TY - JOUR
T1 - Assessment of an automated calibration of the SEBAL Algorithm to estimate dry-season surface-energy partitioning in a Forest-Savanna Transition in Brazil
AU - Laipelt, Leonardo
AU - Ruhoff, Anderson Luis
AU - Fleischmann, Ayan Santos
AU - Bloedow Kayser, Rafael Henrique
AU - Kich, Elisa de Mello
AU - Rocha, Humberto Ribeiro da
AU - Usher Neale, Christopher Michael
N1 - Funding Information:
This research was financially supported by the Brazilian Water Agency (ANA) and by the Brazilian Ministry of Education through the Coordination for the Improvement of Higher Education Personnel (CAPES), under grants 88881.178687/2018-01 and 88887.363040/2019-00. It was also funded by the Brazilian National Council for Scientific Research (CNPq) under grant number 141161/2017-5. The support from the DaughertyWater for Food Global Institute is also greatly appreciated.
Publisher Copyright:
© 2020, by the authors.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - Evapotranspiration (ET) 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 4.2mmday-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.
AB - Evapotranspiration (ET) 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 4.2mmday-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.
KW - Amazon
KW - Cerrado
KW - Deforestation
KW - Evapotranspiration
KW - SEBAL
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U2 - 10.3390/rs12071108
DO - 10.3390/rs12071108
M3 - Article
AN - SCOPUS:85084250615
SN - 2072-4292
VL - 12
JO - Remote Sensing
JF - Remote Sensing
IS - 7
M1 - 1108
ER -