physical method gives definitely physical meaning for each parameter, it is of great complexity. Similar to the above procedure, [START_REF] Li | Physical retrieval of surface emissivity spectrum from hyperspectral infrared radiances[END_REF] retrieved global TIR emissivity spectra from AIRS data using the physical method. However, more work, such as the quality assurance, need to be done before taking this approach for operational process.

Generally, the superiority of this method are: 1) do not need a priori atmospheric corrections; 2) retrieve simultaneously the atmospheric profiles, LST and LSEs; 3) PCA and Tikhonov regularisation can be used to make the solution more stable and accurate.

The limitations and disadvantages are: 1) complexity; 2) low computational efficiency limits the application; 3) require adequate numbers of channels; 4) require an initial guess of LSEs, LST and atmospheric temperature-humidity; 5) the solution is more dependent on the initial guess.

Overview of LSE retrieval methods from passive MW data in the past decades

As TIR remote sensing has its own limitations, the importance of the passive MW remote sensing has been gradually shown in recent years. The first Earth observing satellite was the Russian COSMOS-243 with one of microwave radiometers at 37 GHz onboard. Through the years, remote sensing became more sophisticated and in 1978 the American NIMBUS-7 satellite carried the Scanning Multichannel Microwave Radiometer (SMMR), providing global coverage at several microwave bands. In the next three decades many increasingly advanced missions followed [START_REF] Holmes | The radiative temperature of the earth at microwave frequencies[END_REF].

The most important reason for using MW is its capability to penetrate clouds and to some extent rain to view land surface. Passive MW can be therefore used in nearly all-weather conditions with multi-polarization modes. The second reason for the use of MW sensors is that they are able to penetrate more deeply into vegetation than optical light can. The third reason is that MW and TIR complement to each other under some circumstances. Generally, the microwave remote sensing has some unique advantage on monitoring land surface parameters, particularly soil moisture, LSE and LST monitoring. In recent years, with the widespread use of the passive MW data, there are three main methods dedicated to LSE retrieval. The first is a stepwise retrieval method that determines the LSE and the LST separately. The LST is estimated first, and then the LSE is retrieved. The second is a simultaneous retrieval method that treats both the LST and the LSE as unknowns and resolves both of them from the atmospherically corrected radiances. The third is the artificial neural network (ANN) method which simultaneously retrieves the LST and the LSE without exact knowledge of the complex physics mechanisms.

Stepwise retrieval methods

This type of method retrieves the LSE using two consecutive steps. First, the LST is (semi-)empirically determined from MV measurements at different frequencies and polarizations or physically estimated from TIR measurements. Then, the LSE is estimated by the inversion of the radiative transfer equation in MV, provided that the atmospheric effects could be removed or corrected [START_REF] Jones | Passive microwave remote sensing of cloud liquid water over land regions[END_REF]McFarland et al.1990;[START_REF] Prigent | Microwave land surface emissivities estimated from SSM/I observations[END_REF][START_REF] Jones | Satellite passive microwave remote sensing for monitoring global land surface phenology[END_REF][START_REF] Moncet | Land surface microwave emissivities derived from AMSR-E and MODIS measurements with advanced quality control[END_REF].

Simultaneous LSE and LSTretrieval methods with known atmospheric information

Because the accuracy of the retrieved LSE is primarily dependent on the accuracy of the LST, simultaneous determination of the LSE and the LST has been proposed to improve the retrieval accuracy. Many simultaneous LST and LSE retrieval methods with given known atmospheric information have been developed since the 1990s. These methods can be roughly grouped into two categories: the multi-temporal and multi-spectral retrieval methods. The multi-temporal retrieval methods primarily make use of measurements at different times to retrieve the LSE and the LST under the assumption that the LSE is time-invariant. The representatives of these methods is the two-temperature method [START_REF] Xiang | Feasibility of simultaneous surface temperature-emissivity from retrieval using SSM/I measurements from HAPEX-Sahel[END_REF], aiming to reduce the number of unknowns in the retrievals. The multi-spectral retrieval methods rely on the intrinsic spectral behaviour of the LSE at different polarizations or different frequencies rather than temporal information. The representatives of the multi-spectral methods is the linear emissivity constraint method [START_REF] Fily | A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas[END_REF], which uses the empirical linear relationship between LSEs at different polarizations to increase the number of equations.

Artificial neural network (ANN)method

Over the past decade there have been considerable increases in both the quantity of remotely sensed data available and the use of neural networks. These increases have largely taken place in parallel. An increasing awareness of the significance of neural network technique has been made by some researchers, because the radiative transfer and the interaction of physical processes in the microwave inversion of surface parameters is non-linear.They tried to retrieve surface parameters by neural network for getting higher accuracy product. As the solution and stability is unknown, the resolution is always ill. Using the priori initial value is very important for eliminate these uncertainties. [START_REF] Aires | A new neural network approach including first-guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature and emissivities over land from satellite microwave observations[END_REF] proposed a neural network approach for retrieving atmospheric water vapor, cloud liquid water path, surface temperature, and emissivites over land between 19 and 85 GHz from SSM/I observations. They showed that the neural network with first guess and variational assimilation approaches shares important theoretical concepts and highlight some of the technical differences. The LSEs were retrieved with an accuracy of better than 0.008 in clear conditions and 0.010 in cloudy conditions.

Problems/issues

The LSE has already been recognised as a crucial parameter for the discrimination and sometimes the identification of various surface types and for the determination of LST by radiometry. Although various methods have already been developed, there is still no best method to retrieve LSE from space. All of the methods either rely on statistics relationships or on assumptions and constraints to solve the inherent ill-posed retrieval problem. Therefore, they might not hold true under some circumstances, and it is necessary to choose the optimum approach to estimate LSEs from space for a particular case by taking the sensor characteristics, the required accuracy, computation time, as well as the availability of atmospheric temperature and water vapour profiles into accounts. From the previous discussion, the main restricting factors in the estimates of LSEs from remotely sensed data are actually the following:

(1) The difficulty of the atmospheric corrections: The presence of the atmosphere between the land surface and the sensors at satellite level disturbs the radiances measured by a radiometer at the TOA. These radiances result primarily from the emission/reflection of the surface modulated by the effects of the absorption, diffusion and emission of the atmosphere. To minimise these atmospheric effects, spectral channels are always built in the windows where the atmosphere is most transparent. The atmospheric corrections thus consist of correcting the radiance measured by the sensors for the effects of atmospheric absorption, emission and emission-reflection. These effects can be variable because of the great variability of the vertical profiles of atmospheric water vapour and temperature.

(2) The difficulty in decoupling the LST and LSEs in the measured radiances. Independent of the atmospheric problems presented in all spectral regions, the spectral radiance emitted by a surface is a product of the spectral LSEs of this surface and the spectral radiance of the black body at the LST. Therefore, it is not possible in passive radiometry to separate, on a physical basis, the contributions due to LSE from the contributions due to LST in the observed radiance. For this reason, the LSE determination from space requires not only atmospheric corrections but also knowledge of the LST and vice versa.

(3) The difficulty of physical interpretation of the measurement. The difficulties raised by the atmospheric corrections and the temperature-emissivity coupling are, to some extent, of a technical nature. On the other hand, the scaling problem is much more fundamental because it implies a conceptual analysis of the physical significance of the measured quantities (variables). Indeed, the diversity of continental surfaces involves spatial (vertical and horizontal) and radiometric heterogeneities of surface. Considering that the spatial resolution of the current on board systems varying from 10 -2 to 2500 km2, it is therefore necessary to be able to define and correctly interpret surface parameters (variables) independently of the scale used and the processes necessary to validate this definition.

(4) The difficulty of validation of the LSEs retrieved from space at satellite pixel scale:

Comparisons between LSEs derived from space and in-situ measurements are required to evaluate the reliability and accuracy of the LSE retrieved methods. Although it may be feasible and reasonable to validate LSEs derived from remotely sensed data with traditional measurements, mainly conducted at the "point" scale over uniform areas, problems will be encountered when the validation is performed over complicated land-surface areas.

Main research contents

Focused on the issues/problems identified above through a complete overview of the This thesis is composedof six chapters.

In the above first chapter, the importance of the land surface emissivity is described first, and then the state of the art on the methodologies to estimate regional LSE from satellite data in both TIR and MW spectral regions is given in detail, at the end of this chapter,the structure of this thesis is presented.

Chapter 2 recalls the fundamental principles of physics used in the determination of LSE from satellite data.

Chapter 3 is devoted to describe the sensors, data and models used in this work as well as the areas studied in this work.

Chapter 4 refers to the determination of LSE and LST from passive MV data.

Chapter 5 presents an improved algorithm to simultaneously retrieve both LSE and LST from SEVIRI data.

The conclusions of this work and some future trends and prospects are given in the sixth chapter.

Chapter 2

Fundamental radiometric theory

Thermal radiation

All substances at a finite absolute temperature emit electromagnetic energy [START_REF] Ulaby | Microwave remote sensing: active and passive, Vol. I: microwave remote sensing fundamentals and radiometry[END_REF]. Any object that is hot gives off light known as thermal radiation. The hotter an object is the more energy it emits. As the temperature of the object increases, it emits most of its light at higher energy level (higher energy level means shorter wavelength light). The relationship between the amounts of energy emitted, its wavelength and temperature of the object is an equation known as the Planck Law.

Planck's Blackbody Radiation Law

In general, of the radiation incident upon the surface of a solid (or liquid) substance, a certain fraction is absorbed and the remainder is reflected. A blackbody is defined as an idealized, perfectly opaque material that absorbs all the incident radiation at all frequencies, reflecting none. The quantum-mechanical model of a blackbody may be described as consisting of such a large number of quantized energy levels with a correspondingly large number of allowable transitions, that any photon, whatever its energy or frequency, is absorbed when incident upon the blackbody. In addition to being a perfect absorber, a blackbody is also a perfect emitter, since energy absorbed by a material would increase its temperature if no energy is emitted.

According to Planck's radiation law, the spectral emittance of electromagnetic radiation from a blackbody is related to the temperature T (K) :

1 1 2 2 1 1 2 5 5 2 ( , ) exp 1 exp 1 , C C c h ch E T W m m k T T p l m l l l l - - - - ae ö ae ö é ù == - = - × × ç ÷ ç ÷ ë û è ø è ø (2.1)
where ( , )

E T l is the energy that is quantifiable in terms of spectral emittance, defined as the energy per unit time per unit wavelength crossing an unit area perpendicular to the viewing direction of the sensor;

h is the Plank constant; l is the wavelength in m m ;
c is the speed of light;

k is the Boltzmann constant; A blackbody radiates uniformly in all directions with a spectral radiance ,T

B l given by 2 1 1 , ( , ) ,

T E T B W m sr m l l m p - - - é ù = × × × ë û (2.2)
where ,T

B l is Blackbody spectral radiance and it is the power per unit area per unit solid angle per unit wavelength.

It is sometimes of interest to express the spectral radiance on terms of

, f T B ( 1 , f frequency cm - = ) rather than ,T
B l , the radiance is given by

1 3 2 1 , 2 2 exp 1 , f T hf hf B W m sr cm c kT - - - ae ö é ù = - × × × ç ÷ ë û è ø .
(2.

3)

The total radiance B for a blackbody at a temperature T is the integral of

, f T B (or ,T
B l ) over all frequencies (or all wavelengths). One relationship was obtained, as written ´.

The low-frequency counterpart to the Wien radiation law is known as the Rayleigh-Jeans law. If / 1 hf kT = , the approximation can be used to simplify equation ( 2

.3) to 2 , 2 2 2 2 f T f kT kT B c l = = . (2.5)
This approximation is very useful in the microwave region.

Non-blackbody radiation

A blackbody is an idealized body which, when in thermodynamic equilibrium at a temperature, radiates more energy than any other body at the same temperature. Actual materials emit less than a blackbody does and do not necessarily absorb the entire energy incident upon them. An important parameter connected to the spectral radiance is the spectral emissivity, ( , ) e q l , which is the ratio of the spectral radiance actually emitted by an object at some temperature to the spectral radiance emitted by a blackbody at the same temperature given by equation (2.3). According to the definition, only the black body has an emissivity of 1, and the natural bodies are usually non-black bodies, 0 ( , ) 1 e q f < < ( ) m l m from a non-black body at temperature ( ) T K is given by the spectral emissivity times the Planck's function:

2 5 2 ( , ) ( , ) ( , ) ( , ) exp 1 c h R B T ch k T
q l e q l l e q l l l

= = ae ö - ç ÷ è ø . (2.6)
If the spectral radiance, which may be direction-dependent, is R(θ, λ) and its physical temperature is T, a blackbody equivalent radiometric temperature, which is usually called the brightness temperature, T B (θ), may be defined by.

( , , ) ( , ) ( , ) ( , , )

B B T R B T q l
q l e q l q l = = .

(2.7)

In microwave region, the brightness temperature T B (θ) of the material relative to that of a blackbody at the same temperature becomes:

( ) ( ) B T T q e q = .
(2.8)

Emission and scattering

To examine quantitatively the interaction of radiation with a dielectric slab, the effective reflectivity ρ , the effective transmissivityt and the effective absorptivity α are derived expressions, where the adjective "effective" refers to the steady-state solution incorporating all multiple reflections within the slab. These three parameters are related by:

1 t a r + + = .
(2.9)

If the material remains in thermodynamic equilibrium, its effective emissivity ɛ should be equal to α. Here,

1 t e r + + = , (2.10)
which is a statement of energy conservation. For an opaque material, t = 0, in which case equation (2.10) reduces to 1 e r + = .

(2.11)

In the MW region, the emission and scattering by natural surface are dependent on roughness of surface, complex dielectric constant, temperature and scattering by surface.

Specular reflection is the mirror-like reflections of wave from a surface, the reflectivity of horizontal and vertical polarization ( oh r and ov r ) are calculated by Fresnel's equations: For the specular and opaque surface, the specular emissivity can be expressed in terms of the specular reflectivity through ( , ) 1 ( , ) p p e q r q = -.

2 2 2 cos ' sin cos ' sin oh q e q r q e q - - = + - , ( 2
(2.14)

For the rough surface, more general expressions of the emissivity should be given.

Scattering by a rough surface is characterized by the bistatic scattering cross-section per unit area ( ) 0 0 0 , ; , ; , o s s s p p s q f q f which relates the magnitude of the power scattered in the direction ( s q , s f ) with polarization P s to the power incident in the direction ( 0 q , 0 f ) with polarization 0 p . (See Fig. 2.1). o s is also known as the surface scattering coefficient.

o o h v v h s s = ).
Applying Kirchhoff's radiation law to the rough-surface case leads to the expression for the polarized emissivity ( , , ) s s s p e q f of a surface observed from the direction ( , ) q e q f s q f q f s q f q f q f p q

s s q f in terms of o s : ( ) ( ) 2 /2 0 0 0 0 0 0 0 0 0 0 sin 1 ( , ,
é ù = - + ë û ò ò .
(2.15)

In addition, radiation from remotely sensed objects in the earth's environment is attenuated in its passage through the atmosphere. The atmospheric transmissivity (t) is given by:

( ) ( )

exp exp t d t m - = -= , (2.16)
where δ is the nadir optical depth μ is the cosine of view zenith angle (θ).

The nadir optical depth δ is defined as the integral over the height z of the volume extinction

coefficient k e ( ) ( ) e Z z k z dz d ¥ = ò .
(2.17)

For the total nadir optical depth δ 0 , the integral is taken from z = 0 to z = ∞.

