Articles | Volume 30, issue 4
https://doi.org/10.5194/hess-30-1117-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-1117-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Feb 2026
Research article |
| 26 Feb 2026
| 26 Feb 2026
Uncertainties in long-term ensemble estimates of contextual evapotranspiration over southern France
Unité de Recherche Ecologie des Forêts Méditerranéennes, INRAE, Avignon, France
Université de Toulouse, CESBIO, CNES/CNRS/IRD/UPS/INRAE, Toulouse, France
Unité de Recherche Ecologie des Forêts Méditerranéennes, INRAE, Avignon, France
Jordi Etchanchu
UMR HSM, IRD-CNRS-Université de Montpellier, Montpellier, France
Kanishka Mallick
Université de Toulouse, CESBIO, CNES/CNRS/IRD/UPS/INRAE, Toulouse, France
Aolin Jia
Institute of Agricultural Sciences (IAS), Dept. of Environmental Systems Science (D-USYS), ETH Zürich, Zurich, Switzerland
Jérôme Demarty
UMR HSM, IRD-CNRS-Université de Montpellier, Montpellier, France
Nesrine Farhani
UMR HSM, IRD-CNRS-Université de Montpellier, Montpellier, France
Emmanuelle Sarrazin
Université de Toulouse, CESBIO, CNES/CNRS/IRD/UPS/INRAE, Toulouse, France
Philippe Gamet
Université de Toulouse, CESBIO, CNES/CNRS/IRD/UPS/INRAE, Toulouse, France
Jean-Louis Roujean
Université de Toulouse, CESBIO, CNES/CNRS/IRD/UPS/INRAE, Toulouse, France
Gilles Boulet
Université de Toulouse, CESBIO, CNES/CNRS/IRD/UPS/INRAE, Toulouse, France
Indo-French Cell for Water Sciences (IFCWS), IISc, Bangalore, India
Related authors
No articles found.
Erwan Le Roux, Valentin Wendling, Gérémy Panthou, Océane Dubas, Jean-Pierre Vandervaere, Basile Hector, Guillaume Favreau, Jean-Martial Cohard, Caroline Pierre, Luc Descroix, Eric Mougin, Manuela Grippa, Laurent Kergoat, Jérôme Demarty, Nathalie Rouche, Jordi Etchanchu, and Christophe Peugeot
Hydrol. Earth Syst. Sci., 30, 929–944, https://doi.org/10.5194/hess-30-929-2026, https://doi.org/10.5194/hess-30-929-2026, 2026
Short summary
Short summary
In hydrological science, better accounting for regime shift (abrupt and/or irreversible changes) remains a challenge that could lead to a new paradigm for the adaptation to extreme events (flood , drought). In this article, we present a simple model that can account for a hydrological regime shift in Sahelian watersheds. Based on this model, we find that the Gorouol, Dargol, Nakanbé, and Sirba watersheds have shifted during the droughts of the '70s–'80s.
Arsène Druel, Julien Ruffault, Hendrik Davi, André Chanzy, Olivier Marloie, Miquel De Cáceres, Albert Olioso, Florent Mouillot, Christophe François, Kamel Soudani, and Nicolas K. Martin-StPaul
Biogeosciences, 22, 1–18, https://doi.org/10.5194/bg-22-1-2025, https://doi.org/10.5194/bg-22-1-2025, 2025
Short summary
Short summary
Accurate radiation data are essential for understanding ecosystem functions and dynamics. Traditional large-scale data lack the precision needed for complex terrain. This study introduces a new model, which accounts for sub-daily direct and diffuse radiation effects caused by terrain features, to enhance the radiation data resolution using elevation maps. Tested on a mountainous area, this method significantly improved radiation estimates, benefiting predictions of forest functions.
