V Simonneaux
email: simonneaux@ird.fr

B Duchemin{

D Helson{

S Er-Raki{

A Olioso

A G Chehboun{

A time series of ten high-resolution Landsat TM images, ranging over the crop season, has been acquired over an irrigated area in central Morocco. From this time series, a Normalized Difference Vegetation Index (NDVI) profile was generated for each pixel. In order to get significant profiles, the images were radiometrically corrected, first, using invariant objects located on the scene, based on visual observation of the images, and second, using the reflectance of these objects, estimated from a previously corrected image. In the following step, these NDVI profiles were used to identify four main crop types-bare soil, annual crops, trees on bare soil and trees on annual understory-using a decision tree algorithm. The resulting land cover map and the associated NDVI profiles were then used for an evapotranspiration estimate over the whole area, using the FAO model. Daily outputs of the Moroccan meteorological model ALADIN were used to generate reference evapotranspiration (ET 0 ) maps and K c estimates were determined using the NDVI profiles.

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Journal: International Journal of Remote Sensing Paper: 224952 Title: The use of high-resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco

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Query Remarks The use of high-resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco

Introduction

Water management is a major issue in arid lands, especially those ; < having large irrigated areas. Optimization of irrigation water-using only what is actually needed by plants-is one of the obvious ways to save water. In order to monitor plant water requirements over large areas, remote sensing is a very interesting tool that gives synoptic information about two key parameters for evapotranspiration (ET) assessment: land cover and crop development. Remote sensing capabilities for monitoring vegetation and its physical properties have been identified for years now (Tucker 1979). The interest of image time series for vegetation monitoring has also been pointed out, mainly with regard to low-resolution images, which have been available for a long time [START_REF] Maisongrande | Monitoring seasonal and interannual variations of gross primary productivity, net primary productivity and net ecosystem productivity using a diagnostic model and remotely-sensed data[END_REF]

@

Remote sensing potential for monitoring irrigated crops using high-resolution images was more recently discussed [START_REF] Allen | Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study[END_REF], Bastiaanssen et al. 2000, A [START_REF] Hunsaker | Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index[END_REF].

B

Regarding irrigation, two main approaches may be considered: the first, using optical information to estimate vegetation development through the computation of vegetation indexes-for example, as in the FAO to assess instantaneous energy balance of vegetation and thus its possible stress, as in the SEBAL EO model (Baastianssen 2000). We focus here more specifically on the potential of high-resolution optical image time series for irrigated vegetation monitoring. The usefulness of such time series for operational agronomic applications is currently a subject of investigation (Calera Belmonte et al. 2003), EP as this type of data is becoming more easily available through new missions like ROCSAT/FORMOSAT (Chern et al. 2001), or planned ones like RHEA (Dedieu et al. 2003). The inherent pointing capabilities of these new sensors are similar to those of SPOT HRV, which enables revisiting, thus making them much more efficient than Landsat TM for time series acquisition.

The study area is located in the irrigated Haouz plain, around Marrakech, Morocco. Irrigation management in this area would benefit from better estimates of actual water consumption, as water is being distributed on a fixed, per-area basis. A time series of eight Landsat TM images was acquired over the six-month growing season, from November 2002 to May 2003. This paper presents the full process used for analysing this time series for ET estimate of the vegetation over the area, including the three steps of (i) radiometric correction, (ii) land cover characterization, and (iii) phenology monitoring. We show here how such image time series, apart from allowing an accurate monitoring of the crop cycle, may be useful to simply identify crops, where ordinary classification methods may fail because of the very important variations of phenology within each crop class.

Material and methods

Study area

The Haouz plain, around Marrakech, is located in central Morocco, to the north of the High Atlas mountain range (figure 1). The climate of the area is markedly contrasted over the course of the year: hot and dry in the summer, with almost no rainfall except for some violent storms in the mountains; from autumn to spring, the temperature decreases and there is rainfall. Precipitation varies from 240 mm a 21 in the Haouz plain to about 500 mm a 21 in the mountains located south of the plain, with great inter-and intra-annual variability. Water descending from the mountains is stored in some large dams and is mainly used for irrigation of the Haouz plain. This water is distributed throughout the year, but especially during the rainy season-from late autumn to spring-to complement insufficient rainfall. The irrigated area covers approximately 3000 km 2 . The main crops are tree plantations (olive, citrus trees and apricot), covering about 38% of the irrigated areas, and very often combined with understory composed of various annuals, mainly wheat or grass, which is used as forage. The approximately 60% of the remaining irrigated area is used for herbaceous crop production (mainly annuals, like wheat and barley, with some alfalfa). The actual annual crop area is subject to high variations depending mainly on the water available in the dams at the beginning of the crop season, as well as the rainfall occurring during autumn, which affects the sowings date of the farmers. The period of maximum annual crop density therefore occurs during the winter, while in summer only perennial tree crops remain. To compute the climatic demand ET 0 required by the FAO method, we utilized the daily outputs of the ALADIN EQ model used by the Moroccan Meteorological Services [START_REF] Pailleux | La pre ´vision nume ´rique du temps avec les mode `les Arpe `ge et Aladin: Bilan et perspectives (Numerical weather prediction with the Arpe `ge and Aladin models; assessment and prospects)[END_REF]. This is a regional model designed for Morocco, which is constrained by daily outputs of the global model ARPEGE ER driven by the French National Meteorological Services. ALADIN produces every day, for a regular 16 km resolution, grid maps of the four variables used for Penmann ET 0 computation-temperature, humidity, wind speed and net radiation (all estimated at the standard height of 2 m). The outputs produced every three hours by ALADIN were averaged for each day and interpolated at the resolution of the satellite images (i.e. 30 m), using a simple bilinear interpolation. This rough interpolation was acceptable because the Haouz plain is flat and characterized by a homogeneous climate, although it would not be suitable in the High Atlas mountains, where climate is very heterogeneous and strongly linked to relief.

Data

Radiometric correction

A radiometric correction was necessary to apply a deterministic identification algorithm based on analysis of the Normalized Difference Vegetation Index (NDVI) profile shape, which requires at least a relative correction of the images. These NDVI profiles will be used in a next step to estimate the phenology of the crops, in order to estimate their water consumption, thus we also need to apply an absolute calibration to get reflectance images. Despite the increased availability of satellite images, their use is still impeded by the scarcity of good ground reflectance images, which are heavily dependent on the quality of the atmospheric parameters' inputdata which is still infrequently available today-for physical modelling. This radiometric correction was achieved in two steps. The first step was to find a set of reliable invariant objects in the raw image series, in order to get linear relation between the digital numbers (DN) of these images. Indeed, on flat areas, under reasonable assumptions of spatial homogeneity of the atmosphere, the relation between the DN of two raw images is linear (Schott et al. 1988). In the second step, in order to assign reflectance values to the raw images, the reflectance values of the invariant sites are linearly related to the image's DN. This simple linear correction relies on the fact that in a first approximation (for flat areas), the simplification of the radiative transfer model shows that reflectance is also a linear function of the DN of the raw image (Schott et al. 1988). The reflectance of the invariants was obtained using the previously computed reflectance image dated 8 February 2002. This image was available from a previous work, based on physical modelling corrections using the SMAC ES algorithm [START_REF] Rahman | SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum[END_REF], coupled with a relative calibration [START_REF] Simonneaux | Identifying main crops classes in an irrigated area using high-resolution image time series[END_REF].

Invariant surfaces were identified visually over the scene. The surface of each site should be at least of about 464 pixels to avoid positioning problems that may arise when using poorly georeferenced images. In arid zones, bare soils are widespread in landscapes and few changes occur in land cover over the course of a year. These bare soils are often good invariants (Se ´guis and Puech 1997). To help the identification of those sites, a pair of images was displayed on screen, manually stretched to get comparable colours. The two images were visually overlapped, making them flicker when desired, which is an efficient manner to identify candidates to serve as invariant areas. These areas were sought especially within uncultivated zones, based on the ground knowledge we have of this region. The radiometry of these areas was then displayed on the scatterplot of the same band for two dates, to check that it was on the 'no-change line' (Elvidge et al. 1995, Xiaojun andLo 2000). In arid areas, this no-change line is quite visible on scatterplots for two dates and contains many pixels corresponding to bare soils that remain relatively unchanged between the two dates. This method is, in fact, less usable in temperate areas, where there are more significant changes between two dates. As precipitation is rare in the Haouz plain, and because wet soils usually dry within one or two days after an ordinary rain event, fortunately none of the images comprising the series was affected by a wet soil problem. Indeed, a wet soil occurrence would have hampered the use of this method in modifying the signature of many previously invariant objects (e.g. bare soils), making the no-change line disappear. In this way, more than 20 sites were easily identified. Such a great number may seem excessive at first glance, but it is in fact very useful to detect and eliminate outliers, i.e. pixels that may have lost their invariant property because of surface changes. Invariant corrections based on only two objects-the traditional 'dark' and 'bright' targets-may be erroneous if the observer is not absolutely sure of the quality of these points, which is often difficult to check.

The accuracy of image reflectance was assessed through a comparison with spectral measurements done during the season in wheat fields, using a CROPSCAN field spectrometer including the TM bands. This sensor is handheld, using a 3 m- long boom leaning at 45u, which results in a patch of about 2 m 2 . Because this instrument simultaneously measures both incoming solar radiation and reflected radiation-using upward and downward sensors-it is therefore less susceptible to variations of illumination during the measurements. The calibration of this spectrometer consists only of intercalibrating the upward and downward sensors in order to get correct reflectance readings, without requiring any reference panel.

At the beginning of the season, the CROPSCAN measurements were checked against data collected using an ASD precision spectroradiometer. Each measurement consisted of 10 points, spaced every 10 m along a fixed transect located accurately using the GPS. The 10 points of each transect were averaged and compared with the average of the pixels corresponding to the transect line extracted from the TM image (figure 2). The wheat fields were large enough-100 m6400 mand homogeneous enough to minimize the potential problems of misregistration when extracting pixels related to each transect. The standard deviation (SD) of the 10 points of each transect ranged from 1 to 35%, with an average of 12%. Additional details about this measurement campaign may be found in Duchemin et al. (2006). ET

Land cover characterization

The vegetation development in the area is affected by a great heterogeneity that has consequences for the choice of a land cover classification method using remote sensing. For several crop types, especially annuals, the phenological cycles of different fields may not be synchronous, showing important shifts between fields.

