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Article

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

1
Institute of Hydraulic Research, Federal University of Rio Grande do Sul, Porto Alegre 91501-970, Brazil
2
Daugherty Water for Food Global Institute, University of Nebraska, Lincoln, NE 68588, USA
3
Institute of Atmospheric Sciences, University of Sao Paulo, Sao Paulo 05508-090, Brazil
*
Author to whom correspondence should be addressed.
Received: 20 February 2020 / Revised: 23 March 2020 / Accepted: 27 March 2020 / Published: 31 March 2020
(This article belongs to the Section Environmental Remote Sensing)
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. View Full-Text
Keywords: Amazon; Cerrado; deforestation; evapotranspiration; SEBAL Amazon; Cerrado; deforestation; evapotranspiration; SEBAL
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MDPI and ACS Style

Laipelt, L.; Ruhoff, A.L.; Fleischmann, A.S.; Kayser, R.H.B.; Kich, E.d.M.; da Rocha, H.R.; Neale, C.M.U. Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil. Remote Sens. 2020, 12, 1108. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071108

AMA Style

Laipelt L, Ruhoff AL, Fleischmann AS, Kayser RHB, Kich EdM, da Rocha HR, Neale CMU. Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil. Remote Sensing. 2020; 12(7):1108. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071108

Chicago/Turabian Style

Laipelt, Leonardo, Anderson L. Ruhoff, Ayan S. Fleischmann, Rafael H.B. Kayser, Elisa d.M. Kich, Humberto R. da Rocha, and Christopher M.U. Neale 2020. "Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest–Savanna Transition in Brazil" Remote Sensing 12, no. 7: 1108. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071108

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