Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil

Abstract

Evapotranspiration ( E T ) provides a strong connection between surface energy and hydrological cycles. Advancements in remote sensing techniques have increased our understanding of energy and terrestrial water balances as well as the interaction between surface and atmosphere over large areas. In this study, we computed surface energy fluxes using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm and a simplified adaptation of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for automated endmember selection. Our main purpose was to assess and compare the accuracy of the automated calibration of the SEBAL algorithm using two different sources of meteorological input data (ground measurements from an eddy covariance flux tower and reanalysis data from Modern-Era Reanalysis for Research and Applications version 2 (MERRA-2)) to estimate the dry season partitioning of surface energy and water fluxes in a transitional area between tropical rainforest and savanna. The area is located in Brazil and is subject to deforestation and cropland expansion. The SEBAL estimates were validated using eddy covariance measurements (2004 to 2006) from the Large-Scale Biosphere-Atmosphere Experiment in the Amazon (LBA) at the Bananal Javaés (JAV) site. Results indicated a high accuracy for daily ET, using both ground measurements and MERRA-2 reanalysis, suggesting a low sensitivity to meteorological inputs. For daily ET estimates, we found a root mean square error (RMSE) of 0.35 mm day−1 for both observed and reanalysis meteorology using accurate quantiles for endmembers selection, yielding an error lower than 9% (RMSE compared to the average daily ET). Overall, the ET rates in forest areas were 4.2 mm day−1, while in grassland/pasture and agricultural areas we found average rates between 2.0 and 3.2 mm day−1, with significant changes in energy partitioning according to land cover. Thus, results are promising for the use of reanalysis data to estimate regional scale patterns of sensible heat (H) and latent heat (LE) fluxes, especially in areas subject to deforestation.

Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain

“…In fact, the high accuracy of the in situ flux data used contributed to the good performance of the daily estimates. However, it is important to highlight that this is not a restriction, because hourly radiation data are available for the whole globe and can be easily obtained from several reanalysis products and used to generate trustable and continuous daily ET series [56].…”

Amazon Hydrology From Space: Scientific Advances and Future Challenges

“…While flux tower measurements have shown, at local scales, that land cover changes can impact water and energy fluxes (von Randow et al., 2004), large scale assessment with satellites based on both energy balance and physical‐based equations driven by vegetation phenology and meteorological reanalysis have reinforced these findings (Baker & Spracklen, 2019; Khand et al., 2017; Laipelt et al., 2020; de Oliveira et al., 2019). All these studies demonstrated significantly lower ET rates under pasture, agricultural, and deforested areas than in primary and secondary forests (von Randow et al., 2020).…”

“…Our understanding about energy partitioning in the Amazon biome has improved through RS models (de Oliveira et al., 2019; Laipelt et al., 2020). For example, high resolution ET estimates using SEBAL in the south‐western Amazon demonstrated significant differences among energy and water fluxes in forests and non‐forest areas, such as pasture and cropland.…”

Surface Albedo and Temperature Models for Surface Energy Balance Fluxes and Evapotranspiration Using SEBAL and Landsat 8 over Cerrado-Pantanal, Brazil

“…Among the most used models, the surface energy balance algorithm for land (SEBAL) has been successfully applied in different climatic regions and land covers [6]. SEBAL integrates orbital and meteorological data to compute SEBFs and ET [7].…”