Radiative transfer equation

For the spectral radiance measured by a sensor at the top of the atmosphere (TOA), the atmospheric effects cannot be ignored. Atmospheric effects include absorption, emission and scattering [START_REF] França | Retrieval of land and sea surface temperature using NOAA-11 AVHRR data in northeastern Brazil[END_REF]. (2.20). Part of the spectral radiance emitted and scattered by the atmosphere reaches the surface, the so-called atmospheric downwelling radiance, and then reflected by the surface towards the sensor ([3] in Fig. 2.2). For the MIR spectrum in daytime, direct solar irradiance penetrates the atmosphere and part of it reaches the surface, and then is reflected by the surface towards the sensor ( [4] in Fig. 2.2). The surface emission, the reflected atmospheric downwelling radiance and the reflected direct solar irradiance ( [5] in Fig. 2.2) upwards penetrate the atmosphere and part of them reach the sensor after the absorption and scattering of the atmosphere meanwhile the atmosphere emits and scatters the radiance directly upwards to the sensor ( [START_REF] Conclusions…… | References[END_REF] in Fig. 2.2), which is called the atmospheric upwelling radiance. The total radiance reaching the sensor at wavelength λ is therefore written as:

( ) ( ) ( ) ( ) ( ) , , , , , v G v v v v atm s R R t R R
q l q l q l q l q l --= + + , (2.18a) where ( )

, v
R q l is the total radiance reaching the sensor; λis thewavelength; v q is the view zenith angle;

( )

, G v
R q l is the total spectral radiance measured at ground level;

( )

, v
t q l is the total atmospheric spectral transmittance;

( )

, v atm R q l -
is the upwelling atmospheric spectral radiance resulting from the scattering of solar radiance at zenith angle v q ;

( )

, v s R q l -
is the upwelling atmospheric spectral diffusion radiance resulting from the scattering of solar radiance at zenith angle v q , in term of brightness temperature, it can be expressed as with

( ) ( ) ( ) , , ( , , ) , , , ,
B S v B G v v v atm B T B T t R q l q l q l q l - = + , (2.18b)
( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) , , , , , , 1 , , , , ,
G v B G v v s v b v s sun s atm s R B T B T R R E
q l q l e q l l e q l l l r q q j l q l

¯= = + - + + é ù é ù ë û ë û ,(2.19)
where R G (θ v , λ) is the total spectral radiance measured at ground level;

ɛ (θ v , λ)
is the emissivity of the land surface;

T s is the land surface temperature;

1-ɛ(θ v , λ) is the reflectivity of the land surface;

R atm (λ) is the downwelling atmospheric spectral radiance; R s (λ) is the downwelling hemispheric solar diffusion radiance divided by π ;

ρ b (θ v , θ s , φ,λ) is the bi-directional spectral reflectivity;
φ is the relative azimuth angle between the view azimuth angle and the solar azimuth angle;

θ s is the solar zenith angle;

E sun (θ s , λ) is the direct solar spectral irradiance at ground level.

For the spectral radiance in TIR channels, MW channels and night-time measurements in middle infrared range (MIR) channels, E sun (θ s , λ) = 0 and R s↑ (θ v , λ) = R s↓ (λ) = 0.

As the sensor onboard the satellite has finite range of spectral response, the radiance measured by the sensor in channel i, the so-called channel-averaged radiance, is can be written as (Li et al., 1999):

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) , 0 0 0 0 , , , , , , i v B G v i v v atm s i i v i i f t B T d f R R d B T f d f d l q l q l l l q l q l l q l l l l ¥ ¥ - - ¥ ¥ + é ù ë û = + ò ò ò ò , (2.20)
where ( ) i f l is the spectral response function of the channel i .

Similarly, the radiance measured in channel i at ground level is given by: ( )

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) { } ( ) 0 , 0 , , 1 , , , , , i v s v b v s sun atm s i g i v i f B T R R E d B T f d
l e q l l e q l l l r q q j l l l q l l

¥ ¯¥ + - + + é ù é ù ë û ë û = ò ò (2.21)
where T g,i is the brightness temperature at ground level in the channel i.

For the channels with narrow spectral range (~1.0 µm), without introducing significant errors, Eqs. (2.20) and (2.21) can be approximated as (Li et al., 1999):

( ) ( ) ( ) ( ) ( ) , , , i i v i v i g i v v v atm i s i B T t B T R R q q q q q - - = + + , (2.22) with ( ) ( ) ( ) ( ) ( ) ( ) ( ) , , , , 1 ,
,

i g i v i v i s i v b i v s sun i s atm i s i B T B T R R E
q e q e q r q q j q ¯= + -

+ + é ù ë û . (2.23) Figure 2.
2 Radiative transfer at a wavelength λ (μm), [1] is the emission emitted by the surface; [2] is the space equivalent emission reflected by the surface; [3] is the downwelling atmospheric emission reflected by the surface; [4] is part of direct solar irradiance reaches the surface, and is reflected by the surface; [5] is the surface emission, the reflected atmospheric downwelling radiance and the reflected direct solar irradiance upwards penetrate the atmosphere and part of them reach the sensor; [START_REF] Conclusions…… | References[END_REF] is the upwelling atmospheric emission

In MW region, the brightness temperature at TOA is contributed by four components: the emission by the surface itself ([1] in Fig. 2.2) and attenuated by the atmosphere; the upwelling atmospheric emission ( [START_REF] Conclusions…… | References[END_REF] in Fig. 2.2); the downwelling atmospheric emission reflected by the surface ([3] in Fig. 2.2) and attenuated by the atmosphere, and the space equivalent emission reflected by the surface ([2] in Fig. 2.2) and attenuated by the atmosphere. Therefore, assuming a scatter free atmosphere and a specular reflecting surface, the radiative transfer equation in MW region, expressed as brightness temperature at the TOA in the viewing direction v q , can be written as:

( ) ( ) ( ) , , , , f p v g f p f v au f v T T t T q q q = ´+ , ( 2
, , , , ,

1 g f p v f p v s f p v ad f sp f T T T T t e q
e q e q q é ù = + -+ ë û , (2.25)

, f p e is the surface emissivity at frequency f and polarization mode p; T s is the surface temperature;

T sp is the equivalent temperature of space, and has the constant value T sp = 2.7K;

θ ɛ = 54°, and
( ) ( ) exp f f t q t q é ù = - ë û , (2.26)
in which f t the optical depth of the atmosphere.

Atmospheric scattering

The earth's atmosphere plays an important role in remote sensing. It scatters and absorbs

the incident energy and emits the energy in TIR and MW. Generally, the optical properties (e.g., optical depth, single scattering albedo, phase function) of the medium are determined by the particles that compose the medium and their properties. If the molecular particles in the atmosphere are far smaller than the wavelength ( l ), scattering patterns can be calculated by the Rayleigh scattering law. For spherical particles, their scattering behaviours depend on the refractive index and size parameter defined as:

2 s r p c l = , (2.27)
where s r is the radius of the sphere.

If c is smaller than 0.01, then the Rayleigh scattering formulas are valid. Rayleigh scattering is easy to handle in remote sensing because its principles are easily understood. The only variable is the optical depth, which is quite stable globally. It depends mainly on the surface elevation [START_REF] Russell | Interleukin-2 receptor gamma chain: a functional component of the interleukin-4 receptor[END_REF] and can be calculated by: ( ) l is the wavelength (μm).

Since the optical depth decreases quickly as wavelength increases as shown in Eq. (2.28),

taking Rayleigh scattering into account is meaningful only in the shorter wavelengths in this case.

If the particle size is close to the length of the wavelength ( 0.1 50 c < < ), such as the most aerosol particles in the atmosphere, their scattering behaviour can be characterized by Mie theory.

Absorption by atmospheric gases

Atmospheric absorption is caused mainly by atmospheric gases, such as water vapor, carbon dioxide, ozone, oxygen. Through an understanding of the scattering, absorption, and emission behaviour of atmospheric constituents, microwave remote sensing techniques can be employed to monitor atmospheric parameters and weather conditions. Atmospheric absorption affects mainly the visible and infrared regions. The microwave spectrum offers a wide range of transmission conditions. In the 1-15 GHz region, the atmosphere is practically transparent even in the presence of clouds and moderate rainfall rates, which makes this frequency region especially attractive for terrain and ocean observations from satellite platforms [START_REF] Ulaby | Microwave remote sensing: active and passive, Vol. II: radar remote sensing and surface scattering and emission theory[END_REF].

In addition to oxygen and water vapor, other atmospheric gases and pollutants have absorption lines. The attenuation coefficient is defined as the sum of absorption and scattering coefficients of atmosphere According to the quantum theory, the absorption (or emission) spectrum of a molecule consists of sharply defined frequency lines corresponding to transitions between sharply defined (quantized) energy levels of the molecule.

Water vapor absorption

Water vapor has most significant absorption for electromagnetic radiation, and has rotational absorption lines in both TIR and MW region. Distribution of water vapor varies widely over time and location. The greater the water vapor content, the more serious the absorption is. In TIR region, there are many water vapor absorption bands: two strong absorption bands, 4.9~8.7 µm and 2.27~3.57 µm; two narrow absorption bands, 1.38 µm and 2.0 µm; one narrow weak absorption band, 0.7~1.23 µm. In MW region (1-200 GHz), there are two water vapor absorption lines, one at 22.235 GHz and another at 183.31 GHz.

Carbon dioxide

Carbon dioxide is one of the most important infrared absorption gases in Earth's atmosphere. Its absorption bands are mainly located in the infrared region greater than 2 µm.

There are four main carbon dioxide absorption bands: one around 4 µm, one absorption band from 14~18 µm and two narrow absorption bands at1.38 µm and 2.0 µm.

Ozone absorption

Ozone is a tri-atomic molecule, consisting of three oxygen atoms. It is an allotrope of oxygen that is much less stable than the diatomic allotrope. In the upper atmosphere, the sun's ultraviolet radiation breaks down oxygen molecules into atoms. The oxygen atoms then combined to a three atoms molecule (ozone) in certain conditions. Ozone is mainly in the 10 to 40 km altitude of atmosphere.

Ozone absorbs all the short-wave solar radiation under 0.3 µm, but it is very weak in long-wave absorption, except for a narrow TIR absorption band around 9.6 µm.

Oxygen absorption

Absorption spectrum of oxygen consists of a large number of absorption lines spread out over the 50-70 GHz frequency range (known as the 60 GHz oxygen complex) and an additional line at 118.75 GHz. As characteristic of the lower part of the earth's atmosphere, pressure broadening causes the complex of lines to blend together, forming a continuous absorption band centred around 60 GHz (Ulaby et al., 1980).

Extinction by clouds and precipitation

The interaction of electromagnetic radiation with particles (such as those in clouds, or rain) is different in nature and may involve both absorption and scattering. The volume extinction coefficient is governed by the density, shape, size distribution, and dielectric properties of the particles contained in the volume. In extending the results for a single particle to the ensemble containing many particles, the particles usually are assumed to be randomly distributed within the volume, and therefore the contributions of the individual particles can be summed assuming an incoherent process. In addition to the above assumptions, spherical particles are further assumed.

Chapter 3

Data collection and pre-processing Considering the low resolution of microwave data, another study area encompassed in the red rectangle denoted as C in Fig. 3.1 is our study area (in Saharan Africa spanning from 24.89°N to 29.27°N and 9.89°E to 28.11°E) for LSE retrieval from MW data. This study area is part of the Saharan Africa, with geospatial coverage of latitude 24.89° N to 29.27° N and longitude 9.89° E to 28.11° E.

Study area for retrieving LSE from TIR data (study area B)

A study area encompassed in the red rectangle denoted as B in Fig.

Satellite data and related data

Aqua satellite

The Aqua mission is a part of the NASA-centered international Earth Observing System (EOS).

AMSR-E data

The AMSR-E is a twelve-channel, six-frequency (dual polarized), total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz with two polarized (vertically and horizontally) measurements taken in all frequencies. The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55° and providing a swath width array of six feed horns which then carry the radiation to radiometers for measurement.

Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. The main operating parameters and characteristics of AMSR-E are shown in Table 3.2.

The AMSR-E gridded brightness temperature data produced at 0.25° resolution by the US National Snow and Ice Data Center (NSIDC) are used in this study.

Cloud MASK

Another useful product is the MSG cloud mask. The MSG cloud mask product is an image-based product derived from the results of scenes analysis and provides, for every cycle, information on the possible occurrence of clouds within each pixel. A central objective is to delineate all absolutely cloud-free pixels in a satellite scene with a high degree of confidence.

The MSG cloud mask product provides the following information: (1) no cloud, clear surface;

(2) no cloud, surface types snow/ice; (3) no cloud, sun-glint over sea; (4) cloudy, 50%, 75% and 100% probabilities; (5) no-processed or missing data. The MSG cloud mask data are stored in the Native files and are used to select SEVIRI data for clear sky in this study (http://archive.eumetsat.org).

Synchronous atmospheric quantities

The atmospheric quantities for atmospheric correction are estimated from the atmospheric profile data provided by European Centre for Medium-Range Weather Forecasts (ECMWF) at 00:00, 06:00, 12:00 and 18:00 UTC. Atmospheric profile data have 21 levels and cover from Latitude -10°N to 60°N and Longitude -20°E to 160°E at a spatial resolution of 1°×1° over the whole year 2006 for study area A (see Table 3.5). However, over the whole year 2008 for study area B, 25 levels at a spatial resolution of 0.25°×0.25° are available (see Table 3.6). , 925, 850, 700, 500, 400, 300, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2 and 1hPa , 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2 and 1hPa.

DEM

HYDRO1k, developed at the U.S. Geological Survey's (USGS) EROS Data Center (http://esp.cr.usgs.gov/), is a geographic database providing comprehensive and consistent global coverage of topographically derived data sets. Developed from the USGS' recently released 30 arc-second digital elevation model (DEM) of the world, HYDRO1k provides a standard suite of geo-referenced data sets (at a resolution of 1 km) that will be valuable for all users who need to organize, evaluate, or process geographic information on a continental scale. The DEM is used to modify the atmospheric profiles from ECMWF.

Data pre-processing

MODIS data pre-processing

As MODIS and AMSR-E are both onboard on the same satellite Aqua, MODIS LST product can be used as a true LST detected by MW to retrieve LSE from AMSR-E data. Due to the different spatial resolutions of these two products as described in sections 3.2.2 and 3.2.3, they should be matched spatially. To get LST at AMSR-E pixel scale from MODIS LST product, first MODIS pixels have to be matched spatially with AMSR-E pixel using a simple coordination matching (longitude/latitude matching) method as illustrated in Fig. 3.4 control condition. Secondly, the average of all of MODIS LSTs corresponding to clear sky within an AMSR-E pixel is computed using Eq. (3.1) leading to a LST measured at 0.25° 0.25° (AMSR-E pixel resolution).

Estimation of atmospheric quantities at spatial resolution of AMSR-E pixel from the quantities at 1° 1°

The atmospheric profiles provided by the ECMWF mainly include pressure (P), temperature (T), relative humidity (RH) and geo-potential (GP) for each pressure level from 1000hpa to 1hpa. To make these atmospheric profiles suitable for atmospheric correction with the radiative transfer models, they have to be merged with DEM data to take into account the topographic variation. These modified atmospheric profiles are then used to calculate the atmospheric quantities (transmittance, upwelling and downwelling atmospheric radiance) at resolution of 1° 1° with radiative transfer models such as Modtran and MonoRTM. Finally, the bilinear spatial interpolation method is employed to obtain the atmospheric quantities at spatial resolution of 0.25° 0.25° from those at spatial resolution of 1° 1°.

MODTRAN

MODTRAN (MODerate resolution atmospheric TRANsmission and radiance) code is a computer program designed to model atmosphericpropagation of electromagnetic radiationfor the 100-50,000 cm -1 (0.2 to 100 μm) spectral range. MODTRAN code calculates atmospheric transmittance and radiance for frequencies from 100 to 50,000 cm -1 at moderate spectral resolution, primarily 2cm -1 (20cm -1 in the UV). 3.7.

In addition to the six standard model atmospheres, MODTRAN also provides New Model

Atmosphere option to enable users to define their own atmospheric profiles, such as radio sounding data or ECMWF data.

MODTRAN 4.0 is used in this work to estimate the atmospheric quantities, which are used to perform atmospheric corrections in TIR and middle infrared (MIR).

Introduction

Land surface remote sensing satellite observations are affected by both the land surface and the atmosphere. To retrieve land surface parameters from a satellite measurement, the influence of atmosphere must first be removed.

The influence of the atmosphere on the measurement and retrieval of LSE and LST, using infrared remote sensing data, is obvious and needs to be considered (Li et al.,2013a;Li et al, 2013b;[START_REF] Nerry | Multi temporal regression method for mid infrared [3-5 µm] emissivity outdoor measurements[END_REF][START_REF] Ouyang | Errors analysis on temperature and emissivity determination from hyperspectral thermal infrared data[END_REF]. Compared to optical and infrared remote sensing, passive MW remote sensing is considered to be an effective tool for all-weather monitoring of land surface processes. The long wavelengths used in passive MW remote sensing resist atmospheric disturbances and are even able to penetrate clouds and some rainfall. Therefore, after correcting for the atmosphere, passive MW remote sensing data can be used to retrieve land surface parameters under almost any weather conditions.

Satellite data simulation must be performed with an accurate radiative transfer model to develop methodologies for LSE and LST retrievals from passive MW data. Unfortunately, there is still a lack of the soil-atmosphere radiative transfer model in MW region, the influences of soil physical properties, atmospheric properties, surface roughness and vegetation are also unclear. Therefore, one of the objectives of this chapter is to develop a comprehensive soil-atmosphere radiative transfer model in MW, which is used subsequently to propose LST and LSE retrieval methods.

To date, a great deal of attention has been paid to the use of space-borne passive MW data.

