Título:	Hydropedological digital mapping: machine learning applied to spectral VIS-IR and radiometric data dimensionality reduction.
Autoria:	SANTOS, P. A. dos PINHEIRO, H. S. K. CARVALHO JUNIOR, W. de SILVA, I. L. da PEREIRA, N. R. BHERING, S. B. CEDDIA, M. B.
Afiliação:	PRISCILLA 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.
Ano de publicação:	2023
Referência:	Revista Brasileira de Ciência do Solo, v. 47, e0220149, 2023.
Conteúdo:	Pedosphere-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.
Thesagro:	Sensoriamento Remoto
NAL Thesaurus:	Remote sensing Radiometry
Palavras-chave:	Geoprocessing Hydropedology Applied statistics Geoprocessamento Radiometria Hidropedologia Estatística aplicada
Digital Object Identifier:	https://doi.org/10.36783/18069657rbcs20220149
Tipo do material:	Artigo de periódico
Acesso:	openAccess
Aparece nas coleções:	Artigo em periódico indexado (CNPS)