Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1116841
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dc.contributor.authorJARAMILLO-GIRALDO, C.eng
dc.contributor.authorFERREIRA, W. P. M.eng
dc.contributor.authorFONSECA, H. P.eng
dc.contributor.authorRIBEIRO, M. de F.eng
dc.contributor.authorSILVA, L. M. R.eng
dc.contributor.authorFERNANDES, R. B. A.eng
dc.date.accessioned2019-12-13T18:09:40Z-
dc.date.available2019-12-13T18:09:40Z-
dc.date.created2019-12-13
dc.date.issued2019
dc.identifier.citationJournal of Agricultural Science, v. 11, n. 15, p, 187-199, 2019.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1116841-
dc.descriptionRobust monitoring techniques for perennial crops have become increasingly possible due to technological advances in the area of Remote Sensing (RS), and the products are available through the European Space Agency (ESA) initiative. RS data provides valuable opportunities for detailed assessments of crop conditions at plot level using high spatial, spectral, and temporal resolution. This study addresses the monitoring of coffee at the plot level using RS, analyzing the relationship between the spatio-temporal variability of the Leaf Area Index (LAI) and the crop coefficient (Kc); the Kc being a biophysical variable that integrates the potential hydrological characteristics of an agroecosystem compared to the reference crop. Daily and one-year Kc were estimated using the relation of crop evapotranspiration and reference. ESA Sentinel-2 images were pre-analyzed and atmospherically corrected, and Top-of-the-Atmosphere (TOA) reflections converted to Top-of-the-Canopy (TOC) reflectance. The TOCs resampled at the 10m resolution, and with the angles corresponding to the directional information at the time of the acquisition, the LAI was estimated using the trained neural network available in the Sentinel Application Platform (SNAP). During 75% of the monitored days, Kc ranged between 1.2 and 1.3 and, the LAI analyzed showed high spatial and temporal variability at the plot level. Based on the relationship between the biophysical variables, the LAI variable can substitute the Kc and be used to monitor the water conditions at the production area as well as analyze spatial variability inside that area. Sentinel-2 products could be more useful in monitoring coffee in the farm production area.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectSatellite crop monitoring sentinel-2eng
dc.titleRelationship Between Spatio-Temporal Leaf Area Index and Crop Coefficient When Monitoring Coffee Plots in Brazil.eng
dc.typeArtigo de periódicoeng
dc.date.updated2019-12-13T18:09:40Z
dc.subject.nalthesaurusCrop coefficienteng
dc.subject.nalthesaurusLeaf area indexeng
riaa.ainfo.id1116841eng
riaa.ainfo.lastupdate2019-12-13
dc.contributor.institutionCarolina Jaramillo-Giraldo, Empresa de Pesquisa Agropecuária de Minas Gerais - EPAMIG; WILLIAMS PINTO MARQUES FERREIRA, CNPCa; Humberto Paiva Fonseca; Marcelo de Freitas Ribeiro, Empresa de Pesquisa Agropecuária de Minas Gerais - EPAMIG; Laís Maria Rodrigues Silva, Universidade Federal de Viçosa - UFV/Departamento de Ciência do Solo; Raphael Bragança Alves Fernandes, Universidade Federal de Viçosa - UFV/Departamento de Ciência do Solo.eng
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