In previous studies, at low MW frequencies, the atmospheric effects are usually considered to be negligible. The observed brightness temperature was used to estimate land surface parameters without atmospheric correction. By contrast, at high frequencies, atmospheric effects on observations should be considered, and it is necessary to correct for atmospheric effects before the data can be used. Meteorological reanalysis and radiosonde data have typically been used to estimate the atmospheric parameters and remove atmospheric effects in previous research [START_REF] Prigent | Microwave land surface emissivities estimated from SSM/I observations[END_REF][START_REF] Karbou | Microwave land emissivity calculations using AMSU measurements[END_REF]. However, it is difficult to match both the time and location of different data sources, which may introduce errors into the retrieval process and lead to larger uncertainties in the estimation of land surface parameters.

Although some researches on atmospheric effects and correction algorithms has been performed [START_REF] Snider | Radiometric correction for atmospheric effects in surface sensing from aircraft and satellites[END_REF], there is little quantitative analysis describing the atmospheric effects on AMSR-E data or on when it is necessary to correct for atmospheric conditions. One of the objectives of this chapter is to first analyze atmospheric effects on AMSR-E data and then to propose an atmospheric correction algorithm at 18.7 GHz vertical polarization for LST retrieval.

LSE is essential for deriving land surface geophysical parameters from remotely sensed data. In many applications, the uncertainty in LSE directly affects the accuracies of the estimated parameters, such as LST and soil moisture. In reality, it is difficult to obtain LSE from passive radiometers because the number of measurements is always less than the number of unknowns. Although more accurate physics-based models have been developed in the past decades [START_REF] Chen | The emission of rough surfaces calculated by the integral equation method with a comparison to a three-dimensional moment method simulations[END_REF]Fung et al., 1992;[START_REF] Fung | Microwave scattering and emission models and their applications[END_REF]Tsang et al., 1985;[START_REF] Ulaby | Microwave remote sensing: active and passive, Vol. II: radar remote sensing and surface scattering and emission theory[END_REF], their complexity often makes them difficult to use for retrieving geophysical parameters. To make the solutions deterministic, additional assumptions, semi-empirical relationships and extra constraints have been used in previous studies [START_REF] Choudhury | Effect of surface-roughness on the microwave emission from soils[END_REF][START_REF] Fily | A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas[END_REF][START_REF] Gao | A practical method for retrieving land surface temperature from AMSR-E over the Amazon forest[END_REF]Jones et al., 2007;[START_REF] Mo | A parameterization of the effect of surface-roughness on microwave emission[END_REF][START_REF] Fily | A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas[END_REF][START_REF] Shi | A parameterized multi-frequency-polarization surface emission model[END_REF]Wang and Choudhurry, 1981;[START_REF] Wegmuller | Rough bare soil reflectivity model[END_REF][START_REF] Weng | Physical retrieval of land surface temperature using the special sensor microwave imager[END_REF][START_REF] Kerr | Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) Mission[END_REF]. One of objectives of this chapter is to first develop a parameterized relationship between the vertical and horizontal polarization emissivities that is helpful in understanding the emission of a natural surface and then to propose an algorithm for retrieving LSE from AMSR-E data in combination of MODIS LST product.

Land surface model (AIEM)

Random surface roughness is commonly described by two statistical parameters: root mean square height ( s ) and surface correlation length (l ). Electromagnetically, a surface is considered smooth if its height (z) variations are much smaller than the wavelength of the radiation. As rough surface scatters the incident energy in all direction, its characterization is very important for describing land surface-atmosphere interaction in MW. Root mean square height ( s ) and surface correlation length (l ) are mathematically expressed as:

( ) ( ) 1/2 2 2 1 1 1 N i i s z N z N = é ù ae ö = - ê ú ç ÷ ç ÷ - ê ú è ø ë û å , (4.1) with 1 1 N i i z z N = = å . (4.2)
On the other hand, the surface correlation function ρ (x) and l, are parameters used for the horizontal description of the surface roughness. In the discrete case, the normalised surface correlation function for a spatial displacement x'= (j-1) x is given by ( )

1 2 1 1 1 ' N j N i j i i i i x z z z r + - + - = = = å å , (4.3)
in which z j+i-1 is a point with the spatial displacement from the point x i [START_REF] Fung | Microwave scattering and emission models and their applications[END_REF]. The l is defined as the displacement x' for which ρ (x') between two points inhibits values smaller than 1/e (Euler's Value 2.7183)

( ) 1 l e r = . (4.4)
Thus, the surface correlation describes the statistical independence of two points on a surface and increases with the correlation between two neighbouring points. For a smooth surface l = ∞.

In this chapter, the Advanced Integral Equation Model (AIEM) is used to calculate the emissivity of rough land surface. AIEM is an improved version of Integral Equation Model(IEM), hereafter called advanced IEM (AIEM). Generally, the AIEM model provides a simple and accurate connection between the surface emission at different frequencies and polarization, as well as the commonly used surface roughness parameter measurements. It can be applied to the measurements from both single-pass and multi-pass measurements for the time series analyses.

The AIEM retained the absolute phase term in Green's function so that the upward and downward re-radiation over the surfaces is included [START_REF] Jiang | Evaluation of emission from snow-covered ground for passive microwave remote sensing[END_REF]. AIEM model uses the Dobson module to calculate the soil dielectric constant. This model was used for built a complete theoretical simulation land surface emissivity database. The database is used to the next step calculation; the Fig. 4.1 shows the input parameters and the modules which composited the AIEM model.

Dielectric constant, which is estimated by DOBSON module, is very important input parameters for AIEM model to calculate emissivity and reflectivity. The major input

Atmospheric radiative transfer model (MonoRTM)

The key issue to accurately estimate the land surface radiance from at-sensor radiance is to get the accurate influence of atmosphere. In order to research the influence of atmosphere, this thesis uses the MONOchromaticRadiative Transfer Model (MonoRTM) which is particularly useful in the MW spectral region. The Fig. 4.2 shows the input parameters, the output parameters and the modules which composited the MonoRTM model.

LSE retrieval

This section is devoted to (1) developing an empirical relationship between the vertical and horizontal polarization soil MW emissions and (2) developing a method to retrieve LSE from AMSR-E data, provided that the LST is known or can be estimated in other ways.

Relationship of LSE

As an example, Fig. GHz, 15.375 GHz and 19.35GHz [START_REF] Ulaby | Microwave remote sensing: active and passive, Vol. I: microwave remote sensing fundamentals and radiometry[END_REF].

Most of the extremely-high-frequency (EHF) range from 30 to 300 GHz is used less extensively, although the atmospheric-window region between 30 GHz and 40 GHz (Fig. 4.5) is rather widely used and applications in the neighbourhood of 90 to 100GHz are increasing.

Because of the strong oxygen absorption in the neighbourhood of 60 GHz (Fig. GHz. Radio-astronomy bands exist at 31.4 GHz, 37 GHz, and 89GHz, and these are, of course, used by microwave radiometers for remote sensing as well [START_REF] Ulaby | Microwave remote sensing: active and passive, Vol. I: microwave remote sensing fundamentals and radiometry[END_REF].

In the 1-15 GHz region, the atmosphere is practically transparent even in the presence of clouds and moderate rainfall rates; absorption (and therefore emission) resonances due to water vapour (at 22.2 and 183.3 GHz) and oxygen (in the 50-70 GHz region and at 118.7 GHz) can be used to determine the height profiles of atmospheric water vapour and temperature through radiometric measurements at and near the absorption maxima.

To develop parameterized relationships between emissivity at different frequencies, an emission database for bare surfaces was simulated for the configuration of AMSR-E, i.e., 6.925, 10.65, 18.7, 23.8, 36.5 and 89 GHz at vertical and horizontal polarization with a 55° incident angle as shown in Fig. 4.6. There are 2904 cases in each plots of Fig. 4.6. The soil surface dielectric constants are described by volumetric soil moisture (sm), which is varied from 2% to 44% at 2% intervals by Dobson's dielectric mixing model (Dobson et al., 1985), for a given soil texture. The applicability of the AIEM has proven by comparing the simulation of AIEM with simulated data of a three-dimensional Monte Carlo model and field data [START_REF] Chen | The emission of rough surfaces calculated by the integral equation method with a comparison to a three-dimensional moment method simulations[END_REF]Chen et al., 2010;[START_REF] Shi | A parameterized multi-frequency-polarization surface emission model[END_REF]. The results show that the AIEM is capable of simulating natural surface soil emissions. The surface roughness parameters, which are also necessary input parameters of AIEM, are set with a root-mean-square height (s) from 0.25 to 3.0 cm at a 0.25 cm interval and a correlation length (cl) from 5 to 30 cm at a 2.5 cm interval. A ratio of s to cl (s/cl) that is positively correlated with surface roughness condition is used to describe the surface roughness in the following analysis.

Based on the simulated database, the effect of soil moisture and surface roughness on the bare surface emission and the relationship between vertical (ɛ v ) and horizontal polarization ( Emissivity of 6.925GHz (Vertical) .6

.7

.8

.9

1.0 (b) Emissivity of 10.5GHz (Horizontal)

.4 .6

.8 1.0 Emissivity of 10.5GHz (Vertical) .6

.7

.8

.9

1.0 (c) .6

.7

.8

.9

1.0 4.6 Relationships between vertical and horizontal polarized emissivities at each frequency (6.925GHz,10.5GHz,18.7GHz,23.8GHz,36.5GHz and 89.0GHz) 4.8 The atmospheric upwelling radiation (T au ) and the difference of upwelling and downwelling radiation (T ad -T au ) for six frequencies (6.925GHz,10.5GHz,18.7GHz,23.8GHz,36.5GHz Considering the different effects of soil moisture and surface roughness on emissivity, two empirical parameterized models are developed to characterize the relationship between ɛ v and ɛ h . The relationship between ɛ h and sm is also analyzed.

(1) Parameterized relationship between e h and e v using sm: As shown in Fig. 4.7, a linear function can be established between e v and e h in the direction of varying surface roughness for a given soil moisture. The coefficients of the function may vary with the relative volumetric soil moisture. Therefore, the slopes and intercepts of a group of sub-parallel lines are dependent on the volumetric soil moisture for a certain frequency, i.e., A comparison between the simulated and estimated e v is shown in Fig. 4.12. The estimated e v from Eq. (4.6) shows that the absolute root-mean-square errors (RMSEs) are usually below 0.003, which is sufficient for the applications. (2) Parameterized relationship between ɛ h and ɛ v using s/cl: The direction in which the soil moisture varies is also important. Although the relationship between the vertical and horizontal polarization emissivities is weakly nonlinear for a varying soil moisture within the full range of emissivities, a linear relationship can also be established under certain conditions. Using the emissivity at 10.65 GHz as an example, this linear relationship can be written as:

( ) ( )

( / ) ( / ) v h E s cl F s cl e e = ´+ , (4.8)
where the E and F are linear functions of s/cl. The RMSE of the estimated ɛ v at 10.65 GHz using Eq. (3) Evaluation and application of the relationships using actual AMSR-E data:

Neglecting atmospheric effects, the land surface vertical and horizontal polarization emissivities at 10.65 GHz were computed using the brightness temperature data of AMSR-E and the LST data of the Moderate Resolution Imaging Spectroradiometer (MODIS) as: The computed emissivities are plotted with the simulated data in Fig. 4.16. The emissivities are discretely distributed due to the coupling between the effects of surface roughness and soil moisture in the study area. Because the surface roughness is less variable than soil moisture at a given place for a period, the emissivities may be described by an approximate linear relationship, as in Eq. ( 4.8). To further illustrate these phenomena, eight sites with predominantly bare soils are chosen and labelled A through H.

30 ° W 15 ° W 0 ° 15 ° E 30 ° E 45 ° E 60 ° E 0 °
The computed emissivities for these points for all clear days in 2004 are shown in Fig. 4.17. There are clear linear relationships between the vertical and horizontal polarization emissivities, which agree well with the analysis above. The regression lines and value of s/cl estimated by Eq. ( 4.9) are also shown in Fig. 4.17.

The estimated values of s/cl are largely consistent in between the eight sites. However, the absolute values of the estimated s/cl are near the maximum used in the simulated data, which is inconsistent with the fact that the surface should be relatively smooth in the study area. The difference between the simulated and actual data may contribute to the discrepancy between the scale of modelling and that of observation (Wu and Li, 2009).

LSE retrieval from AMSR-E data in combination with MODIS LST

In the AMSR-E microwave frequencies range, for a given path zenith angle, the brightness temperature (T B,f,p ) observed by the satellite instrument can be expressed by Eq.

(2.25). Provided that LST (T s ) is known and the atmospheric quantities (t f , T au,f , T ad,f ) are estimated using MonoRTM with atmospheric profiles, LSE ( ,

f p e ) can be directly derived from ( ) 2 , , , , , , sp B f p au f ad f f f f p s sp ad f f f T T T t T t T T T t t e - - ´- = - - ´´, (4.11)
where subscripts p and f represent polarization (vertical or horizontal) and frequency, respectively. T sp is the equivalent temperature of space, T sp = 2.7K.

In this study, to retrieve LSE from T B , f,p , LST (T s ) at AMSR-E pixel is calculated from MODIS LST product using Eq. ( 4.11), and the atmospheric profiles given by ECMWF data are used to estimate the atmospheric quantities (t f , T au,f , T ad,f ) with MonoRTM.

As an example, maps of the monthly mean LSEs in February 2006 over whole China retrieved using this method at 18.7 GHz and 36.5 GHz for vertical and horizontal polarizations are displayed in Fig. 4.18. Note that, each composite LSE value in this figure is the average value of all LSEs available in one month.

To analyze the variation of LSE with different land surface types, eight sites, which represent sandy desert, rocky desert, forest, wetland, grassland and barren/sparsely vegetated surface according to ESA/ESA Glob cover Project classification, are selected. Table 4.2 lists the monthly mean LSEs of February, June and September at two polarizations and mean difference of LSE at two polarizations for 8 sites (Taklimakan, Junggar, Donggebi, Linzhi, Nameless, Zhalong, Tianshan, Zhongcang). From this table, one can notice that the polarization difference of LSEs is dependent of land surface type. The vegetated land surfaces have small polarization difference of LSEs and the sandy desert has the largest polarization difference of LSEs. The polarization differences of LSEs at 18.7 GHz are commonly larger than those at 36.5 GHz, especially over the rocky desert and grassland.

Atmospheric correction for retrieving LST from AMSR-E data

In this section, a simulated database is first built that covers various atmospheric and land surface conditions and reflects AMSR-E data. Atmospheric effects on AMSR-E observations are then analyzed and an atmospheric correction algorithm at 18.7 GHz vertical polarization is finally proposed.

Database

A simulated database of passive microwave land surface brightness temperatures, configured to represent AMSR-E data, is built using the modified microwave MonoRTM, which is used for analyzing atmospheric effects. To generate simulated database, atmosphere profile data, LSE data and LST data are all required. Descriptions of the data used in this study are partly provided in section 4.2, and partly given below.

LST is necessary input parameter to simulate brightness temperature of land surface. T o make the value of LST more reasonable, LST was set within a given range based the temperature (T 0 ) of the bottom layer of atmosphere. Specifically, LST varied from -5 K to 15 K, with an interval of 5 K, for T 0 280 K, and from -10 K to 10 K, with an interval of 5 K, for T 0 280 K.

Using this input data, a simulation database containing 638880 AMSR-E brightness temperature observations and covering a range of atmospheric and land surface conditions were established using the MonoRTM model. All of following analysis work is based on the simulated data.

Analysis of atmospheric effects

To show the effects of atmosphere on AMSE-E observations, the simulated brightness temperature at the top and the bottom of the atmosphere (T Bp-TOA and T Bp-BOA ) has been compared to the land surface emitted brightness temperature (T B,S ). According to radiative transfer theory, the satellite-observed brightness temperature at polarization mode, p, and a

given frequency and incidence angle can be written as:

, au Bp TOA Bp BOA T T t T - - = ´+ (4.12)
where T Bp_BOA is the brightness temperature at polarization, p, (vertical or horizontal) observed at the bottom of atmosphere. It can be expressed as:

(1 )

sp p Bp BOA Bp land ad T T T T t e - - é ù ë û = + + ´´- , (4.13)
and T Bp-land is given by Eq. (2.8). The simulated data set shows that the mean differences are greater than zero, with the exception of DT B89V . This means that the atmosphere acts as an emission source rather than an absorption layer in most cases. The STD resulting from a comparison of T Bp-TOA and T Bp-BOA is 0.44 K and 0.65 K, respectively, at a frequency of 6.925 GHz for the vertical and horizontal polarizations. This means that the atmospheric effect on observed brightness temperatures at 6.925 GHz can be neglected and the T B6.925p-TOA can be directly used as T B6.925p-BOA without introducing significant errors. Furthermore, because the mean value is 0.6 K and the STD is 0.57 K, T B10.65v-TOA can be used as T B10.65v-BOA without considering atmospheric effects. For the other channels of AMSR-E, the mean and STD are larger than 1 K, with the exception of the STD at 10.65 GHz with horizontal polarization and the mean at 36.5 GHz with vertical polarization. Atmospheric effects on these channels should be considered. Comparing T Bp-TOA and T Bp_land shows that both the mean and the STD are larger than 1 K in every AMSR-E channel. This means that atmospheric effects should be considered when estimating T Bp_land using AMSR-E data. Further analysis shows that atmospheric effects are less critical to the temperature of the atmosphere than to the atmospheric water content across a range of atmospheric conditions. A comparison of T Bp-TOA and T Bp-BOA shows that, when the atmospheric water content is less than 20 kg/m 2 , the mean and STD are lower than 1.5 K at 18.7 GHz vertical polarization. At 36.5 GHz vertical polarization, the mean and STD are less than 2 K. At 89 GHz vertical polarization, the mean and STD are lower than 2 K when the atmospheric water content is less than 10 kg/m 2 . Due to the low values of land surface emissivity at horizontal polarization, atmospheric effects are more pronounced than at vertical polarization. The results show that atmospheric effects on observed brightness temperatures at horizontal polarization should be considered.

