Delbart

Le Maire

Keywords: remote sensing, global vegetation model, ORCHIDEE, carbon, biomass, height 37

Global Vegetation Models (GVMs) simulate CO2, water and energy fluxes at large scales, typically no smaller than 10 x 10 km. GVM simulations are thus expected to simulate the average functioning, but not the local variability. The two main limiting factors in refining this scale are 1) the scale at which the pedo-climatic inputs -temperature, precipitation, soil water reserve, etc. -are available to drive models and 2) the lack of geospatial information on the vegetation type and the age of forest stands. This study assesses how remotely sensed biomass or stand height could help the new generation of GVMs, which explicitly represent forest age structure and management, to better simulate this local variability. For the ORCHIDEE-FM model, we find that a simple assimilation of biomass or height brings down the root mean square error (RMSE) of some simulated carbon fluxes by 30-50%. Current error levels of remote sensing estimates do not impact this improvement for large gross fluxes (e.g. terrestrial ecosystem respiration), but they reduce the improvement of simulated net ecosystem productivity, adding 13.5-21% of RMSE to assimilations using the in situ estimates. The data assimilation under study is more effective to improve the simulation of respiration than the simulation of photosynthesis. The assimilation of height or biomass in ORCHIDEE-FM enables the correct retrieval of variables that are more difficult to measure over large areas, such as stand age. A combined assimilation of biomass and net ecosystem productivity could possibly enable the new generation of GVMs to retrieve other variables that are seldom measured, such as soil carbon content.

Introduction

Along with the growing concern about climate change, international agreements and regional markets have appeared and now give an economical value to carbon. These developments have naturally led to look at forest services from a carbon perspective: what is the carbon budget of a country's forests and how will it evolve? How can forest management increase the carbon balance of a forest stand and how can this mitigation effort be rewarded? To answer these questions, policy makers and foresters have progressively turned to forest models.

Stand-scale growth-and-yield models -such as FAGACEES [START_REF] Dhôte | Changements de productivité dans quatre forêts de chênes sessiles depuis 1930 : une approche au niveau du peuplement[END_REF], SILVA [START_REF] Pretzsch | The single tree-based stand simulator SILVA: construction, application and evaluation[END_REF] or CO2FIX [START_REF] Masera | Modeling carbon sequestration in afforestation, agroforestry and forest management projects: the CO2FIX V.2 approach[END_REF] -or process-based models -such as CASTANEA [START_REF] Dufrene | Modelling carbon and water cycles in a beech forest Part I: Model description and uncertainty analysis on modelled NEE[END_REF] or GRAECO [START_REF] Porté | Modélisation des effets du bilan hydrique sur la production primaire et la croissance d'un couvert de pin maritime (Pinus pinaster Ait.) en lande humide[END_REF] -offer trustworthy local simulations of carbon stocks. Nevertheless, the former require a local site productivity index and the latter tend to require an intensive local calibration, based on extensive field observations. It renders both types of models very site-specific and unfit to simulate regional fluxes and stocks. Another solution is to use generic global vegetation models such as Ecosytem Demography [START_REF] Moorcroft | A method for scaling vegetation dynamics: The ecosystem demography model (ED)[END_REF], Biome-BGC [START_REF] Thornton | Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests[END_REF], LPJ [START_REF] Sitch | Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model[END_REF] or ORCHIDEE [START_REF] Krinner | A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system[END_REF]. Although these GVMs tend to perform worse than well calibrated stand-scale models on a site-by-site basis [START_REF] Loustau | Modeling climate change effects on the potential production of French plains forests at the sub-regional level[END_REF], they provide estimates of carbon stocks and fluxes at regional scales, or where data is lacking for a local calibration. Their estimates of continental carbon budgets are within the range given by other methods [START_REF] Lindner | Specific Study on Forest Greenhouse Gas Budget[END_REF], but uncertainty remains high: for the carbon balance of European forests (EU25), the estimates of three similar GVMs exhibit a 2-fold difference [START_REF] Luyssaert | The European carbon balance. Part 3: forests[END_REF].

The bigger uncertainty of GVM estimates has two main causes: first the pedo-climatic inputs driving the models are too coarse and do not capture local variations, which in turn can generate errors due to the non-linear response of physiological processes to these inputs.

Second most GVMs do not explicitly simulate management [START_REF] Le Quere | Trends in the sources and sinks of carbon dioxide[END_REF], and therefore fail to correctly simulate age-dependent variables such as aboveground biomass. This pitfall is being increasingly addressed [START_REF] Zaehle | The importance of age-related decline in forest NPP for modeling regional carbon balances[END_REF][START_REF] Desai | Regional carbon fluxes from an observationally constrained dynamic ecosystem model: Impacts of disturbance, CO2 fertilization, and heterogeneous land cover[END_REF][START_REF] Sato | SEIB-DGVM: A new dynamic global vegetation model using a spatially explicit individual-based approach[END_REF]Bellassen et al., 2010a).

In even-aged stands, local variations of soil fertility, climate, and stand age can be combined in the notion of "growth stage". Forest yield tables indeed show that most characteristics -stand density, height, basal area, diameter, aboveground biomass, ... -of two stands with different fertility classes follow the same evolution with age, albeit not at the same pace [START_REF] Vannière | Tables de production pour les forêts françaises[END_REF]JRC, 2009). A 50 year old forest standing on a productive site may be considered to be at the same "growth stage" than a 100 year old forest standing on an unfertile soil, as they have similar biomass and height. Information about the "growth stage" of forest stands could be used to account for sub-grid heterogeneity in GVM simulations and increase the match between simulations and local measurements. Initializing a GVM with a spatially explicit map of growth stage may improve simulations in two ways: 1) at the local level, information on growth stage recreates some intra-pixel variability in simulations which may improve the fit to site data and 2) at the continental level, a map of growth stage provides a spatially more precise initialization than the current regional or national age averages of forest inventories. The initialization of carbon pools has already been shown to play an important role in the ability of GVMs to reproduce local flux data [START_REF] Carvalhais | Identification of Vegetation and Soil Carbon Pools out of Equilibrium in a Process Model via Eddy Covariance and Biometric Constraints[END_REF].

