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dc.contributor.authorSantos, Leonardo Laipelt dospt_BR
dc.contributor.authorRuhoff, Anderson Luispt_BR
dc.contributor.authorFleischmann, Ayan Santospt_BR
dc.contributor.authorKayser, Rafael Henrique Bloedowpt_BR
dc.contributor.authorKich, Elisa De Mellopt_BR
dc.contributor.authorRocha, Humberto Ribeiro dapt_BR
dc.contributor.authorNeale, Christopherpt_BR
dc.date.accessioned2020-09-26T04:09:14Zpt_BR
dc.date.issued2020pt_BR
dc.identifier.issn2072-4292pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/213790pt_BR
dc.description.abstractEvapotranspiration (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 di erent 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.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofRemote Sensing. Basel, Switzerland. Vol. 12, no. 7 (Apr. 2020), [Article] 1108, 23 p.pt_BR
dc.rightsOpen Accessen
dc.subjectDesmatamentospt_BR
dc.subjectAmazonen
dc.subjectCerradoen
dc.subjectSensoriamento remotopt_BR
dc.subjectEvapotranspiração : Mediçãopt_BR
dc.subjectDeforestationen
dc.subjectEvapotranspirationen
dc.subjectCalibração automáticapt_BR
dc.subjectSEBAL (Surface Energy Balance for Land)en
dc.titleAssessment of an automated calibration of the SEBAL algorithm to estimate dry-season surface-energy partitioning in a forest–savanna transition in Brazilpt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001115574pt_BR
dc.type.originEstrangeiropt_BR


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