Chandrika Pinnepalli, Jean-Louis Roujean, and Mark Irvine
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 325–330, https://doi.org/10.5194/isprs-annals-X-3-2024-325-2024, https://doi.org/10.5194/isprs-annals-X-3-2024-325-2024, 2024
Lammert Kooistra, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, Jean-Louis Roujean, Miriam Machwitz, Martin Schlerf, Clement Atzberger, Egor Prikaziuk, Dessislava Ganeva, Enrico Tomelleri, Holly Croft, Pablo Reyes Muñoz, Virginia Garcia Millan, Roshanak Darvishzadeh, Gerbrand Koren, Ittai Herrmann, Offer Rozenstein, Santiago Belda, Miina Rautiainen, Stein Rune Karlsen, Cláudio Figueira Silva, Sofia Cerasoli, Jon Pierre, Emine Tanır Kayıkçı, Andrej Halabuk, Esra Tunc Gormus, Frank Fluit, Zhanzhang Cai, Marlena Kycko, Thomas Udelhoven, and Jochem Verrelst
Biogeosciences, 21, 473–511, https://doi.org/10.5194/bg-21-473-2024, https://doi.org/10.5194/bg-21-473-2024, 2024
Short summary
Short summary
We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
Aolin Jia, Shunlin Liang, Dongdong Wang, Lei Ma, Zhihao Wang, and Shuo Xu
Earth Syst. Sci. Data, 15, 869–895, https://doi.org/10.5194/essd-15-869-2023, https://doi.org/10.5194/essd-15-869-2023, 2023
Short summary
Short summary
Satellites are now producing multiple global land surface temperature (LST) products; however, they suffer from data gaps caused by cloud cover, seriously restricting the applications, and few products provide gap-free global hourly LST. We produced global hourly, 5 km, all-sky LST data from 2011 to 2021 using geostationary and polar-orbiting satellite data. Based on the assessment, it has high accuracy and can be used to estimate evapotranspiration, drought, etc.
Bimal K. Bhattacharya, Kaniska Mallick, Devansh Desai, Ganapati S. Bhat, Ross Morrison, Jamie R. Clevery, William Woodgate, Jason Beringer, Kerry Cawse-Nicholson, Siyan Ma, Joseph Verfaillie, and Dennis Baldocchi
Biogeosciences, 19, 5521–5551, https://doi.org/10.5194/bg-19-5521-2022, https://doi.org/10.5194/bg-19-5521-2022, 2022
Short summary
Short summary
Evaporation retrieval in heterogeneous ecosystems is challenging due to empirical estimation of ground heat flux and complex parameterizations of conductances. We developed a parameter-sparse coupled ground heat flux-evaporation model and tested it across different limits of water stress and vegetation fraction in the Northern/Southern Hemisphere. The model performed particularly well in the savannas and showed good potential for evaporative stress monitoring from thermal infrared satellites.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
Short summary
Short summary
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
L. Dumas, V. Defonte, Y. Steux, and E. Sarrazin
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 167–175, https://doi.org/10.5194/isprs-annals-V-2-2022-167-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-167-2022, 2022
Xueyuan Gao, Shunlin Liang, Dongdong Wang, Yan Li, Bin He, and Aolin Jia
Earth Syst. Dynam., 13, 219–230, https://doi.org/10.5194/esd-13-219-2022, https://doi.org/10.5194/esd-13-219-2022, 2022
Short summary
Short summary
Numerical experiments with a coupled Earth system model show that large-scale nighttime artificial lighting in tropical forests will significantly increase carbon sink, local temperature, and precipitation, and it requires less energy than direct air carbon capture for capturing 1 t of carbon, suggesting that it could be a powerful climate mitigation option. Side effects include CO2 outgassing after the termination of the nighttime lighting and impacts on local wildlife.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
Short summary
Short summary
The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
E. Sarrazin, M. Cournet, L. Dumas, V. Defonte, Q. Fardet, Y. Steux, N. Jimenez Diaz, E. Dubois, D. Youssefi, and F. Buffe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 383–390, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-383-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-383-2021, 2021
Jaber Rahimi, Expedit Evariste Ago, Augustine Ayantunde, Sina Berger, Jan Bogaert, Klaus Butterbach-Bahl, Bernard Cappelaere, Jean-Martial Cohard, Jérôme Demarty, Abdoul Aziz Diouf, Ulrike Falk, Edwin Haas, Pierre Hiernaux, David Kraus, Olivier Roupsard, Clemens Scheer, Amit Kumar Srivastava, Torbern Tagesson, and Rüdiger Grote
Geosci. Model Dev., 14, 3789–3812, https://doi.org/10.5194/gmd-14-3789-2021, https://doi.org/10.5194/gmd-14-3789-2021, 2021
Short summary
Short summary
West African Sahelian and Sudanian ecosystems are important regions for global carbon exchange, and they provide valuable food and fodder resources. Therefore, we simulated net ecosystem exchange and aboveground biomass of typical ecosystems in this region with an improved process-based biogeochemical model, LandscapeDNDC. Carbon stocks and exchange rates were particularly correlated with the abundance of trees. Grass and crop yields increased under humid climatic conditions.