Both average stand height and aboveground biomass could be estimated at large scales with active remote sensing techniques such as RADAR and LiDAR. These two variables can therefore be used to initialize growth stage in a model simulation. P-band RADAR has been particularly used for estimating biomass, as the reflected signal is more specific to the woody components of trees, and therefore saturates at higher levels of biomass than other techniques (Le [START_REF] Toan | conifer plantation at Thetford Forest, UK[END_REF]. The LiDAR signal is less specific and most often used to estimate canopy height structure. Many studies however derive biomass estimates from LiDAR measurements, using the allometric relationship between biomass and height [START_REF] Lim | Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators[END_REF]Lefsky et al., 2005b;[START_REF] Stephens | Estimation of carbon stocks in New Zealand planted forests using airborne scanning LiDAR[END_REF][START_REF] Naesset | Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data[END_REF]. Except for a few data from the GLAS LiDAR satellite (Lefsky et al., 2005a;[START_REF] Boudreau | Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec[END_REF], all P-band RADAR and LiDAR studies relied on airborne campaigns, and were therefore limited to the local scale. As the European Space Agency is currently assessing the need for a P-band RADAR satellite (Le [START_REF] Toan | conifer plantation at Thetford Forest, UK[END_REF] and a LiDAR satellite [START_REF] Durrieu | Lidar for Earth and Forests[END_REF], there is a pressing need to quantify the benefits of remotely sensed biomass or height for the new generation GVMs which simulate different stand growth stages.

To this aim the current study compares the standard version of the ORCHIDEE GVM, with steady-state equilibrium forests, to a more recent version, ORCHIDEE-FM, that simulates forest management and the resulting tree height and biomass in a generic -that is even-agedmanaged stand. The ability of ORCHIDEE-FM to simulate the growth stages of a forest stand is checked with forest inventory plots. Then, pseudo-RADAR and LiDAR estimates of height and biomass are generated, based on existing in situ measurements from forest inventories and a global flux database. These pseudo-measurements are used to initialize ORCHIDEE-FM, and the improvement brought by this basic data assimilation is quantified for the simulation of volume increment, gross primary production (GPP), total ecosystem respiration (TER) and net ecosystem productivity (NEP). These quantified improvements provide a first assessment of the knowledge that could be gained from remotely sensed "growth stage" maps for large-scale carbon and water flux estimates.

Material and Methods

Field data

Two in situ datasets are used in this study: the French national forest inventory (IFN) plots in two different regions, and the global flux and biometry database of [START_REF] Luyssaert | CO2 balance of boreal, temperate, and tropical forests derived from a global database[END_REF].

French national forest inventory (IFN)

The first in situ dataset used in this study comes from the French national forest inventory (IFN). The IFN conducts yearly field measurement campaigns covering the entire French metropolitan territory. A systematic inventory grid is visited and inventoried following the IFN protocol [START_REF] Ifn | Observer la forêt française : mission première de l'IFN[END_REF]: circumference at breast height, width of the last five rings, height and species are recorded for a representative sample of trees. IFN allometric rules are used to estimate commercial volume and volume increment. For even-aged stands, a few tree cores sampling all growth rings are used to estimate stand age class, with age class widths between 10 and 20 years. Raw data for each plot is available on the IFN website (www.ifn.fr).

When necessary, estimated standing volume is converted to total aboveground biomass, using the default branch ratio and carbon density parameters of ORCHIDEE-FM (Table 1). Furthermore, commercial wood increment is used to estimate annual woody Net Primary Productivity (NPP), using Eq. 1.

    c w i b woody d d BEF V br T I NPP          1  (1)
where NPPwoody is the annual woody NPP in gC m -2 yr -1 , I is the estimated commercial wood increment in m 3 m -2 yr -1 , Tb is the annual turnover of branches in yr -1 , br is the total branch ratio (no unit), V is the estimated standing commercial wood in m 3 m -2 , ε is the averaged increment of trees that died over the last five years before measurement in m 3 m -2 yr -1 , BEFi is the biomass expansion factor for volume increment (no unit), dw is the wood density in gDM m -3 (grams of dry matter), and dc is the carbon density in gC gDM -1 . At most a few percent of trees commonly die over 5 years. They are usually smaller trees and not all die right before measurement date. ε is therefore much smaller than the wood increment of trees which survived, and neglected in the calculations. For parameter values, see Table 1.

For this study, the results of three campaigns -2005, 2006 and 2007 -are pooled, and only even-aged stands are used. To assess the performance of ORCHIDEE-FM, we selected regions that filled the following criteria:

-As REMO climate input data have a 0.25° x 0.25° resolution (see section 2.3.2) we selected grid points where climate does not vary strongly at the considered spatial scale so that a single ORCHIDEE-FM simulation should be representative of neighbouring IFN plots -A second criteria is to have enough IFN plots within a 0.5° radius for statistical purposes.

Two highly forested regions fit these criteria for both broadleaves and needleleaves:

southwestern and northeastern France. We therefore selected one southwestern gridpoint, hereafter referred to as "Landes", and one northeastern gridpoint, hereafter referred to as "Vosges", with respectively 215 and 324 IFN plots within a 0.5° radius of the grid point centre. The characteristics of these two grid points are summarized in Table 2. The IFN dataset thus provides a high number of in situ estimates for age, average height, standing aboveground biomass, and woody NPP at various growth stages (stand ages vary from 2 to 200 years). However, it does not provide direct carbon fluxes measurements.

Global dataset for carbon fluxes

Luyssaert et al. (2007) compiled a dataset of carbon flux measurements on forest sites, heavily building on the eddy covariance FLUXNET network (Baldocchi et al., 2001). This dataset also gives additional site information, when available, regarding stand age, average height and biomass. All managed sites in temperate and boreal biomes informed for stand age, average height and aboveground biomass, and either GPP, TER or NEP were retained for this study, thus reducing the database to a subsample of 31 sites. When several years of measurements were available for a given variable, the average was retained. The resulting dataset, hereafter referred to as the "global flux dataset", is presented in Appendix A.

Three peculiar site configurations are beyond the expected validity domain of ORCHIDEE-FM and therefore excluded for the quantification of performance improvement:

 Forest stands younger than 20 years (8 sites): in such young stands, NEP is expected to be heavily influenced by the site history before stand establishment (eg. afforested farmland or clearcut old stand) for which we have no information. This hypothesis is tested by analyzing the difference in simulations with two extreme sets of initial conditions: forest regrowth -initial conditions corresponding to the clear-cut of a mature forest -and reforestation -initial conditions corresponding to a cropland.

 Carbon sources (2 sites): our dataset only contains growing managed stands younger than 103 years old. Such stands are not expected to be net sources of carbon for several years in a row, unless management events such as heavy thinnings or clear-cuts take place within the footprint of the flux tower. Again, the absence of information on such heavy management events makes it hazardous to simulate these sites.

 Collelongo (1 site): GPP at this 103 years old Italian site is twice larger than TER, which makes it an outlier in most analysis of the global flux database [START_REF] Luyssaert | Toward a consistency cross-check of eddy covariance flux-based and biometric estimates of ecosystem carbon balance[END_REF]. In our case, climate is a possible explanation as it exhibit strong local variationsuncaptured by the 0.25° resolution forcing data -in this part of Italy.

The resulting dataset of 20 sites is hereafter referred to as the "screened dataset".

Generation of pseudo remote sensing data

The in situ estimates of average stand height and biomass from the two datasets are used to construct a set of pseudo remote sensing estimates of these variables. To this end, a random error is added to the in situ estimates of height and biomass. This random error is drawn from a normal probability law centred on the in situ estimate and with a standard deviation equal to the typical RMSE of LiDAR and P-band RADAR estimates of average height and biomass. This implies three assumptions: in situ estimates are assumed to be perfect, remote sensing observations are assumed to be unbiased and their error is assumed to be independent of the estimate value. The typical RMSE assigned to these pseudo-remote sensing observations are averages of RMSE from relevant literature studies (see Table 3): the typical RMSE of LiDAR is lower than that of RADAR for average height (1.66 vs 2.34 m) and higher for average biomass (23.7 vs 18.5 tC ha -1 ). The procedure of "pseudo data" generation is repeated 10 000 timesprocedure based on the Monte Carlo technique [START_REF] Rubinstein | Simulation and the Monte Carlo Method[END_REF] -for each in situ estimate in order to generate a representative pseudo-dataset.

Model

ORCHIDEE and ORCHIDEE-FM

The ORCHIDEE global vegetation model ("ORganizing Carbon and Hydrology In Dynamic

Ecosystems") is designed to operate from regional to global scales [START_REF] Krinner | A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system[END_REF].

ORCHIDEE typically represents an average mature forest at steady-state equilibrium in a "bigleaf" approach. For a given climatology, it simulates the carbon, water and energy budget at the pixel scale. For carbon, ORCHIDEE computes its assimilation (GPP), allocates photosynthates to the different biomass compartments where they are respired or stored, and recycles carbon through constant tree mortality and soil respiration. This "standard" version of ORCHIDEE (V1.9.4) is intended to simulate forests that have reached a steady-state equilibrium between growth and mortality. Its uses the allocation framework of [START_REF] Friedlingstein | Toward an allocation scheme for global terrestrial carbon models[END_REF], and does not simulate the nitrogen cycle, recently included in ORCHIDEE-N by Zaehle and Friend (2010).

This version of ORCHIDEE, hereafter referred to as the standard version, is intended to simulate forests that have reached a steady-state equilibrium between growth and mortality.

The standard version does not represent important processes driving the evolution of stand structure such as competition, forest management, or the age-limitation of NPP and is therefore not suited to simulate managed forests, or forests recovering from past disturbance.

As a consequence of its formulation all carbon pools, including biomass, need to be put to equilibrium before studying the effect of varying climate and CO2 conditions. This equilibrium is obtained by a "spin-up", that is an initial simulation which stops when carbon and water pools are in equilibrium with a fixed climate which can take up to 10 000 years.

In order to simulate forest management, several processes have been added to the standard version of ORCHIDEE, among which a Forest Management Module (FMM) inspired from the stand-level model FAGACEES [START_REF] Dhôte | Changements de productivité dans quatre forêts de chênes sessiles depuis 1930 : une approche au niveau du peuplement[END_REF]. The key concept is to add to the "average tree" representation of ORCHIDEE an explicit distribution of individual trees, which is the basis for a process-based simulation of mortality. The aboveground "stand-scale" wood increment simulated by ORCHIDEE is distributed among individual trees according to the rule of [START_REF] Deleuze | A flexible radial increment model for individual trees in pure even-aged stands[END_REF]: the basal area of each individual trees grows proportionally to its circumference. Tree mortality is then determined by the structure of the stand. Mortality due to natural competition relies on the self-thinning rule of [START_REF] Reineke | Perfecting a stand-density index for even-aged forests[END_REF] while another set of rules drives the mortality processes due to human interventions such as thinnings or clearcuts.

Some other small refinements have been added to ORCHIDEE such as a height limitation on leaf area index (LAI) and an age-related decline in photosynthesis efficiency. As a result, this new version of the model, called ORCHIDEE-FM, is able to simulate the carbon budget and detailed stand structure of forests of varying ages [START_REF] Bellassen | Modeling forest management within a global vegetation model -Part 2: model validation from tree to continental scale[END_REF]. Its equations are fully described in Bellassen et al. (2010a). For both versions of the model, the standard value of the maximum rate of carboxylation, Vcmax, is set to the optimized values for 6 sites in France and Germany found by [START_REF] Santaren | Optimisation des paramètres du modèle de biosphère ORCHIDEE à partir de mesures sur site des flux de carbone, d'eau et d'énergie[END_REF] for needleleaves and broadleaves, that is respectively 42.6 and 52.2 in µmol m -2 s -1 .

Simulations

Input data

For simulations at the sites of the global flux dataset, the climate forcing data comes from the 2.5° resolution NCEP -National Centers for Environmental Prediction -reanalysis, adjusted with the 0.5° CRU -Climate Research Unit -data for temperature and precipitation and interpolated to a 0.5° resolution [START_REF] Kalnay | The NCEP/NCAR 40-year reanalysis project[END_REF][START_REF] Mitchell | An improved method of constructing a database of monthly climate observations and associated high-resolution grids[END_REF]. As in most global simulations [START_REF] Krinner | A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system[END_REF], the soil bucket is uniformly taken to be 2 m deep and its texture is evenly distributed between clay, sand and silt, corresponding to a uniform water holding capacity of 300 mm

For the IFN sites, we use the 0.25° resolution REMO reanalysis [START_REF] Vetter | Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models[END_REF], which covers Europe. Maps of soil depth and texture were derived from FAO and IGBP products [START_REF] Vetter | Analyzing the causes and spatial pattern of the European 2003 carbon flux anomaly using seven models[END_REF].

