Abstract
Abstract: The objective of this work was to evaluate the relationship between different remote sensing data, derived from satellite images, and interrill soil losses obtained in the field by using a portable rainfall simulator. The study was carried out in an area of a hydrographic basin, located in Médio Paraíba do Sul, in the state of Rio de Janeiro - one of the regions most affected by water erosion in Brazil. Evaluations were performed for different vegetation indices (NDVI, Savi, EVI, and EVI2) and fraction images (FI), derived from linear spectral mixture analysis (LSMA), obtained from RapidEye, Sentinel2A, and Landsat 8 OLI images. Vegetation indices are more adequate to predict soil loss than FI, highlighting EVI2, whose exponential model showed R2 of 0.74. The best prediction models are generated from the RapidEye image, which shows the highest spatial resolution among the sensors evaluated.
Highlights
In agricultural lands of tropical regions, water erosion contributes most to soil degradation, which is facilitated and accelerated by man, through inadequate agricultural management practices and exploitation of natural resources (Mello et al, 2013).The Paraíba do Sul river basin is one of the Brazilian regions most influenced by water erosion, since more than 20% of its area is in high or very high vulnerability to erosion (Machado et al, 2008)
Many studies are based on the mapping of different soil cover classes, which are related to a degree of susceptibility to erosion and used, for instance, to estimate the soil cover C factor of the revised universal soil loss equation (Rusle) (Ganasri & Ramesh, 2016; Gelagay & Minale, 2016)
Indices that correlate with vegetation cover are used such as the normalized difference vegetation index (NDVI) (Rouse et al, 1974) which is the most used in erosion prediction models
Summary
The Paraíba do Sul river basin is one of the Brazilian regions most influenced by water erosion, since more than 20% of its area (one million hectares) is in high or very high vulnerability to erosion (Machado et al, 2008). Temporal and spatial information on soil loss are used as tools to assist soil and water conservation programs, and can be generated by erosion prediction models which commonly use remote sensing techniques to represent the vegetation cover (Renard et al, 1997; De Jong et al, 1999; Hazarika & Honda, 2001). Indices that correlate with vegetation cover are used such as the normalized difference vegetation index (NDVI) (Rouse et al, 1974) which is the most used in erosion prediction models
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