International Journal of Remote Sensing res112815.3d

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The Charlesworth Group, Wakefield +44(0)1924 369598 -Rev 7.51n/W (Jan 20 2003)

COLOUR FIGURE

Image time series for evapotranspiration estimate 5 The phenology is indeed very dependent on agricultural practices, which may vary greatly among farmers. For financial and organizational reasons, people follow neither the same time schedule nor the same technical itineraries. For example, the sowing period for wheat ranges from November to January (Duchemin et al. 2006, Hadria et al. 2006a). One other feature of the land cover is that the cover types encountered are complex. Among tree plantations for example, there are great variations of density and age. Tree plantations also often include an understory of annual crops-wheat, grass, etc.-cultivated as forage. Thus, the land cover classes usually get very heterogeneous spectral signatures, regardless of the date considered.

Moreover, there are a lot of overlapping signatures between these classes, producing spectral confusions.

For the above-mentioned reasons, we chose to focus on the analysis of the temporal behaviour of vegetation, instead of trying to use all of the spectral information available from the TM sensor through the usual classification methods such as 'maximum likelihood'. Because of the time shifts observed in agricultural practices for any particular crop, explicitly taking into account the temporal dimension of the spectral signature-for example, through the study of the vegetation index time profile-may help significantly in discriminating between classes. The identification of land cover classes based on NDVI profiles-which had previously been used mainly for low-resolution global land cover characterization [START_REF] Lloyd | A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery[END_REF], Friedl and Brodley 1997, Chern et al. 2001), given that only lowresolution satellite data was readily available-has been utilized in recent years for high-resolution time series composed of fewer images by [START_REF] Ray | Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS[END_REF]. Using such time profiles, the identification of distinct classes may either be based on a parameterization of the NDVI profile-and on subsequent discrimination based on the estimated parameters [START_REF] Liang | Land-cover classification methods for multi-year AVHRR data[END_REF])-or it may be based on the use of simple phenological criteria, which are taken into account mainly through what are often called 'decision trees' [START_REF] Lloyd | A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery[END_REF], Chern et al. 2001[START_REF] Ray | Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS[END_REF]. These phenological criteria-such as the minimum, the maximum or the range of NDVIhave the advantage of bearing an ecological signification. Consequently, they are more robust than statistical criteria, which are more directly linked to the studied dataset and are therefore more difficult to generalize. The method used in our case was successfully applied during the previous season (2001/2002), using a time series of nine SPOT HRV images and a ground truth set of 120 plots that had been acquired over a smaller area [START_REF] Simonneaux | Identifying main crops classes in an irrigated area using high-resolution image time series[END_REF]. This method was adapted for the 2002/2003 season, with the assumption that we had to validate some parameters, for example the NDVI threshold used to distinguish between bare soil and vegetation. This validation was achieved based on a set of 187 field observations made during the 2002/2003 season.

Evapotranspiration estimate

The FAO model was chosen for the ET estimate [START_REF] Allen | Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 (Rome[END_REF][START_REF] Allen | Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study[END_REF]. This model is based on the assumption that any crop can be compared to a standard crop-'a well-watered grass'-from which it differs by a simple multiplicative coefficient, the 'crop coefficient', which varies over time according to the crop development.

ET c ~ET 0 K c ð1Þ
where ET c is the crop evapotranspiration assuming no water stress, ET 0 is the The ET 0 value is computed using the Penman-Monteith equation and the following climatic parameters: air temperature, air humidity, wind speed and net radiation. The measurements fixing these parameters may be recorded on the ground in the usual way, using standard climatologic recording stations. Here we used instead the daily outputs of the ALADIN model described in §2.2, which offers a spatialization of these variables over the plain.

The main problem is to estimate the crop coefficients in space and time. For a given crop, the temporal evolution of this coefficient is modelled according to the FAO method by a trapeze curve (figure 3), with, successively, (i) a period corresponding to the seed development without significant vegetation ('initial period'); (ii) an ascending stage corresponding to the growing season ('crop development'); (iii) a stability stage corresponding to the biomass and ear development ('mid-season'); and (iv) a decreasing stage corresponding to maturity and senescence ('late season').

K c is, in fact, a global coefficient encompassing the soil evaporation fraction of water fluxes (named K e ) and the transpiration fraction named K cb for 'basal' K c .

K c ~Ke zK cb ð2Þ
Without any information about the actual crop development during the season studied, the K c profiles used are usually the average ones found in the literature [START_REF] Allen | Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 (Rome[END_REF], and the beginning point of growth is arbitrarily and equally chosen for all plots, according to knowledge of the crop calendars for the region. On the other hand, if remote sensing data is available, it is possible to estimate the K cb International Journal of Remote Sensing res112815.3d evolution for each pixel using image time series. The spectral information included in the images is usually closely related to vegetation phenology, especially when using vegetation indexes. Many authors have confirmed the fact that NDVI and K cb can be related through a linear relation (Bausch and Neale 1987[START_REF] Ray | Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS[END_REF], Duchemin et al. 2006[START_REF] Hadria | Monitoring of irrigated wheat in a semi-arid climate using crop modeling and remote sensing data: impact of satellite revisit time frequency[END_REF]. This empirical observation is easy to understand, as the K cb is roughly proportional to the crop fraction cover. After the coverage reaches 100%, even if the biomass (and thus the LAI)

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continues to increase, the transpiration is limited by the increasing shadow and the roughness of the surface. In this case, the vegetation indexes obtained from spectral data also saturate, in correlation with the potential ET of the crop. The NDVI-K cb relation was taken from the FAO guidelines [START_REF] Allen | Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 (Rome[END_REF]:

K cb ~1:64| NDVI{NDVI min ð Þ ð 3Þ
with NDVI min 50.15.

Although related to wheat, this relation will be used for all annual crops, considering that wheat is by far the main crop, and that other cereals have very similar K cb . Thus, using remote sensing, it is possible to estimate NDVI for the image dates, then to interpolate a NDVI profile for each day of the vegetation season and subsequently to compute the K cb using relation (3). Depending on the number of NDVI values available (i.e. the number of cloud-free dates of imagery), the interpolation method may differ. If a lot of points are available (e.g. every week), then a simple linear interpolation may be suitable. But if only three or four points are available, the linear interpolation between these sparse points would lead to an unrealistic shape of the NDVI profile. In this case, it is more reliable to assume a standard shape for the NDVI profile (i.e. a trapeze), and fit this trapeze to the points available (Duchemin et al. 2003). This latter method was used here to adjust an NDVI trapeze for each pixel from the points available from images. A program was coded in C + + to fit a trapeze to the available points. Seven variables were considered in adjusting the shape of the trapeze: the minimum NDVI (bare soil), the maximum NDVI (vegetation max), the sowing date and the length of the four development periods (figure 4). Theses parameters were tested iteratively using realistic value ranges and increments. The result of this fitting is-for each pixel-a specific trapeze profile matching the image points.

Unlike K cb , no spatiotemporal information was available to estimate K e . Although it may vary considerably in space and time, depending on soil water content, an arbitrary value was assumed over the growing season. However, this value was modulated for each pixel depending on its vegetation fraction cover, in order to minimize K e when vegetation coverage is increasing. As proposed by the FAO guidelines [START_REF] Allen | Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 (Rome[END_REF], we used the following equation:

K e ~1{f c ð Þ|K e, max ð4Þ
where f c is the vegetation fraction cover: f c 51.186(NDVI-NDVI min ). K e,max was approximated depending on the frequency of water inputs, affecting the proportion of days with wet soil evaporating [START_REF] Allen | Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 (Rome[END_REF]. Taking into consideration the frequency of irrigations (four to five times per season) and the rainfall events (100-250 mm rainfall during the season, very irregular), K e,max was fixed to a constant value of 0.3.

Finally, the K c resulting from combining equations ( 3) and ( 4) was multiplied by the daily estimates of ET 0 to get the estimated ET c of the crops. maximize the probability that invariant areas for these two dates would remain invariant for other dates. We selected 33 invariant sites in the scene, in nonvegetated areas, as flat as possible, outside the irrigated plain where there is a greater probability of their remaining stable over the course of the season. We also checked that these points were spread well over the reflectance range of the image, in order to obtain reliable regressions. Using these sites, the regression lines were computedfor each band, between pairs of images-to check for the presumed invariance of the signatures. We obtained very good determination coefficients (figure 5), usually above 0.9, showing that the areas selected are actual invariant features. Indeed, the hypothesis that all these areas might have changed such that their signatures remain proportional is highly unlikely, the invariant hypothesis being far more probable. The 2003 images were then compared to the image dated 8 February 2002 for reflectance correction. The invariants' quality was again confirmed at this stage by the very good determination coefficients obtained. These coefficients were in the range 0.88-0.99, with an average of 0.96 (figure 6). Reflectance images were then computed using the estimated linear relations. The critical point of this correction method appears to be the source of ground reflectance information, more than the assumption of linear relation based on invariants, which appears very robust in our case.

To validate the reflectances, CROPSCAN spectral measurements were done on various dates from January to May, in 14 wheat fields in the irrigated area (Duchemin et al. 2006). We selected only the 69 measurements that were done within two days of an image date. The comparison with TM reflectance data shows good agreement (figure 7) despite determination coefficients lower than those resulting from the image-to-image comparison. This is normal, since it is difficult to make field measurements comparable with image data because of the very different processes of data acquisition [START_REF] Simonneaux | De ´tection d'invariants dans une se ´rie temporelle d'images satellitales en zone aride. Application a `l'intercalibration des images et a `la correction radiome ´trique d'images brutes[END_REF]. The main differences are the point of view and atmospheric conditions of the measurement; the time and date of the measurement; and, especially, the spatial extent of the object being measured.

