Impact of spatial resolution on soil loss estimation: a case study of abandoned quarries in Morocco

Soil erosion poses significant challenges to sustainable land management. The Revised Universal Soil Loss Equation (RUSLE) has emerged as a valuable tool for predicting soil-erosion risk, providing essential insights for effective soil-conservation practices. However, the accuracy of RUSLE predictions strongly depends on the quality and suitability of the input data. This study investigated the impact of variability in the input data on the performance of the RUSLE model in quantifying soil-erosion rates for abandoned quarries in the Bouguergouh commune in Morocco using two sets of input data. The first set represents coarse resolution data (30 m) extracted from different available sources (ISRIC for soil data, NASA database for weather data, Landsat 8 for land use, and ASTER Digital Elevation Model (DEM) for elevation data). These data were compared with a high-resolution (10 m) dataset comprising field data for soil, observed weather data from the Bouregreg and Chaouia Hydraulic Basin Agency data extracted from the Mohammed VI satellites at 0.5 m, and a DEM from Sentinel-1A at 10 m. Our findings reveal that the erosion rates obtained from both the measured (10 m) and international (10 m) databases, using the LS (10 m) factor set, exhibit significant and comparable values. Specifically, the erosion rates ranged from 1.28 to 7.7 t/ha/yr for the measured database and from 1.25 to 7.74 t/ha/yr for the international database. Based on the results of this study, international databases can estimate soil losses using high-resolution DEMs instead of investing in time-consuming soil sampling and analysis results or acquiring expensive high-resolution satellite images, however, conducting the same comparison in other areas can generate global conclusions.