Atmospheric correction algorithm

(1) Algorithm: the results above show that atmospheric correction is necessary for AMSR-E data, with the exception of the 6.925 and 10.65 GHz bands at vertical polarization and 6.925 GHz at horizontal polarization. Using the simulated data, a theoretical description of the required atmospheric correction and a correction algorithm are presented in this section. The approximate equivalency of ɛ v18.7 and ɛ v23.8 [START_REF] Weng | Physical retrieval of land surface temperature using the special sensor microwave imager[END_REF], as well as the obvious differences between the atmospheric effects at 18.7 and 23.8 GHz vertical polarization, makes it possible to remove the atmospheric effects on observations at 18.7

GHz vertical polarization. Based on the classic split window technique, which is usually used in thermal infrared remote sensing [START_REF] Li | Evaluation of six methods for extracting relative emissivity spectra from thermal infrared images[END_REF], an atmospheric correction method for the 18.7 GHz vertical polarization channel of AMSR-E is established.

First, using the simulated data, the following empirical relationships were established. For observations of AMSR-E under clear sky conditions, T au and T ad are closely related and can be expressed as: Eq. ( 4.14) demonstrates that it is reasonable to consider Tau and Tad to be approximately equal [START_REF] Njoku | Retrieval of land surface parameters using passive microwave measurements at 6 to 18 GHz[END_REF], especially under relatively moist atmospheric conditions.

Next, some linear relationships between t and w (the atmospheric water content, kg/m2) at 18.7 and 23.8 GHz were also established: Using the approximate expression for effective radiating temperature (T ae ), T au can be expressed as [START_REF] Njoku | Retrieval of land surface parameters using passive microwave measurements at 6 to 18 GHz[END_REF]:

(1 ) ), (4.19) where r p is the soil reflectivity (related to the soil emissivity, ɛ p , by ɛ p =1-r p ).

Assuming that ɛ v18.7 ≈ ɛ v23.8 and ignoring some small terms in Eqs. (4.18)

B V TOA B B B land T A T A T T = ´+ ´- , ( 4

.20)

where A 1 and A 2 are unknown coefficients.

To improve the accuracy of Eq. (4.20), correction terms were added, and the coefficients were determined from the simulated data. Fig. 4.23 shows that this algorithm can correct for the atmospheric effects and that RMSE may decrease from 6.04 K to 0.99 K. Using this algorithm, the land surface emission can be estimated without using any other auxiliary data. Moreover, if ɛ 18.7v is known, LST can be estimated from AMSR-E data using the following equation. ´-

´-= + --

(4.22)
Using Eq. (4.22), the estimated value of LST from the simulated data is shown in Fig. 4.24. The RMSE of the estimated LST is 1.17 K. The results show that, if DT B18.7V-TOA (DT B23.8V-TOA ) is 1 K, DT s will be approximately 1.98 K (-0.85 K). Fig. 4.26 shows the data based on the calculation in Eq. (4.25). The bias and STD of Eq. ( 4.25) are -325.41 K and 32.77 K, respectively. This means that, if Dɛ 18.7v is 0.01, DT s will be approximately -3.25 K. Eq.( 13): bias = 1.98 STD = 0.37

Introduction

LST is one of the primary factors that govern energy exchange between the land surface and the atmosphere. This factor is used in global change studies to estimate radiation budgets in heat balance studies and is also a key parameter in climate models [START_REF] Mallick | Estimation of land surface temperature over Delhi using Landsat-7 ETM+[END_REF].

Due to the considerable difficulties of acquiring in situ LST representations at the satellite pixel scale, the need for satellite-based LST measurements is highlighted, as these measurements are the only way to provide the LST on a regional or global scale. Therefore, various LST retrieval algorithms have been proposed and developed over the past three decades (Li et al., 2013a). These algorithms can be roughly grouped into two categories based on the wavelength used: TIR techniques and MW techniques. However, because of the smaller range in variation of LSE in the TIR domain and the stronger dependence of the radiance on temperature, there are fewer uncertainties involved in TIR-based LST retrieval than in microwave-based LST retrieval [START_REF] Sun | Retrieval and application of land surface temperature[END_REF]. Therefore, TIR techniques are widely used to estimate the LST from space.

Currently, three major TIR-based LST retrieval methods have been developed, including the single-channel method, the split-window (SW) method and the multi-angle method, and all of these methods require a priori knowledge of the LSE [START_REF] Dash | Land surface temperature and emissivity estimation from passive microwave data[END_REF]Li et al., 2013a).

The single-channel method retrieves the LST from the radiance measured in a single atmospheric window channel and corrects for atmospheric effects using a confident knowledge of accurate atmospheric profiles [START_REF] Price | Estimating surface temperatures from satellite thermal infrared data-A simple formulation for the atmospheric effect[END_REF]. However, these accurate atmospheric profiles are not available with sufficient spatial density or at the same time as the passage of the satellite. The SW method is based on the different atmospheric absorptions in two adjacent channels to correct for atmospheric effects and does not require knowledge of accurate atmospheric profiles. This method was first proposed by McMillin to determine sea surface temperature [START_REF] Mcmillin | Estimation of sea surface temperature from two infrared window measurements with different absorptions[END_REF] and extended to LST retrieval by Price [START_REF] Price | Land surface temperature measurements from the split window channels of the NOAA 7 AVHRR[END_REF].

Following the application of this method to LST retrieval, a variety of SW algorithms have been developed and modified with some success to retrieve LST from space instruments such as the MODIS and AVHRR [START_REF] Coll | A split-window algorithm for land surface temperature from advanced very high resolution radiometer data: Validation and algorithm comparison[END_REF]Li et al., 2013b;Wan and Dozier, 1996;). The multi-angle method is based on a similar principle as that used in the SW method but uses different absorption results from the different atmospheric path-lengths that correspond to different observation angles. However, this method assumes that the surface temperature is angular independent.

Descriptions of all datasets used in the Study

The study area B described in section 3.1.2 of chapter 3 is used in this work. The main payload of MSG-2, SEVIRI, provides data in 12 spectral bands every 15 minutes; it is therefore particularly suited to determining LSE on the day/night TISI concept and then deriving the LST using the SW method.

The primary product of the MSG is the MSG Level 1. [START_REF] Wan | New refinements and validation of the MODIS land-surface temperature/emissivity products[END_REF][START_REF] Wan | Radiance-based validation of the V5 MODIS land-surface temperature product[END_REF]. LSEs from the V5 MOD11B1 product, on the other hand, have not been validated. Therefore, the cross-calibration in this study is carried out in terms of LST. In addition to day and night LSTs for each pixel, information on LSE, VZA, viewing time, and quality control (QC), which are the key input parameters for the cross-calibration, is also stored.

Because the LST significantly varies in space and time, this cross-validation of SEVIRI-derived LST with MODIS LST under clear skies (QC=0) must be conducted on the same site and within as short a time period as possible. Considering the facts that the SEVIRI provides LSTs every 15 minutes and the SEVIRI measurements closest in view time to the MODIS are eligible to be used in this study, only the pixels with a view time difference of less than 7.5 minutes are considered for this cross-validation. In addition, to solve the issue of inconsistency in spatial resolution, a radiance-based pixel aggregation method is employed to match coordinates between the two LST products [START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF]. Furthermore, because

Meteosat is a geostationary meteorological satellite, its sun-satellite viewing geometry results in the observation of sunlit scenes; MODIS, on the other hand, obtains the LSTs over the entire study area from a wide range of viewing perspectives, and its sun-satellite viewing geometry leads to the detection of a portion of shadow surfaces. To reduce the difference in LST caused by the shadow surfaces, only pixels with MODIS VZAs less than 30° are considered in this study.

All of the datasets used in this study are recapitulated in Table 5.1.

LSE Retrieval

(1)Principle of day/night TISI-based method: This method uses the property that, in the MIR channel (approximately at 3.7 mm), the radiance emitted by the land surface itself and the reflected radiance due to solar irradiation during the day are on the same order of magnitude if the surface reflectance in this channel is approximately 0.1. [START_REF] Li | Feasibility of land surface temperature and emissivity determination from AVHRR data[END_REF] and [START_REF] Li | A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data[END_REF] proposed and improved method to retrieve the LSE using the sun as an active source by defining the two channel TISI ij without the contribution of solar illumination between channels i and j as Eq. ( 5.1) through the power-law approximation of Planck's function (Jiang et al., 2006;Li et al., 2013b;[START_REF] Nerry | Emissivity signatures in the thermal IR band for Remote Sensing: calibration procedure and method of measurement[END_REF]Tang et al., 2009).

1/ , , 1/ ( , ) ( , ) [ ] [ ] j i n i v g i j v g j n ij i i j j R T R T TISI m C m C q q - = (5.1) with , , 1 1 (
) atm i atm i i i s i R R C B T R ¯ǣ ö ae ö = - - ç ÷ ç ÷ è ø è ø
where n i and m i are the exponents of the power law approximation of channel radiance:

( ) i n i i B T m T = .
The surface temperature T s involved in the calculation of C i is taken as the maximum surface temperature calculated with LSE=0.98 in channels 7, 9 and 10.

Assuming that the TISI ij (i= 4, j = 9 or 10) in the daytime without the contribution of solar illumination is the same as the TISI ij in the night-time, the bi-directional reflectivity (r i (q v , q s , j)) in the MIR channel (i=4 for SEVIRI channel 4) can then be extracted through a comparison of the day and night TISI ij (see Eq. ( 5.2)):

/ , , , , , , ( , )
[ ] ( , ) ( , ) ( , ) ( , , ) i j day day n n j v g i night i v g i day night night i v g i j v g i i v s sun i sun i R T C R T R T C R T E E q q q q r q q j = - (5.2) with / i j n n i j C C C - = .
Because the MSG-2/SEVIRI provides data every 15 minutes with a fixed VZA for a given pixel, various values of r i (q v , q s , j)with different solar illuminated directions during the daytime can be extracted for the same pixel; the LSE in the MIR channel (ε i (q), see Eq.

(5.4)) is then estimated to be a complement to the hemispheric-directional reflectivity, which is the integration of the angular variation of the bi-directional reflectivities described by the RossThick-LiSparse-R model [START_REF] Roujean | A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data[END_REF][START_REF] Lucht | Considerations in the parametric modeling of BRDF and albedo from multiangular satellite sensor observations[END_REF].

2

/2 0 0 ( ) 1 ( , , )sin( ) cos( )

i v i v s s s s d d p p
e q r q q j q q q j = -ò ò

(5.3) , , , , , ( , , ) ( , , ) ( , , )
i v s iso i vol i vol i v s geo i geo i v s K K f K f
r q q j q q j q q j = + ´+ ´ (5.4) where K iso,i is the isotropic scattering term in channel i, K vol,i and K geo,i are the coefficients of the volumetric kernel f vol,i and the geometric kernel f geo,i in channel i, respectively.

Assuming that the ratio of the vertical radius to the horizontal width of the spheroid crown is equal to unity and that the distance to the centre of the crown above the ground is equal to the vertical radius, for a given view zenith ranging from 0° to 80°, [START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF] calculated the integration of f vol,I and f geo,I (If q q q j q q q j q = = + ò ò

(5.5) 2 /2 2 1 , , 0 0 0 ( ) ( , , )sin( ) cos( ) exp[ 2( ) ] / 2 v c geo i v i geo v s s s s B If f d d B p p
q q q q q j q q q j w w p -= = + -ò ò

(5.6)

TISI

e q e q =

(5.7)

(2) Atmospheric corrections: It is worth noting that the TISI-based method requires the quantity R i (θ v ,T g,i ) (i= 4, 9, 10) to construct the TISI and that atmospheric corrections must be performed for the SEVIRI MIR and TIR channels. In this study, after data checking, cloud screening and land/sea water masking procedures, the atmospheric correction is performed using MODTRAN 4.0 with ECMWF atmospheric profile data, as the TISI-based method is not sensitive to the error of atmospheric correction. However, because ECMWF atmospheric profiles have lower temporal and spatial resolutions than the SEVIRI data, modified schemes are used to approach this problem. At each synoptic time, the spatial bilinear interpolation method is used to interpolate the atmospheric quantities estimated with the ECMWF atmospheric profile data using MODTRAN 4.0, and these interpolated atmospheric quantities are then used to correct for the atmospheric effects for SEVIRI pixels. For the data measured at times other than the synoptic time, different schemes are employed to correct for the atmospheric effects in the MIR and TIR channels.

For the MIR channel (SEVIRI channel 4), time-nearest atmospheric quantities are used to correct for atmospheric effects because the measurements in this channel are less sensitive to changes in the atmospheric WVC. However, because SEVIRI channel 4 cannot be regarded as a channel with a narrow spectral range, the use of Eqs. (2.22) and (2.23) will introduce large errors in the radiance at ground level in this channel. Jiang et al. (2006) and [START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF] introduced a temperature-dependent channel-averaged transmittance of SEVIRI channel 4 (τ 4 (θ v , T g,4 )) defined by Eq. ( 5.8):

( ) ( , , ) ( , ) ( , ) ( ) ( , , ) v g v v g v g f R T d T f R T d
l q l t q l l t q l q l l ¥ ¥ = ò ò

(5.8)

Consequently, the channel-averaged radiance in channel 4 (L 4 (θ v , T 4 )) can be rewritten as:

4 4 4 , 4 4 ,4 ,4 ,4 ( , )
( , ) ( , ) ( ) ( ) v v g v g v v atm s R T T R T R R q t q q q q - - = + + (5.9)
As shown in Eq. ( 5.9), to obtain R 4 (θ v , T g , 4 ), the quantity τ 4 (θ v , T g, 4) is a key parameter that depends on the estimation of T g,4 . The change in τ 4 (θ v , T g, 4) was observed to be less than 0.6% when T g,4 varies by 10 K (Jiang et al., 2006;[START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF], which indicates that the quantity τ 4 (θ v , T g,4 ) can be obtained with sufficient accuracy using an approximate temperature. Therefore, the quantity R 4 (θ v , T g,4 ) can be obtained as follows:

1) Computation of the approximate brightness temperature (T g,4a ) in channel 4 at ground level using Eq. (2.22);

2) Estimation of the average transmittance in SEVIRI channel 4 (τ 4 (θ v , T g,4a )) using Eq.

(5.8) with T g,4a ;

3) Inversion of Eq. ( 5.9) to obtain R 4 (θ v , T g,4 ) with the estimated τ 4 (θ v , T g,4a ) in the second step.

Because the TIR channels (SEVIRI channels 9 and 10) are very sensitive to changes in the atmospheric water vapor content (WVC), a large error would be produced when using the time-nearest atmospheric data to obtain R j (θ v , T g,j ) (j = 9 and 10) for the images acquired at times other than the synoptic times (UTC: 05:57, 11:57, 17:57 and 23:57). Therefore, to address this limitation, a two-part semi-empirical diurnal temperature cycle (DTC) model with six unknown parameters [START_REF] Göttsche | Modeling of diurnal cycle of brightness temperature extracted from METEOSAT data[END_REF] is used for the temporal interpolation to obtain T g,j ( j= 9 and 10) at any times from those used at synoptic times.

The DTC model is described by the following equations:

j j j s j d j j j b t t b b b a - = - 1, , , 2, cos( ( )
) j j j j s j d j j b a b t t b b = + - -
where β j is the angular frequency in channel j (j=9 and 10), t d,j is the time where the T g,j reaches its maximum in channel j, t s,j is the starting time of attenuation in channel j, and α j is the decay coefficient at night-time in channel j.

