Spatial-temporal improvement of long-term evapotranspiration estimates for all sky conditions in tropical biomes using satellite- and reanalysisbased datasets

Abstract

This study 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) model. 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 days to daily) resolutions, minimising gaps in cloud cover, and adjusting for specific tropical characteristics of diverse vegetation and microclimate types. We compared the results of ESTIMET with those of 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 estimates in 25 Brazilian catchments. The results obtained from the ESTIMET model were also used to analyse the variability and trends of ET over the past 20 years in contrasting Brazilian biomes (Caatinga and Pantanal) and in regions with land-use changes caused by the occurrence of wildfires and deforestation. Overall, the ESTIMET estimates captured the daily seasonal variations of the EC data, especially in the Caatinga, Pantanal, and Cerrado sites located in these three biomes. The comparisons of the 8-day cumulative ET show that 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. In addition, the analysis of ET estimates over the past 20 years highlighted that both biomes exhibit ET variation cycles strongly correlated with precipitation variability, with a general trend of decreasing in different land uses in both biomes and in areas affected by wildfires and deforestations, indicating that factors such as water availability and changes in vegetation cover are important drivers in explaining ET variability in these ecosystems. Thus, ESTIMET improves remote sensing-based ET estimates in tropical biomes, operating at a finer spatiotemporal scale and with lower latency (i.e. monthly) under all sky conditions, and it is capable of representing the variability of ET in natural surfaces and under anthropogenic changes over decades.