Cited articles
Allies, A., Demarty, J., Olioso, A., Moussa, I. B., Issoufou, H. B. A., Velluet, C., Bahir, M., Maïnassara, I., Oï, M., Chazarin, J. P., and Cappelaere, B.: Evapotranspiration estimation in the sahel using a new ensemble-contextual method, Remote Sens., 12, https://doi.org/10.3390/rs12030380, 2020.
Allies, A., Olioso, A., Cappelaere, B., Boulet, G., Etchanchu, J., Barral, H., Bouzou Moussa, I., Chazarin, J.-P., Delogu, E., Issoufou, H. B.-A., Mainassara, I., Oï, M., and Demarty, J.: A remote sensing data fusion method for continuous daily evapotranspiration mapping at kilometric scale in Sahelian areas, J. Hydrol., 607, 127504, https://doi.org/10.1016/j.jhydrol.2022.127504, 2022.
Bonett, D. G.: Confidence interval for a coefficient of quartile variation, Comput. Stat. Data Anal., 50, 2953–2957, https://doi.org/10.1016/j.csda.2005.05.007, 2006.
Boulet, G., Mougenot, B., Lhomme, J.-P., Fanise, P., Lili-Chabaane, Z., Olioso, A., Bahir, M., Rivalland, V., Jarlan, L., Merlin, O., Coudert, B., Er-Raki, S., and Lagouarde, J.-P.: The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat, Hydrol. Earth Syst. Sci., 19, 4653–4672, https://doi.org/10.5194/hess-19-4653-2015, 2015.
Carlson, T.: An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery, Sensors, 7, 1612–1629, https://doi.org/10.3390/s7081612, 2007.
Carlson, T. N., Gillies, R. R., and Perry, E. M.: A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover, Remote Sens. Rev., 9, 161–173, https://doi.org/10.1080/02757259409532220, 1994.
Delogu, E., Boulet, G., Olioso, A., Coudert, B., Chirouze, J., Ceschia, E., Le Dantec, V., Marloie, O., Chehbouni, G., and Lagouarde, J.-P.: Reconstruction of temporal variations of evapotranspiration using instantaneous estimates at the time of satellite overpass, Hydrol. Earth Syst. Sci., 16, 2995–3010, https://doi.org/10.5194/hess-16-2995-2012, 2012.
Eilers, P. H. C. and Goeman, J. J.: Enhancing scatterplots with smoothed densities, Bioinformatics, 20, 623–628, https://doi.org/10.1093/bioinformatics/btg454, 2004.
Etchanchu, J., Demarty, J., Dezetter, A., Farhani, N., Thiam, P. B., Allies, A., Bodian, A., Boulet, G., Chahinian, N., Diop, L., Mainassara, I., Ndiaye, P. M., Ollivier, C., Olioso, A., and Roupsard, O.: Multiscale analysis of existing actual evapotranspiration products over agropastoral Sahel, J. Hydrol., 651, 132585, https://doi.org/10.1016/j.jhydrol.2024.132585, 2025.
