02849naa a2200361 a 450000100080000000500110000800800410001902200140006002400540007410000250012824501390015326000090029252017850030165000120208665000120209865000240211065000200213465000150215465000120216965000140218165000110219565000140220665000250222065300250224565300310227070000190230170000230232070000300234370000190237370000260239270000250241877300440244321255322021-08-25       2020    bl uuuu     u00u1 u    #d  a2352-00947 ahttps://doi.org/10.1016/j.geodrs.2020.e002532DOI1 aPADILHA, M. C. de C.  aUsing Landsat and soil clay content to map soil organic carbon of oxisols and Ultisols near São Paulo, Brazil.h[electronic resource]  c2020  aAbstract: Quantification of soil organic carbon (SOC) is a low-cost and necessary practice to meet increasing agricultural demands. Studies show that remote sensing (RS) is important for SOC prediction and its use has become crucial in agricultural management. In this study, a Multiple Linear Regression (MLR) model was constructed to predict SOC in a site in Piracicaba, São Paulo, Brazil. As predictor variables, we used the optical-satellite data of OLI/Landsat-8 sensor (bands 5 and 7, specifically), clay concentration, and the Normalized Difference Vegetation Index (NDVI). We collected 218 samples at the sampling points in the field to quantify clay and SOC in the laboratory as a calibration procedure. An Exposed Soil Mask (ESM) was created using the method GEOS3 technology, which showed pixels with greater variability of bare soil. The pixels were evaluated with their respective surface reflectance values obtained by the satellite sensor and their respective NDVI index values. We evaluated the model predictive performance based on the adjusted coefficient of determination (R2), the Root Mean-Squared Error (RMSE), and the Ratio of Performance to Interquartile Range (RPIQ) obtained in data validation. The MLR model presented R2 values 0.79 and 0.81 for calibration and validation, respectively. We obtained important RMSE and RPIQ values, 0.14 and 2.32, respectively. The high RPIQ indicated significative sampling distribution around the trendline. After construction, the model was applied to the C spatial distribution using the predictive variables as layers, predominant concentrations of 0.65 to 0.79 g. Kg-1 in 51 (23.4%) soil samples. The analysis presented here offer possibilities for SOC prediction using Geographic Information Systems (GIS) tools.  aLandsat  aOxisols  aSoil organic carbon  aSoil properties  aArgissolos  aCarbono  aLatossolo  aOxisol  aSatélite  aSensoriamento Remoto  aDigital soil mapping  aMultiple linear regression1 aVICENTE, L. E.1 aDEMATTÊ, J. A. M.1 aLOEBMANN, D. G. dos S. W.1 aVICENTE, A. K.1 aURBINA SALAZAR, D. F.1 aGUIMARÃES, C. C. B.  tGeoderma Regionalgv. 21, e00253, 2020.