Image pixels are very large, even compared with transects made by handheld radiometers. Moreover, neighbouring pixels are never completely independent because of signal mixing. Thus, it is almost impossible to match the same object using field measurements, despite collecting many measurements along transects. Nevertheless, these graphs indicate that, on the whole, the reflectance values obtained are correct. However, the relations observed show slight biases between the two data types. These biases may be due either to some problem in the radiometric correction of images-for example, that the image used as the basis for reflectance was biased-or from some bias in the spectrometer. Assuming here that CROPSCAN measurements were more reliable than the image reflectances, an additional correction was applied using the observed linear relations.

From the corrected image series, an NDVI profile was computed including the eight dates. Three images of the time series were partly cloudy, for dates that are very important for monitoring the crop development (27 February 2003, 31 March 2003and 16 April 2003). Thus, instead of entirely rejecting the cloudy images, we masked the affected pixels. The clouds were small cumulus clouds, which are cold objects that are easy to identify using a simple threshold on the Landsat TM thermal band (re-sampled at 30 m). The cloud shadows were also easy to identify because they had the lowest reflectance in the near infrared (NIR) band. In order to avoid mixed pixels at the boundary of clouds and shadows, a single-pixel dilatation was applied to the generated masks. As the clouds were rarely at the same place, at least two of the three dates were usually usable for computing the NDVI profiles.

Land cover classification

3.2.1 Method designed using the 2001/2002 dataset. The crop identification method was designed based on a set of 120 field observations. This field data was made up of 18 thematic classes, including all the species encountered and their combinations (trees and understory). It seemed obvious that this detailed typology would not be correctly discriminated based only on the analysis of the NDVI profiles. The visual examination of the profiles showed that they might allow for a reliable discrimination of four main classes, taking into account simple phenological criteria like the minimum and maximum values of the NDVI profile and the range of NDVI over the season. These classes were bare soils, annual vegetation, trees on bare soil and trees over annual understory. The typical profiles of these four classes are shown in figure 8.

The discrimination criteria were established on the basis of three threshold values, adjusted to obtain a good discrimination of classes. These criteria were (i) the NDVI value discriminating between bare soil and vegetation (Sn50.18); (ii) the maximum NDVI for trees on bare soil (Sa50.40), assuming that higher NDVI depicts trees on annual understory; and (iii) the minimum NDVI range for trees on annual understory (Sr50.2), considering that the presence of annuals would imply a . The total number of only four classes may seem low, but it nevertheless presents valuable information for water budget assessment.

Bare soils are easy to discriminate, as they show flat and low NDVI profiles, always below a threshold Sn, estimated to 0.18. Sn may vary according to the soil properties, but no evidence was shown for that effect here, despite the presence of a large range of soil properties-texture, roughness and humidity-in the study area. These bare soils may represent either true bare soils (fallow land with almost no vegetation), or very poorly developed wheat with low NDVI.

Annual crops are defined by NDVI values rising above Sn, depicting significant vegetation biomass, combined with some values below Sn, showing non-permanent vegetation coverage (i.e. a period of bare soil). This non-vegetated period permits distinguishing them from plantations of evergreen trees, which occur frequently here. This class includes mainly cereals like wheat and barley, and some vegetables when the vegetation coverage is sufficient for NDVI rising above Sn. Tree classes are considered as NDVI profiles always above Sn, considering that the majority of them are evergreen trees (olive and citrus trees). The biomass of theses trees is relatively constant over time, despite some slight variations due to growing stages and annual pruning, so their NDVI is therefore also generally constant.

Trees on bare soil are clearly identified by their having all NDVI values below a threshold Sa50.45 and limited variations of NDVI (variation range Sr,0.15). All other tree profiles, having either high NDVI values or high NDVI ranges are considered to be Trees on annual understory. The distinction of these two classes is useful for water management.

The Sn value is critical here, because it discriminates very different classes (i.e. annuals or bare soil), which may have significant consequences when this information is later used to forecast water needs over the course of the season. But in our case, the development of the vegetation (related to K c ) is also taken into account to assess daily water requirements. Thus, the confusion between poorly developed wheat, which consumes little water, and an actual bare soil is not crucial in terms of water budgeting. The maximum value of NDVI is higher for irrigated crops, and lower for rainfall wheat. These two types of crops are usually well discriminated, but in many cases irrigated wheat that is suffering from a shortage of water will behave like rainfall wheat.

It appears that the shape of the wheat profiles is rarely a trapeze as modelled by the FAO method. However, this fact does not preclude using the method because the algorithm fitting the trapeze also fits the length of the mid-season phase and can thus fit a shorter one, as can be seen in figure 4.

Confusion may occur with some of the annual crops that show no value below Sn over the period of observation. This may be the case for cereals with early development compared to the first date we had available (7 November 2002). It is also the case for permanent crops like alfalfa, never showing very low NDVI values even after cutting, but which is hopefully not widespread in the study area. All these particular herbaceous crops may be confused with the tree classes, which is a more critical problem for water budgeting because these classes may show different crop coefficients.

Another problem affects young plantations, which do not show a high NDVI and are not detected on the image, even though they consume much less water than older ones. Moreover, these young plantations are usually combined with an understory of well-developed annual crops, which use most of the irrigation water, and are classified as annuals, with the related water consumption estimate.

Regarding the 2001/2002 season-for which the method was designed-the accuracy of the classification was 85% [START_REF] Simonneaux | Identifying main crops classes in an irrigated area using high-resolution image time series[END_REF]. As a basis of comparison, maximum likelihood classifications were conducted on the same dataset, using the 18 detailed classes as primary spectral classes, then combining them further to obtain the four final classes. Regardless of the number of images used-whether all of them or only a selection of dates, well-ranged over the season-the accuracy was never better than 50%. In fact, this poor result was not really surprising, considering the special features of the land cover in this area: great variability of profile shapes for some classes, and their shift on the time axis, especially for the annual covers. From the perspective of cost optimization, the NDVI profiles method was also tested using fewer images. equally as good as those obtained by using the full series (85%). When considering only three images-as long as they were temporally well positioned at the beginning, peak and end of the season-the accuracy was 83%. Although the four classes were easily distinguished on this image, it was representative of a relatively small area in comparison with the extent of the Landsat images; also, the area was mainly covered with wheat. Thus, an additional field trip was carried out over the Haouz plain providing 187 observations. The confusion matrix computed using this data (table 2) gave a global accuracy of 83.7% (between 77% and 91%, depending on the class), which is close to the previous year's assessment. This positive result is not surprising, considering the simplicity of the typology and the robustness of the algorithm based on physical criteria, i.e. vegetation index analysis (the criteria were the same for both years). Unlike the method proposed by [START_REF] Ray | Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS[END_REF], it is not linked to particular dates, but can be applied to any time series, assuming a reasonable distribution over the season. This is an important point from the perspective of operational application.

Application

Because trees are long-lasting vegetation, it may be planned, in the scope of a permanent operational tool, to map such vegetation by means other than the NDVI profile shape, for example using very high resolution satellite images at metric resolution. Such a data source would produce more accurate tree mappingreducing confusion with herbaceous perennial vegetation like alfalfa-and would improve the detection of very young trees.

Evapotranspiration estimate

The computation of ET c was carried out only for annual vegetation (considered as wheat), because no accurate K c was available for trees. The product of K c and ET 0 was calculated for the whole vegetation cycle (i.e. from the beginning of the initial stage to the end of the senescence stage). The cumulated ET c was computed for the 530 000 pixels of annual crops (48 000 ha) in the Haouz plain, giving an average value of 446 mm. The distribution of the values (figure 9) shows a peak from 220 mm to 550 mm, with a mode at 415 mm. The upper tail of the histogram spreads up to 1300 mm, and probably corresponds to misclassified pixels-actually either trees or alfalfa, both of which consume much more water. The modal ET c (415 mm) therefore seems to be more representative of the average field of annual crop. These estimates are consistent with the standard water needs as estimated by the local agency in charge of irrigation, which are 525 mm for wheat and 450 mm for barley (values issued from the application of the FAO method). Since the studied year cannot be considered as dry-with a total amount of precipitation of 300 mm for that year, compared with the climatic average of 240 mm-the difference with our estimates points to the fact that crop development is rarely optimal, owing to the aforementioned crop management problems.

For a subset of the area covered mainly with wheat (figure 10), values of cumulated ET c vary from 200 mm to 500 mm. This very high variability is, in itself, an interesting result, which justifies the spatiotemporal approach offered by remote sensing. This high variability is not surprising; it illustrates the above-mentioned variability of farming practices-which are often not optimal, for example, with regard to the sowing or irrigation dates-more than it does the variability of intrinsic soil conditions. The higher values-around 500 mm-are those of wellconducted wheat plots experiencing almost no stress. The lower values of 200 mm correspond to wheat that is poorly developed because of water shortage or nitrogen stress that can induce large yield reduction (Hadria et al. 2006b). Considering the 250 mm rainfall that occurred that year from November to May, and taking into account the rainfall efficiency reducing the actual amount available for vegetation, rainfall wheat might have been able to develop and reach the lower values of ET c observed here (around 200 mm). On the other hand, both because of problems with the availability of water and because of differences among farming practices, much of the so-called 'irrigated wheat' does not actually reach high values of ET c . Since A first validation of these remote sensing estimates was possible thanks to ET measurements of three wheat plots, using eddy correlation systems providing measurements of the actual evapotranspiration (AET) of these plots (Duchemin et al. 2006[START_REF] Hadria | Monitoring of irrigated wheat in a semi-arid climate using crop modeling and remote sensing data: impact of satellite revisit time frequency[END_REF]. The daily ET c profiles estimated by remote sensing were extracted for the three pixels corresponding to the three eddy correlation systems (see location in figure 2). Figure 11 shows the two types of daily ET values for the three plots. The eddy correlation measurements show some gaps-especially the B123 plot-because of technical problems occurring during the experiment. On the whole, we observe concordance between the ground and remote sensing values. At the daily time step, however, there may be marked discrepancies. For the whole measurement set available, the average daily ET c was 3.32 mm, with a daily absolute deviation of 0.89 mm (26.9%). We computed an estimate of this absolute deviation at the weekly time scale, but as there were many gaps in the time series, subsets of seven days were artificially aggregated in removing the missing days. For the 22 periods of seven days obtained, the mean daily absolute deviation was reduced to 0.61 mm (18.25%). Finally, for the whole period of measurement-about 160 days available, considering the three sites-the average daily estimate of ET c was 3.48 mm, which means a difference of only 4.8% compared with the measurements. COLOUR FIGURE 16 V. Simonneaux et al. A bigger discrepancy appears between Julian days 90 and 98 for the three plots, especially the B27 plot, where the AET decreases whereas the estimated ET c remains at high values. This discrepancy is due to the quick rise of ET 0 during this period, coupled with a lack of irrigation, inducing stress in the vegetation. As mentioned in §2.4, the vegetation stress is not accounted for by the methodology used here. Early AET measurements were only available for plot B123, and show values much higher than the estimated ET c for Julian days 36 to 43. This high AET corresponds to an irrigation of the plot applied on day 35, while the vegetation is still relatively undeveloped, generating a high soil evaporation that is not accounted for by our method at the daily scale, but only at the seasonal scale.