Model "spinup"

Following a standard method in GVM modelling, a model "equilibrium spinup" is performed before all simulations to define the initial conditions of subsequent simulations, in particular for soil carbon. For this "spinup", ORCHIDEE is repeatedly run for the climate of the 10 years preceding stand establishment until all ecosystem carbon and water pools, including soil, reach a steady-state equilibrium. For the ORCHIDEE-FM simulations, the runs start with a clear-cut:

stems are exported and all the remaining biomass -branches, roots and leaves -goes to the litter pools, except for a small fraction corresponding to the initial stand structure assumed by the model.

Simulation set up

The reference simulation -STD -uses the standard version of ORCHIDEE. This simulation is compared to seven simulations using ORCHIDEE-FM, the age-explicit version of the model. For While it is technically feasible to assimilate biomass in the standard version, as in the CASA steady-state equilibrium model [START_REF] Carvalhais | Identification of Vegetation and Soil Carbon Pools out of Equilibrium in a Process Model via Eddy Covariance and Biometric Constraints[END_REF], the constant mortality assumed by ORCHIDEE, and the absence of wood removals, lead to a very fast equilibrium between NPP and TER, and therefore a limited ability to take advantage of biomass data. We therefore chose to discuss directly the relative merit of ORCHIDEE-FM with biomass or height assimilation against the standard version without data assimilation.

Example of application: maps of NEP

Results

Simulation of age-related trends by ORCHIDEE-FM

Age retrieval from assimilation of height or biomass data

The in situ estimate of stand age is correctly retrieved by assimilating biomass -FMgb simulation -or height -FMgh simulation -for both PFTs: the shape of the frequency distribution of age differences between simulations and observations is close to a zero-centered Gaussian curve (

Figure 3). The spread of the distribution is larger toward positive differences, leading to a positive mean age difference of 5 to 34 years between simulations and observations, for ages measured between 10 and 250 years. This bias is comparable to the precision of the measurement (the width of measured age classes varies between 10 and 20 years). The narrower shape of height histograms indicates than assimilating average height in ORCHIDEE-FM is slightly more discriminating than assimilating biomass.

No quantified improvement in simulated woody NPP

The ability of ORCHIDEE-FM to simulate an age-related decline in woody NPP (

Improvement in simulated GPP, TER and NEP

Simulation of carbon sources and sinks

Despite the expected presence of outliers in the global flux dataset, ORCHIDEE-FM -FMga simulation -is better able than ORCHIDEE to reproduce the observed cross-sites gradient of NEP across sites ( Figure 5). Interestingly, it is able to simulate the huge carbon source of -606 gC m -2 yr -1 measured at the very young site of Vancouver Island.

The use of our "screened dataset" is justified by the exponential decrease over time of the difference in simulated TER between reforested cropland and regrowing clearcut forests ( Figure 6). This difference can reach 1 000 gC m -2 yr -1 for younger stands but is below 300 gC m -2 yr -1 for all the sites of the screened dataset, older than 20 years. These figures compare to a typical simulated interannual variability of 300 gC m -2 yr -1 .

Quantification of improvement in simulated carbon fluxes

The standard version of ORCHIDEE correctly reproduces the spatial gradient of GPP across the screened dataset, despite a systematic positive bias, but not that of TER (Figure 7). Due to its representation of stand growth, ORCHIDEE-FM improves the simulation of TER, despite a systematic positive bias, and consequently the simulation of NEP. This improvement is quantified in Figure 8: the RMSE is improved (i.e. reduced) by 40-50% for TER, and by 20-40%

for NEP. More importantly for NEP, ORCHIDEE-FM is able to reproduce the observed variability, with slopes of data-simulations linear regressions between 0.8 and 1. This is a clear improvement from the NEP simulated by standard version of ORCHIDEE, which correlates very poorly with observations.

Whereas large gross fluxes (GPP, TER) are unaffected by an additional error on pseudo remote sensing data (FMrb, FMlb, FMrh and FMlh simulations), the simulation of NEP deteriorates with decreasing precision of the biomass data assimilated: the assimilation of in situ biomass (FMgb)

improves RMSE by 37% against only 23% for pseudo-lidar biomass (FMlb), and 28% for pseudoradar biomass (FMrb). The slope of the linear regression is also worsened from 1.01 to respectively 0.81 and 0.86.

Assimilating height or biomass leads to broadly similar improvements in flux simulations.

Biomass assimilation nevertheless seems most beneficial: while height assimilation further reduces RMSE for GPP and TER, this reduction comes at the price of a degraded linear regression. For NEP, biomass assimilation brings the strongest improvement in both RMSE and slope of linear regression.

Application: maps of NEP

Our simple assimilation procedure -using data-derived maps of height or biomass to select an age-class in ORCHIDEE-FM simulations -produces less uniform and likely more realistic maps of NEP for France (Figure 9). In particular, the older forests of central and north-eastern France are detected from their higher height and biomass, leading to a lower simulated NEP than the simulation without information on growth stage (Figure 9a).

Discussion

Flux simulation improvement delivered by height/biomass data

Our results show that a simple height or biomass data assimilation already yields a 30-50% decrease in RMSE for both TER and NEP. The tested range of measurement errors (representing Lidar or Radar errors) does not affect the improvement in large gross fluxes (GPP and TER).

However, the precision of NEP simulation is impacted: a 18.5 tC ha -1 uncertainty on the assimilated biomass data, typical of P-band radar measurements, increases the simulation error by 20 gC m 2 yr -1 (7.5% of observed average NEP), and a 23.6 tC ha -1 uncertainty, typical of LiDAR derived estimates, increases the simulation error by 31 gC m 2 yr -1 (11.5% of observed average NEP). Thus, a reduced error on remote sensing estimates of biomass would not be useful for the simulation of large fluxes, but could further reduce the error on simulated NEP by 20-31 gC m 2 yr -1 down to 149 gC m 2 yr -1 , that is the error obtained by assimilating the in situ estimate.

These results are both site-and model-dependent. For instance, the strong influence of landuse history on the NEP of younger stands limits the benefits of the assimilation procedure ( Figure 6). More elaborate distinctions in the usefulness of the method could possibly be made based on PFT or climate, but the small size of the screened dataset does not allow to draw robust conclusions.

The error reduction in simulated fluxes could also be further improved by improvements in the structure and in the parameterisation of ORCHIDEE-FM. In particular, our results point to systematic positive biases in the simulation of GPP and TER which offset one another in the simulation of NEP. Zaehle et al. (2010) demonstrated that modelling the nitrogen cycle eliminates this systematic bias. This new version of ORCHIDEE, ORCHIDEE-N, remains however unable to simulate the large positive NEP typical of growing forest sites. In the near future, when ORCHIDEE-FM is merged with ORCHIDEE-N, we can expect to reduce the bias in GPP and TER, and further decrease the error on NEP by assimilating age, height or biomass.

Our assumption that remote sensing measurement error is independent on measured value and normally distributed is not realistic. Remote sensing measurements of biomass for example are known to carry a larger error for larger biomass values due to signal saturation (Le [START_REF] Toan | conifer plantation at Thetford Forest, UK[END_REF]. We nevertheless opted for this simplistic approach of error modelling due to the lack of quantification of these error patterns. An alternative to improving error modelling would be to use real remote sensing data where it coincides with in situ measurements of carbon fluxes, but this additional requirement would further reduce an already small dataset.

The simple data assimilation framework tested in this study could easily be applied at continental scale, provided reliable biomass or height estimates are available. It would be most meaningful in temperate and boreal regions, where carbon fluxes are most impacted by management and age effects [START_REF] Grant | Changes in net ecosystem productivity with forest age following clearcutting of a coastal Douglas-fir forest: testing a mathematical model with eddy covariance measurements along a forest chronosequence[END_REF][START_REF] Magnani | The human footprint in the carbon cycle of temperate and boreal forests[END_REF][START_REF] Nunery | Forest carbon storage in the northeastern United States: Net effects of harvesting frequency, post-harvest retention, and wood products[END_REF]: most temperate forests are thinned or harvested, and large fires are the dominant source of disturbance in boreal forests.

These large-scale applications are very promising: TER is the component of NEP for which existing GVM estimates are most uncertain [START_REF] Mitchell | Multiple sources of predictive uncertainty in modeled estimates of net ecosystem CO2 exchange[END_REF], and it is also the flux for which simulation improvement is largest in our framework. This large improvement in TER comes from the process-based mortality and wood removals simulated by ORCHIDEE-FM. For a test broadleaf site in northeastern France, Bellassen et al. (2010a) showed that the delay in litterfall and the wood removals were responsible for 60% and 40% of the sink over one forest rotation respectively. The combination of these two processes allows the simulation of a realistic disequilibrium in biomass and soil carbon.

Scale issues in spatial heterogeneity

The ability of ORCHIDEE-FM to simulate correctly the across-site gradient in "growth stages" of the global flux dataset does not yield a quantitative improvement in the simulation of woody NPP for the IFN dataset. This difference points out the limits in the notion of "growth stage".

While it applies relatively well to some characteristics such as NEP (these characteristics evolving similarly in all stands, albeit not at the same pace), other variables such as woody NPP cannot be easily explained in this light only. Forests standing on poor soils for example will never have strong woody NPP, no matter how long one waits. As illustrated in Figure 10, the large variability in woody NPP for stands of similar ages highlights the importance of other factors than growth stage: the growth stage-dependent allocation and photosynthesis efficiency simulated in ORCHIDEE-FM cannot be expected to reproduce such a wide range of observed values.

The same phenomenon explains that the spread of age deviation distributions presented in Figure 3 is larger on the positive (younger ages) than on the negative (older ages) side. The assimilation framework can go very far towards younger simulated ages in order to match a small observed biomass, but the reverse is not true for high observed biomass: the old stands of average productivity simulated by ORCHIDEE-FM cannot reach the biomass of some observed high productive stands which are only slightly younger.

This large residual heterogeneity is most likely explained by species or local fertility, which could also be integrated in ORCHIDEE-FM. Model parameters in particular could be adapted to distinguish between fast-growing and slow-growing species. The main limitation for these developments is the ability to get proxies for these factors on a large scale. Fine resolution maps of species or site productivity are even more difficult to obtain than equivalent maps of height or biomass, although they may not be completely unmanageable [START_REF] Nabuurs | Mapping the continent: High resolution forest resource analyses of European forests[END_REF].

Biomass vs. height

The application of our method on the global flux dataset points to biomass as a more suitable candidate than average height for assimilation in ORCHIDEE-FM. The smaller RMSE obtained for GPP and TER in the FMgh, FMlh, and FMrh simulations are indeed misleading. They result from an overestimate of stand age which activates age-related decline processes in ORCHIDEE-FM:

while the previously discussed positive biases in GPP and TER are consequently reduced, they are probably not reduced for the correct reason since the RMSE in NEP is higher than for the FMgb simulation. As shown by Figure 11, the assimilation of height in ORCHIDEE-FM indeed leads to an overestimate of stand age for other plant functional types than temperate summergreen broadleaves and temperate evergreen needleleaves. This overestimate probably comes from the height-circumference allometry and the self-thinning relationship of ORCHIDEE-FM, which have only been tested rigorously for temperate summergreen broadleaves and temperate evergreen needleaves.

The IFN dataset, which is restricted to these two plant functional types, provides a different picture. The narrower and more centered distributions of age deviation for FMgh simulations (see

Figure 3) point to height as a more useful variable for assimilation in ORCHIDEE-FM. Height is indeed expected to be less sensitive than biomass to varying intensities of management.

These contradicting results make it difficult to draw a general and definitive conclusion on the relative merits of height vs. biomass assimilation in ORCHIDEE-FM. While height seems theoretically more promising, the allometric and self-thinning rules of ORCHIDEE-FM may not be currently generic enough to make the best use of it.

Retrieval of unmeasured variables

Some variables, such as stand age or soil carbon content, are difficult to measure and therefore seldom available at a fine resolution over large areas. Ours results show that ORCHIDEE-FM is able to use biomass or height data, in addition to pedo-climatic conditions, to correctly retrieve stand age. If such data were available over large areas, this simple assimilation method could therefore produce a new set of stand age maps. The method is independent of the combination of inventory data and remote sensing of disturbances used by [START_REF] Pan | Age structure and disturbance legacy of North American forests[END_REF] to produce age maps over north America, and the disagreements between the two would undoubtedly provide useful insights on the strengths and weaknesses of both methods.

