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Remote sensing based evapotranspiration modeling for sugarcane in Brazil using a hybrid approach

dc.contributor.authorBispo, R. C. [UNESP]
dc.contributor.authorHernandez, F. B.T. [UNESP]
dc.contributor.authorGonçalves, I. Z.
dc.contributor.authorNeale, C. M.U.
dc.contributor.authorTeixeira, A. H.C.
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionDaugherty Water for Food Global Institute
dc.contributor.institutionUniversidade Federal de Sergipe (UFS)
dc.date.accessioned2023-03-01T20:10:55Z
dc.date.available2023-03-01T20:10:55Z
dc.date.issued2022-09-01
dc.description.abstractThe increasing pressure on water resources in agricultural areas requires the implementation of innovative tools and solutions to improve irrigation water management. Against that background, this research presents the application of a remote sensing-based methodology for estimating actual evapotranspiration (ETa) based on two-source energy balance model (TSEB) and remote sensing-water balance (RSWB) coupling for sugarcane crop in Brazil using the hybrid model Spatial EvapoTranspiration Modeling Interface (SETMI). Estimated results through SETMI and field data using the eddy covariance system (EC) considering two growing seasons were used to validate the energy balance components and ETa. In addition, the basal crop coefficient as a function of the spectral reflectance (Kcbrf) was developed through the soil-adjusted vegetation index (SAVI) and observed ET. Modeled energy balance components showed a strong correlation to the ground data from EC, with ET presenting R2 equal to 0.94 and a Pearson correlation coefficient (ρ) equal to 0.88. Regarding Kcbrf, the Kcb-SAVI relationship for sugarcane presented a high correlation with an R2 value of 0.85 and an “ρ” equal to 0.92. On average, considering the whole season, Kcb was equal to 0.75 and 0.73 for the 4th ratoon and 5th ratoon, respectively. Overall, the average Kc throughout the period was 0.73 and 0.70 for the 4th and 5th ratoons respectively, and the maximum Kc of about 1.23 for both growing seasons. On average, accumulated ETa presented 1025 mm resulting in ETa rates of 2.9 mm per day considering the two seasons. Crop water productivity (WP) obtained values similar between the seasons, averaging 12.6, 21.7, and 12.3 kg m−3 for WPp+i, WPi and WPET, respectively. The SETMI hybrid model produced suitable estimated daily ETa values over the two growing seasons through remote sensing based on the Kcb-SAVI relationship and good performance of TSEB model during the evaluated growing periods confirming the applicability of the model under tropical conditions in Brazil focusing on improving irrigation management in sugarcane crop.en
dc.description.affiliationSão Paulo State University – UNESP
dc.description.affiliationUniversity of São Paulo – USP/ESALQ
dc.description.affiliationUniversity of Nebraska Daugherty Water for Food Global Institute
dc.description.affiliationFederal University of Sergipe
dc.description.affiliationUnespSão Paulo State University – UNESP
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipIdFAPESP: 2.009/52.467-4; 2020/08365-1
dc.description.sponsorshipIdCNPq: 404.229/2013-1
dc.description.sponsorshipIdCAPES: 88881.189165/2018-01
dc.identifierhttp://dx.doi.org/10.1016/j.agwat.2022.107763
dc.identifier.citationAgricultural Water Management, v. 271.
dc.identifier.doi10.1016/j.agwat.2022.107763
dc.identifier.issn1873-2283
dc.identifier.issn0378-3774
dc.identifier.scopus2-s2.0-85132441212
dc.identifier.urihttp://hdl.handle.net/11449/240302
dc.language.isoeng
dc.relation.ispartofAgricultural Water Management
dc.sourceScopus
dc.subjectIrrigation management
dc.subjectSaccharum officinarum
dc.subjectSatellite image
dc.subjectSETMI
dc.titleRemote sensing based evapotranspiration modeling for sugarcane in Brazil using a hybrid approachen
dc.typeArtigo
dspace.entity.typePublication

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