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
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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...