Farhani, N., Etchanchu, J., Boulet, G., Gamet, P., and Olioso, A.: Spatially remotely sensed evapotranspiration estimates in Sahel region using an ensemble contextual model with automated heterogeneity assessment, Sci. Remote Sens., 11, 100229, https://doi.org/10.1016/j.srs.2025.100229, 2025.
Gallego-Elvira, B., Olioso, A., Mira, M., Castillo, S. R., Boulet, G., Marloie, O., Garrigues, S., Courault, D., Weiss, M., Chauvelon, P., and Boutron, O.: EVASPA (EVapotranspiration Assessment from SPAce) Tool: An overview, Procedia Environ. Sci., 19, 303–310, https://doi.org/10.1016/j.proenv.2013.06.035, 2013.
Gamet, P., Marcq, S., Delogu, E., Binet, R., Boulet, G., Olioso, A., Roujean, J.-L., Bhattacharya, B., and Maisongrande, P.: TRISHNA: towards daily evapotranspiration from remote sensing thermal data, in: International Workshop on HR Thermal EO, Turin, Italy, https://hal.inrae.fr/hal-05201208v1 (last access: 16 February 2026), 2023.
Goward, S. N., Cruickshanks, G. D., and Hope, A. S.: Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape, Remote Sens. Environ., 18, 137–146, https://doi.org/10.1016/0034-4257(85)90044-6, 1985.
Hu, T., Mallick, K., Hitzelberger, P., Didry, Y., Boulet, G., Szantoi, Z., Koetz, B., Alonso, I., Pascolini-Campbell, M., Halverson, G., Cawse-Nicholson, K., Hulley, G. C., Hook, S., Bhattarai, N., Olioso, A., Roujean, J., Gamet, P., and Su, B.: Evaluating European ECOSTRESS Hub Evapotranspiration Products Across a Range of Soil-Atmospheric Aridity and Biomes Over Europe, Water Resour. Res., 59, https://doi.org/10.1029/2022WR034132, 2023.
ICOS-cp.eu: Ecosystem final quality (L2) product in ETC-Archive format – release 2024-1, ICOS ERIC, https://doi.org/10.18160/G5KZ-ZD83, 2024.
Jia, A., Mallick, K., Lin, Z., Sulis, M., Szantoi, Z., Zhang, L., Corbari, C., Torralbo, P., Nieto, H., Roujean, J., Etchanchu, J., Demarty, J., Mwangi, S., Olioso, A., Merlin, O., and Boulet, G.: Sensitivity of thermal evapotranspiration models to surface and atmospheric drivers across ecosystems and aridity, Agric. For. Meteorol., 376, 110930, https://doi.org/10.1016/j.agrformet.2025.110930, 2026.
Jiménez, C., Prigent, C., Mueller, B., Seneviratne, S. I., McCabe, M. F., Wood, E. F., Rossow, W. B., Balsamo, G., Betts, A. K., Dirmeyer, P. A., Fisher, J. B., Jung, M., Kanamitsu, M., Reichle, R. H., Reichstein, M., Rodell, M., Sheffield, J., Tu, K., and Wang, K.: Global intercomparison of 12 land surface heat flux estimates, J. Geophys. Res. Atmos., 116, 1–27, https://doi.org/10.1029/2010JD014545, 2011.
Koetz, B., Barat, I., Baschek, B., Bastiaanssen, W., Berger, M., Bernard, F., Blommaert, J., Bolea, A., Buongiorno, M., D'Andrimont, R., Defourney, P., Drinkwater, M., Duca, R., Gamet, P., Gascon, F., Ghent, D., Guzinski, R., Hoogeveen, J., Hook, S., Kerr, Y., Lagouarde, J.-P., Manolis, I., Martimort, P., Masek, J., Massart, M., Mementi, M., Notarnicola, C., Olioso, A., Sandholt, I., Sobrino, J., Strobl, P., Such, M., Udelhoven, T., and Wu, Z.: LSTM Mission, in: International Workshop on HR Thermal EO, Turin, Italy, https://hal.inrae.fr/hal-05201260v1 (last access: 16 February 2026), 2023.
