Title:	Estimation of Soybean Evapotranspiration Using SSEBop Model with High-Resolution Imagery from an Unmanned Aerial Vehicle.
Authors:	CASARI, R. A. das C. N. NEUMANN, M. B. RIBEIRO JUNIOR, W. Q. OLIVETTI, D. TAVARES, C. J. PEREIRA, L. F. RAMOS, M. L. G. PEREIRA, A. F. SILVA NETO, S. P. da ROIG, H. L.
Affiliation:	RAPHAEL AUGUSTO DAS CHAGAS NOQUELI CASARI, UNIVERSIDADE DE BRASILIA; MARINA BILICH NEUMANN, UNIVERSIDADE DE BRASILIA; WALTER QUADROS RIBEIRO JUNIOR, CPAC; DIOGO OLIVETTI, UNIVERSIDADE DE BRASÍLIA; CÁSSIO JARDIM TAVARES, INSTITUTO FEDERAL GOIANO; LUCAS FELISBERTO PEREIRA, INSTITUTO FEDERAL GOIANO; MARIA LUCRÉCIA GEROSA RAMOS, UNIVERSIDADE DE BRASÍLIA; ANDRE FERREIRA PEREIRA, CPAC; SEBASTIAO PEDRO DA SILVA NETO, CPAC; HENRIQUE LLACER ROIG, 5INSTITUTO FEDERAL GOIANO.
Date Issued:	2024
Citation:	Revista Brasileira de Meteorologia, v. 39, e39240007, 2024.
Description:	Evapotranspiration (ET) is one of the most important processes in the hydrologic cycle, constituting the main responsible for water losses at the surface. Several evapotranspiration models use information from surface temperature and vegetation indices captured by remote sensors such as MODIS and LANDSAT to estimate the ETc value. The objective of this study is to apply SSEBop model to estimate ETc of soybean in a field experiment under four water regimes, using high-resolution multispectral and thermal images collected from remotely piloted aircraft (RPA). Surface temperature and NDVI maps were generated as sources for evapotranspiration estimation. From a Python script, spatial variability maps of ETc were generated at different phenological stages of the crop. The quality of the model for ETc estimates was performed by comparing the modeling results with leaf transpiration data measured in the field using an infrared gas analyzer, whose results showed a good correlation (R2 = 0.76). These results demonstrated the possibility of transferring a model originally developed for processing low to medium-resolution satellite images to high-resolution spatial-temporal images acquired by RPA with small adaptations in the original algorithm, generating great potential for new studies on an experimental and field scale.
Thesagro:	Soja Glycine Max
DOI:	http://dx.doi.org/10.1590/0102-77863910007
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CPAC)