Publications

Claudino, CMA; Bertrand, GF; Nóbrega, RLB; Almeida, CD; Gusmao, ACV; Montenegro, SMGL; Silva, BB; Patriota, EG; Lemos, FC; Coutinho, JV; Sousa, JWGD; Andrade, JM; Melo, DCD; Rodrigues, DFB; Oliveira, LM; Xuan, YQ; Moura, MSB; Montenegro, AAA; Brocca, L; Corbari, C; Jin, YF; Suvocarev, K; Bezerra, B; de Lima, JRS; Souza, E; Anache, JAA; Coelho, VHR (2025). ESTIMET: Enhanced and Spatial-Temporal Improvement of MODIS EvapoTranspiration algorithm for all sky conditions in tropical biomes. REMOTE SENSING OF ENVIRONMENT, 325, 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 providing a nearreal-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 (rho c) 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 rho c of 0.63, greater than that of MOD16A2GF (rho c = 0.58), GLEAM 4.1a (rho c = 0.47), and PML_V2 (rho c = 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.

DOI:
10.1016/j.rse.2025.114771

ISSN:
1879-0704