Estimates of soil carbon content could also be retrieved from ORCHIDEE-FM by assimilating simultaneously biomass and NEP. Assuming that the model simulates correctly NPP and litterfall once it has been initialised for biomass, the resulting discrepancy between measured and simulated NEP would be due to a faulty soil carbon content, which could then be corrected in the model to match the NEP measurements.

Conclusion

Large-scale information on biomass derived from remote sensing estimates would provide valuable constraints for the simulation of carbon fluxes in ORCHIDEE-FM: the RMSE of simulated NEP is decreased by up to 30% for a global flux dataset. Most importantly, this improvement results from the ability of ORCHIDEE-FM, initialized with the correct "growth stage", to reproduce spatial gradients in NEP, an ability that is lacking in the standard steadystate equilibrium version of ORCHIDEE.

At a smaller spatial scale, where climate conditions are comparable, remotely sensed information on "growth stage" does not bring a useful constraint on woody NPP, probably because of the relatively higher importance of local factors such as soil fertility and species mix.

The notion of "growth stage" may also be less relevant for woody NPP than for NEP.

Nevertheless, our simple assimilation framework for height or biomass correctly retrieves stand age, despite a large standard deviation.

The simulated error of pseudo remote sensing estimates of biomass or height does not impact the improvement of large gross fluxes (GPP and TER). For simulated NEP however, this additional source of uncertainty increases the total error by 13.5% and 21% for P-band radar and lidar respectively.

Finally, while the results of our simple assimilation framework are promising, they represent only a first assessment of the potential of large-scale data assimilation in DGVMs. New developments in ORCHIDEE (Zaehle and Friend, 2010), and more refined assimilation frameworks, including other remotely sensed variables such as leaf area index [START_REF] Demarty | Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model[END_REF] or CO2 concentration [START_REF] Sarrat | Mesoscale modelling of the CO2 interactions between the surface and the atmosphere applied to the April 2007 CERES field experiment[END_REF], will doubtlessly optimize the use that ORCHIDEE-FM can make of remote sensing estimates of biomass and height.

appreciated the expert comments of Thuy Le Toan (CESBIO) and Patrick Chazette (LSCE) on remote sensing estimates.

This work was made possible thanks to a research grant from the French ministry for research.

Appendixes 523 Appendix A. Subsample of the global carbon flux dataset [START_REF] Luyssaert | CO2 balance of boreal, temperate, and tropical forests derived from a global database[END_REF] indicates that the simulated age is higher than the measurement. Baldocchi, D., Falge, E., Gu, L.H., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X.H., Malhi, Y., Meyers, T., Munger, W., Oechel, W., U, K.T.P., Pilegaard, K., Schmid, H.P., Valentini, R., Verma, S., Vesala, T., Wilson, K., Wofsy, S., 2001. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society 82, 2415-2434. Balzter, H., Luckman, A., Skinner, L., Rowland, C., Dawson, T., 2007a. Observations of forest stand top height and mean height from interferometric SAR and LiDAR over a

  Figure 1 and simulations names are summarized in Appendix E, together with other

  Data-derived maps of height and volume in French forests can be obtained by a smooth interpolation of IFN data[START_REF] Bellassen | Modeling forest management within a global vegetation model -Part 2: model validation from tree to continental scale[END_REF]: each grid cell is attributed the averaged height and standing volume over IFN plots within a 50 km radius from its centre. In order to illustrate the potential of remote sensing data assimilation in ORCHIDEE-FM, three maps of average simulated NEP in the 1990s are then produced for France. One represents the NEP simulated by ORCHIDEE-FM without prior knowledge of the growth stage of French forests: all French forests are assumed to belong to the 40-50 years age class. The two others -one assimilating height and the other volume -show how the initial map can be refined with information on growth stage. From 15 ORCHIDEE-FM simulations over France representing stands aged between 0 and 150 years, each grid cell is attributed the NEP of the simulation with closest height or biomass to the corresponding data-derived map.

  Figure2). The age-related decline is however steeper in IFN estimates than in simulations. In

Figure 2 )

 2 Figure 2) does not translate in a quantified improvement in the fit to IFN-derived estimates

Figure 1 . 7 Figure 2 .Figure 5 .

 1725 Figure 1. Combination of input data (italic) and models (bold) used in the simulation

Figure 6 . 3 Figure 7 . 4 Figure 8 .

 63748 Figure 6. Difference in simulated TER between forest regrowth and reforestation

Figure 9 .

 9 Figure 9. Maps of simulated NEP assimilated data-derived maps of height and volume

Figure 10 .

 10 Figure 10. Evolution of woody NPP with age for broadleaves in the IFN dataset

Figure 11 .

 11 Figure 11. Age retrieval by assimilating biomass (a) or height (b) in ORCHIDEE-FM

broadleaves (gC m -2 yr -1 )

  Wilmott performance indexes for the simulation of volume increment (IFN dataset) Abbreviations for the names of performance indexes are given in Appendix B. For details on simulations names, see part 2.3.2, Appendix E and

