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ESTIMET: Enhanced and Spatial-Temporal Improvement of MODIS EvapoTranspiration algorithm for all sky conditions in tropical biomes

Cinthia M.A. Claudino, Guillaume F. Bertrand, Rodolfo L.B. Nóbrega, Cristiano das N. Almeida, Ana Cláudia V. Gusmão, Suzana M.G.L. Montenegro, Bernardo B. Silva, Eduardo G. Patriota, Filipe C. Lemos, Jaqueline V. Coutinho, José Welton Gonçalo de Sousa, João M. Andrade, Davi C.D. Melo, Diogo Francisco B. Rodrigues, Leidjane M. Oliveira, Yunqing Xuan Orcid Logo, Magna S.B. Moura, Abelardo A.A. Montenegro, Luca Brocca, Chiara Corbari, Yufang Jin, Kosana Suvočarev, Bergson Bezerra, José Romualdo S. de Lima, Eduardo Souza, Jamil A.A. Anache, Victor Hugo R. Coelho

Remote Sensing of Environment, Volume: 325, Start page: 114771

Swansea University Author: Yunqing Xuan Orcid Logo

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DOI (Published version): 10.1016/j.rse.2025.114771

Abstract

We developed an ET model, namely the Enhanced and Spatial-Temporal Improvement of MODIS EvapoTranspiration (ESTIMET), for local-to-regional ET monitoring and applications in the tropics, based on the original MOD16 evapotranspiration (ET) algorithm. The main distinguishing features of ESTIMET are pr...

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Published in: Remote Sensing of Environment
ISSN: 0034-4257
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69361
Abstract: We developed an ET model, namely the Enhanced and Spatial-Temporal Improvement of MODIS EvapoTranspiration (ESTIMET), for local-to-regional ET monitoring and applications in the tropics, based on the original MOD16 evapotranspiration (ET) algorithm. The main distinguishing features of ESTIMET are providing a near-real-time product with increased spatial (from 500 to 250 m) and temporal (from 8-day to daily) resolutions, minimising gaps in cloud cover and adjusting specific tropical characteristics of diverse vegetation and microclimate types. We compared the results of ESTIMET with the MOD16A2GF, PML_V2, and GLEAM 4.1a ET products, using eddy covariance (EC) data from 14 sites in Brazil, as well as the water balance-based annual ET in 25 Brazilian catchments. Overall, the ESTIMET estimates captured the daily seasonal variations of the EC data, especially in the Caatinga, Pantanal, and Cerrado biomes, with concordance correlation coefficients (ρc) ranging from 0.45 to 0.80 at eight sites located in these three biomes. The comparisons of the 8-day cumulative ET show that the ESTIMET algorithm exhibits a mean ρc of 0.63, greater than that of MOD16A2GF (ρc = 0.58), GLEAM 4.1a (ρc = 0.47), and PML_V2 (ρc = 0.45). Similarly, for the catchment water balance, ESTIMET exhibits a better representation of annual ET than other ET products in the three major South American biomes, i.e. the Amazon, Atlantic Forest, and Cerrado, which cover over 85 % of the Brazilian territory. Thus, ESTIMET improves remote sensing-based ET estimates in tropical biomes, operating at a finer spatiotemporal scale and latency (i.e. monthly) under all sky conditions.
Keywords: Remote sensing; MODIS; Evapotranspiration; Tropical biomes; Brazil
College: Faculty of Science and Engineering
Funders: The authors would like to thank the financial support provided by 1) the Fundação de Apoio à Pesquisa do Estado da Paraíba (FAPESQ-PB) for the Master and PhD scholarships; 2) the National Council for Scientific and Technological Development (CNPq; Grant REFs: 443320/2023-3, 313614/2020-2, 3113392/2020-0, and 309303/2022-2); 3) the Federal University of Paraíba (UFPB; Grant REF: PIA13145-2020); 4) the National Observatory of Water and Carbon Dynamics in the Caatinga Biome (NOWCDCB), supported by FACEPE (Grant REFs: APQ-0498-3.07/17 INCT 2014, and APQ-0500-5.01/22), CNPq (Grant REFs: INCT 465764/2014-2, 406202/2022-2, 310105/2022-6, 313493/2020-0, and 409990/2018-3), and CAPES (Grant REFs: 88881.981697/2024-01, 88887.136369/2017-00, and 88881.318207/2019-01); 5) the National Observatory of Water Security and Adaptative Management (Grant REF: CNPq 406919/2022-4); 6) the Newton/NERC/FAPESP Nordeste project (Grant REFs: NE/N012526/1 ICL 652, and NE/N012488/1 UoR); and 7) the São Paulo Research Foundation (FAPESP, Grant REFs: 2015/50488-5, 2019/11835-2, 2021/10639-5, and 2022/07735-5).
Start Page: 114771

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