Because atmospheric quantities are only available at four synoptic times in one day for each location, the DTC model is underdetermined with four measurements. To reduce the number of unknowns in the model and to make the model deterministic, t d,j (j = 9 and 10) and t s,j in the model are taken as known parameters and are pre-determined from the quantity T j by assuming that the atmosphere between the ground and the satellite does not change t d,j and t s,j . The DTC model described by Eqs. (5.10) and ( 5.11) with pre-determined t d,j and t s,j values is then fitted with a Levenberg-Marquardt least squares algorithm. However, at some locations, because of the constraint in the model related to sunrise time or cloud contaminations, only three T g,j values are available for fitting the DTC model, or only one T g,j valueis eligible before t s,j even if four T g,j values are available.

In this case, the parameter α j in the DTC model is also assumed to be the same at the satellite and ground levels. Furthermore, due to the large fluctuation in temperature and the inaccuracy of the DTC model around sunrise, data from two hours before sunrise to two hours after sunrise are excluded to construct a more accurate TISI (and therefore to retrieve more accurate LSEs).

The procedures for performing atmospheric corrections of the TIR images are shown in Fig. 5.1. These procedures are primarily divided into three steps: 1) with the aid of MODTRAN 4.0, the atmospheric quantities at a spatial resolution of 0.25°/0.25° latitude/longitude at the synoptic times are estimated with the ECMWF profiles and other ancillary data required in MODTRAN; the atmospheric effects for the SEVIRI images at each synoptic time are then corrected with spatial interpolated atmospheric quantities corresponding to SEVIRI pixels estimated with a bilinear interpolation method; 2) the parameters in the DTC model are derived from T j (j = 9 and 10); 3) the T g,j (j=9 and 10) at times other than the synoptic times are estimated from those at the synoptic times using the DTC model with pre-determined t d,j , t s,j or α j . (3) LSE retrieval: To construct the TISI, the radiance R i (q v ,T g,i ) (i=4, 9 and 10) is calculated using the spatially interpolated atmospheric quantities estimated from the ECMWF data and then combined with the DTC model aforementioned. Using these R i (q v ,T g,i ) values, the LSE can be retrieved with the following procedures:

1) The E sun,4 value at any local time is predicted by assuming that E sun,4 simply varies as a cosine function (see Eq. ( 5.12)). (5.12)

where t is the local solar time and A s , B s and β s are unknown coefficients that can be pre-determined using various E sun,4 values. To accelerate the processing of ECMWF data, the E sun,4 quantity is calculated every two hours rather than every 15 minutes (the temporal resolution of the SEVIRI).

2) The values of r i (q v , q s , j) in channel 4 with different solar illumination directions are calculated assuming C day /C night = 1.0 in Eq. 5.2, which is a rather good approximation of the r 4 (q v , q s , j) estimation (Li et al., 2006;[START_REF] Li | Physical retrieval of surface emissivity spectrum from hyperspectral infrared radiances[END_REF]. In this procedure, all of the daytime data are used, and data at 23:57 UTC are selected because (a) the TISI at night is stable (constant value during the night-time); (b) ECMWF profile data are only available at 00:00, 06:00, 12:00, 18:00 UTC every day; therefore, the SEVIRI data acquired at 23:57 UTC can be atmospherically corrected using ECMWF atmospheric profiles; and (c) for the studied region, all of the pixels observed at 23:57 UTC are measured during the night when no direct or diffuse solar radiation is present.

3) Based on the RossThick-LiSparse-R model and Kichhoff's law, ε 4 (θ v ) is derived using Eqs. ( 5.3) and (5.4). A Levenberg-Marquardt minimisation scheme is utilised to determine the parameters K iso,4 , K vol,4 and K geo,4 in Eq. ( 5.4) for each pixel using various values of r 4 (q v , q s , j) with different solar illumination directions. In the fitting procedure, if the absolute difference between the measured and modelled bi-directional reflectivity is two times greater than the root mean square error (RMSE), this measurement is eliminated in the next fitting procedure until the number of measurements is reduced by 30%; in this manner, the bi-directional reflectivity model is not sensitive to the errors in the reflectivities and the parameters K iso,4 , K vol,4 and K geo,4 can be correctly inverted. However, in Jiang's work [START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF], the minimum number of measurements was set to 7, which may result in local optimal solutions rather than global optimal solutions. 4) Due to the stability of the TISI at night and the availability of ECMWF data, the night-time TISI 4,9 and TISI 4,10 at 23:57 UTC are calculated and used to estimate e 9 (q v ) and e 10 (q v ) according to Eq. (5.7). However, Jiang et al. (2006) and Jiang ( 2007) derived e 10 (q v ) from e 9 (q v ) using TISI 9,10 . This derivation resulted in the propagation of error in e 9 (q v ) to e 10 (q v ).

LST retrieval method

It is worth noting that the estimated errors in LSE in SEVIRI channels 9 and 10 with the TISI method are not independent; in particular, the error in the emissivity difference between the two adjacent channels, De, is very small. Because the LST retrieval using the single-channel method is sensitive to the error of atmospheric corrections but not the LSE retrieval using the TISI method, and given that the error in De is small, the SW method can be used to derive the LST with an acceptable accuracy [START_REF] Li | Feasibility of land surface temperature and emissivity determination from AVHRR data[END_REF]. In this study, a generalized SW method (GSW) proposed by Becker and Li (1990) and developed by Wan and Dozier (1996) is used, in which the LST is retrieved from the MSG-2/SEVIRI data in the form of Eq. (5.13). + ´+ ´´+ + ´+ ´´ (5.13) where e is the averaged emissivity [e = (e 9+ e 10 )/2];De = (e 9-e 10 ); A 0 , A 1 , A 2 , A 3 , B 1 , B 2 and B 3 are unknown coefficients for a given VZA and a given sub-range of ε, LST and WVC which must be pre-determined.

The coefficients A i , B i , and A 0 (i= 1, 3) in Eq. ( 5.13) can be pre-determined either from the ground LST measured synchronously with satellite brightness temperatures (T j , j = 9, 10) or from simulated data under various atmospheric and surface conditions by statistical regression.

To render the algorithm operational, the determinations of the GSW coefficients A i , B i (i= 1, 3) and A 0 areperformed using numerical simulations, as insufficient high quality simultaneous in situ measurements of the LST for a wide range of surface types and atmospheric conditions are available to permit a good determination of the coefficients.

In the simulations using MOTRAN 4.0 (Berk et al., 1998) to acquire the values of coefficients A i , B i (i=1, 2, 3) and A 0 in Eq. ( 5.13), 1413 clear-sky atmosphere profiles are used in combination with various land surface conditions (LSE and LST), VZA and spectral response functions of SEVIRI channels 9 and 10 to drive MODTRAN to simulate T j (j = 9 and 10) (Gao et al., 2013;Tang et al., 2008). For each VZA (eleven VZAs at the surface (0º, 10.16º, 20.33º, 30.52º, 35.63º, 40.76º, 45.91º, 51.08º, 56.31º, 61.6º, 67º) are considered in this work), various values of T j (j =9 or 10) with different atmospheric profiles and surface conditions are simulated according to Eqs.(2.22) and (2.23). Furthermore, to improve the accuracy of LST retrieval for each VZA, the WVC, LSE and LST are divided into several tractable sub-ranges, i.e., WVC: 0-1.5, 1.0-2.5, 2.0-3.5, 3.0-4.5, 4.0-5.5 and 5.0-6.5 g/cm 2 ; e: 0.90-0.96 and 0.94-1.0; LST: 280.0 K, 275-295 K, 290-310 K, 305-325 K, and 320 K. The coefficients A 0 , A 1 , A 2 , A 3 , B 1 , B 2 and B 3 in Eq. ( 5.13) for each sub-range of e, LST and WVC can be determined using the minimisation procedure with the simulated data. The results indicate that the RMSE of the retrieved LST varies with the VZA and the atmospheric WVC and that the RMSEs are within 1.0 K for all sub-ranges where the VZA<30° and WVC<4.25 g/cm 2 .

Furthermore, because the atmospheric WVC is used to select the optimal coefficients A i , B i (i = 1, 2, 3) and A 0 in the GSW method, an accurate WVC is not required provided that the estimated WVC is within the same range as the actual WVC. The method proposed by [START_REF] Li | A new approach for retrieving precipitable water from ATSR2 split-window channel data over land area[END_REF] is used to estimate the WVC at the spatial resolution of several pixels from the SEVIRI measurements of channels 9 and 10. If this method fails (if the square of the correlation coefficient is less than 0.95), the WVCs provided by the ECMWF data are used to obtain the WVC corresponding to the relevant pixel with a temporal and spatial linear interpolation.

In practice, the LST is estimated in two steps. First, the approximate LST is estimated with the coefficients for the entire LST; then, a more accurate LST is determined using the coefficients for the LST sub-range containing the approximate LST [START_REF] Jiang | Split-window algorithm for land surface temperature estimation from MSG1-SEVIRI data[END_REF]. (2) Atmospheric correction: with the aid of MODTRAN 4.0, the atmospheric effects for the SEVIRI channels 4, 9 and 10 are corrected. For channels 9 and 10, the DTC at ground level has the same parameters (t d,j and t s,j , or α j ,t d,j and t s,j (j=9 and 10))as the DTC at the satellite level rather than β j and t s,j ;. (3) LSE retrieval: LSEs in channels 4, 9 and 10 are derived based on the TISI concept and the bi-directional reflectivity model (RossThick-LiSpare-R model). In the LSE retrieval, the schemes used to fit the bi-directional reflectivity model and retrieve the LSE in the SEVIRI channel 10 are improved; (4) LST retrieval: the LST is estimated using the GSW (Eq.( 5.13)) with the knowledge of T j (j=9 and 10), the pre-determined coefficients [A i , B i (i = 1, 3) and A 0 ] in Eq.( 5.13), ε j (θ v ) (j=9 and 10), and the WVC either estimated with the method proposed by [START_REF] Li | A new approach for retrieving precipitable water from ATSR2 split-window channel data over land area[END_REF] or provided by the ECMWF data.

Results

To analyze the performance of the algorithm described in section 5.3 under different climatic and atmospheric conditions, the algorithm is applied to these datasets on four clear-sky days (August 22, 2009;March 13, 2009;July 3, 2008 andMarch 2, 2008) As mentioned in section 5.3, some improvements are adopted for the retrievals of the LSE/LST from the MSG-2/SEVIRI data. The effects of these improvements on results are analyzed with the datasets on the aforementioned four clear-sky days in terms of the following three aspects:

(1) The DTC at ground level has the same parameters (t d,j and t s,j , or α j ,t d,j and t s,j (j=9 and 10))

as the DTC at the satellite level rather than β j and t s,j . As illustrated in Fig. 5.2, unreasonable DTC coefficients are usually derived with pre-determined β j and t s,j values when only one T g,j value is eligible before t s,j, even if four T g,j values are available, which results in the failure of the atmospheric correction with the DTC model for the TIR data. (2006) and [START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF], the differences between the SEVIRI LSE1 and the SEVIRI LSE2 in channels 4, 9, 10 and the differences between the SEVIRI LST1 and SEVIRI LST2 at 11:12 are calculated. On the previously mentioned four days (August 22, 2009;March 13, 2009;July 3, 2008 andMarch 2, 2008), the LSE differences are within 0.1 for the most cases in channel 4 and within 0.05 in channels 9 and 10; the LST differences are within 2 K in most cases. As an example, the LSE/LST differences on August 22, 2009 (UTC time)

and the corresponding histograms of these differences are displayed in Fig. 5.11. It is worth nothing that the LSE differences in channel 4 are considerably higher than those in channels 9 and 10; more than 85% of the LSE differences are within 0.05 for channel 4 and 0.02 for channels 9 and 10; more than 85% of the LST differences are within 1.0 K, though, in some instances, differences greater than 2.0 K are also observed. Moreover, the diurnal cycles of SEVIRI LST1/LST2 on August 22, 2009 and their differences in two homogeneous areas (geographic coordinates: 35.176°N, 5.001°W; 34.493°N, 2.527°W) covered by trees and shrubs, respectively, are displayed in Fig. 5.12. It is worth noting that the differences between SEVIRI LST1 and SEVIRI LST2 over the tree-covered area are within 0.65 K, while the differences over the shrub-covered area range from -1.0 K~-1.5

K.

Preliminary cross-validation

LST over the entire study area on the two clear-sky days (August 22, 2009 andJuly 3, 2008).

The LST differences between the SEVIRI LST1 and the MODIS LST as a function of the MODIS LST and the histograms of the LST differences are shown in Fig. 5.13. It is noteworthy that the differences between the SEVIRI LST1 and the MODIS LST have no obvious relationship with the MODIS LST; approximately more than 70% of the LST differences are within 2.5 K on both August 22, 2009 andJuly 3, 2008, and the differences in LST tend to be lower at night than during the day, which may be explained by the relatively homogeneous thermal conditions of the Earth's surface at night. However, in several instances, differences between the SEVIRI LST1 and the MODIS LST on August 22, 2009 reach approximately 10 K, which may have been caused by the misdetection of clouds. LST differences tend to be lower at night than in the day, which may result from the homogeneous thermal conditions at night.

Perspectives

The results of this study open interesting perspectives. In the LSE retrieval from SEVIRI data, the accuracy of the estimated LSEs is mainly dependent on the accuracy of atmospheric corrections and the performance of the BRDF model. Therefore, the atmospheric correction scheme and the BRDF model still need to be improved. In the LSE retrieval from AMSR-E data, efforts should be made on the simultaneous retrieval of LSE and LST.

  les anciens capteurs MW ont un faible rapport signal/bruit, conduisant à une grande erreur sur la LSE. Les travaux présentés dans ce mémoire de thèse visent à: (1) améliorer la méthode de d'obtention de la LSE à partir du capteur SEVIRI (SpinningEnhanced Visible and InfraRed Imager) à bord du satellite géostationnaire MSG (Météosat Seconde Génération), (2) à élaborer des méthodes d'extraction LSE à partir des données MW du capteur AMSR-E (Advanced Microwave Scanning Radiometer-EarthObserving System).

  simple et précise entre l'émission de surface à des fréquences différentes et celles qui ont une polarisation différente, ainsi que les mesures des paramètres de rugosité de surface couramment utilisés. Pour éliminer les effets atmosphériques dans la région MW, le modèle MonoRTM (MONOchromatic Radiative Transfer Model) a été utilisé pour obtenir l'influence exacte de l'atmosphère. Finalement, le modèle SARTM (Soil-Atmosphere Radiative Transfer Model (SARTM)) a été construit à partir des modèles MonoRTM et AIEM. Le deuxième objectif de ce chapitre est construire une base de données de LES à partir de données du capteur AMSR-E et de LST obtenues à partir de données MODIS. La section suivante est consacrée à (1) développer une relation empirique entre les émissions de surface dans la région MW avec polarisation verticale et horizontale (2) développer une méthode pour récupérer les LSE à partir des données AMSR -E, à condition que la LST soit connue ou puisse être estimée d'une autre façon.

  ............................................................................................ 2.3 Radiative transfer equation ......................................................................................... 2.4 Atmospheric scattering ............................................................................................... 2.5 Absorption by atmospheric gases ............................................................................... 2.5.1 Water vapor absorption ................................................................................... 2.5.2 Carbon dioxide ................................................................................................ 2.5.3 Ozone absorption ............................................................................................. 2.5.4 Oxygen absorption .......................................................................................... 2.5.5 Extinction by clouds and precipitation ............................................................ Chapter 3 Data collection and pre-processing .....................................................................

  ................................................................................................... 3.3.1 MODIS data pre-processing ............................................................................ 3.3.2 Estimation of atmospheric quantities at spatial resolution of AMSR-E pixel from the quantities at 1°×1° .................................................................... 3.3.3 MODTRAN ..................................................................................................... Chapter 4 LSE and LST retrieval from AMSR-E data ...................................................... 4.1 Introduction ................................................................................................................

Figure 3 . 3

 33 Figure 3.3 Normalized spectral function response of SEVIRI channels 4, 7, 9 and 10 ...........

Figure 3 . 4

 34 Figure 3.4 Illustration of coordination matching between AMSR-E and MODIS pixels. The red rectangle represents an AMSR-E pixel and small blue rectangle represents an MODIS pixel ...................................................................................................................

Figure 4 . 1

 41 Figure 4.1 Simulation of LSE from AIEM ..............................................................................

Figure 4 .

 4 Figure 4.2 MonoRTM ..............................................................................................................

Figure 4 . 3

 43 Figure 4.3 Land surface-atmosphere radiative transfer model, module 1 is used to read atmospheric parameters and land surface parameters; module 2 is used to calculate the atmospheric cross-section data and simulate surface emissivity; module 3 is used to complete the radiative transfer computation to get the zenith observations of microwave brightness temperature .................................................................................