The discrepancies observed between what remote sensing offers and the AET measured on the ground mean that whatever the accuracy of NDVI and K cb estimates, the ET estimated here is basically not AET. Our ET estimates are based on NDVI-i.e. on vegetation development-but soil evaporation and vegetation stress, contributing to AET are not accurately taken into account here. The evaporation fraction of the soil, related to K e , is considered constant, and issued empirically from the observed frequency of irrigations and rainfall events. This approximation is not satisfactory from the perspective of a spatialized monitoring at a few days' time step. The actual values of K e may vary greatly, depending on farming practices and water availability. One remote sensing solution to overcome this problem would be the use of radar images to estimate soil moisture, but this is another field of research, currently far from being operational. The second process not being considered here is the stress of the vegetation, occurring when the soil water content is low. In the case of moderate stress, the vegetation still has the same NDVI despite a decrease in actual transpiration coefficient [START_REF] Moran | Estimating crop water deficit using the relation between air-surface temperature and spectral vegetation index[END_REF]. To account for this stress, the FAO method introduces the stress coefficient, Ks, to be multiplied with K cb . From a remote sensing perspective, this kind of stress may be monitored using thermal information as the reduction of transpiration makes the temperature of the plant rise, using, for example, the Water Stress Index proposed by [START_REF] Jackson | Canopy temperature as a crop water stress indicator[END_REF]. Thus, the ET estimated here from remote sensing is a 'pseudoevapotranspiration'; it is both overestimated, because of potential vegetation stress, and underestimated, because of soil evaporation. In fact, the difference with AET is mainly driven by irrigation practices. For the season studied here, we see from figure 11 that the two error factors may essentially offset one another. Additionally, one should point out that the ET estimated using this method is not the potential ET resulting from ideal growing conditions. Indeed, if the stress is not considered instantaneously when it occurs, it has subsequent effects on plant growth, modifying the development of the vegetation and thereby the NDVI. The pseudoET estimated here is, then, close to the actual ET most of the time, but usually below the potential ET of optimal conditions (water and nutrients).

Considering the level of complexity of the modelling approach of plant stress and of soil humidity estimates using remote sensing, one simple alternative approach would be an empirical estimate of this factor adjusted to this area, as this was done for K e .

Conclusion

Considering the worldwide increase of pressure on water resources, which is mainly due to its agricultural use, the accurate monitoring of agricultural water consumption over large areas is critical for planners and decision makers. In this work, we show how satellite image series may be used to estimate the evapotranspiration of crops over large areas. The availability of such satellite data is rather new and still expensive, but this should change in the next years thanks to planned or recently launched missions, such as SPOT, IRS, FORMOSAT,

FX

Venus, etc.

Among the main problems to overcome, the first is the radiometric correction of satellite data to make them comparable and utilizable for physical modelling. Despite the progress made in the knowledge of atmospheric parameters, these are still often lacking or are not known accurately enough. A combined method based on physical modelling and relative correction based on invariants was proposed, which proved to be very useful for improving the results of the physical models considered alone. Regarding the relative accuracy obtained using images taken on different dates, and for a set of invariant objects, this method gave correlations between images around 0.98. The second phase of the work was to assess land cover-a key input in estimating water consumption-using most available models, including the FAO model, which was chosen here for its compliance with and utility for remote sensing applications. The high spectral confusions between classes were overcome, giving priority to temporal analysis of the signature instead of focusing on spectral signatures. The land cover obtained is simple (annual crops, trees, annual crops + trees, bare soils) but accurate (85% well classified); it is better than the spectral classification, and relevant for the application of the FAO method. Indeed, the resulting classes match the main groups that can be distinguished in terms of water management. Problems still remain with regard to the clear identification of alfalfa, as well as in distinguishing between different types of trees. Further analysis based on more field work and using multiyear imagery would probably allow for further refinement of this first typology, permitting greater discrimination of classes-especially among trees-through the use of refined criteria.

The ultimate phase of the work was the computation of water consumption for wheat, using crop coefficients obtained from the image series. We saw that whatever the accuracy of the input variables-i.e. climatic demand (ET 0 ), and phenology (crop coefficients)-the main limitation is the lack of account taken of soil water content. Indeed, soil water is a key variable for water budgeting because it controls both water evaporation (when there is too much water in top soil) and plant stress (when water is lacking). The forthcoming availability of passive microwave data (SMOS FO mission) and the use of thermal data, associated with more complex models, should help to overcome these problems. For the time being, the simple method described here gives a first estimate of plant water consumption, assuming that top soil water evaporation is not too important, and that plant water stress is not too frequent, or at least that rough estimates of these two factors are available from empirical ground knowledge. Under this hypothesis, we show that remote sensing estimates of ET compare very satisfactorily with ground measurements.

Image time series for evapotranspiration estimate 19 19, pp. 3141-3168. DUCHEMIN, B., GOUBIER, J. and COURRIER, G., 1999

  The eight TM scenes acquired, which range over the six months of vegetation development, are dated as follows: 7 November 2002, 25 December 2002, 26 International Journal of Remote Sensing res112815.3d 6/3/07 17:18:19 The Charlesworth Group, Wakefield +44(0)1924 369598 -Rev 7.51n/W (Jan 20 2003) 2003, 27 February 2003, 31 March 2003, 16 April 2003, 18 May 2003. An image dated 8 February 2002, already corrected for reflectance, was also used as a basis for the radiometric correction of this time series.

InternationalFigure 1 .

 1 Figure 1. Overview of the study area. Colour composition dated 18 May 2003 (Thematic Mapper bands 7,4,3 respectively in R,G,B). The vegetation appears in green, corresponding mainly to irrigated crops in the Haouz plain and to natural vegetation in the High Atlas mountains. Red on the location plan indicates the frame of the full TM scenes processed. In blue on the image, the location of figure 2 (CROPSCAN); in red the area for which the cumulated ET c is shown in figure 10.

Figure 2 .

 2 Figure 2. Location of the CROPSCAN measurements on the Thematic Mapper (TM) image dated 11 February 2003 (TM bands 7,4,3 respectively in R,G,B). The irrigated wheat displayed in various green values shows the high heterogeneity of crop development.

  standard crop and K c is the crop coefficient of the vegetation coverage studied.

  Figure 3. The Kc evolution of an annual crop, taken from Allen et al. (1998).

International

  Journal of Remote Sensing res112815.3d 6/3/07 17:18:43 The Charlesworth Group, Wakefield +44(0)1924 369598 -Rev 7.51n/W (Jan 20 2003) Radiometric calibration and computation of NDVI profiles To choose invariant areas, two images with very different vegetation development stages were selected-dated 7 November 2002 and 11 February 2003-in order to

International

  Figure 4. Modelling of the Normalized Difference Vegetation Index (NDVI) profile, example of a wheat plot with eight NDVI measurements obtained from Landsat TM images.

International

  Figure 5. Comparison of raw image values for the 33 invariants. Example from images of 7 November 2002 and 27 February 2003, TM3 and TM4 bands.

Figure 6 .

 6 Figure 6. Relation between raw digital number (DN) values and reflectance values from 8 February 2002, for the 33 invariants. Example of 27 February 2003, TM3 and TM4 bands.

Figure 7 .

 7 Figure 7. Relation between reflectances (%) from Thematic Mapper (TM) images and CROPSCAN ground measurements achieved between January and May 2003, for TM3 and TM4 bands.

Figure 8 .

 8 Figure 8. Normalized Difference Vegetation Index (NDVI) profiles of the main vegetation covers of the Haouz plain. Samples were visually selected on the basis of their typical shape.

  Five images from the 2001/2002 time series, well-distributed over the season, produced results that were Image time series for evapotranspiration estimate 13

  to the 2002/2003 time series. Because annual crops change, for the 2002/2003 season a new validation set was used, based on a QuickBird very high resolution image from 24 March 2003 over a 9 km 2 subset of the area. This image was enhanced by applying a fusion of the multispectral bands (2.4 m resolution) with the panchromatic one (0.6 m resolution), giving a very sharp image on which almost all trees were visible, except for very young ones.

International

  Figure 9. Histogram of cumulated ET c for the pixels of the annual class over the whole Haouz plain.

Figure 10 .

 10 Figure 10. ET c of annual crops cumulated over the season (mm) for an extract of the Haouz plain (see location on figure 1).

International

  Figure 11. Comparison of ET c estimated using FAO

  .G.M.,MOLDEN, D.J. and MAKIN, I.W., 2000a, Remote sensing for irrigated agriculture: examples from research and possible applications. Agricultural Water Management, 46, pp. 137-155. BASTIAANSSEN, W.G.M., 2000b, SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of Hydrology, 229, pp. 87-100. BAUSCH, W.C. and NEALE, C.M.U., 1987, Crop coefficients derived from reflected canopy radiation: a concept. Transactions of the ASAE, 30, pp. 703-709. CALERA BELMONTE, A., JOCHUM, A.M. and CUESTA GARCIA, A., 2003, Space-assisted irrigation management: towards user-friendly products. In ICID Workshop on Remote Sensing of Crop Evapotranspiration, 17 September, Montpellier, France FP . CARLOTTO, M.

