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T. E. , NSE (sans unité) et R² (sans unité)-d'E3S non interpolé (E3S) et interpolé (E3Sv1.a, E3Sv1.b et E3Sv1.c) par rapport aux observations gafillées et aux simulations de SiSPAT (entre parenthèses) acquises sur les sites de Wankama, Performances statistiques journalières-biais (en mm.jour-1 ), RMSE (en mm.jour-1 )

A. , Les auteurs ont également évalué SEBAL en regard de simulations de deux modèles hydrologiques sur deux sites en Égypte. Selon les sites, SEBAL a été utilisé à partir de données acquises par Landsat-TM ou NOAA-AVHRR et METEOSAT. Le modèle a par la suite été évalué sous une large gamme de conditions climatiques et à partir de données dérivés de différents capteurs, Desertification-threatened Areas (EFEDA), au Sahel, dans le cadre du projet Hydrologic Atmospheric Pilot Experiment in the Sahel (HAPEX-Sahel), 2000.

. Jiménez-bello, 2008) et notamment en régions semi-arides, 2006.

. Teixeira, , 2009.

. Wang, Les auteurs ont ainsi montré que l'assignation de cette forme rectangulaire pouvait engendrer des erreurs dans la distribution spatiale du flux H. Aussi ils ont proposé une version modifiée de SEBAL, nommée ModifiedSEBAL (M-SEBAL), pour imposer une forme trapézoïdale dans le modèle SEBAL. De plus M-SEBAL détermine automatiquement le bord sec à partir du trapézoïde T s-f COVER et le bord humide est définit par la moyenne de T a observée. La validation du modèle sur un site instrumenté, dans le cadre du projet Soil-Moisture-Atmosphere Coupling EXperiment (SMACEX), dans l'Iowa aux États-Unis, a montré que M-SEBAL était plus performant que SEBAL. L'effet de la sélection des pixels déterminant les bords humide et sec sur la, ont mené une analyse de sensibilité du modèle SEBAL en le testant sur des couverts plus ou moins dense au Nouveau Mexique. Les auteurs ont montré que SEBAL était très sensible à la définition des pixels dits « sec » et « humide », et suggère que le choix de ces pixels devrait être automatisé pour éviter toute subjectivité lié à l'opérateur dans ce choix, 1994.

A. , ) ont proposé le modèle Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) pour une utilisation sur des cultures irriguées. La principale différence entre METRIC et SEBAL réside dans le fait que, contrairement à SEBAL, METRIC ne fait pas l'hypothèse que H = 0 et LE = R n-G au pixel humide/froid et H = R n-G et LE = 0 au pixel sec/chaud, BEAREX07-08) menée au Texas, et validé en regard de mesures issues de lysimètres sur deux cultures irriguées et 2 champs en jachère, 1998.

. Santos, METRIC convertit ensuite l'ET instantanée en ET journalière en utilisant l'ET 0 comme support d'extrapolation. Le modèle METRIC a plutôt été créé à destination de l'ingénierie pour la gestion des volumes irrigués et a, en ce sens, fait l'objet de peu d'évaluation et toujours sur des cultures irriguées, L'évaporation du pixel sec est quant à elle calculée à partir d'une routine de bilan d'eau dans le sol forcée par des données météorologiques in situ, 2005.

. Sur-les-principes-des-modèles-sebal, . Metric, and . Senay, Le modèle calcul dT pour les quatre pixels extrêmes délimitant le trapézoïde et représentant les conditions extrêmes de pixel nu sec et saturé, et de végétation stressée et non stressée, se traduisant par la résolution d, ) ont proposé un nouveau modèle 1-source contextuel nommé SSEBop, 1994.

. Lhomme, Kustas (1990) a montré que l'application des modèles SEB 1-source était en ce sens très limitée pour des surfaces partiellement végétalisées. Même si des ajustements de r a sont possibles, ils ne sont généralement pas applicables à toutes les conditions de couvert. Pour éviter ces problèmes, des modèles 2-sources, qui traitent le sol et la végétation séparément, ont été développé pour être utilisés à l'origine sur des couverts hétérogènes, A.5.3. Modèles SEB 2-sources Les modèles SEB 1-source ne font pas la distinction entre l'évaporation du sol et la transpiration de la végétation, 1994.

. Kustas-&-norman-;-anderson, Les modèles SEB 2sources ne requièrent pas de calibration in situ comme le nécessitent généralement les modèles 1-source, et présentent donc une plus large gamme d'applicabilité, 1997.