	524 525 536 580	used in this NEP (gC m -2 yr -1 ) YE 3 ME (years) Age 583 1895 103 178 1928 70 35 1931 68 410 1972 22 -32 1954 44 589 1987 12 675 1973 25 497 1982 18 202 1936 61 514 1930 67 104 1960 40 -61 1900 99 233 1964 36 289 1933 66 279 1924 76 407 1969 31 608 1987 9 249 1918 82 360 1970 34 309 1942 60 b MAE RMSE RMSEs RMSEu GPP (gC m -2 yr -1 ) RECO (gC m -2 yr -1 ) 1 127 591 1 215 1 030 690 638 960 550 1 303 1 334 2 762 2 087 2 606 1 944 1 788 1 233 1 287 1 058 1 690 1 335 1 000 932 1 312 1 404 1 012 782 1 165 835 1 452 1 163 1 833 1 451 1 774 1 166 na na 1 232 953 1 379 1 071 d r2 71.0 328 0.05 624.71 228.4 276.4 267.4 PFT 1 ASH 2 (m) Biomass (gC m-2) TeBS 19.0 9 860 BoBS 20.3 1 997 BoNE 13.0 1 997 BoNE 5.0 3 090 TeNE 36.0 9 267 TeNE 10.0 2 285 TeNE 19.0 8 271 TeNE 14.0 5 128 TeBS 25.0 9 900 TeBS 25.3 5 715 BoNE 4.7 1 770 BoNE 28.0 11 135 BoNE 15.5 5 900 BoBS 24.0 7 490 TeBS 26.5 8 720 TeNE 19.0 7 008 TeNE 8.0 3 700 TeBS 30.0 6 288 BoNE 16.0 6 063 TeBE 6.0 5 424 Simulation obs average sim average obs sd sim sd N study ("global flux dataset") ID Site name Latitude Longitude 1003 Collelongo 41.75 °N 13.75 °E 1004 Prince_Albert_SSA_(SOAS) 53.75 °N 106.25 °W 1006 Prince_Albert_SSA_(SOJP) 53.75 °N 104.75 °W 1007 Thompson_NSA_(NYJP) 55.75 °N 98.25 °W 1012 Bayreuth/Weiden_Brunnen 50.25 °N 11.75 °E 1014 Slash_pine_Florida_Mid 29.75 °N 82.25 °W 1015 Slash_pine_Florida_old 29.75 °N 82.25 °W 1089 Duke_Forest 35.75 °N 79.25 °W 1092 Harvard 42.75 °N 72.25 °W 1093 Walker_Branch 35.75 °N 84.25 °W 1095 Flakaliden_C 64.25 °N 19.25 °E 1096 Norunda 60.25 °N 17.25 °E 1097 Hyytiala 61.75 °N 24.25 °E 1101 Willow_Creek 45.25 °N 90.25 °W 1106 Morgan_Monroe 39.25 °N 86.25 °W 1109 Le_Bray 44.75 °N 0.75 °W 1110 Balmoral 42.75 °S 172.75 °E 1154 Oak_ridge_liriodendron 35.75 °N 84.25 °W 1168 Skyttorp2 60.25 °N 17.75 °E 1169 Puechabon 43.75 °N 3.75 °E Woody NPP -needleleaves (gC m -2 yr -1 ) a STD 481.5 650.9 219.1 69.9 0.48 0.03 FM ga 483.1 617.1 193.8 70.4 172 0.05 592.82 189.1 237.7 227.3 69.5 0.46 0.02 FM gb 481.5 648.2 219.1 72.8 328 0.06 620.94 225.2 274.8 265.3 71.6 0.48 0.03 FM gh 481.5 640.5 219.1 70.8 328 0.06 613.97 220.6 269.9 260.8 69.7 0.48 0.03 FM lb 481.5 651.6 219.1 71.6 328 0.05 627.34 227.3 277.6 268.5 70.7 0.48 0.02 FM rb 481.5 649.4 219.1 72.7 328 0.06 621.86 225.0 275.4 265.9 71.5 0.48 0.03 FM lh 481.5 642.1 219.1 71.1 328 0.05 616.99 222.3 271.5 262.3 70.0 0.47 0.03 FM rh 481.5 641.8 219.1 70.6 328 0.06 615.26 220.7 270.6 261.6 69.4 0.48 0.03 GPP (gC m -2 yr -1 ) OA SA OS SS N a b MAE RMSE RMSEs RMSEu d r2 STD 1296 1579 502 582 18 1.12 129 286 325 289 0.91 0.93 FM ga 1296 1562 502 593 18 1.14 87 269 315 274 0.92 0.93 FM gb 1296 1562 502 601 18 1.15 71 279 319 276 0.92 0.92 FM gh 1296 1472 502 613 18 1.15 -22 219 274 190 0.94 0.89 FM lb 1296 1544 502 596 18 1.13 73 269 310 258 0.92 0.91 FM rb 1296 1552 502 597 18 1.14 75 273 313 265 0.92 0.92 FM lh 1296 1475 502 619 18 1.17 -43 219 272 198 0.94 0.90 FM rh 1296 1475 502 620 18 1.17 -45 220 272 198 0.94 0.90 TER (gC m -2 yr -1 ) OA SA OS SS N a b MAE RMSE RMSEs RMSEu d r2 STD 1032 1493 403 569 18 1.31 142 462 520 477 0.76 0.86 FM ga 1032 1288 403 425 18 0.96 302 263 311 257 0.86 0.82 FM gb 1032 1324 403 423 18 0.96 333 292 336 292 0.85 0.84 FM gh 1032 1269 403 412 18 0.94 296 250 285 239 0.88 0.85 FM lb 1032 1331 403 443 18 0.99 310 301 354 301 0.84 0.81 FM rb 1032 1330 403 436 18 0.98 321 300 350 299 0.84 0.82 FM lh 1032 1262 403 416 18 0.95 278 241 278 231 0.89 0.85 FM rh 1032 1261 403 416 18 0.95 277 241 278 230 0.89 0.85 NEP (gC m -2 yr -1 ) OA SA OS SS N a b MAE RMSE RMSEs RMSEu d r2 STD 269 94 152 70 20 0.12 62 185 229 219 0.46 0.06 FM ga 269 282 152 176 20 0.79 69 97 129 34 0.82 0.47 FM lb 269 226 152 215 20 0.79 15 134 180 59 0.74 0.40 Tables Parameter Conifers Broadleaves Unit Source BEFi 1.1 1.2 no unit (IPCC, 2003) Tb 0.025 0.025 yr -1 (Bellassen et al., 2010a) br 0.25 0.38 no unit (Bellassen et al., 2010a) dc 0.5 0.5 gC gDM -1 (Bellassen et al., 2010a) dw 0.4*10 6 0.6*10 6 gDM m -3 (Bellassen et al., 2010a) Table 1. Parameter values Name Landes Vosges Latitude 43.875°N 48.125°N Longitude 0.875°W 6.875°E 2001-2005 average temperature (°C) 14.3 9.7 2001-2005 average rainfall (mm yr -1 ) 925 971 Number of broadleaf plots within a 0.5° radius 78 133 Number of needleleaf plots within a 0.5° radius 137 191 Average volume increment of neighbouring broadleaf plots (m 3 ha -1 ) 12.2 14.0 Average volume increment of neighbouring needleleaf plots (m 3 ha -1 ) 19.8 22.2 Average age of neighbouring broadleaf plots (years) 92 97 Average age of neighbouring Appendix C. Figure 1. FM gb 269 250 152 214 20 0.99 -17 110 149 19 0.81 0.50 FM gh 269 213 152 255 20 0.98 -49 155 210 56 0.70 0.34 needleleaf plots (years) 42 71
		1170 Dooary FM rb	269	52.75 °N 235	7.25 °W 152 212 20 0.84 10 127 169 TeNE 8.0 6 162	2 001 47	1 141	860 1989 0.76 0.42	15
		1178 Takayama FM lh	269	36.25 °N 137.25 °E 223 152 244 20 0.96 -34 146 194 TeBS 20.0 13 488	1 050 47	833	217 1962 0.73 0.37	35
	544	1185 Hyytiala_12 1246 Hyytiala_75 Simulation obs average sim average obs sd sim sd N 61.75 °N 24.25 °E BoNE 4.0 61.75 °N 24.25 °E BoNE 25.0 FM rh 269 223 152 244 20 0.96 -34 146 194 250 6 700 a b MAE RMSE RMSEs RMSEu 854 752 102 1991 918 566 352 1927 47 0.73 0.37	11 74 d r2
	545	1328 Skyttorp3 1364 Espirra STD FM ga	426.7 468.9	60.25 °N 17.75 °E 38.75 °N 8.75 °W 520.7 241.9 523.4 280.5	BoNE 18.0 TeBE 20.0 55.5 211 0.00 518.81 214.0 263.8 258.0 7 310 na na 370 1938 4 212 1 495 876 619 1991 55.4 0.36 0.00 64 13 41.3 86 -0.02 533.51 234.1 292.9 290.1 40.6 0.20 0.02
	546	1378 Bartlett 1482 Chibougamau_EOBS 1507 Vancouver_Island_HDF00 1508 Vancouver_Island_HDF88 1509 Vancouver_Island_DF49 FM rb 426.7 FM lb 426.7 FM gb 426.7 FM gh 426.7	44.25 °N 71.25 °W 49.75 °N 74.25 °W 49.75 °N 125.25 °W 49.75 °N 124.75 °W 49.75 °N 125.25 °W 533.8 241.9 536.2 241.9 534.1 241.9 530.2 241.9	TeBS 19.0 BoNE 14.0 TeNE 1.0 TeNE 8.0 TeNE 29.0 39.0 211 -0.03 545.63 223.8 272.8 270.1 10 730 1 053 790 263 1925 4 500 584 580 4 1909 2 775 435 1 041 -606 1999 5 700 1 214 1 347 -133 1988 10 550 1 991 1 737 337 1949 38.3 0.34 0.03 80 95 52 14 37.5 211 -0.02 545.14 222.9 272.1 269.6 37.1 0.35 0.02 3 38.6 211 -0.02 543.00 221.5 271.4 268.7 38.2 0.35 0.02 37.6 211 -0.01 535.58 219.3 268.0 265.4 37.4 0.34 0.01
		FM lh	426.7	530.1 241.9	38.5 211 -0.01 536.03 219.3 268.3 265.6	38.3 0.34 0.01
		FM rh	426.7	531.9 241.9	38.2 211 -0.02 538.57 220.2 269.4 266.7	37.9 0.34 0.01