FQ , 1998 ,

 1998 Spectral shape classification of Landsat Thematic Mapper imagery. Photogrammetric Engineering and Remote Sensing, 64, pp. 905-913. CHEHBOUNI, A., ESCADAFAL, R., DEDIEU, G., ERROUANE, S., BOULET, G., DUCHEMIN, B., MOUGENOT, B., SIMONNEAUX, V., SEGHIERI, J. and TIMOUK, F., 2003, A multidisciplinary program for assessing the sustainability of water resources in semi-arid basin in Morocco: SUDMED. In EGS-AGU-EUG Joint Assembly, 6-11 April, Nice, France FR . CHERN, J.-S., LEE, L.-C., WANG, H.-C. and CHU, F.-H., 2001, An introduction to NSPO and ROCSATs missions. In 3rd IAA Symposium on Small Satellites for Earth Observation, 2-6 April, Berlin, Germany FS . DEDIEU, G., CABOT, F., CHEHBOUNI, A., DUCHEMIN, B., MAISONGRANDE, P., BOULET, G. and PELLENQ, J., 2003, RHEA: a micro-satellite mission for the study and modeling of land surfaces through assimilation techniques. EGS-AGU-EUG Joint Assembly, 6-11 April, Nice, FranceFT . DE FRIES, R.S., HANSEN, M., TOWNSHEND, J.R.G. and SOHLBERG, R.

FU,

  1998, Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote Sensing,

Table 1 .

 1 Criteria used for Normalized Difference Vegetation Index (NDVI) profiles classification. Sn50.18, Sa50.4 and Sr50.2.

	Conditions for NDVI values	Conditions for NDVI range	Class
	All NDVI,Sn	No	Bare soil (SN)
	Some NDVI.Sn and some	No	Annual crops (H)
	NDVI,Sn
	All NDVI.Sn	Max-Min,Sr AND All	Trees on bare soil (ASN)
		NDVI,Sa
		max-min.Sr or some	Trees + annual understory
		NDVI.Sa	(AH)

12

V. Simonneaux et al.

Table 2 .

 2 Confusion matrix of the 2002/2003 land cover classification (in pixels).

	224952
	International Journal of Remote Sensing res112815.3d 6/3/07 17:18:47	The Charlesworth Group, Wakefield +44(0)1924 369598 -Rev 7.51n/W (Jan 20 2003)

  , Monitoring phenological key-stages and cycle duration of temperate deciduous forest ecosystems with NOAA-AVHRR data. Remote Sensing of Environment, 67, pp. 51-67. DUCHEMIN, B., ER-RAKI, S., GENTINE, P., MAISONGRANDE, C., CORET, L., BOULET, G., RODRIGUEZ, J.C., SIMONNEAUX, V., CHEHBOUNI, A., DEDIEU, G. and GUEMOURIA, N., 2003, Estimating cereal evapotranspiration using a simple model driven by satellite data. In Proceedings of the International IEEE 2003 Geoscience and Remote Sensing Symposium (IGARSS'03), 21-25 July, Toulouse, France, 4, pp. 2762-2764. DUCHEMIN, B., HADRIA, R., ER-RAKI S., BOULET, G., MAISONGRANDE, P., CHEHBOUNI, A., ESCADAFAL, R., EZZAHAR, J., HOEDJES, J., KARROUI, H., KHABBA, S., MOUGENOT, B., OLIOSO, A., RODRIGUEZ, J-C., SIMONNEAUX, V. and TIMOUK, F., 2006, Monitoring wheat phenology and irrigation in center of Morocco: on the use of relationship between evapotranspiration, crops coefficients, leaf area index and remotely sensed vegetation indices. Agricultural Water Management, 79, pp. 1-27. ER-RAKI, S., CHEHBOUNI, A., GUEMOURIA, N., DUCHEMIN, B., EZZAHAR, J. and HADRIA, R., 2006, Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region. Agricultural Water Management (accepted) FV . ELVIDGE, C.D., YUAN, D., RIDWAY, D.W. and LUNETTA, R.S., 1995, Relative radiometric normalization of Landsat Multispectral Scanner (MSS) data using an automatic scattergram-controled regression. Photogrammetric Engineering and Remote Sensing, 66, pp. 1255-1260. FRIEDL, M.A. and BRODLEY, C.E., 1997, Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61, pp. 399-409. SCHOTT, J.R., SALVAGGIO, C. and VOLCHOCK, W.J., 1988, Radiometric scene normalization using pseudoinvariant features. Remote Sensing of Environment, 26, pp. 1-16. TUCKER, C.J., 1979, Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, pp. 127-150. XIAOJUN, Y. and LO, C.P., 2000, Relative radiometric normalization performance for change detection from multi-date satellite images. Photogrammetric Engineering and Remote Sensing, 66, pp. 967-980.

			20 22	Image time series for evapotranspiration estimate Image time series for evapotranspiration estimate Image time series for evapotranspiration estimate	21
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Acknowledgment

This study was realized within the framework of the SudMed project: 'Management of hydrological and ecological resources in semi-arid areas: characterization, modelling and forecasting' (Chehbouni et al. 2003). This ongoing project currently involves the collaboration of CESBIO ('Center for Biosphere Study from Space', Toulouse, France) and a team of the Semlalia faculty (Cadi Ayyad University, Marrakech, Morocco), thanks to the financial support of IRD (Institute for Research in Development, France). We are grateful to the CNES (National Center for Space Studies, France) for its financial support during this work and for providing SPOT images and to the DMN (National Meteorological Directorate of Morocco) for providing us with the outputs of the ALADIN model.


Air pollution: an old problem 1 2 Air pollution episode Date Cause Consequences

1930 High concentrations of SO 2 in the atmosphere during a temperature inversion 63 people died and thousand sick Donora, Pennsylvania, USA 1948 High concentrations of SO 2 coupled with temperature inversion and foggy weather 20 people died due to cardiac respiratory disease and about half of town's 12,000 residents complained of cough, respiratory tract irritation, chest pain, headaches, nausea, and vomiting Poza Rica, Mexico 1950 Natural gas plant inadvertently released hydrogen sulphide coupled with temperature inversion and foggy weather 22 people died and 320 were hospitalized London, England 1952 A five day temperature inversion trapped deadly acid aerosols in the atmosphere 8000 deaths. Over 4000 succumbed to bronchitis, pneumonia, and respiratory and cardiac disease

Part 1 -Why does chemical composition matter?

"the simple act of breathing is killing 7 million people a year and harming billions more" Dr. T.A. Ghebreyesus, head of the World Health Organization

Air pollution: an invisible pandemic?

• Impact of our emissions is defined by atmospheric chemistry

• Air pollution is atmospheric chemistry

• Air pollution is an urgent problem

• And it will get worse in the future 2

• The only vaccine is to understand it and legislate against it

• But there is still so much that we don't yet understand Gen. 1 Gen. 2

Gen. 3

Gen. 4

Gen. 5

. .

The export of pollution The health effects of air pollution The ozone hole

Climatic effects

Conclusions: Part 1 -Why does air pollution matter?

• Air pollution is estimated to kill millions of people each year

Ar

How has Earth's atmosphere changed? A paradox: low concentration, big effect

• How can things at such low concentrations be so important?

• Possibility 1: the mass flux is high

•

VOCs and ecosystems

Conclusions: Part 2 -Getting to know the atmosphere The atmosphere as a combustion engine

Getting things started…

•
- 𝑑 𝐴𝐵 𝑑𝑡 = 𝐽 × [𝐴𝐵]
• J, the photolysis frequency, is described as follows:

𝐽 = න λ1 λ2 𝐹 λ × Φ λ × σ λ
Where: F(λ) = actinic flux σ(λ) = absorption cross section Φ(λ) = quantum yield

Φ(λ)= #𝑝ℎ𝑜𝑡𝑜𝑛𝑠 𝑙𝑒𝑎𝑑𝑖𝑛𝑔 𝑡𝑜 𝑟𝑒𝑎𝑐𝑡𝑖𝑜𝑛 #𝑝ℎ𝑜𝑡𝑜𝑛𝑠 𝑎𝑏𝑠𝑜𝑟𝑏𝑒𝑑 • Example: NO 2 + hv → NO + O( 3 P) J(NO 2 )
However, there are other processes besides dissociation…

Oxidation in the troposphere • Ambient concentration: ~1 × 10 6 molecule cm -3

• OH has the balance between high reactivity and high enough concentration to be an effective cleaner of the atmopshere

•O-H

The hydroxyl radical

•O-H

Hydroxyl radical reactions The nitrate radical

•Cl

Atomic chlorine

• Atomic chlorine is one of the most reactive oxidants known • Typically, higher concentrations are seen in coastal environments • This is not always the case with longer-lived precursors such as ClNO 2

• Sinks: rapid reactions with organic compounds • Ambient concentration: ~1 × 10 4 molecule cm -3

• Hydrogen abstraction

Atomic chlorine reactions

•Cl

• O 2 + hν → 2O( 3 P) • O 2 + O( 3 P) + M → O 3 + M • O 3 + hν → O 2 + O( 1 D) • O( 1 D) + M → O( 3 P) + M • O( 3 P) + O 3 → 2O 2 O O 3 2O 2 + O 2 + hν, -O 2 + O + hν Initiation Termination Sydney Chapman (1930)
The Chapman Cycle

The Chapman profile -Quiz -

Altitude

  1 https://www.who.int/news-room/detail/02-05-2018-9-out-of-10-peopleworldwide-breathe-polluted-air-but-more-countries-are-taking-action 2 Lelieveld, J. et al. The Contribution of Outdoor Air Pollution Sources to Premature Mortality on a Global Scale. Nature 2015, 525 (7569), 367-371 3 https://www.worldometers.info/coronavirus/coronavirus-death-tollshowing the global daily fatalities due to air pollution/ COVID-19

  Possibility 2: a molecule has a catalytic effect • Possibility 3: high toxicity • Possibility 4: extremely sensitive effect [isoprene] Field concentration: ppts sources sinks Atmospheric emission: 5-7.5 x 10 6 tons yr -1 e.g. stratospheric chlorine sources (see later) high specific toxicity / high collective toxicity aerosol nucleation / infrared absorption / catalytic behaviour / biological role

Part 3 -

 3 Chemical reactions in the troposphere • Much like an engine, the atmosphere is an oxidizing environment • The volatile organic compounds (VOCs) emitted to the atmosphere represent the fuel • What we learned at school: Fuel + O 2 → → CO 2 + H 2 O • However, under ambient (and most other) conditions, these things don't react together…

•

  Photodissociation• Reaction AB + hv → A + B • Rate of reaction is given by:

Ozone•••••-

  Photolysis threshold

•

  Ozone production is a balance between number density, photon flux and absorption cross sections of O 2 and O 3 Remember all those reactions that are available to oxidize chemicals in the troposphere?• What happens to this molecule?