. Le-modèle-seb-2-sources-le-plus-largement-utilisé-est-celui-développé-par-norman, Dans les modèles SEB 2-sources comme TSEB, le flux H est décomposé en une composante sol (H sol ), 1995.

&. Long and . Singh-;-moran, Les sommets supérieurs du trapézoïde sont définies en résolvant l'équation du bilan radiatif comme les températures T veg,max (température de la surface totalement végétalisé la plus sèche) et T sol,max (température de la surface de sol nu la plus sèche). La température de l'air T a constitue la limite inférieure du trapézoïde. La valeur de T s d'un pixel donné au sein de l'espace T s-f COVER est décomposée en une composante sol (T sol ) et couvert (T veg ) en interpolant la pente des limites théoriques du trapézoïde et en interprétant la variation de T s avec f COVER pour chaque isoplèthe traversant le pixel. La transpiration de la végétation et l'évaporation du sol sont ensuite paramétrés séparément. Le modèle TTME a été appliqué sur le site SMACEX au centre de l'Iowa aux États-Unis. De manière analogue, Yang & Shang (2013) puis Tang & Li (2017) ont proposés des modèles SEB 2-sources, pour lesquels les températures T veg et T sol sont calculés à partir du trapézoïde ou triangle T s-VI. De même, 1994.

. Sun, Le modèle estime l'ET comme la somme des composantes sols et végétation dont les températures de surfaces respectives sont estimées à partir du Triangle T s-VI. Sim-ReSET a notamment été appliqué sur l'Afrique de l'Est, ont également développé un modèle 2-sources à paramétrage contextuel nommé Simple Remote Sensing Evapotranspiration (Sim-ReSET), 2009.

. Loukas, ont illustré la relation entre le NDVI et l'ET en utilisant des relations linéaires entre des composites de NDVI mensuels et l'ET simulée par un modèle hydrologique, pour estimer l'évapotranspiration réelle de quatre bassins versants grecs. Nagler et al. (2005a, 2005b) ont estimé l'ET de ripisylves à partir de simples régressions linéaires basées sur l'EVI MODIS et la température journalière maximale de l'air. Des comparaisons avec des données issues de tour à flux le long de trois grands systèmes fluviaux au sud-ouest des États-Unis acquises entre 2000 et 2004, montrent que l'ET journalière peut être simulée avec des valeurs de R² de 0.76. Dans la même veine, Nagler et al. (2007) ont corrélé des valeurs d'EVI MODIS avec des observations d'ET issues de tour à flux sur des pâturages semi-arides en Arizona. Dans cette étude, une simple régression linéaire multiple permet de relier l'ET, l'EVI et les précipitations avec un R² de 0.74. Scott et al. (2008) ont proposé une approche similaire pour laquelle une régression linéaire de forme exponentielle permet de relier l'ET à EVI et T s issus du capteur MODIS. Le modèle a ensuite été révisé par Bunting et al. (2014) pour donner une forme multiplicative, A.6. Autres méthodes A.6.1. Méthodes empiriques Les modèles empiriques utilisent des méthodes statistiques permettant de relier l'évapotranspiration à différentes variables explicatives plus facilement mesurable par satellite ou in situ, 2005.

. Wang, ont proposé une méthode empirique basée sur une régression linéaire multiple entre Rn, T a , T s et le NDVI pour spatialiser des mesures de flux effectuées sur 8 stations météorologiques dans les Grandes Plaines aux États-Unis. Wang & Liang, (2008) ont ensuite étendu le modèle à l'échelle globale avec une résolution temporel mensuelle en utilisant des données issue de l'ISLSCP-II 1 et un NDVI obtenu à partir de mesures de réflectance par le capteur AVHRR, 2007.

. Knipper, Le modèle calcul l'ET en modulant l'ETP calculée à partir de la formule de Priestley-Taylor par un facteur multiplicatif d'humidité du sol. Ce dernier est calculé à partir d'estimations d'humidité de surface issue du capteur SMOS, dont la résolution native est désagrégée à la résolution MODIS à, 2017.

Y. , pour prédire l'évapotranspiration sur l'ensemble du continent américain à partir de trois variables issues de la télédétection (T s , EVI et l'occupation du sol) et une variable issue de station météorologique (Rg), Les auteurs rapportent une RMSE relative de 23 % et un R² de 0.75. D'autres auteurs ont utilisé des réseaux de neurones artificiels (ANN) pour relier différents observables de télédétection à l'ET, 2006.