Kpemlie, E. K.: Assimilation variationnelle de données de télédétection dans des modèles de fonctionnement des couverts végétaux et du paysage agricole, Université d'Avignon, https://hal.inrae.fr/tel-00555416v1 (last access: 16 February 2026), 2009.
Lambert, S. J. and Boer, G. J.: CMIP1 evaluation and intercomparison of coupled climate models, Clim. Dyn., 17, 83–106, https://doi.org/10.1007/pl00013736, 2001.
Mallick, K., Jarvis, A. J., Boegh, E., Fisher, J. B., Drewry, D. T., Tu, K. P., Hook, S. J., Hulley, G., Ardö, J., Beringer, J., Arain, A., and Niyogi, D.: A Surface Temperature Initiated Closure (STIC) for surface energy balance fluxes, Remote Sens. Environ., 141, 243–261, https://doi.org/10.1016/j.rse.2013.10.022, 2014.
Mallick, K., Toivonen, E., Trebs, I., Boegh, E., Cleverly, J., Eamus, D., Koivusalo, H., Drewry, D., Arndt, S. K., Griebel, A., Beringer, J., and Garcia, M.: Bridging Thermal Infrared Sensing and Physically-Based Evapotranspiration Modeling: From Theoretical Implementation to Validation Across an Aridity Gradient in Australian Ecosystems, Water Resour. Res., 54, 3409–3435, https://doi.org/10.1029/2017WR021357, 2018.
Menenti, M., Bastiaanssen, W., van Eick, D., and Abd el Karim, M. A.: Linear relationships between surface reflectance and temperature and their application to map actual evaporation of groundwater, Adv. Sp. Res., 9, 165–176, https://doi.org/10.1016/0273-1177(89)90482-1, 1989.
Mira, M., Olioso, A., Gallego-Elvira, B., Courault, D., Garrigues, S., Marloie, O., Hagolle, O., Guillevic, P., and Boulet, G.: Uncertainty assessment of surface net radiation derived from Landsat images, Remote Sens. Environ., 175, 251–270, https://doi.org/10.1016/j.rse.2015.12.054, 2016.
Moran, M. S., Clarke, T. R., Inoue, Y., and Vidal, A.: Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index, Remote Sens. Environ., 49, 246–263, https://doi.org/10.1016/0034-4257(94)90020-5, 1994.
Morcrette, J. J., Barker, H. W., Cole, J. N. S., Iacono, M. J., and Pincus, R.: Impact of a new radiation package, McRad, in the ECMWF integrated forecasting system, Mon. Weather Rev., 136, 4773–4798, https://doi.org/10.1175/2008MWR2363.1, 2008.
Mwangi, S., Boulet, G., and Olioso, A.: Assessment of an extended SPARSE model for estimating evapotranspiration from directional thermal infrared data, Agric. For. Meteorol., 317, 108882, https://doi.org/10.1016/j.agrformet.2022.108882, 2022.
Mwangi, S., Olioso, A., and Boulet, G.: Influence of Thermal Radiation Directionality (TRD) on Surface Flux Retrieval: Point-Scale Surface Energy Balance Experiments, in: IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, 4742–4745, https://doi.org/10.1109/IGARSS52108.2023.10283072, 2023a.
Mwangi, S., Boulet, G., Le Page, M., Gastellu-Etchegorry, J. P., Bellvert, J., Lemaire, B., Fanise, P., Roujean, J.-L., and Olioso, A.: Observation and Assessment of Model Retrievals of Surface Exchange Components Over a Row Canopy Using Directional Thermal Data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 16, 7343–7356, https://doi.org/10.1109/JSTARS.2023.3297709, 2023b.
