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dc.contributor.authorFONGARO, C. T.eng
dc.contributor.authorDEMATTÊ, J. A. M.eng
dc.contributor.authorRIZZO, R.eng
dc.contributor.authorSAFANELLI, J. L.eng
dc.contributor.authorMENDES, W. de S.eng
dc.contributor.authorDOTTO, A. C.eng
dc.contributor.authorVICENTE, L. E.eng
dc.contributor.authorFRANCESCHINI, M. H. D.eng
dc.contributor.authorUSTIN, S. L.eng
dc.date.accessioned2019-11-19T18:06:17Z-
dc.date.available2019-11-19T18:06:17Z-
dc.date.created2019-11-19
dc.date.issued2018
dc.identifier.citationRemote Sensing, v. 10, n. 10, p. 1-21, 2018. Article 1555.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1114592-
dc.descriptionAbstract: Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0?20 cm depth, 919 points) from an area of 14,614 km 2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R 2 = 0.83; RMSE = 65.0 g kg − 1 ) and sand (R 2 = 0.86; RMSE = 79.9 g kg − 1 ). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectMapeamento do soloeng
dc.subjectImagem de satéliteeng
dc.titleImprovement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images.eng
dc.typeArtigo de periódicoeng
dc.date.updated2019-11-19T18:06:17Z
dc.subject.thesagroSensoriamento Remotoeng
dc.subject.thesagroSatéliteeng
dc.subject.thesagroSolo Arenosoeng
dc.subject.thesagroSolo Argilosoeng
dc.subject.nalthesaurusSoil mapeng
dc.subject.nalthesaurusRemote sensingeng
dc.subject.nalthesaurusMultispectral imageryeng
dc.subject.nalthesaurusSatelliteseng
dc.subject.nalthesaurusClay soilseng
dc.subject.nalthesaurusSandy soilseng
dc.subject.nalthesaurusReflectance spectroscopyeng
dc.subject.nalthesaurusPrecision agricultureeng
dc.subject.nalthesaurusSoil degradationeng
riaa.ainfo.id1114592eng
riaa.ainfo.lastupdate2019-11-19
dc.identifier.doihttps://doi.org/10.3390/rs10101555eng
dc.contributor.institutionCAIO TROULA FONGARO, ESALQ-USP; JOSE ALEXANDRE MELO DEMATTE, ESALQ-USP; RODNEI RIZZO, CENA-USP; JOSE LUCAS SAFANELLI, ESALQ-USP; WANDERSON DE SOUSA MENDES, ESALQ-USP; ANDRE CARNIELETTO DOTTO, ESALQ-USP; LUIZ EDUARDO VICENTE, CNPMA; MARSTON HERACLES DOMINGUES FRANCESCHINI, Wageningen University; SUSAN L USTIN, University of California-Davis.eng
Aparece en las colecciones:Artigo em periódico indexado (CNPMA)

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