. Lu-&-zhuang, Par exemple, Lu and Zhuang (2010) ont développé un ANN permettant de relier la fraction évaporative à cinq observables de télédétection (NDVI, NDWI 1 , LAI et le rayonnement photosynthétiquement actif) et deux variables issus de stations météorologiques, 2010.

J. , ) ont proposé un modèle statistique non-linéaire permettant de relier les flux latent et sensible simulés par divers modèles de surface (GSWP2, ISBA et ORCHIDEE) à des mesures de télédétection dans les domaines visibles, procheinfrarouge, infra-rouge thermique et hyperfréquence. Une fois calibré, le modèle statistique reproduit les flux simulés par les modèles sur différents, ont développé une méthode d'apprentissage appelée « Model Tree Ensemble (MTE) » pour relier l'évapotranspiration mensuelle à des mesures de fraction de radiation photosynthétiquement active (f APAR ), 2009.

. Ryu, 1991) et d'une forme quadratique de l'équation de Penman-Monteith. Le modèle BESS a été utilisé pour fournir des estimations de GPP et d'ET à l'échelle globale et à la résolution kilométrique à partir de produits atmosphériques (aérosols, couvert nuageux, teneur en vapeur d'eau, profil atmosphérique) et de surface (?, T s , LAI, occupation du sol) issus du capteur MODIS et de données météorologiques issus des réanalyses NCEP/NCAR 2, A.6.2. Méthodes basées sur le couplage GPP-ET L'évapotranspiration étant un processus à la croisé des cycles de l'eau, de l'énergie et du carbone, certains modèles établissent une connexion entre l'activité photosynthétique ou la production primaire brut (GPP pour Gross Primary Production) et l'ET. Par exemple, 1980.

. Néanmoins-;-yang, ce type de modèle demande d'importante base de données et des temps et une puissance de calcul nécessairement beaucoup plus long pour être appliqué à de large échelle, 2013.

. De-moran, une variante du triangle T s-VI Lieu et données : Bassin versant de Peyne, France Capteur (résolution spatiale / temporelle) : ASTER (90 m / instantanée) Jeu d'évaluation : simulations Hydrus 1-D + 2 stations (vignes) / 1 site / 11 dates Conclusions : · S-SEBI produit de meilleurs résultats que l'approche WDI Référence : Tang et al. (2011b) Modèles comparés : SEBS / TSEB / Triangle T s-VI Lieu et données : Station expérimentale de Yucheng, Chine Capteur (résolution spatiale / temporelle) : MODIS (1 km / instantanée) Jeu d'évaluation : 1 station (LAS + EC / cultures rotation maïs-blé) / 1 site / 7 dates Conclusions : · TSEB est le modèle le plus performant devant SEBS · Le Triangle T s-VI, Normalized Difference Water index 2 National Centers for Environmental Prediction/the National Center for Atmospheric Research Référence : Galleguillos et al. (2011a) Modèles comparés : S-SEBI / approche Water Deficit Index (WDI), 1994.

, Il apparait que les modèles TSEB et SEBS sont très sensible aux erreurs sur T s tandis que le Triangle T s-VI y est relativement insensible, · Les trois modèles ont été testés avec des données T s MODIS brutes et débiaisées à partir des mesures in situ

, États-Unis Capteur (résolution spatiale / temporelle) : MODIS (1 km / instantanée) Jeu d'évaluation : 11 stations (Bowen ratio (BR)) / 1 site / 16 dates Conclusions : Le Triangle T s-VI est plus performant que S-SEBI. Les raisons évoquées pour ces meilleures performances sont les suivantes : · la forme triangulaire prédéfinit de l'espace T s-VI permet de mieux définir les bords humides et secs comparativement à l'espace T s-albedo, Modèles comparés : S-SEBI / Triangle T s-VI Lieu et données : Southern Great Plains, 2011.

, · l'équation de PT utilisée dans le Triangle T s-VI exprime le contrôle de la température de l'air et de l'élévation sur ET à travers ? et ? respectivement, ce qui améliore la précision des estimations de EF

&. , ET étant très dépendante de la densité du couvert, l'utilisation du NDVI plutôt que de l'albédo améliore l'estimation de EF

. Référence-:-chirouze, Yaqui Valley, Mexique Capteur (résolution spatiale / temporelle) : ASTER (90 m / instantanée) et FORMOSAT-2 (8 m / instantanée) Jeu d'évaluation : 7 stations (EC) + simulations ICARE (non étalonné et forcé par mesures in situ) / 1 site / 7 dates Conclusions : · SEBS, S-SEBI et TSEB présentent des résultats comparables et dans le même ordre de grandeur que ICARE mais avec un comportement opposé dans la répartition des flux turbulents : ICARE tend à sous, 2014.

