Are remote sensing evapotranspiration models reliable across South American ecoregions? - Melo, D.C.D.; Anache, J.A.A.; Borges, V.P.; Miralles, D.G.; Martens, B.; Fisher, J.B.; Nobrega, R.C.B.; Moreno, A.; Cabral, O.M.R.; Rodrigues, T.R.; Bezerra, B.; Silva, C.M.S.; et al. (Wiley, 2022)
Many remote sensing-based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman–Monteith Mu model (PM-MOD), and Penman–Monteith Nagler model (PM-VI). E ET was predicted satisfactorily by all four models, with correlations consistently higher (20.6ER) for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of percent bias ( 1010EPBIAS%). As for PM-VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land-use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristics of climates and ecosystems in South America.MELO ET AL.© 2021. American Geophysical Union. All Rights Reserved.Are Remote Sensing Evapotranspiration Models Reliable Across South American Ecoregions?D. C. D. Melo1, J. A. A. Anache2, V. P. Borges1, D. G. Miralles3, B. Martens3, J. B. Fisher4, R. L. B. Nóbrega5, A. Moreno6, O. M. R. Cabral7, T. R. Rodrigues2, B. Bezerra8,9, C. M. S. Silva8,9, A. A. Meira Neto10, M. S. B. Moura11, T. V. Marques9, S. Campos9, J. S. Nogueira12, R. Rosolem13, R. M. S. Souza14, A. C. D. Antonino15, D. Holl16, M. Galleguillos17, J. F. Perez-Quezada17,18, A. Verhoef19, L. Kutzbach16, J. R. S. Lima20, E. S. Souza21, M. I. Gassman22,23, C. F. Perez22,23, N. Tonti22, G. Posse24, D. Rains3, P. T. S. Oliveira2, and E. Wendland251Federal University of Paraíba, Areia, PB, Brazil, 2Federal University of Mato Grosso do Sul, Campo Grande, MS, Brazil, 3Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium, 4Schmid College of Science and Technology, Chapman University, Orange, CA, USA, 5Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, UK, 6Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT, USA, 7Brazilian Agricultural Research Corporation, Embrapa Meio Ambiente, Jaguariúna, SP, Brazil, 8Department of Atmospheric and Climate Sciences, Federal University of Rio Grande do Norte, Natal, RN, Brazil, 9Climate Sciences Graduate Program, Federal University of Rio Grande do Norte, Natal, RN, Brazil, 10Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, USA, 11Brazilian Agricultural Research Corporation — Embrapa Tropical Semi-arid, Petrolina, PE, Brazil, 12Federal University of Mato Grosso, Cuiabá, MT, Brazil, 13University of Bristol, Bristol, UK, 14Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX, USA, 15Department of Nuclear Energy, Federal University of Pernambuco, Recife, PE, Brazil, 16Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany, 17Department of Environmental Science and Renewable Natural Resources, University of Chile, Santiago, Chile, 18Institute of Ecology and Biodiversity, Santiago, Chile, 19Department of Geography and Environmental Science, The University of Reading, Reading, UK, 20Federal University of the Agreste of Pernambuco, Garanhuns, PE, Brazil, 21Federal Rural University of Pernambuco, Serra Talhada, PE, Brazil, 22Department of Atmospheric and Ocean Sciences, FCEN — UBA, Buenos Aires, Argentina, 23National Council for Scientific and Technical Research, CONICET, Buenos Aires, Argentina, 24Instituto de Clima y Agua. Instituto Nacional de Tecnología Agropecuaria (INTA), Hurlingham, Argentina, 25Department of Hydraulics and Sanitary Engineering, University of São Paulo, São Carlos, SP, BrazilKey Points:•Four remote sensing evapotranspiration (ET) models were evaluated using 25 flux towers from across South America•Performance of all models is reduced in dry environments•Comparisons with flux tower-based ET showed that Global Land Evaporation Amsterdam Model and Priestley–Taylor Jet Propulsion Laboratory produced higher correlations whereas RMSE was similar for all modelsSupporting Information:Supporting Information may be found in the online version of this article.Correspondence to:D. C. D. Melo,melo.dcd@gmail.comCitation:Melo, D. C. D., Anache, J. A. A., Borges, V. P., Miralles, D. G., Martens, B., Fisher, J. B., et al. (2021). Are remote sensing evapotranspiration models reliable across South American ecoregions? Water Resources Research, 57, e2020WR028752. https://doi.org/10.1029/2020WR028752Received 26 APR 2021Accepted 11 OCT 202110.1029/2020WR028752RESEARCH ARTICLE1 of 23