Mwangi, S., Olioso, A., Boulet, G., Farhani, N., Etchanchu, J., Demarty, J., Ollivier, C., Hu, T., Mallick, K., Jia, A., Sarrazin, E., Gamet, P., and Roujean, J.-L.: Ensemble Estimation of Evapotranspiration Using EVASPA: a Multi-Data Multi-Method Analysis, in: IGARSS 2024 – 2024 IEEE International Geoscience and Remote Sensing Symposium, 2475–2478, https://doi.org/10.1109/IGARSS53475.2024.10642831, 2024.
Norman, J. M., Kustas, W. P., and Humes, K. S.: Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature, Agric. For. Meteorol., 77, 263–293, https://doi.org/10.1016/0168-1923(95)02265-Y, 1995.
Olioso, A., Boulet, G., Castillo-Reyes, S., Gallego-Elvira, B., Mira, M., Courault, D., Marloie, O., Lecerf, R., Weiss, M., Chehbouni, G., Baret, F., Jacob, F., Lagouarde, J.-P., Sobrino, J. A., and Hamimed, A.: Suivi spatio-temporel de l'évapotranspiration dans les domaines thermiques et solaires: développement d'une chaîne de traitements de données, https://hal.science/hal-01336998 (last access: 16 February 2026), 2012.
Olioso, A., Allies, A., Boulet, G., Delogu, E., Demarty, J., Elvira, B. G., Mira, M., Marloie, O., Chauvelon, P., Boutron, O., Buis, S., Weiss, M., Velluet, C., and Bahir, M.: Monitoring Evapotranspiration with Remote Sensing Data and Ground Data Using Ensemble Model Averaging, in: IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, 7656–7659, https://doi.org/10.1109/IGARSS.2018.8517532, 2018.
Olioso, A., Allies, A., Desrutins, H., Carrière, S., Farhani, N., Sobrino, J., Skoković, D., Demarty, J., Etchanchu, J., Boulet, G., Buis, S., and Weiss, M.: Evapotranspiration mapping from remote sensing data: uncertainties and ensemble estimates based on multimodel – multidata simulations, in: International Workshop on High-Resolution Thermal EO, Turin, Italy, https://hal.science/hal-04153021 (last access: 16 February 2026), 2023.
Olioso, A., Boulet, G., Gamet, P., Roujean, J.-L., Bhattacharya, B., Irvine, M., Ogée, J., Buis, S., Weiss, M., Prevot, L., Mwangi, S., Penot, V., Farhani, N., Jia, A., Matteo Herrera, B., Sarrazin, E., Marcq, S., Delogu, E., Binet, R., Maisongrande, P., Hu, T., Malick, K., Adams, J., Damm, A., Naegeli, K., Sobrino, J., Rivalland, V., Merlin, O., Hagolle, O., Etchanchu, J., Demarty, J., Ollivier, C., and Jacob, F.: Daily Evapotranspiration TRISHNA Level-2 products, EVASPA, in: International Science Workshop on High-Resolution Thermal Earth Observation, https://hal.inrae.fr/hal-05037175v1 (last access: 16 February 2026), 2024.
