Por favor, use este identificador para citar o enlazar este ítem: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1187841
Registro completo de metadatos
Campo DCValorLengua/Idioma
dc.contributor.authorKLINKE NETO, G.
dc.contributor.authorOLIVEIRA, A. H.
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorBERGIER, I.
dc.contributor.authorGOULART, A. J. H.
dc.date.accessioned2026-06-25T17:48:28Z-
dc.date.available2026-06-25T17:48:28Z-
dc.date.created2026-06-25
dc.date.issued2026
dc.identifier.citationSustainability, v. 18, n. 13, 6473, July 2026.
dc.identifier.issn2071-1050
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1187841-
dc.descriptionAccurate monitoring of hydrological dynamics in complex perennial landscapes is a cornerstone for tropical agricultural sustainability. Traditional energy balance models based on orbital optical data often face methodological bottlenecks due to cloud cover and the “greening myth,” where optical indices fail to capture immediate water stress due to the non-linear decoupling between stomatal closure and pigment loss. This study developed a cloud-integrated multisensor framework to estimate actual evapotranspiration (ETa) at a refined 100 m resolution in mountain coffee systems, utilizing active microwave proxies from Sentinel-1. We fused polarimetric metrics—Degree of Polarization (DoP) and Shannon Entropy (SE)—with land surface temperature and soil moisture data. Multiple Linear Regression (MLR) was compared against non-linear algorithms (Random Forest and SVR) to prioritize model parsimony and physical interpretability. The results show that MLR emerged as the most parsimonious and suitable model within this localized dataset scope (R2 = 0.872; RMSE = 2.916 mm/8-day), outperforming complex “black-box” architectures. Soil moisture emerged as the dominant environmental driver of ETa variability, while SAR-based metrics served as sensitive mechanical proxies for canopy geometric heterogeneity and macro-structural variations. Cross-correlation analysis revealed a 16-day lag, empirically indicating that biophysical water shifts temporally precede geometric canopy alterations. Operationally, this framework ensures temporal continuity under persistent cloud cover and provides high-fidelity spatial detailing for precision water management. This approach offers an auditable and scalable tool for watershed planning and climate resilience in tropical agriculture.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectFusão de dados
dc.subjectÍndice polarimétrico
dc.subjectSustentabilidade
dc.subjectMicro-ondas ativos
dc.subjectCafé de montanha
dc.subjectActive microwaves
dc.subjectData fusion
dc.subjectGoogle Earth Engine
dc.subjectPolarimetric indices
dc.subjectSustainability
dc.titleCloud-based fusion of Sentinel-1 Radar, MODIS and soil moisture data for resolution-refined evapotranspiration mapping in mountain coffee systems.
dc.typeArtigo de periódico
dc.subject.thesagroEvapotranspiração
dc.subject.thesagroUmidade do Solo
riaa.ainfo.id1187841
riaa.ainfo.lastupdate2026-06-25
dc.identifier.doihttps://doi.org/10.3390/su18136473
dc.contributor.institutionGUSTAVO KLINKE NETO, UNIVERSIDADE ESTADUAL DE CAMPINAS; ANNA HOFFMANN OLIVEIRA, UNIVERSIDADE FEDERAL DE SÃO CARLOS; EDSON LUIS BOLFE, CNPTIA; IVAN BERGIER TAVARES DE LIMA, CNPTIA; ANTONIO JOSÉ HOMSI GOULART, UNIVERSIDADE DE SÃO PAULO.
Aparece en las colecciones:Artigo em periódico indexado (CNPTIA)

Ficheros en este ítem:
Fichero TamañoFormato 
AP-Cloud-based-fusion-2026.pdf2,1 MBAdobe PDFVisualizar/Abrir
Mostrar el registro sencillo del ítem

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace