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

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. • ESTIMET aims at increasing the spatiotemporal resolution of MOD16 evapotranspiration (ET). • ESTIMET reduces the losses caused by cloud contamination in tropical regions. • ESTIMET provides daily ET with a 250 m spatial resolution and monthly latency. • ESTIMET is locally and regionally tested over the major tropical biomes. • ESTIMET exhibits consistent daily metrics and 8-day results better than other products.

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Publication Details

Journal
Remote Sensing of Environment
Published
2025-04-26
DOI
https://doi.org/10.1016/j.rse.2025.114771
Citations
4
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
4.42
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article

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

João Andrade, Victor Coelho, Rodolfo Nóbrega, Bergson Guedes Bezerra et al.
4 citations
Remote Sensing of Environment
Plant Water Relations and Carbon Dynamics
4.42
article

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

João Andrade, Victor Coelho, Rodolfo Nóbrega, Bergson Guedes Bezerra, Chiara Corbari, Suzana Maria Gico Lima Montenegro, Ana Cláudia Villar e Luna Gusmão, Cristiano das Neves Almeida, M. S. B. de Moura, Leidjane Maria Maciel de Oliveira, Yunqing Xuan, Kosana Suvočarev, Jaqueline Vígolo Coutinho, Bernardo Barbosa da Silva, Guillaume Bertrand, Jamil Alexandre Ayach Anache, Yufang Jin, J. Sousa, José Romualdo de Sousa Lima, Luca Brocca, Eduardo Soares de Souza, Davi de Carvalho Diniz Melo, Diogo Francisco Borba Rodrigues, Abelardo Antônio de Assunção Montenegro, Eduardo Gonçalves Patriota, Filipe Carvalho Lemos, Cinthia M.A. Claudino
article en
4 citations

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. • ESTIMET aims at increasing the spatiotemporal resolution of MOD16 evapotranspiration (ET). • ESTIMET reduces the losses caused by cloud contamination in tropical regions. • ESTIMET provides daily ET with a 250 m spatial resolution and monthly latency. • ESTIMET is locally and regionally tested over the major tropical biomes. • ESTIMET exhibits consistent daily metrics and 8-day results better than other products.

Remote Sensing of EnvironmentVol. 325
Centre National de la Recherche Scientifique (FR), Universidade Federal da Paraíba (BR), Brazilian Agricultural Research Corporation (BR), Universidade Federal de Pernambuco (BR), Gower College Swansea (GB), University of Bristol (GB), Swansea University (GB), Universidade Federal de Campina Grande (BR), Research Institute for Geo-Hydrological Protection (IT), National Research Council (IT), Université Marie et Louis Pasteur (FR), Universidade Federal Rural de Pernambuco (BR), Université Savoie Mont Blanc (FR), Politecnico di Milano (IT)
Climate action
Openalex Percentile: Top 6%
Plant Water Relations and Carbon Dynamics
4.42
DOIOpen Access PDFOpenAlex
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