Figure 4 . 4

 44 Figure 4.4 Simulation of brightness temperature at sensor ......................................................

Figure 4 . 5

 45 Figure 4.5 Percentage transmission through the U.S. standard 1976 model atmospheric profile, under clear sky conditions ..................................................................................

Figure

  Figure 4.6 Relationships between vertical and horizontal polarized emissivities at each frequency(6.925GHz, 10.5GHz, 18.7GHz, 23.8GHz, 36.5GHz and 89.0GHz) ............

Figure 4 . 7

 47 Figure 4.7 Relationship between vertical and horizontal polarization emissivities at 10.65 GHz .................................................................................................................................

Figure

  Figure 4.8 The atmospheric upwelling radiation (T au ) and the difference of upwelling and downwelling radiation (T ad -T au ) for six frequencies (6.925GHz, 10.5GHz, 18.7GHz, 23.8GHz, 36.5GHz and 89.0GHz) ..................................................................................

Figure 4 .Figure 4 .Figure 4 .Figure 4 .Figure 4 .Figure 4 .Figure 4 .Figure 5 . 1

 444444451 Figure 4.20 Comparison between the T Bp-TOA and T Bp-BOA at vertical polarization (a) and horizontal polarization (b) .............................................................................................. 78 Figure 4.21 Comparison between theT Bp_toa and T Bp_land at vertical polarization (a) and horizontal polarization (b) .............................................................................................. 79 Figure 4.22 Linear relationships between w and t at 18.7 and 23.8 GHz ................................ 81 Figure 4.23 Difference between simulated values of T B18.7V-TOA and T B18.7v_land . • T 1 is the difference between the simulated values of T B18.7v-TOA and T B18.7v_land . • T 2 is the difference between the estimated values of T B18.7v_land using Eq. (4.21) and T B18.7v_land . 82 Figure 4.24 Difference betweenT s estimated by Eq. (4.22) and actual value of T s .................. 83 Figure 4.25 Histograms of the results of Eqs. (4.23) and (4.24) using the simulated data ...... 84 Figure 4.26 Histogram of the results computed by Eq. (4.25), based on the simulated data ... 84

  regional emissivity estimation methods from remotely sensed data, this work thus concerns on the methodological development permitting to determine both the temperature and surface emissivity from data of the SEVIRI (Spinning Enhanced Visible and Infra-Red Imager) instrument flown on the Second Generation of Meteosat satellites (MSG) and data from AMSR-E and MODIS instruments onboard the same Aqua platform. It is part of the CEOP-AEGIS project (Coordinated Asia-European long-term Observing system of Qinghai -Tibet Plateau hydrometeorological processes and the Asian monsoon systEm with Ground satellite Image data and numerical Simulations) funded by the European Commission under the programme FP7 for a period of 5 years from May 1, 2008. One of the objectives of this project is to develop methodologies for retrieving the geophysical parameters from satellite data.

  as the Stefan-Boltzmann law. Here, s is called the Stefan-Boltzmann constant.

  the land complex dielectric constant; θ is the angle of incidence; p is the polarisation(p = v or h); ρ (the surface reflectivity) is a function of zenith angle, frequency, polarization and other physical parameters, e.g. temperature and salinity.

Figure 2 . 1

 21 Figure 2.1 Geometry of incident and scattered radiation[START_REF] Ulaby | Microwave remote sensing: active and passive, Vol. II: radar remote sensing and surface scattering and emission theory[END_REF]

  Fig. 2.2illustrates the different terms in the radiative transfer equation at a wavelength λ , all of these different terms together form the spectral radiance measured at TOA. As seen from this figure, the surface emission, which is a function of LST and LSE is denoted as[1] and expressed by the first term in the right-hand side of Eq.

  temperature at ground level;

Figure 3

 3 Figure 3.1 The NDVI over world of the 2007

  3.1 is our study area for mapping LSE from TIR data. This study area is composed of the Iberian Peninsula and part of the Maghreb, with geospatial coverage of latitude 30° N to 45° N and longitude 15° W to 15° E. The Iberian Peninsula is the westernmost section of the European continent, lying between the Mediterranean Sea and the Atlantic Ocean, occupied by Spain and Portugal. It is dominated by two major climatic groups, namely, the Oceanic or Western Coastal Maritime to the North and the Mediterranean climate, which influences the southern two-thirds of the peninsula. As displayed in land use/land cover (LULC) map (Fig. 3.2), which is generated from the global land cover 2000 produced by the Institute for Environment and Sustainability (IES), the representative LULC in Iberian Peninsula typically consist of cultivated and managed areas (about 53%), tree cover (about 35%) and shrub cover (about 9%). The Maghreb is primarily defined through a series of physical geographic features that separate the area from the rest of Africa. It includes the Northern African nations of Algeria, Tunisia and Morocco and is covered by the Sahara desert and Atlas Mountains. The north of the Maghreb in the study area is dominated by shrub (about 9%) and herbaceous (about 26%), while the south is dominated by bare area (about 52%). Climatically, the Maghreb has a Mediterranean climate in the north, and has the arid in the south.

Figure 3 . 2

 32 Figure 3.2 The land cover of the study area, generated from the Global Land Cover 2000 produced by IES (http://bioval.jrc.ec.europa.eu/products/glc2000/products.php)

  Figure 3.3 Normalized spectral function response of SEVIRI channels 4, 7, 9 and 10

  -derived LST corresponding to the ith MODIS pixel within an AMSR-E pixel; N is the total number of MODIS clear pixels within an AMSR-E pixel. If N<20, the AMSR-E pixel is discarded in this study.

Figure 3 . 4

 34 Figure 3.4 Illustration of coordination matching between AMSR-E and MODIS pixels. The red rectangle represents an AMSR-E pixel and small blue rectangle represents an MODIS pixel

MODTRAN 4 .

 4 0 is the current version of MODTRAN. Compared with MODTRAN 3, it adds some new features, such as two Correlated-K (CK) options, azimuth dependent DISTORT option, upgraded ground surface modelling, high-speed option, making the calculation more accurate than the former version (Berk et al., 1998; MODTRAN 4.0 User's Manual). MODTRAN prescribes six standard model atmospheres: Tropical, Mid-Latitude Summer, Mid-Latitude Winter, Sub-Arctic Summer, Sub-Arctic Winter and 1976 U.S. Standard. The air temperatures of the first boundary and the total column water vapor contents of these six standard atmospheres are listed in Table

Figure 4 . 3 Figure 4 . 4

 4344 Figure 4.2 MonoRTM

Frequency

  4.5 shows the atmospheric transmittance calculated by our SARTM in function of frequency for the U.S. standard 1976 model atmospheric profile. It shows that the percentage of transmittance through the U.S. standard 1976 model atmospheric profile under clear sky conditions in microwave region increase with the increase of wavelength.

Figure 4 . 5

 45 Figure 4.5 Percentage transmission through the U.S. standard 1976 model atmospheric profile, under clear sky conditions Although MW has a strong penetrating ability to penetrate clouds and rain, the atmosphere is not completely transparent. There are still scattering, absorption and emission behaviour of atmospheric in the microwave region. As shown in the Fig. 4.5, in the low frequency region (6.925 GHz and 10.5 GHz), the atmosphere is practically transparent even in the presence of clouds and moderate rainfall rates.

  4.5), frequencies in the 40-70 GHz region are not used by active systems. However, multi frequency radiometers operating in the 50-60 GHz range are used for retrieving the atmospheric temperature profiles from radiometric observations. Radars are operated for remote sensing in the 32-36 GHz region, and some military imaging radars are around 95

  ɛ h ) emissivity were analyzed. The resulting scatter plots of the vertical and horizontal polarization emissivities at different frequencies are shown in Fig. 4.7.There are two clear trends. From points A to B, the value of s/cl changes from 0.055 to 0.333, while the volumetric soil moisture remains unchanged at 2%. From B to C, the volumetric soil moisture increases to 44%, while the value of s/cl is unchanged. From C to D, the value of s/cl decreases from 0.333 to 0.055, while the volumetric soil moisture is constant at 44%. For a given soil moisture condition and in the direction of varying surface roughness, the vertical and horizontal polarization emissivities are negatively correlated, and their relationship is almost linear. The slope and intercept of the linear function may depend on the soil moisture condition. For a given surface roughness and in the direction of varying soil moisture, the vertical and horizontal polarization emissivities are positively correlated, and their relationship is weakly nonlinear over the full range of emissivities for a given surface roughness condition. The slope and intercept are thought to be a function of s/cl.

Fig. 4 .Fig. 4 .

 44 Fig. 4.8 (a,b) displays the upwelling and downwelling atmospheric brightness temperature (T au and T ad computed by our SARTM versus the channels of AMSR-E for six standard atmospheres.

Figure

  Figure 4.6 Relationships between vertical and horizontal polarized emissivities at each frequency(6.925GHz, 10.5GHz, 18.7GHz, 23.8GHz, 36.5GHz and 89.0GHz)

Figure 4 . 7

 47 Figure 4.7 Relationship between vertical and horizontal polarization emissivities at 10.65 GHz

Figure

  Figure4.8 The atmospheric upwelling radiation (T au ) and the difference of upwelling and downwelling radiation (T ad -T au ) for six frequencies(6.925GHz, 10.5GHz, 18.7GHz, 23.8GHz, 36.5GHz and 89.0GHz)

Figure 4 . 9 Fig. 4 .Figure 4 .

 4944 Figure4.8 The atmospheric upwelling radiation (T au ) and the difference of upwelling and downwelling radiation (T ad -T au ) for six frequencies(6.925GHz, 10.5GHz, 18.7GHz, 23.8GHz, 36.5GHz and 89.0GHz)

Fig. 4 .

 4 Fig. 4.11 shows A and B versus sm, which can be expressed by quadratic functions of sm.

Figure 4 .Figure 4 .

 44 Figure 4.11 Coefficients A and B versus the volumetric soil moisture

Figure 4 .

 4 Figure 4.13 Comparison between the vertical polarization AIEM simulated soil surface emissivity and the corresponding emissivity calculated using Eq. (4.7)

,

  Figure 4.14 Comparison of the estimated and actual s/cl

  the land surface emissivity at polarization p; T Bp is the brightness temperature of AMSR-E; T s is the LST from MODIS data. Considering the low resolution of microwave data, a study area in Saharan Africa, as indicated by the red rectangle in Fig. 4.15, is considered. The vertical and horizontal polarization microwave emissivities at 10.65 GHz in 2004 are computed for this study area using Eq. (4.10).

Figure 4 .

 4 Figure 4.15 Land surface brightness temperature at 10.65 GHz vertical polarization for one day June 2th in 2004. The study area is bounded by the red rectangle and the locations of the eight evaluation sites are labeled from A to H

Figure 4 .

 4 Figure 4.16 Vertical and horizontal polarization emissivities at frequency 10.65 GHz; red: actual data; blue: simulated data

Figure 4 .

 4 Figure 4.17 Scatter plots of vertical and horizontal polarization emissivities for the eight sites in the Sahara Desert

Figure 4 .

 4 Figure 4.18Composite monthly mean LSE at 18.7 GHz and 36.5 GHz for horizontal and vertical polarizations in February 2006

Figure 4 .

 4 Figure 4.19 Daily evolution of LSE at Taklimakan site for February, June and September of 2006 at two polarizations

Fig. 4 .

 4 Fig. 4.20 shows the difference between T Bp-TOA and T Bp-BOA at vertical (DT BV ) and horizontal (DT BH ) polarization using the simulated data for all of the AMER-E channels. The mean and standard deviation (STD) for the difference between DT BV and DT BH are given inTable 4.3.

Figure 4 .

 4 Figure 4.20 Comparison between the T Bp-TOA and T Bp-BOA at vertical polarization (a) and horizontal polarization (b)

Fig 4 .

 4 Fig 4.21 shows the difference between T Bp-TOA and T Bp_land at vertical (DT' BV ) and horizontal (DT' BH ) polarization using simulated data for all of the channels of AMER-E. The mean and STD of the difference between DT' BV and DT' BH are given in Table 4.4.

Figure 4 .

 4 Figure 4.21 Comparison between the T Bp_toa and T Bp_land at vertical polarization (a) and horizontal polarization (b)

Figure 4 .

 4 Figure 4.22 Linear relationships between w and t at 18.7 and 23.8 GHz

Fig. 4 .

 4 Fig. 4.23 shows the difference between T B18.7V-TOA and T B18.7v_land (DT 1 ) and the difference between T B18.7v_land estimated by Eq. (4.20) and T B18.7v_land in the simulated database (DT 2 ).

Figure 4 .

 4 Figure 4.23 Difference between simulated values of T B18.7V-TOA and T B18.7v-land . T 1 is the difference between the simulated values of T B18.7v-TOA and T B18.7v-land . T 2 is the difference between the estimated values of T B18.7v-land using Eq. (4.21) and T B18.7v-land

Figure 4 .

 4 Figure 4.24 Difference between T s estimated by Eq. (4.22) and actual value of T s

Fig. 4 .

 4 Fig. 4.25 shows the results of Eqs. (4.23) and (4.24), computed using the simulated data.

Figure 4 .

 4 Figure 4.25 Histograms of the results of Eqs. (4.23) and (4.24) using the simulated data

Figure 5 . 2

 52 Figure 5.2 Measured/Modelled brightness temperatures at the satellite and ground levels in channel 9 with different known parameters (a) for the site located at 33.225° N, 6.378° E on March 2, 2008 and (b) for the site located at 38.024° N, -3.834° E on March 13, 2009

Fig. 5 .

 5 Fig. 5.3 shows the key procedures for retrieving the LSE and LST from SEVIRI data. These procedures are composed of four parts. (1) Data preparation: the MSG-2/SEVIRI radiances, corresponding cloud masks, land/water masks, ECMWF profiles and other ancillary datasets, such as view geometry data and geolocation information (latitudes and longitudes) are extracted or pre-processed using SPT software and self-developed C++ programs. (2) Atmospheric correction: with the aid of MODTRAN 4.0, the atmospheric

Figure 5 . 3 5 . 4

 5354 Figure 5.3 Procedures for retrieving the LSE/LST from the MSG-2/SEVIRI data

Figure 5 . 4

 54 Figure 5.4 Maps of the LSEs in SEVIRI channels 4, 9 and 10 and the LSTs at 11:12 (UTC time) retrieved from the SEVIRI data on August 22, 2009. (a) LSE in channel 4; (b) LSE in channel 9; (c) LSE in channel 10; (d) map of the retrieved LSTs at 11:12

Figure 5 . 5 Figure 5 . 6 Figure 5 . 7 -Figure 5 .

 5556575 Fig. 5.7. It is worth nothing that the value of LBST1 is lower than the value of LBST2 in most cases, and the differences between LBST1 and LBST2 are within 1 K. Similar results are obtained for March 13, 2009 and March 2, 2008.

Figure 5 .Figure 5 .

 55 Figure 5.11 The histograms of the differences between the SEVIRI LSE1 and the SEVIRI LSE2 on August 22, 2009 and the corresponding differences between the SEVIRI LST1 and the SEVIRI LST2 at 11:12 (UTC time). SEVIRI LSE1/LST1 is the LSE/LST retrieved using the algorithms proposed in this study, and SEVIRI LSE2/LST2 is the LSE/LST retrieved based on the original schemes proposed byJiang et al.(2006 and[START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF]

Figure 5 .