·. Tseb, ICARE sont les deux modèles qui estiment le mieux la réponse au stress hydrique mais avec des comportements opposés : TSEB est plus performant pour des LAI forts et montre des difficultés à détecter les périodes de stress durant la senescence ; tandis que ICARE a tendance à surestimer le stress pour les LAI forts mais est plus précis que TSEB pour les LAI faibles

, · SEBS est peu performant pour les situations de LAI faible et de sol nu

S. ·-comme-attendu, . Est, and . Lorsqu, il existe dans l'image un fort contraste d'humidité de surface (en particulier au printemps et au début de l'été) et à tendance à accentuer les contrastes d'humidité dans des conditions d'humidité de surface plus homogènes

. Référence-:-chen, Lieu et données : Asie (ChinaFlux, AsiaFLUX, LathuileFLUX) Capteur (résolution spatiale / temporelle) : forçages météorologiques (MERRA-GMAO 1 ), NDVI/LAI (MODIS et AVHRR) / résolution 0.1° / pas de temps mensuel, pp.1982-2009, 2003.

, 3 forêts de feuillus sempervirents, 1 forêt de conifères sempervirents, 7 cultures, 7 prairies, 3 forêts mixtes) / 22 sites Conclusions : · Une forte variabilité inter-modèle est observée : sur la Chine, l'ET varie de 535 à 852 mm.an-1 entre les huit modèles, Jeu d'évaluation : 23 stations (EC / 1 forêt de feuillus caducifoliés, 1 forêt de conifères caducifoliés

, · Les modèles à bases physiques (PT-JPL et MOD16) sont plus performants que les modèles empiriques dont les performances dépendent grandement du jeu d'apprentissage

, · Les auteurs suggèrent qu'un bon moyen d'améliorer un produit d'ensemble d'ET, serait d'évaluer, en regard d'observations, ces produits dans les régions pour lesquels la variabilité inter-produit est importante

:. Référence and . French, Modèles comparés : METRIC / TSEB Lieu et données : Maricopa, Arizona, États-Unis Capteur (résolution spatiale / temporelle) : aéroporté (1 m) et Landsat 5 & 7 (120 m) / instantanée Jeu d'évaluation : ET dérivée de 112 sondes à neutrons (culture irriguée de coton) / 1 site / 7 vols (aéroporté) et 17 dates (Landsat) Conclusions : · METRIC et TSEB présentent des performances similaires pour les deux résolutions testées, 2015.

, · METRIC est plus sensible aux changements de T s sur les pixels avec les plus faibles couverts alors que TSEB est plus sensible durant les phases de maturité du coton

, Chine Capteur (résolution spatiale / temporelle) : MODIS (1 km / instantanée) Jeu d'évaluation : 1 station (LAS / essentiellement des cultures) / 1 site / 23 dates Conclusions : · Le Triangle T s-VI présente des valeurs de RMSE nettement supérieures à celles obtenues avec SEBAL, Modèles comparés : SEBAL / Triangle T s-VI Lieu et données : Yucheng, 2015.

·. Le, Triangle T s-VI fournit des valeurs de EF plus faibles que celles de SEBAL

. Référence-:-yang, Modèles comparés : HTEM (Yang & Shang, 2013) / TSEB / MOD16, 2015.

, Lieu et données : Bassin versant de Heihe, Chine (données campagne MUSOEXE-12 1 )

, EC) dont 1 avec partitionnement transpiration-évaporation à partir d'une méthode isotopique (1 steppe désertique, 2 zones sableuses désertiques, 1 zone humide, 17 cultures) / 1 site / 6 dates Conclusions : · HTEM est le plus performant (RSME sur LE de 42.3 W.m-2 ) suivi de près par TSEB (RSME = 49.8 W.m-2 ), Capteur (résolution spatiale / temporelle) : ASTER (90 m / instantanée) Jeu d'évaluation : 21 stations

, · HTEM tend à surestimer l'ET dans des conditions de forte advection

, Les résultats montrent également que l'heure d'acquisition a une forte influence sur les erreurs d'estimation d'ET d. Les méthodes ET 0 et A donnent les meilleurs résultats pour des mesures acquises entre 11h et 15h, tandis que les performances optimales pour les autres méthodes sont obtenues pour une acquisition à midi. Les auteurs soulignent enfin que les cinq méthodes sont peu sensibles à la variation du LAI, autrement dit au stade de développement du couvert, ET 0 et A sont les plus performantes

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