02864naa a2200325 a 450000100080000000500110000800800410001902400550006010000220011524501520013726000090028952018470029865000150214565000190216065000250217965300230220465300260222765300210225365300180227465300190229265300180231165300160232970000230234570000270236870000200239570000190241570000190243470000180245377300670247121571512023-12-07       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.36783/18069657rbcs202201492DOI1 aSANTOS, P. A. dos  aHydropedological digital mappingbmachine learning applied to spectral VIS-IR and radiometric data dimensionality reduction.h[electronic resource]  c2023  aPedosphere-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.  aRadiometry  aRemote sensing  aSensoriamento Remoto  aApplied statistics  aEstatística aplicada  aGeoprocessamento  aGeoprocessing  aHidropedologia  aHydropedology  aRadiometria1 aPINHEIRO, H. S. K.1 aCARVALHO JUNIOR, W. de1 aSILVA, I. L. da1 aPEREIRA, N. R.1 aBHERING, S. B.1 aCEDDIA, M. B.  tRevista Brasileira de Ciência do Sologv. 47, e0220149, 2023.