•

  It impacts the lives of billions • It's less problematic now than it was (in

	Part 2 -Getting to know the atmosphere Physical attributes (structure) Physical attributes (atmospheric pressure) Physical attributes (in context) Chemical composition (major components) Chemical composition (trace components)
	the developed world) • But it's becoming worse (in the developing world) tobacco (a lifestyle choice) →→ mass of atmosphere ≈5.13 x 10 18 kg apple… • Air pollution now kills more people than • The atmosphere is categorized as a set of layers • We spend almost all of our time in the planetary boundary layer 𝑝 𝑧 = 𝑝 𝑧 0 exp 𝑧 -𝑧 0 𝐻 𝐻 = Gas Mixing ratio Source(s) 𝑅𝑇 𝑔 H = 8.3145 (J K -1 mol -1 )/0.02897 (kg mol -1 ) x 288 (K) ≈ 8.5 (km) eyelash lying next to the Ar 99.96% • In terms of mass: it is a single 9.80665 (m s -2 ) where: • In terms of spatial extent: the atmosphere is to the solid to its diameter O 2 Ar = 0.93% Earth, what an apple's skin is Carbon dioxide 420 (ppm) Combustion, ocean, biosphere N 2 = 78.08% O 2 = 20.95% Methane 1.7 (ppm) Biogenic, anthropogenic
	• Breathing is not a lifestyle choice, it is a necessity! • + we should not forget the climatic effects that will impact the whole world N 2	18 Clean air Top of ionosphere = 1.74 x 10 -33 hPa z • From a physical point of view, the atmosphere is therefore a Most of the atmosphere is very thin, insubstantial layer boring!
		700 km

are produced in a collectively autocatalytic set

	• Atmospheric chemistry But life is never that easy… is a very cold, very slow
	NO 3 , O 3 , Cl … RH	Products	combustion • There are many branching channels • Eventually, a "fuel" may to consider fragmentation isomerization and high capacity for • RO 2 and RO have a be converted to CO 2 and H 2 O
			• Separate chemical cycles involving nitrogen and sulphur are important
			• What if R contains a heteroatom?

• VOCs are emitted from many sources: trees, vehicles, industries, etc. • They are consumed by OH, but other oxidants are important (NO 3 , O 3 , Cl) • Oxidants • Here, VOCs are transformed into a sequence of carbonyls of diminishing size → CO 2 However… Adapted from: Jenkin (1998) AEAT-4182/ 20150/003 RH • But

the path through this space is not simple or direct

	Meandering through
	oxidation space
	• At what point is
	atmospheric chemistry
	unknowable/
	unpredictable?

Source: Kroll et al. (2011)

• Also known as the "detergent of the atmosphere" • Daytime sources: O 3 + hv, HONO + hv, RCHO + hv • Nighttime source (minor): alkene ozonolysis • Sinks: reactions with VOCs, reaction with NO 2

•

  The nitrate radical can be the most effective oxidant at night • Nitrate is part of the following equilibrium: NO 2 + NO 3 ⇌ N 2 O 5 • NO 3 is most important in areas that are affected by anthropogenic NO x and ozone • Sinks: photolysis, reactions with alkenes and NO • Ambient concentration: ~5 × 10 8 molecule cm -3• NO 3 has a very high photolysis cross section in the actinic region, so it is effectively lost during the day NO 3

	Nitrate radical reactions Nitrate radical reactions	NO 3 NO 3
	• Hydrogen abstraction • Nitrate addition	• Generally very low • High reactivity towards
		reactivity towards activated alkenes
	+ HNO 3	hydrogen abstraction • Aldehydic C-H bonds are • Adds nitrate and epoxy functionality to alkenes
	+ HNO 3	an exception to this rule

Cl and ClO: active chlorine HCl and ClONO 2 : important Cl reservoirs HOCl and CH 3 OCl

  CFCl 3 + hν → Cl + CFCl 2 ClO + CH 3 OO → CH 3 OCl + O 2 ClO + HO 2 → HOCl + O 2

	Catalytic ozone destruction Catalytic ozone destruction
	Termination:
	ClO + NO 2 + M → ClONO 2 + M Cl + HO 2 → HCl + O 2 NO + O 3 → NO 2 + O 2 NO 2 + O → NO + O 2 NO 2 + hν → NO + O NO + ClO → Cl + NO 2 Cl + O 3 → ClO + O 2 Net: O 3 + O → 2O 2 O + ClO → Cl + O 2 Cl + O 3 → ClO + O 2 Propagation: Cl + CH 4 → HCl + CH 3	• CFC-11 (CFCl 3 ) was observed in the air samples of James Lovelock in 1972 stratosphere… this presents to the anticipated the threat that • Together with Crutzen they transport • This indicates upward decades/ centuries molecules survive for determined that these • Molina and Rowland
	Net: 2O 3	→ 3O 2

Initiation:

e.g. : less important Cl reservoirs

The ozone hole

• Observation of the ozone hole was very sudden and unexpected • This led to a rapid international effort to understand this process further

In the polar night

In the polar spring Approaches to studying the atmosphere


Kinetic parameters of the atmosphere

• Photolysis:

• O 3 + hv → O( 1 D) + O 2
• Thermal decomposition:

• CH 3 C(O)OONO 2 → CH 3 C(O)OO + NO 2
• Radical-molecule:

• OH + isoprene → products

• Radical-radical:

• RO 2 + RO 2 → RO + RO + O 2
• Termolecular reactions:

• OH + SO 2 + M → → H 2 SO 4 + M
• Equilibria:

• NO 2 + NO 3 ⇌ N 2 O 5
• Phase transfer:

• Gas-phase → aerosol-phase

• Heterogeneous reactions:

• 2NO 2 + H 2 O + surface → HONO + HNO 3 • … + more

Carte du jour

Why we study chemical kinetics

• So, the atmosphere has lots of different chemical reactions that can happen • Many of these reactions are in competition with each other • Normally, the faster the rate of reaction, the more important it is • That is, the more competitive it is

Types of competition

• For a simple compound like methane, we could ask: which oxidant is most important?

• For more complex compounds, we could ask: which reactive sites are more important?

• For compounds of a difference size, we could ask: which mechanism is most important?

H H H H H H H H H H H H H H H H H H H H H H H H H H RO 2 + RO 2 → RO + RO + O 2 RO 2 + RO 2 → ROOR

Atmospheric lifetime

• Atmospheric chemists often use the lifetime with respect to OH as a general measure of lifetime:

𝜏 = 1 𝑘[OH]
• Lifetime is a major control on the distance over which pollution can be transported Why is the lifetime so important? For example:

For example:

The total atmospheric lifetime

• Reaction with OH is generally important for destroying pollution • But there are many other processes that can be important • Therefore, it is more useful to consider the lifetime as follows:

• Also, depending on the location in the atmosphere, individual processes may dominate, e.g. photolysis is faster in the stratosphere, but hydrolysis is very slow, so we can express lifetime as follows:

1 τ total = 1 τ OH + 1 τ NO 3 + 1 τ O 3 + 1 τ Cl + 1 τ photolysis + 1 τ hydrolysis + ⋯ 1 τ total = 1 τ troposphere + 1 τ stratosphere + 1 τ mesosphere
Either way, it all depends on k (or J)

𝜏 = 1 𝑘[OH] 𝜏 O 3 = 1 𝑘[O 3 ] etcetera … 𝜏 oxidant = 1 𝑘 oxidant [oxidant] similarly … 𝜏 photolysis = 1 𝐽
The rate coefficient, k

• Simple atmospheric chemical reactions are first-order, A → B (e.g. unimolecular, photolysis):

• Many atmospheric reactions are bi-molecular and second-order, A + B → C (e.g. OH addition):

• Kinetics prefer to work under pseudo-first-order conditions, where:

] [ ] [ A k dt A d = - kt A A t = ] [ ] [ ln 0 ] ][ [ ] [ B A k dt A d = - kt B A B A B A t t = - ] [ ] [ ] [ ] [ ln ] [ ] [ 1 0 0 0 0 Relative concentration change with time (convenient) Absolute concentration change with time (inconvenient) 𝐵 ≫ 𝐴 ∴ [𝐵] 0 ≈ [𝐵] 𝑡 ] [ ] [ A k dt A d obs = - ] [B k k obs  =

Changes in concentration and time…

The rate coefficient, k

• But what is k? • k is a parameter that describes efficiency • If k is big, the reaction process is efficient • If k is small, the reaction process is inefficient • If k is 100% efficient,

then every collision between molecule A and molecule B results in a reaction

• This is known as the "gas kinetic limit" or "collision limit"

The gas kinetic limit

• Begin by considering how fast a gas molecule can move • Let's consider something simple like N 2 at room temperature:

• Next, consider how far a molecule travels before it hits another molecule (mean free path):

• Now we can calculate how many collisions are happening:

• And, if you divide by the number density of 1 atmosphere of gas at room temperature:

ҧ 𝑣 = 8𝑘 𝐵 𝑇 𝜋𝑚 = 8 × 1.38 × 10 -23 (𝑚 2 𝑘𝑔 𝑠 -2 𝐾 -1 ) × 298(𝐾) 3.14 … × 4.65 × 10 -26 (𝑘𝑔) = 474.7 𝑚 𝑠 -1 𝜆 = 𝑅𝑇 2𝜋𝑑 2 𝑁 𝐴 𝑃 = 8.314(𝑚 3 • 𝑃𝑎 • 𝐾 -1 • 𝑚𝑜𝑙 -1 ) × 298(𝐾)
2𝜋(3.64 × 10 -10 ) 2 (𝑚 2 ) × 6.02 × 10 23 (1 𝑚𝑜𝑙) × 101325(𝑃𝑎) = 6.898 × 10 -8 𝑚 𝑍 = ҧ 𝑣 𝜆 = 474.7(𝑚 𝑠 -1 ) 6.898 × 10 -8 (𝑚) = 6.88 × 10 9 𝑠 -1 𝑘 = 𝑍 𝑛 = 6.88 × 10 9 (𝑠 -1 ) 2.46 × 10 19 (𝑚𝑜𝑙𝑒𝑐𝑢𝑙𝑒 𝑐𝑚 -3 ) = 2.79 × 10 -10 𝑐𝑚 3 𝑚𝑜𝑙𝑒𝑐𝑢𝑙𝑒 -1 𝑠 -1

What controls the size of k?