Woody NPP -

Table 2 .

 2 Description of the IFN plots used in this study 581

Table 3 .

 3 RMSE of LiDAR and P-band radar for height and biomass 583

TeNE: tempereate needleleaf evergreen, TeBE: temperate broadleaf evergreen, TeBS: temperate broadleaf summergreen, BoNE: boreal needleleaf evergreen, BoBS: boreal broadleaf summergreen

Average stand height / 2. Year of establishment /

Measurement year 526

Acknowledgements

We want to acknowledge the contribution of Antoine Colin (IFN), without whom the work on the dataset he manages would have been both impossible and meaningless. We also

FMlb: Simulation with closest biomass to the pseudo-lidar estimate, selected from a set of ORCHIDEE-FM simulations separated by 10-years intervals.

FMrb: Simulation with closest biomass to the pseudo-radar estimate, selected from a set of ORCHIDEE-FM simulations separated by 10-years intervals.

FMlh: Simulation with closest average stand height to the pseudo-lidar estimate, selected from a set of ORCHIDEE-FM simulations separated by 10-years intervals.

FMrh: Simulation with closest average stand height to the pseudo-radar estimate, selected from a set of ORCHIDEE-FM simulations separated by 10-years intervals.

Model assessment and quantification of simulation improvement

Assessment of stand growth simulation

The expected improvement of ORCHIDEE-FM simulations relies on the assumed ability of the model to correctly simulate the evolution of forest stand variables such as height and biomass as a function of age. To check this ability, two tests are performed: first, the simulated evolution of woody NPP with age is compared to the IFN plots estimates. Second, since stand age, biomass and height are correlated with each other, ORCHIDEE-FM should be able to retrieve stand age when biomass or height is assimilated. For simulations FMgb and FMgh where stand age is adjusted in the simulations to minimize the error on respectively biomass and height, the inferred stand age can be cross-validated by the in situ data on stand age.

Quantification of simulation improvement

Quantified evaluation criteria

To quantify the improvement in simulations resulting from the use of ORCHIDEE-FM in conjunction with external estimates of biomass or height, we focus on four carbon flux variables available in the datasets: GPP, TER, NEP, and woody NPP, this last being the indirect result of the allocation of NPP between the different organs of trees. While thirteen model evaluation criteria are computed (Appendix B) for each simulation according to the recommendations of [START_REF] Willmott | Some Comments on the Evaluation of Model Performance[END_REF], we especially focus our analysis on two of them: the root mean square error (RMSE) as an indicator of average simulation error, and the slope of the Appendix B. Wilmott performance indexes [START_REF] Willmott | Some Comments on the Evaluation of Model Performance[END_REF] References [START_REF] Nelson | A portable airborne laser system for forest inventory[END_REF]Balzter et al., 2007a;Balzter et al., 2007b;[START_REF] Stephens | Estimation of carbon stocks in New Zealand planted forests using airborne scanning LiDAR[END_REF][START_REF] Breidenbach | Quantifying the influence of slope, aspect, crown shape and stem density on the estimation of tree height at plot level using lidar and InSAR data[END_REF]) [START_REF] Neeff | Tropical forest measurement by interferometric height modeling and P-band radar backscatter[END_REF][START_REF] Dubois-Fernandez | The Compact Polarimetry Alternative for Spaceborne SAR at Low Frequency[END_REF][START_REF] Hajnsek | Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign[END_REF] Aboveground biomass RMSE (tC ha -1 ) 23.66