 5 Figure 5.13 The differences between the SEVIRI LST1 and the MODIS LST as a function of the MODIS LST and the corresponding histograms of their LST differences onAugust 22, 2009 and July 3, 2008. The SEVIRI LST1 is the LST retrieved in this study, and the MODIS LST is the LST extracted from the V5 MOD11B1 product

  Overview of LSE retrieval methods from TIR data in the past decades ...................... 1.1.1 Semi-empirical methods .................................................................................... 1.1.2 Multi-channel temperature/emissivity separation methods ............................... 1.1.3 Physically based methods (PBM) ..................................................................... 1.2 Overview of LSE retrieval methods from passive MW data in the past decades ...... 1.2.1 Stepwise retrieval methods .............................................................................. 1.2.2 Simultaneous LSE and LSTretrieval methods with known atmospheric information ....................................................................................................... 1.2.3 Artificial neural network (ANN)method ......................................................... 1.3 Problems/issues .......................................................................................................... 1.4 Main research contents ............................................................................................... Thermal radiation .......................................................................................................

	analysés et un algorithme de correction atmosphérique à 18,7 GHz avec une polarisation
	verticale est finalement proposé.
	Utilisant la base de données simulée, qui couvre un large éventail de conditions de surface Parce que les LST et LSE sont les principales caractéristiques de la surface de la Terre
	terrestres et atmosphériques, une analyse quantitative des effets atmosphériques sur les dans le domaine thermique, leurs restitutions à partir de l'espace a été étudié depuis plusieurs
	données AMSR-E a été faite. Les résultats montrent que l'atmosphère a un effet évident sur décennies. Sur la base des études de Jiang et al. (2006) et Jiang (2007), un algorithme
	l'observation du capteur AMSR-E, à l'exception des bandes 6.925 et 10,65 GHz avec une opérationnel amélioré est développé dans ce document pour déterminer à la fois le LSE et
	polarisation verticale et 6.925 GHz avec une polarisation horizontale. LST à partir des mesures MSG-2/SEVIRI. Trois modules de l'algorithme TISI jour/nuit sont
	améliorés : les corrections atmosphériques, la mise en place du modèle de réflectivité bi-
	Une correction atmosphérique est nécessaire avant d'utiliser les données AMSR-E pour directionnelle et la récupération de la LSE dans le canal SEVIRI 10. La méthode GSW
	estimer les paramètres de la surface terrestre. En utilisant des relations empiriques et des proposé par Becker et Li (1990) et amélioré par Wan et Dozier (1996) est ensuite utilisée pour
	obtenir la LST.
	e	, f p	, , B f p T	, au f ad f , T T T s ---	2 = f t T t f sp f f T t , ad f sp T t ´--´´
					x

d'abord construite. Les effets atmosphériques sur les observations AMSR-E sont ensuite ( ) hypothèses raisonnables, un algorithme de correction atmosphérique a été proposé. D'après les observations de deux canaux AMSR-E (18,7 et 23,8 GHz avec une polarisation verticale), les effets atmosphériques sur les observations à 18,7 GHz de polarisation verticale sont négligeables et peuvent être corrigés et la radiance émisse par la surface terrestre dans ce canal peut être estimée. De même, la LST peut être estimée en utilisant la méthode proposée et une émissivité connue. Les résultats montrent que l'algorithme de correction est performant. Avec des données simulées, l'erreur quadratique moyenne diminue de 6,04 K à 0,99 K, et l'erreur quadratique moyenne de la LST est estimée à 1,17 K. Ces résultats indiquent que la méthode proposée est essentielle pour améliorer les estimations des paramètres de la surface terrestre. Le chapitre 5 présente un algorithme amélioré pour obtenir simultanément LSE et LST à partir des données du capteur SEVIRI à bord du satellite MSG-2. La méthode des TISI (Temperature Independent Spectral Indices) a été révisée et sera utilisé pour déterminer l'émissivité. Du fait des faibles résolutions temporelles et spatiales des descriptions des profils atmosphériques disponibles, un logiciel pour adapter les corrections atmosphériques a été développé et amélioré avec le but d'estimer LSE à partir du concept des TISI. Enfin, cet algorithme amélioré est appliqué à plusieurs données SEVIRI sur la zone d'étude. Avec plusieurs cas détaillés, il est démontré que ces améliorations sur la méthode de détermination de la LSE sont efficaces et raisonnables. Ce chapitre est organisé comme suit. La section 5.2 décrit toutes les données utilisées dans cette étude. Les principes des méthodes d'extraction de LSE et LST et de leurs applications aux données SEVIRI sont détaillés à la section 5.3. La section 5.4 présente les résultats préliminaires obtenus à partir des données MSG-2/SEVIRI et xi les résultats de leur validation croisée avec les produits dérivés de LST MODIS. La section 5.5 est consacrée aux conclusions. L'application de notre algorithme aux données SEVIRI sous différentes conditions climatiques et atmosphériques a révélé que le nouvel algorithme de correction atmosphérique résout des problèmes qui existaient dans l'algorithme original proposé par Jiang et al. (2006) et Jiang (2007). En effet dans le cas particulier des corrections atmosphériques dans les régions avec une heure de levé du soleil tardive, les résultats obtenus par le nouvel algorithme sont plus réalistes, ce qui montre que l'algorithme amélioré est plus efficace. Pour valider résultats de LST (SEVIRI LST1) issus des mesures SEVIRI obtenus en utilisant l'algorithme amélioré, des validations croisées sont menées sur deux jours clairs (22 Août 2009 et 3 Juillet 2008) sur toute la zone d'étude avec les LST MODIS. Le résultat montre que dans plus de 70 % des cas, la différence entre le SEVIRI LST1 et MODIS LST est inférieur à 2,5 K. Les différences de LST ont tendance à être plus basses la nuit que pendant la jour en raison des conditions thermiques relativement homogènes de la surface de la Terre pendant la nuit. Toutefois, d'autres travaux de validation doivent être effectués en utilisant des mesures de terrain. Les conclusions de ce travail et les perspectives sont données dans le sixième chapitre. xii xiii Contents: Chapter 1 Introduction………. ............................................................................................... 1.1 Chapter 2 Fundamental radiometric theory ....................................................................... 2.1 2.1.1 Planck's Blackbody Radiation Law ................................................................ 2.1.2 Non-blackbody radiation ................................................................................. 2.2 Emission and scattering ..

  Satellite data and related data .....................................................................................

3.1 Study areas ................................................................................................................. 3.1.

1 Study area for retrieving LSE from MW data (study area A and C) .............. 3.1.2 Study area for retrieving LSE from TIR data (study area B) .......................... 3.2 3.2.1 Aqua satellite ................................................................................................... 3.2.2 AMSR-E data .................................................................................................. 3.2.3 MODIS data .................................................................................................... 3.2.4 MSG-2/SEVIRI data ....................................................................................... 3.2.5 Cloud MASK................................................................................................... 3.2.6 Synchronous atmospheric quantities ............................................................... 3.2.7 DEM ................................................................................................................ 3.3 Data pre-processing .

  ....................................................................Figure 3.1 The NDVI over world of the 2007..........................................................................Figure 3.2 The land cover of the study area, generated from the Global Land Cover 2000

produced by IES (http://bioval.jrc.ec.europa.eu/products/glc2000/products.php) .........

  ) .................................................................................. AandB versus the volumetric soil moisture ...................................... 67 Figure 4.12 Comparison between the vertical polarization AIEM simulated soil surface emissivity and the corresponding emissivity estimated by Eq. (4.6) .............................. 68 Figure 4.13 Comparison between the vertical polarization AIEM simulated soil surface emissivity and the corresponding emissivity calculated using Eq. (4.7) ........................ 68 Figure 4.14 Comparison of the estimated and actual s/cl ........................................................ 69 Figure 4.15 Land surface brightness temperature at 10.65 GHz vertical polarization for one day June 2th in 2004. The study area is bounded by the red rectangle and the locations of the eight evaluation sites are labeled from A to H ...................................... 70 Figure 4.16 Vertical and horizontal polarization emissivities at frequency 10.65 GHz; red: actual data; blue: simulated data ..................................................................................... 71 Figure 4.17 Scatter plots of vertical and horizontal polarization emissivities for the eight sites in the Sahara Desert ................................................................................................ 72

	Figure 4.9 Relationship between two polarized brightness temperatures at 6 frequencies
	simulated by SARTM through the U.S. standard 1976 model atmospheric profile (sm
	= 2% to 44% at step 2%,

s = 3 cm and cl = 30 cm) ........................................................

Figure 4.10 Relationship between simulated vertical and horizontal polarized brightness temperature from SARTM Model at 36.5GHz frequency for the 30 th June in 2006 at UTC time 0600, the location longitude 84°and latitude 38° where is sandy desert in China ............................................................................................................................... xvi Figure 4.11 Coefficients Figure 4.18 Composite monthly mean LSE at 18.7 GHz and 36.5 GHz for horizontal and vertical polarizations in February 2006 .......................................................................... 74 Figure 4.19 Daily evolution of LSE at Taklimakan site for February, June and September of 2006 at two polarizations ............................................................................................ 76

of Tables:

  Based on the schemes proposed in this paper and (b) based on the schemes proposed byJiang et al. (2006 and[START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF] ................................................ Jiang et al. (2006 and 2007) ...................................................... , respectively .......................................................... LST2 is the LSE/LST retrieved based on the original schemes proposed by Jiang et al.(2006 and 2007) ...........................................................................................

	Figure 5.6 Maps of the brightness temperature for surface-leaving radiances at 11:12,
	March 13, 2009. (a) Figure 5.7 Maps of the differences between the LSBT1 and LSBT2 at 11:12 (a) on August,
	22, 2009 and (b) on July 3, 2008 (UTC time) and the corresponding histogram of
	these differences. LSBT1 and LSBT2 are the brightness temperature for
	surface-leaving radiances based on the schemes proposed in this study and those
	derived using the original schemes proposed by Jiang et al. (2006 and 2007),
	respectively ...................................................................................................................
	Figure 5.8 Maps of the LSEs in channel 4 retrieved with the reflectivities on August 22,
	2009 using different schemes. (a) Using the schemes proposed in this study; (b)
	using the schemes proposed by Jiang et al. (2006 and 2007) .......................................
	Figure 5.9 Maps of the LSEs in channel 4 retrieved with the reflectivities March 13, 2009
	using different schemes. (a) Using the schemes proposed in this study; (b) using the
	schemes proposed by Figure 5.10 Histogram of the differences between the LSE1 10 and LSE2 10 on August 22,
	2009 (a) and July 3, 2008 (b). LSE1 10 and LSE2 10 are the LSEs in channel 10 derived
	from the LSEs in channels 4 and 9Figure 5.11 The histograms of the differences between the SEVIRI LSE1 and the SEVIRI
	LSE2 on August 22, 2009 and the corresponding differences between the SEVIRI
	LST1 and the SEVIRI LST2 at 11:12 (UTC time). SEVIRI LSE1/LST1 is the
	LSE/LST retrieved using the algorithms proposed in this study, and SEVIRI
	LSE2/Figure 5.12 The diurnal cycles of LST on August, 22, 2009 retrieved with our method and
	Jiang's method, at two respective sites (a) 35.176°N, 5.001°W, covered by trees; (b)
	34.493° N, 2.527°W, covered by shrubs ......................................................................
	Figure 5.13 The differences between the SEVIRI LST1 and the MODIS LST as a function
	of the MODIS LST and the corresponding histograms of their LST differences on
	August 22, 2009 and July 3, 2008. The SEVIRI LST1 is the LST retrieved in this
	study, and the MODIS LST is the LST extracted from the V5 MOD11B1 product ....
	....... 96
	Figure 5.2 Measured/Modelled brightness temperatures at the satellite and ground levels in
	channel 9 with different known parameters (a) for the site located at 33.225° N,
	6.378° E on March 2, 2008 and (b) for the site located at 38.024° N, -3.834° E on
	March 13, 2009 ............................................................................................................... 97
	Figure 5.3 Procedures for retrieving the LSE/LST from the MSG-2/SEVIRI data ............... 101
	Figure 5.4 Maps of the LSEs in SEVIRI channels 4, 9 and 10 and the LSTs at 11:12 (UTC
	time) retrieved from the SEVIRI data on August 22, 2009. (a) LSE in channel 4; (b)
	LSE in channel 9; (c) LSE in channel 10; (d) map of the retrieved LSTs at 11:12 ...... 102
	Figure 5.5 Maps of the brightness temperature for surface-leaving radiances at 11:12,
	March 2, 2008. (a) Based on the schemes proposed in this paper and (b) based on the
	schemes proposed by Jiang et al. (2006 and 2007) ...................................................... 103
	xvii

...............................................................................................................xviii List

Table 3 .

 3 1 Aqua (EOS PM-1) obit information ......................................................................... Table 3.2 AMSR-E performance characteristics ...................................................................... Table 3.3 Channel characteristics ............................................................................................. Air temperature at the first boundary and the total water vapor contents of the six standard model atmospheres prescribed in MODTRAN ................................................ .................................................................................................................

	Table 3.4 Spectral channel characteristics of MSG-2/SEVIRI instrument ..............................
	Table 3.5 Description of the ECMWF data of the year 2006 used for atmospheric
	correction of AMSR-E data ............................................................................................
	Table 3.6 as table 0.5, but for atmospheric correction of SEVIRI data in year 2008 ..............
	Table 3.7 Table 4.1 Coefficients in equation Eq. (4.7) for all frequencies of AMSR-E sensor ..............
	Table 4.2 Mean emissivity of February, June and September at horizontal (H) and vertical
	(V) polarizations and mean difference of LSE at two polarizations (	V e e e D = -) over H
	8 sites in 2006

Table 4 .

 4 3 Mean and standard deviation (STD) of the T BV and T BH for all channels of AMSR-E .........................................................................................................................

Table 4 .

 4 4 Mean and STD of theT' BV and T' BH for all channels of AMSR-E ...........................

  If 0 p

	scattering coefficient, respectively, and if 0 p and s p are different, it is known as the
	cross-polarized scattering coefficient ( ( ) ,	( ) ,
	and s p are both v or h, o s is called the vertically polarized or horizontally polarized

  g,f,p is the brightness temperature measured at ground by a channel having frequency f and polarization mode p; t f is the atmospheric transmittance at frequency f; T ad,f is the downwelling atmospheric equivalent temperature at frequency f,

	with

.24) T f,p is the brightness temperature measured at TOA by a channel having frequency f and polarization mode p; T

  Launched from Vandenderg Air Force Base on May 4th, 2002, the satellite has six different Earth-observing instruments onboard for studies of water on the Earth's surface and

	in the atmosphere. These six instruments include Advanced Microwave Scanning
	Radiometer-EOS (AMSR-E); Moderate Resolution Imaging Spectroradiometer (MODIS);
	Advanced Microwave Sounding Unit (AMSU-A); Atmospheric Infrared Sounder (AIRS);
	Humidity Sounder for Brazil (HSB) and Clouds and the Earth's Radiant Energy System
	NASA;	May 4 th , 2002;	98.14°;	708km;	691km;	98.4minutes;

(CERES). Aqua is polar orbiting with 1:30 A.M. (descending pass)/P.M. (ascending pass) in local time. It is a near-polar low-Earth orbit.

Table 3.1 Aqua (EOS PM-1) orbit information Operator; Launch date; Inclination; Apoapsis; Periapsis; Orbital period;

Table 3

 3 three visible and near-infrared bands, eight infrared bands and a visible broadband band, see Table3.4 every 15 minutes, making it particularly suitable for LSE determination on the day/night TISI concept and then deriving LST with split-window method. The spectral responses function for SEVIRI channels 4, 7, 9 and 10 are shown in Fig.

		.2 AMSR-E performance characteristics
	Polarization		Horizontal and Vertical
	Incidence angle (degree)			55
	Cross-polarization (dB)			Less than -20
	Swath (km)			1445
	Center frequency (GHz)	6.925	10.65	18.7	23.8	36.5	89.0
	Bandwidth (MHz)	350	100	200	400	1000	3000
	Sensitivity (K)	0.3	0.6	0.6	0.6	0.6	1.1
	IFOV (km)	74×43 51×30 27×16 31×18	14×8	6×4
	Sampling interval (km)	10×10 10×10 10×10 10×10 10×10	5×5
	Integration time (msec)	2.6	2.6	2.6	2.6	2.6	1.3
	Main beam efficiency (%)	95.3	95.0	96.3	96.4	95.3	96.0
	Beamwidth (degree)	2.2	1.4	0.8	0.9	0.4	0.18
	3.2.3 MODIS data

MODIS is a key instrument aboard the Terra (EOS AM) the Aqua (EOS PM) satellites.

Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths. Its channel characteristics are listed in Table

3.3

. Daily global LST product (MOD11C1/MYD11C1 Daily CMG LST) at 0.05 degree latitude/longitude resolution are used (1) as a reference LST to cross validate LST derived from SEVIRI data using the method improved in this thesis; (2) as a true LST detected by MW channels to retrieve LSE from AMSR-E data. by European Space Agency (ESA) and EUMETSAT, the latest satellite MSG-2 was launched in December 2005. Its main payload SEVIRI images an Earth disk with view zenith angle (VZA) ranging from 0° to 80° with 3-km nadir spatial resolution, and provides data in 12 spectral bands (

Table 3 .

 3 5 Description of the ECMWF data of the year 2006 used for atmospheric correction of AMSR-E data Data: Pressure (hPa), air temperature (K), relative humidity (%) and geo-potential (GP)

	Sampling	Step	Limits
	Temporal	6hours
	Geographical: Latitude	1°	10°S-60°N
	Geographical: Longitude	1°	20°W-160°E
	Vertical	21 levels*	To altitude about 48 km
	*Levels of pressure are 1000

Table 3

 3

	.6 As table 3.5, but for atmospheric correction of SEVIRI data in year 2008
	Data: Pressure (hPa), air temperature (K), relative humidity (%) and geo-potential (GP)
	Sampling	Step	Limits
	Temporal	6hours
	Geographical: Latitude	0.25°	0°N-60°N
	Geographical: Longitude	0.25°	20°W-40°E
	Vertical	25 levels*
	*Levels of pressure are 1000

Table 3 .