• Some reactions are highly efficient, i.e. ≈ gas kinetic limit (e.g. reactions with chlorine and alkenes, k = 10 -10 ) • Others are a lot less efficient (e.g. alkene ozonolysis, k = 10 -22 ) • So, some reactions are a trillion times more efficient than others! • Where does this inefficiency come from?

• There are several possibilities…

The Arrhenius equation

k(T) = Zexp(-E a /RT)
• Other (steric) factors are also important:

k(T) = Zρexp(-E a /RT)
• Leading to the familiar Arrhenius equation: How do we measure kinetic parameters?

k(T) = Aexp(-E a /RT)
Let's consider a bi-molecular reaction (A + B → C), we generally need three different things:

A. An environment where A and B can react together (a reactor)

• Appropriate temperature/ pressure, absence of secondary chemistry

B. An analytical tool to measure (at least one of) A, B or C

• Needs to be sensitive enough, and rapid enough C. A measurement of time • This is done with the relative rate approach:

 0 ( ) 0 1 L c R  = - ( )     1 abs abs L c R abs L   = -+  0 1 1 1 abs abs abs c                = -
• This can be very precise and effective, but it is limited by the accuracy of the reference

Structure-activity relationships (SARs)

• There are many types of SARs that can be used • The most popular approach is the group-additivity method • This method makes use of the idea that the observed k is the summation of all reaction channels:

• This has the advantage of providing estimates for product branching ratios • This is ideal for chemical models, where we want to know the lifetime of a chemical, but also the secondary pollutants that are formed

Example calculation from: Kwok and Atkinson (1995)

Structure-activity relationship principles

• Fundamentally, these are parameterizations, so, regardless of the approach, the idea is to describe the process of interest in a simplified way • For SARs, this is linked to the molecular structure • But the molecular property that is chosen for parameterization will change depending on the reaction mechanism • For example, for the hydrogen abstraction reaction: RH + OH → H 2 O + R, a description of the strength of the R-H bond may be best • On the other hand, for OH addition reactions: R 1 (R 2 )C=C(R 3 )R 4 + OH → R 1 (R 2 )(OH)C=C(R 3 )R 4 , a description of the electron density of the C=C bond may be the best approach Structure-activity relationship performance

•

  Many reactions are temperature dependent • And over a small temperature range, they may obey the Arrhenius equation • But over larger ranges, we observe curvature • And this curvature can be described by the modified Arrhenius equation • This is referred to as non-Arrhenius behaviour k(T) = Aexp(-E a /RT) k(T) = A(T/300) n exp(-E a /RT) Non-Arrhenius behaviour • Several reasons for this: • Multiple (i) reactions sites, each with their own different T-dependence • Since the total reaction rate is the sum of each: + Non-Arrhenius behaviour

Further reading

• This was a really quick introduction to the science of kinetics, if you would like to learn more about it, consider the following book:

Conclusions

• Kinetics has important effects in all aspects of atmospheric chemistry • Kinetics is a fundamental quantity that can be measured with a variety of analytical and experimental techniques • This quantity often varies according to the atmospheric conditions (temperature and pressure effects) • Atmospheric chemistry produces a huge variety of chemical products • This leads to an enormous parameter-space (too many to measure) • So, we also need to think how we can estimate those things that we don't have time to measure


Dramatic activities and ICTE in Language Acquisition: Learning a foreign language for professional/specific purposes through Role Plays and Online Games

Keywords: blended learning, drama, ESP, higher education, language learning, language teaching, online games, virtual environments

Researchers in performative teaching have laid the stress on the motivation of learners who can express their creative fiber freely thanks to the value of dramatic activities in language classes. Drama and dramatic activities promote the development of competences along the lines of the Common European Framework for language learning. They help learners to broaden their knowledge of the world, to strengthen or acquire new skills and know-how, to consolidate their existential competences as well as their ability to learn. They also promote the improvement of communicative language competences (in terms of linguistic skills, sociolinguistic abilities and pragmatic capacities). They are particularly relevant in the context of an action-oriented approach to language learning for university students in Business English. Putting them into realistic situations through dramatic activities facilitates their learning of English. However, the differences of capacities among learners might hamper their acknowledgment of all the benefits of drama. The increase of IT tools with a pedagogical purpose multiplies the strategies to strengthen the aforementioned competences. Therefore, virtual environments like Second Life, in which games can be implemented, might be an alternative to improve the learners' competences.

The value of drama activities in language classes has been demonstrated for numerous years now.

Researchers have laid the stress on the motivation of learners who can express their creative fiber freely [START_REF] Aden | La créativité artistique à l'école : refonder l'acte d'apprendre[END_REF]. Drama and dramatic activities promote the development of general competences along the lines of the Common European Framework for language learning. They help learners to broaden their knowledge of the world, to strengthen or acquire new skills and know-how, to consolidate their existential competences as well as their ability to learn. They also promote the improvement of communicative language competences (in terms of linguistic skills, sociolinguistic abilities and pragmatic capacities). They are particularly relevant in the context of an action-oriented approach to language learning for university students in Business English. Putting university students into realistic situations through dramatic activities facilitates their learning of English.

However, it cannot be denied that the differences of capacities and personalities among learners might hamper their acknowledgment of all the benefits of drama. Therefore, the increase of IT tools with a pedagogical purpose multiplies the strategies to strengthen the competences mentioned above. Indeed, the creation of avatars on virtual environments like Second Life might be an alternative or an additional channel to improve the learners' competences. This paper presents the observations drawing on an experiment carried out over three months with two groups of second year students in Business English. The students were given the same pedagogical scenarios, they had the same pragmatic and linguistic objectives, but the educational devices differed. One group was required to practice online, the other in the classroom.

After explaining what the preparation phase consisted in so as to determine the extent to which both drama activities and virtual worlds improve the communicative language competences, we shall examine, from the feedback of the students, how both devices lead to the autonomy of the learners in real business situations.

Preparation phase

The Common European Framework was considered as a reference document and as the starting point for this experiment. The objective was to make the students feel they could become independent communicative social and professional speakers in English. After studying the educational value of drama for a long time, we thought we could blend it with a new increasingly popular device in language learning, virtual worlds, the use of which can be associated to that of serious games. We have been interested in the playful aspect of language learning (also known as "edutainment") since we got inspired by the work of Johann Huizinga who wrote about the value of play in humanity across the times in Homo Ludens as well as the writings of Winnicott who thought that "playing facilitates growth and therefore health" (Winnicott,56). Winnicott stated that "playing involves creativity and in being creative, we discover ourselves". (Winnicott,72) Then discovering ourselves "helps us to be aware of our capacities and therefore learn better" (Winnicott,72).

In this part, we shall explain briefly the steps to implement the plays and the games, what the scenarios consisted in and what was expected from the students.

a) Creating the scenarios

The idea of devising pedagogical scenarios for theatre plays, virtual worlds and online games originated from a lecture we were in charge of at the beginning of September 2013 about customer relationship management. Since we did not have enough classes to have the students practice, we asked for nine volunteers to participate in the scenarios. The notion of "scenarios" is really important if we use synthetic worlds and video games for an educational purpose. As a matter of fact, in his PhD thesis, Alvarez explains that the pedagogical scenario, which meets a precise objective, is the condition for a video game to become a serious game. (Alvarez,14) 1 . We did not have any difficulty in finding them even though this represented between three and five supplementary English classes for them. The students were seduced by the multimodality and the playful quality of the devices.

The original plan was to create three groups of three students: one practicing online, another only in class, and the last, alternatively in class and online along the lines of blended learning. Our idea was to check which method was the most effective since plays and games are activities where the students can be creative and free, which are motivating factors in language learning. The choice of the best and most accurate games that could be used online for free and that could meet the set objectives was asked to a couple of experts we had met at e-learning gatherings previously. Not only were they ready to help us, but they also insisted on embarking with us on our educational journey. That is how we made up a team of specialists: the English teacher was in charge of checking the linguistic competences of the students, a teacher in marketing was the specialist in CRM, the vice director of the virtual university of Lyon 3 (France) and another teacher in virtual learning were the administrative experts; lastly a sound and light engineer was in charge of the technical support. The five of us met many times before the games and plays could start because many details had to be agreed upon: notably the scenarios, the objectives, the dates, the places and the tasks.

