C-factor estimate for soil loss equations using transformation function (Near, Gaussian and Symmetric Linear) and remote sensing data

The study proposes a methodology to calculate the C-factor using remote sensing data: NDVI from LANDSAT image and MAPBIOMAS Land Use (LU) classification to Atibaia river watershed, Brazil, to improve the estimation of risk of soil loss using equations such as USLE and RUSLE. The methodology follows the procedures: first the NDVI was calculated, then the resulting image was rescaled to the range 0 to 1, applying the Near, Gaussian and Symmetric Linear transformation functions, with value below threshold 1, value above threshold 0 and scale 1 in the Rescale by function tool. Among the three models presented the Symmetric Linear model showed the best results for the distribution of C-factor values between the LU classes, while in the Gaussian model the same value was recorded, 0.70, for the Pasture and Rocky Outcrop classes and the average of the values was low 0.22 (Near) and 0.31 (Gaussian).