 3 7 Air temperature at the first boundary and the total water vapor contents of the six standard model atmospheres prescribed in MODTRAN

	Model atmosphere	T 0 (K)	W(g/cm 2 )
	Tropical	299.7	4.11
	Mid-Latitude Summer	294.2	2.92
	Mid-Latitude Winter	272.2	0.85
	Sub-Arctic Summer	287.2	2.08
	Sub-Arctic Winter	257.2	0.42
	1976 U S Standard	288.2	1.42
	*T 0 is the air temperature at the first boundary and W represents the total column water vapor
		content.

Table 4 .

 4 1 Coefficients in equation Eq. (4.7) for all frequencies of AMSR-E sensor

Table 4 .

 4 2 Mean emissivity of February, June and September at horizontal (H) and vertical (V) polarizations and mean difference of LSE at two polarizations ( ) over 8 sites in 2006 As an example, Fig. 4.19 displays the daily LSE retrieved from AMSR-E at Taklimakan site for February, June and September of 2006.

	V e e e D = -	H

Table 4 .

 4 3 Mean and standard deviation (STD) of the T BV and T BH for all channels of AMSR-E

	Frequency	Vertical Polarization	Horizontal Polarization
	(GHz)	mean(K)	STD	mean	STD (K)
			(K)	(K)
	6.925	0.44	0.44	1.81	0.65
	10.65	0.60	0.57	2.40	0.98
	18.7	1.33	1.78	6.91	4.22
	23.8	1.23	3.27	12.07	7.05
	36.5	0.48	2.11	8.71	4.38
	89	-1.97	4.28	9.69	4.98

Table 4 .

 4 4 Mean and STD of the T' BV and T' BH for all channels of AMSR-E

	Frequency (GHz)	Vertical Polarization	Horizontal Polarization
		mean (K)	STD(K)	mean (K)	STD (K)
	6.925	1.88	1.25	5.99	1.89
	10.65	2.16	1.50	7.14	2.52
	18.7	4.40	4.13	17.23	9.98
	23.8	6.95	7.64	32.62	19.98
	36.5	3.70	4.24	22.27	10.74
	89	1.71	5.04	32.94	16.71

  LST data at different atmospheric and land cover conditions on the two days are available. The MODIS LST product with a spatial resolution of 5.6 km was constructed using the results produced by the day/night LST algorithm and has been validated to have an accuracy greater than 1 K in most cases through field campaigns in 47 clear-sky cases

			5 product, which can be read by
	SEVIRI Pre-processing Toolbox (SPT) software. In this chapter, to illustrate the
	improvements on LSE and LST retrievals, the MSG datasets on August 22, 2009, March 13,
	2009, July 3, 2008 and March 2, 2008 are used. To delineate all possible occurrences of
	clouds within each pixel, the corresponding MSG cloud masks are used. In addition, the
	atmospheric effect for each pixel is corrected using the atmospheric radiative transfer code
	(MODTRAN 4.0) with the aid of the atmospheric profiles provided by ECMWF. The
	ECMWF reanalysis operational deterministic model data provide 25-level profiles of pressure,
	temperature, relative humidity, and geo-potential with spatial resolutions of 0.25°/0.25°
	latitude/longitude at four synoptic UTC times: 00:00; 06:00; 12:00; 18:00.
	Ancillary input data related to the LSE retrieval consisted of data on view/sun geometry,
	digital elevation model (DEM) and geolocation information (latitudes and longitudes) and
	land/sea water mask for each pixel.
	Moreover, to acquire the coefficients from simulated data obtained using the SW method
	for LST retrieval, 1413 clear-sky atmospheric profiles are extracted from the Thermodynamic
	Initial	Guess	Retrieval	(TIGR)	dataset
	(http://ara.lmd.polytechnique.fr/htdocs-public/products/TIGR), which represents a worldwide
	set of atmospheric situations from polar to tropical atmospheres.
	Due to the difficulty of obtaining in situ LST measurements at the satellite pixel scale
	(~km 2 ), the MODIS-derived LST over the entire study area, which is extracted from the V5
	MOD11B1 product (denoted as MODIS LST), is used to validate the SEVIRI-derived LST in
	this study. In this chapter, the validation is performed with the data on July 3, 2008 and
	August 22, 2009 to ensure that the validation results are representative, as more valid
	SEVIRI-derived

Table 5

 5

		.1 Datasets used in this study
		Dataset
		MSG Level 1.5 product
		MSG cloud mask
		ECMWF atmospheric profiles
	LSE retrieval	View/Sum geometry data
		DEM data
		Geolocation information
		Land/sea water mask
	LST retrieval	TIGR atmospheric profiles
	Cross-validation	MODIS LST extracted from V5 MOD11B1 product

LSE and LST Retrieval from MSG-2/SEVIRI Data 5.3.1 Radiative Transfer Model

  Based on the radiative transfer theory introduced in chapter 2, for a clear-sky atmosphere in local thermodynamic equilibrium, the radiance, R i (θ v ,T i ) measured in channel i at the VZA,

	θ v , is given by Eqs. (2.22) and (2.23).

  vol,i and If geo,i ) with a step of 0.05° for both the solar zenith angle and the solar azimuth and observed that the Exponential Growth function (Eq. (5.5)) and the Gauss function (Eq. (5.6)) provided very good fits to If vol,i and If geo,i , respectively. The fitting parameters of Eqs. (5.5) and(5.6) are shown in Table5.2.

	If	, vol i	( ) v	2 0 p p 0	/2	f	, i vol	( , , )sin( ) cos( ) v s s s	s d d	0 A A 1	1 exp( / ) v t

Table 5 .

 5 2 Fitting parameters of the integration of volumetric kernel (If vol ) and geometric kernel (If geo )

	Function	A 0 (B 0 )	A 1 (B 1 )	t 1 (w) (degree)	θ c (degree)
	If vol,i (θ v )	-0.02990	0.01278	21.43823
	If geo,i (θ v )	-2.01124	-29.40855	68.81710	90.95449
	Finally, the LSE in the TIR channel (j=9 and 10 for SEVIRI channels 9 and 10), ε j (θ v ), can
	be derived from Eq. (5.7):
		j	( ) v	i	/ n n i night ( ) v	j
					, i j

Chapter 4 LSE and LST retrieval from AMSR-E data

parameters of DOBSON include frequency, temperature, soil moisture and some land surface parameters (volumetric contents of sand, volumetric contents of soil, volumetric contents of clay). The soil temperature is set to be between 0 to 40 with a step of5 ; according to soil physics, soil moisture is set to be between 2% to 44% with a step of 2%; the volumetric contents of soil solid is 0.4 to 0.7, step is 0.05; the combination of sand and clay is based on the international soil texture classifications which is a qualitative classification tool used to determine classes for soils based on their physical texture, the range is from 0% to 100% with a step of 10%.

The incidence angle, land surface correlation length and land surface root mean square (RMS) height are inputs for AIEM model. We set the land surface correlation length from 2.5cm to 30cm with a step of 2.5cm and the land surface RMS height from 0.25cm to 3.0cm with a step of 0.25cm. The emissivities and reflectivities are outputs of AIEM model. calculation by Gamache [START_REF] Payne | Air-broadened half-widths of the 22-and 183-GHz water-vapor lines[END_REF]. The self-broadened half width of the 325-GHz line is from Koshelev (2007). The temperature dependences of the half widths and the pressure shifts for these four water lines are from Gamache's calculations.

Chapter 5

LSE and LST retrieval from MSG-2/SEVIRI data

The MSG satellite is a new generation of geostationary meteorological satellite (http://www.eumetsat.int). The main payload in this satellite, the SEVIRI, provides data in four visible and near infrared (VNIR) channels and eight infrared channels every 15 minutes, which provides the possibility of obtaining the LST frequently. From these data, a SW method is operationally used to generate an MSG/SEVIRI LST product by the Land Surface Analysis of Satellite Application Facility (LSA SAF) (http://landsaf.meteo.pt/). However, the LSE used in this SW method is estimated using the vegetation cover method, which relies on the land cover obtained from land classification maps and on corresponding laboratory measurements of LSE extracted from spectral libraries [START_REF] Caselles | Determination of frosts in orange groves from NOAA-9 AVHRR data[END_REF][START_REF] Peres | Emissivity maps to retrieve land surface temperature from MSG/SEVIRI[END_REF]. Therefore, the accuracy of the retrieved LSE significantly depends on both the accuracy of the land classification and the LSE values assigned to each class (Li et al., 2013b). Jiang et al. (2006) and [START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF] estimated the LST from MSG-1/SEVIRI data using a SW method proposed by Becker and Li (1990) and improved by Wan and Doizer (1996); in this method, the LSE was derived using the day/night temperature-independent spectral indices (TISI)-based method proposed by Becker and Li (1990) and improved by [START_REF] Li | Feasibility of land surface temperature and emissivity determination from AVHRR data[END_REF] and [START_REF] Li | A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data[END_REF]. This method provides a new approach for retrieving the LSE/LST from SEVIRI data. Nevertheless, there are some deficiencies in the algorithms of these methods, such as the atmospheric correction failures in regions with a late local sunrise time and the sensitivity of the bi-directional reflectivity model to errors in the reflectivities. Therefore, based on the work of Jiang et al. (2006) and [START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF], an improved algorithm for retrieving the LSE and LST from MSG-2/SEVIRI data is developed in this thesis. Three components of this algorithm are improved: the atmospheric corrections, the fitting of the bi-directional reflectivity model and the retrieval of the LSE in channel 10 of the SEVIRI. Furthermore, a preliminary validation of the LST retrieved from SEVIRI data using the improved algorithm is performed with MODIS-derived validated LST data extracted from the V5 MOD11B1 product.

This chapter is organized as follows. Section 5.2 describes all data used in this study. The principles of the LSE and LST retrieval methods and their application to SEVIRI data are detailed in section 5.3. Section 5.4 presents the preliminary results derived from the MSG-2/SEVIRI data and the results of their cross-validation with the MODIS-derived LST product. Section 5.5is devoted to conclusions.

ECMWF data of two continuous days

Corrected TIR (channels 9 and 10) brightness temperature at ground level (5:57, 11:57,17:57,23:57) Atmospheric quantities with spatial resolution of 0.25 degree at UTC times: 00:00, 06:00,12:00,18:00 [START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF] assumed that the values of β j and t s,j (j = 9 and 10) in the DTC model derived from T j were the same as those derived from T g,j . To illustrate the improvement proposed in this study, two typical sites located at (33.225° N, 6.378° E) and (38.024° N, -3.834° E) are selected from the SEVIRI data from March 2, 2008 and March 13, 2009, respectively, and these data are atmospherically corrected using the DTC model with the different known parameters. Fig. 5.2 shows the modelled T 9 , T g,9 at four synoptic times, the DTC simulated temperatures at ground level with known t s,9 , t d,9 and α 9 (because only one T g,9 at this site is eligible before t s,9 , the DTC at ground level is fitted with known values of t s,9 , t d,9 and α 9 ) and that simulated with pre-determined values of β 9 and t s,9 . It is noteworthy that in Fig. 5.2, an unreasonable t d,9 value (9.85 on March 2, 2008, 9.73 on March 13, 2009) is derived when the DTC model at ground level is fitted with known β 9 and t s,9 values, and the DTC fitted with the pre-determined β 9 and t s,9 values is obviously abnormal; the DTC fitted with known t s,9 , t d,9 and α 9 values, on the other hand, is consistent with the actual situation. This result demonstrates that the assumption proposed in this study is more reasonable.

(denoted as Scheme 1) rather than setting the minimum number of measurements to 7 (denoted as Scheme 2), which ensures that the bi-directional reflectivity model is not sensitive to the errors in the reflectivities and more reasonable values of e 4 (q v ) are obtained. To illustrate this improvement, we retrieve the e 4 (q v ) using the same values of r 4 (q v , q s , j) on August 22 and March 13, 2009 using Schemes 1 and 2, respectively; the results of this analysis are shown in Figs. 5.8 and 5.9, respectively. It is obvious that there are less abnormal LSEs (black pixels) (see Fig. 5.8 (a) and Fig. 5.9 (a)) over the two small areas (red boxes) after improvement. (3) The e 10 (q v ) value is derived from e 4 (q v ) rather than from e 9 (q v ). The differences between the e 10 (q v ) value retrieved from e 4 (q v )(denoted as LSE1 10 ) and the e 10 (q v ) value derived from e 9 (q v ) (LSE2 10 ) are calculated; most of the differences between LSE1 10 and LSE2 10 on the four days are within 0.005. As an example, the corresponding histogram of these differences on August 22, 2009 and July 3, 2008 is shown in Fig. 5.10.

Chapter 6

Conclusions

Conclusions

The objectives of this thesis were twofold: (1) improve the current method for retrieving land surface emissivity and temperature from SEVIRI data;

(2) investigate the potential of passive microwave data (AMSR-E data) for retrieval of land surface emissivity and temperature in all-weather conditions. The primary work includes the development of a comprehensive soil-atmosphere radiative transfer model in passive microwave region, the methodological development for retrieving land surface emissivity and temperature from AMSR-E data, the improvement of algorithm for land surface emissivity and temperature retrievals from SEVIRI data.

The major findings and implications of this thesis are summarized below:

(1) A comprehensive soil-atmosphere radiative transfer model in MW was developed to simulate the brightness temperatures at the top of the atmosphere for microwave frequencies, based on the AIEM and MonoRTM models.Using this model, a microwave emission database was simulated for bare surfaces with a wide range of surface roughness and dielectric properties under the configuration of the AMSR-E, which is used subsequently to propose LST and LSE retrieval methods.

(2) Several empirical parameterized relationships between vertical and horizontal polarization emissivities were developed. With the proposed relationships, the differing effects of soil moisture and surface roughness on the microwave emission of bare surfaces can be separated efficiently. Simulated results using the proposed relationships are compared with those of AIEM model. These results show that the proposed relationships are accurate, with absolute root-mean-square errors (RMSEs) of 0.0025, and they can be used as a reliable boundary condition to retrieve other surface geophysical parameters.

(3) Quantitative analysis of the atmospheric effects on AMSR-E data was performed. The differences between observed brightness temperatures at the top of the atmosphere and at the bottom of the atmosphere were analysed using a database of simulated observations, which were configured to replicate AMSR-E data. The differences between observed brightness temperatures at the top of the atmosphere and land surface-emitted brightness temperatures were also computed. Quantitative results show that the atmosphere has different effects on brightness temperatures in different AMSR-E channels. Atmospheric effects can be neglected at 6.925 and 10.65 GHz, when the standard deviation is less than 1 K. However, at other frequencies and polarizations, atmospheric effects on observations should not be neglected.

(4) An atmospheric correction algorithm was proposed at 18.7 GHz vertical polarization, based on the classic split-window algorithm used in thermal remote sensing. With this correction algorithm, atmospheric effects on observations at 18.7 GHz vertical polarization can be removed effectively and the emission of land surfaces, at 18.7 GHz vertical polarization, can be estimated with RMSE=0.99 K using the measurements of two AMSR-E channels (18.7 and 23.8 GHz vertical polarization). Provided that the land surface emissivity is known, land surface temperature (LST) can be retrieved with an RMSE of 1.17 K using the simulated data.

(5) An improved algorithm was developed for simultaneously retrieving both the LSE and the LST from SEVIRI data. Three components of the day/night TISI-based algorithm for retrieving the LSE from SEVIRI data are improved: the atmospheric correction, the fitting of the bi-directional reflectivity model and the retrieval of the LSE in SEVIRI channel 10.

The GSW method proposed by Becker and Li (1990) and improved by Wan and Dozier (1996) was adapted to retrieve the LST from SEVIRI dada with the knowledge of the retrieved LSE and the coefficients in equation (5.13) [A i , B i (i= 1, 3) and A 0 ] that were pre-determined during a numerical simulation under various atmospheric and surface conditions.

(6) Applying our improved algorithm to the SEVIRI data under different climatic and atmospheric conditions revealed that the improved atmospheric correction algorithm resolves the problem that prevented the original algorithm proposed by Jiang et al. (2006) and [START_REF] Jiang | Retrievals of land surface emissivity and land surface temperature from MSG1-SEVIRI data[END_REF] from being applied to specific issues successfully, especially for the atmospheric correction in regions with a late local sunrise time. Less abnormal LSEs were obtained with the improved algorithm, illustrating that the improved algorithm is more reasonable and efficient.

(7) To preliminarily validate the LST data (SEVIRI LST1) derived from the SEVIRI measurements obtained using the improved algorithm, cross-validations were conducted on two clear-sky days (August 22, 2009 andJuly 3, 2008) over the entire study area with the MODIS-derived validated LST (MODIS LST). It is noteworthy that more than 70% of the differences between the SEVIRI LST1 and MODIS LST are within 2.5 K and that the