They eventually recommended downloading the software called Second Life. Advertisers of the software write that "training simulations are incredibly powerful in Second Life because they simulate complex processes in the physical world and avatars can take on different roles to enhance learning". This, as well as the fact it was free, is one of the reasons why the experts advised to download it. We were really curious to see the potential of that software. Since it took a long time to get ready, the team agreed to have only two scenarios and not three as originally thought. The teachers and the students had to create their own avatars, which enabled them, as players, for the first time in history to become the spectators of their own actions (translating Tisseron in "Sens et Fonctions des avatars"). The students (and the teachers) were thus both actors and spectators, an idea that echoes the concept of "spect-actors" coined by dramaturg Augusto Boal whose goal was to liberate the individuals through forum drama. To some extent, the use of plays and games are also a means to liberate the students who can learn freely.

The avatars of the students were all members of a virtual company we had imagined called "Copets". In Second Life, the technical expert built the virtual offices of the company while in the classroom, we could not materialize it concretely. The activity of the company was the selling of pet toys in the UK.

The first scenario consisted in managing the anger of a dissatisfied customer calling the headquarters of "Copets". This customer (played by the English teacher) was angry due to the possible death of her dog after it played with a toy bought at one of "Copets" ' stores in London. The customer explained that a part of the toy had come off engendering the suffocation of her dog. The students had to deal with the customer and find solutions for both the customer and the defective toy.

The second scenario was linked to the first one. It took place three weeks later. The members of the marketing department of "Copets" realized that due to numerous incidents with toys from one supplier in particular they had lost customers (this information was given to them). They needed to find a new strategy to win customers back. So, they gathered and discussed new plans.

Because the software was not particularly easy to master, and because some students could not download it correctly or could use neither a microphone nor headphones properly (due to technical problems that the technical expert could not fix), only 6 students could participate in the experiment. The decision to create two groups of three was made, and the idea of blended learning was given up for that year.

b) What was expected from the students

From the very beginning the students were told what was expected of them and what they were going to be evaluated on in terms of general and linguistic competences. They were indeed given a summary of the scenarios. Therefore, they had been advised to go through the marketing lectures and English courses they had learnt before and that would enable them to communicate properly in the situations indicated. We followed the recommendations of the Common European Framework as far as the learning of languages was concerned so that the students were taught in the most appropriate manner. On the other hand, using new technologies also helped them to meet the recommendations of the European key competencies for lifelong learning document (key competence 4: digital competence).

The courses were based on a series of tasks that would enable the students to reach the objective of the scenarios progressively. Added to this, our personal objective was to help them transfer their knowledge and put into practice what they had learnt before but with a professional purpose and still in the context of the learning process (not in reality)2 .

The skills and abilities that had to be consolidated were classified along three groups: grammar, vocabulary and pragmatics. In this respect, both virtual worlds and role plays had their importance since they required the students to work not only with their minds but also with their bodies, therefore, to manage space. Aden, who has long demonstrated the values of drama in English classes, indeed observed that the bodies of students are not mere standard lamps which would carry thinking heads. 3 The interaction that was advocated by the teachers could thus be verbal or non-verbal or both (especially when they could not remember a word in particular), the goal was to make oneself understood. Second Life was here again the perfect choice since the publicists would write that "Second Life was designed as a social networking platform -to encourage social interaction. It enables deeply immersive, meaningful, and memorable experiences." And we personally think that this experiment was memorable if we look at the feedback we got.

Feedback

We will first start with the impressions of the team of teachers and experts supervising the experiment before giving an account of what the students felt.

a) From the teachers/ impressions when implementing the scenarios during and after the experience

From our personal viewpoint, we can acknowledge many advantages of the devices as far as learning was concerned. The students could express themselves freely, they had to be creative, when words or grammar were missing, they could rely on body interaction and on their peers. It is true that body interaction was more resorted to in the classroom than in Second Life since the software is not very easy to master, but the students managed well to express what they wanted to express. However, some limits to both practices could be noticed. We consider it more important to highlight the obstacles so as to offer solutions and devise a more efficient plan for future classes Firstly, the implementation of the simulation was time consuming. In such an experiment, one must take into account the time spent before the learning situations. The preparation required a lot of organization since it was necessary to draw up an agenda, create the scenarios, build up the team etc. Then, it was crucial to try the software and teach the students how to manipulate it. Because of the various capacities of the students, this training did not cover only one hour, but many. Even during the performance, there were still technical problems, despite the intervention of the technical expert. Our wish would have been that the simulation on the virtual world was not synchronous but that was impossible.

We became aware that the drama techniques were far easier and faster to implement than serious games on virtual worlds because there were obviously fewer technical problems.

The marketing teacher could not stay until the end of the experiment because she had other responsibilities calling her elsewhere. We deeply regret this situation. So, it is really important to find a reliable team from the very beginning so that the learning process might be more coherent for both teachers and students.

In the light of all this, the team of teachers became facilitators to teach linguistic and general competences. This idea is supported by Alvarez when he asserts that the presence of a medium is a master reformater ses schèmes pour s'adapter à la situation en tirant parti de compétences : on recourt à des compétences déjà éprouvées et, de fait, on les conceptualise en les réinvestissant." 3 cf. Aden, J., 2009, p. 179 : "il est temps que l'école s'intéresse aux apprenants dans leur globalité et qu'elle cesse de considérer leur corps comme des lampadaires qui soutiendraient des têtes pensantes." It was really important for us to get some feedback from the students about the experiment since their remarks could help us improve the use of the devices. In this case, we wanted to know if we could keep on using both devices in the future.

We can classify their remarks along two lines: the improvement of their linguistic competences, and the development of their general abilities. The students who participated in the online games in Second Life are called a, b, c and those who practiced in the classroom are students d, e and f.

The improvement of their linguistic capacities

• student a (who was one of the best students) liked the fact that he could practice English in a small group. Indeed, his English was good before the experiment, but he had never dared talk in front of the whole class. • student b said he was relieved not to have to talk to real people. It was easier for him to find his words and take care over his grammar and pronunciation. He felt no pressure so he could take his time to think about what he was going to say and how he was going to say it.

• student c asserted that she liked the fact it was a small group. Through this experiment, she came to realize how spontaneous and reactive she could become when speaking English.

• student d was an anxious student, always ready to participate in experiments so as to improve her English level. As a matter of fact, her grammar was not reliable, but she learnt by heart all the words we had told them to learn since their first year, so she did not lack any vocabulary. She had phonological problems though. She said that she felt reassured by the smallness of the room which facilitated the proximity between the students right from the start and so enhanced confidence. She really appreciated the fact that she could support her colleagues when they were at a loss for words. She liked the fact that she could talk freely without being marked by the teacher. Yet she regretted the fact that the atmosphere was too relaxed at times and because of this lack seriousness, her linguistic performances were not at their maximum.

• student e appreciated the fact that he could practice in a realistic situation what he had learnt before.

He could then realize that he was able to talk in English for one hour even if his grammar, phonology and lexicon were not reliable enough. He liked the fact that the performances were recorded because he could criticize what he had said and how he had behaved. • student f could see that she could rely on the others when she was at a loss for words.

The evolution of their general competences

• student a was frustrated not to know how to use the software to its full potential. He wished he had had a better training beforehand and had experienced fewer technical difficulties.

• student b pointed out the freedom he had throughout the whole experience. He was the student who knew how to use the software best. • student c had a lot of problems (technical mainly) with the software and because of that, could not concentrate on and follow the conversations.

• student d also pointed out the freedom they had in finding solutions, proposing strategies etc, the relaxed atmosphere (but to some extent only as she had already observed), the correction by her peers.

• student e also pointed out the degree of creativity required and liked the playful aspect of the device,

• student f really appreciated the fact that she was immersed in the situation and had to cope with all she had.

Generally speaking, the students would recommend the use of such devices but advised the teachers to give the full detailed scenarios well before the performance to avoid silences (in Second Life) or misunderstandings (one student thought that the dog was dead when she had recovered). They also shed light on the importance of having feedback from the teachers immediately after the performances so as to not make the same mistakes in the future.

Conclusion

To conclude, technological devices such as online games on virtual worlds are good pedagogical devices for the best students (or even professionals) because they need to master the new technologies of information and communication (which might be a new obstacle for learning languages) and that dramatic activities and theatre plays are best for students less at ease with new technologies because there is no new technology to master. It is precisely because they are complementary that both theatre plays and online games lead to the autonomy of the learner. We do not think that online games convey autonomy alone since it is really important to interact with real people in real life (our bodies being part of our personalities and individual beings), and we are sure that some people cannot freely express themselves through dramatic activities from the start and that sometimes they need some distance from the learning process to be able to get back to it in a more efficient manner. In the future,

component 4

 4 of the learning process as shown in the following graph (reproduced fromAlvarez, p

"Quoi qu'il en soit, ce "scénario pédagogique" qui entre en jeu dès la conception de l'application pour répondre à un 'objectif pédagogique' nous semble être la composante qui pourrait peut-être spécifier le serious game, sur le plan informatique. Avec cette approche, nous pouvons avancer l'idée qu'un serious game se distinguerait ainsi du jeu vidéo dont on détourne a posteriori le principe d'utilisation. Puisque dans ce dernier cas, il s'agit d'ajouter l'objectif pédagogique a posteriori, par une approche purement cognitive, sans implémenter de ce fait un 'scénario pédagogique'."

cf. Bourdet, p. 43 "Nous proposons, pour ce faire, de réfléchir à la notion de potentiel de transférabilité, ce qui renvoie à la construction d'un ensemble d'attitudes (au sens de la psychologie sociale qui coordonne ici représentations et schèmes d'actions). C'est l'existence de ces attitudes qui va permettre, en contexte différent, d'appréhender les variations et de

cf.Alvarez, p. 117 : "Ce qui nous renvoie à l'approche de Winnicott et à sa notion 'd'aire intermédiaire'. Ainsi le jeune apprenant se situerait entre ce qu'il peut apprendre par lui-même et ce qu'il peut apprendre grâce notamment à un tiers. La présence de ce dernier semble donc être une composante maîtresse dans le champ de l'apprentissage."

the team of teachers agreed to start a reflection on the segmentation of competences so that all kinds of students may access both devices. We deeply encourage this practice and will inscribe our experience within blended learning in the future with new chosen students.

Game

Learning process potential learning space