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y. W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Ribeca, A., van Ingen, C., Zhang, L., Amiro, B., Ammann, C., Arain, M. A., Ardö, J., Arkebauer, T., Arndt, S. K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L. B., Bergeron, O., Beringer, J., Bernhofer, C., Berveiller, D., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Boike, J., Bolstad, P. V., Bonal, D., Bonnefond, J. M., Bowling, D. R., Bracho, R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns, S. P., Buysse, P., Cale, P., Cavagna, M., Cellier, P., Chen, S., Chini, I., Christensen, T. R., Cleverly, J., Collalti, A., Consalvo, C., Cook, B. D., Cook, D., Coursolle, C., Cremonese, E., Curtis, P. S., D’Andrea, E., da Rocha, H., Dai, X., Davis, K. J., De Cinti, B., de Grandcourt, A., De Ligne, A., De Oliveira, R. C., Delpierre, N., Desai, A. R., Di Bella, C. M., di Tommasi, P., Dolman, H., Domingo, F., Dong, G., Dore, S., Duce, P., Dufrêne, E., Dunn, A., Dušek, J., Eamus, D., Eichelmann, U., ElKhidir, H. A. M., Eugster, W., Ewenz, C. M., Ewers, B., Famulari, D., Fares, S., Feigenwinter, I., Feitz, A., Fensholt, R., Filippa, G., Fischer, M., Frank, J., Galvagno, M., Gharun, M., Gianelle, D., Gielen, B., Gioli, B., and Gitelson, A.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7, 1–27, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Price, J. C.: Using spatial context in satellite data to infer regional scale evapotranspiration, IEEE Trans. Geosci. Remote Sens., 28, 940–948, https://doi.org/10.1109/36.58983, 1990.
Roerink, G. J., Su, Z., and Menenti, M.: S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance, Phys. Chem. Earth, Part B Hydrol. Ocean. Atmos., 25, 147–157, https://doi.org/10.1016/S1464-1909(99)00128-8, 2000.
Senay, G. B., Bohms, S., Singh, R. K., Gowda, P. H., Velpuri, N. M., Alemu, H., and Verdin, J. P.: Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach, JAWRA J. Am. Water Resour. Assoc., 49, 577–591, https://doi.org/10.1111/jawr.12057, 2013.
Su, Z.: The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes, Hydrol. Earth Syst. Sci., 6, 85–100, https://doi.org/10.5194/hess-6-85-2002, 2002.
Tang, R., Li, Z. L., and Tang, B.: An application of the Ts-VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: Implementation and validation, Remote Sens. Environ., 114, 540–551, https://doi.org/10.1016/j.rse.2009.10.012, 2010.
Thompson, D. R., Basilio, R., Brosnan, I., Cawse-Nicholson, K., Chadwick, K. D., Guild, L., Gierach, M., Green, R. O., Hook, S., Horner, S. D., Hulley, G., Kokaly, R., Miller, C. E., Miner, K. R., Lee, C., Limonadi, D., Luvall, J., Pavlick, R., Phillips, B., Poulter, B., Raiho, A., Reath, K., Uz, S. S., Sen, A., Serbin, S., Schimel, D., Townsend, P., Turner, W., and Turpie, K.: Ongoing Progress Toward NASA's SBG Mission, in: IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, 5007–5010, https://doi.org/10.1109/IGARSS46834.2022.9884123, 2022.
Wan, Z. and Li, Z. L.: A physics-based algorithm for retrieving land-surface emissivity and temperature from eos/modis data, IEEE Trans. Geosci. Remote Sens., 35, 980–996, https://doi.org/10.1109/36.602541, 1997.
Ward, J. H.: Hierarchical Grouping to Optimize an Objective Function, J. Am. Stat. Assoc., 58, 236, https://doi.org/10.2307/2282967, 1963.
Willmott, C. J.: On the validation of models, Phys. Geogr., 2, 184–194, https://doi.org/10.1080/02723646.1981.10642213, 1981.
Zhang, K., Kimball, J. S., and Running, S. W.: A review of remote sensing based actual evapotranspiration estimation, Wiley Interdiscip. Rev. Water, 3, 834–853, https://doi.org/10.1002/wat2.1168, 2016.
Short summary
The EVapotranspiration Assessment from SPAce (EVASPA) algorithm that will generate TRISHNA evapotranspiration (TRISHNA ET) products shows good agreement with observations. Analyses conducted on 4 variables used in contextual ET modelling showed that land surface temperature and evaporative fraction methods introduce the largest uncertainty in ensemble estimates, followed by radiation. Soil heat flux methods contribute the least. Robust ET products will require optimizing the sensitive variables.
The EVapotranspiration Assessment from SPAce (EVASPA) algorithm that will generate TRISHNA...
