Logotipo do repositório
 

Publicação:
Remote sensing-based evapotranspiration modeling using geeSEBAL for sugarcane irrigation management in Brazil

dc.contributor.authorGonçalves, I. Z.
dc.contributor.authorRuhoff, A.
dc.contributor.authorLaipelt, L.
dc.contributor.authorBispo, R. C. [UNESP]
dc.contributor.authorHernandez, F. B.T. [UNESP]
dc.contributor.authorNeale, C. M.U.
dc.contributor.authorTeixeira, A. H.C.
dc.contributor.authorMarin, F. R.
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionInstitute of Hydraulic Research - Federal University of Rio Grande do Sul
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionDaugherty Water for Food Global Institute
dc.contributor.institutionUniversidade Federal de Sergipe (UFS)
dc.date.accessioned2023-07-29T12:32:24Z
dc.date.available2023-07-29T12:32:24Z
dc.date.issued2022-12-01
dc.description.abstractIrrigated agriculture requires the implementation of innovative tools to improve irrigation water management and accurate estimation of actual evapotranspiration (ETa) such as remote sensing-based methodology. This study aimed to evaluate the irrigation management and estimating evapotranspiration through the geeSEBAL, a new tool for automated estimation of ETa based on the Surface Energy Balance Algorithm for Land (SEBAL) and a simplified version of the Calibration using Inverse Modeling at Extreme Conditions (CIMEC) process for the endmembers selection, implemented into the Google Earth Engine (GEE) environment. GeeSEBAL has not been used yet in Brazil for irrigation proposes, and in this research, it was applied to estimate ETa using Landsat images and ERA5-Land as meteorological inputs in the largest sugarcane producing region of the world in Brazil for two ratoon seasons by comparing daily ETa with values obtained from eddy covariance (EC) data, Energy balance components using geeSEBAL were consistent with the measured data and daily ETa presenting RMSE of 0.46 mm with R2 = 0.97. Modeled ETa and Kc were similar for the two seasons, although somewhat overestimated for the fifth ratoon when compared to the EC data, mainly during high atmospheric demand (crop mid-stage). Still, the Kc values were similar to the standard values available in the literature and measured flux tower data for the two ratoons seasons. With ETa from geeSEBAL it was possible to identify water stress over the growing seasons using the remote sensing-based soil water balance, which occurred mainly during the phase after the crop reached the peak Kc (full cover stage) when the irrigation depth required was very high. This analysis showed that geeSEBAL has a significant potential for assessment of ETa for irrigation monitoring and management, even in missing climate data areas, allowing important advances in water resources management for sugarcane and other irrigated crops at field or regional scales.en
dc.description.affiliationUniversity of São Paulo (USP) “Luiz de Queiroz” College of Agriculture (Esalq), SP
dc.description.affiliationInstitute of Hydraulic Research - Federal University of Rio Grande do Sul
dc.description.affiliationSão Paulo State University – UNESP
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.sponsorshipAmerican Nurses Association
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipUniversidade Federal do Rio Grande do Sul
dc.description.sponsorshipUniversidade Estadual Paulista
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2020/08365–1
dc.description.sponsorshipIdFAPESP: 2021/00720–0
dc.identifierhttp://dx.doi.org/10.1016/j.agwat.2022.107965
dc.identifier.citationAgricultural Water Management, v. 274.
dc.identifier.doi10.1016/j.agwat.2022.107965
dc.identifier.issn1873-2283
dc.identifier.issn0378-3774
dc.identifier.scopus2-s2.0-85140317878
dc.identifier.urihttp://hdl.handle.net/11449/246127
dc.language.isoeng
dc.relation.ispartofAgricultural Water Management
dc.sourceScopus
dc.subjectEddy covariance
dc.subjectERA5
dc.subjectLandsat images
dc.subjectWater productivity
dc.titleRemote sensing-based evapotranspiration modeling using geeSEBAL for sugarcane irrigation management in Brazilen
dc.typeArtigo
dspace.entity.typePublication

Arquivos

Coleções