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

Leonardo Laipelt,Christopher Michael Usher Neale,Humberto Ribeiro Da Rocha,Anderson Luis Ruhoff,Elisa De Mello Kich,Rafael Henrique Bloedow Kayser,Ayan Santos Fleischmann

doi:10.3390/rs12071108

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.

Highlights

Evapotranspiration (ET), defined as the sum of evaporation and vegetation transpiration leaving the surface and entering the atmosphere as water vapor, is a key process in the terrestrial water, carbon, and energy cycles [1]
Our results indicate that the use of ground measurements or global reanalysis data (MERRA-2) as meteorological inputs leads to low sensitivity on Surface Energy Balance Algorithm for Land (SEBAL) accuracy to estimate surface energy and water fluxes
The comparison between SEBAL estimates against ground measurements yielded high accuracy for Rn, we found a high bias for G, suggesting a regional calibration

Summary

Introduction

Evapotranspiration (ET), defined as the sum of evaporation and vegetation transpiration leaving the surface and entering the atmosphere as water vapor, is a key process in the terrestrial water, carbon, and energy cycles [1]. In the current scenarios of land cover changes associated with deforestation and increasing agricultural areas in the tropics, monitoring surface energy fluxes and ET over large areas is a fundamental requirement to assess changes in the water cycle. Assessing the effects of large-scale surface–atmospheric interactions and hydrological changes may be accomplished with remote sensing techniques using multispectral and thermal images to calculate the energy balance [16,17,18]. To estimate the spatial and temporal patterns of ET, many types of models are currently in use, from regional to continental and global scales, grouped into two general classes: (i) vegetation index-based and (ii) land surface temperature (Ts ) methods [17,20]. Despite significant advances in ET modelling in the past decades at multiple temporal and spatial scales [31,32,33], several challenges remain to increase the accuracy of estimations [34,35,36] towards higher spatial and temporal resolution and larger spatial and temporal coverage and monitoring [1]

Methods

Results

Conclusion