Por favor, use este identificador para citar o enlazar este ítem: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1157151
Registro completo de metadatos
Campo DCValorLengua/Idioma
dc.contributor.authorSANTOS, P. A. dos
dc.contributor.authorPINHEIRO, H. S. K.
dc.contributor.authorCARVALHO JUNIOR, W. de
dc.contributor.authorSILVA, I. L. da
dc.contributor.authorPEREIRA, N. R.
dc.contributor.authorBHERING, S. B.
dc.contributor.authorCEDDIA, M. B.
dc.date.accessioned2023-10-09T13:24:24Z-
dc.date.available2023-10-09T13:24:24Z-
dc.date.created2023-10-09
dc.date.issued2023
dc.identifier.citationRevista Brasileira de Ciência do Solo, v. 47, e0220149, 2023.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1157151-
dc.descriptionPedosphere-hydrosphere interface accounts for the association between soil hydrology and landscape, represented by topographic and Remote Sensing data support and integration. This study aimed to analyze different statistical radiometric and spectral data selection methods and dimensionality reduce environment-related data to support the classification of soil physical-hydric properties, such as soil basic infiltration rate (bir) and saturated hydraulic conductivity (Ksat); as well as to act in data mining processes applied to hydropedological properties digital mapping. Accordingly, research integrated information from Visible to Infrared (VIS-IR) spectral indices and Sentinel's 2A mission Multispectral Instrument (MSI) sensor bands, terrain numerical modeling and aerogeophysics set to model soil-water content in two soil layers (0.00-0.20 m and 0.20-0.40 m). Pre-processed data were subjected to statistical analysis (multivariate and hypothesis tests); subsequently, the methods were applied (variation inflation factor - VIF, Stepwise Akaike information criterion - Stepwise AIC, and recursive feature elimination - RFE) to mine covariates used for Random Forest modeling. Based on the results, there were distinctions and singularities in spectral and radiometric data selection for each adopted method; the importance degree, and contribution of each one to soil physical-hydric properties have varied. According to the applied statistical metrics and decision-making criteria (highest R2 and lowest RMSE / MAE), the chosen methods were RFE (0.00-0.20 m layers) and Stepwise AIC (0.20-0.40 m layers) - both concerned with the assessed variables (bir and Ksat). This approach captured the importance of environmental variables and highlighted their potential use in hydropedological digital mapping at Guapi-Macacu watershed.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectGeoprocessing
dc.subjectHydropedology
dc.subjectApplied statistics
dc.subjectGeoprocessamento
dc.subjectRadiometria
dc.subjectHidropedologia
dc.subjectEstatística aplicada
dc.titleHydropedological digital mapping: machine learning applied to spectral VIS-IR and radiometric data dimensionality reduction.
dc.typeArtigo de periódico
dc.subject.thesagroSensoriamento Remoto
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusRadiometry
riaa.ainfo.id1157151
riaa.ainfo.lastupdate2023-10-09
dc.identifier.doihttps://doi.org/10.36783/18069657rbcs20220149
dc.contributor.institutionPRISCILLA AZEVEDO DOS SANTOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; HELENA SARAIVA KOENOW PINHEIRO, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; WALDIR DE CARVALHO JUNIOR, CNPS; IGOR LEITE DA SILVA, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; NILSON RENDEIRO PEREIRA, CNPS; SILVIO BARGE BHERING, CNPS; MARCOS BACIS CEDDIA, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO.
Aparece en las colecciones:Artigo em periódico indexado (CNPS)

Ficheros en este ítem:
Fichero Descripción TamañoFormato 
Hydropedological-digital-mapping-2023.pdf2.1 MBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro sencillo del ítem

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace