Improving RUSLE predictions through UAV-based soil cover management factor (C) assessments: A novel approach for enhanced erosion analysis in sugarcane fields

Abstract

The Universal Soil Loss Equation (USLE) and its derivatives express the combined effects of crop cover and rainfall patterns by the cover and management factor (C). Thus, the C-factor links the combined effect of soil surface roughness, vegetation, biomass cover, and rainfall patterns on soil erosion. This evaluation should be at each phenological stage. Due to the significant time and effort needed to access this factor for a crop, simplified methods are often used, disregarding the expected intra-annual variability and consequently increasing the uncertainty for soil loss modeling. In this scenario, we proposed a framework to collect input data at a fine-scale to estimate the C-factor by the original approach. For this, we collected data with a low-cost UAV at the middle of each phenological stage of sugarcane: sprouting, tillering, elongation, and maturation. We used orthomosaics, three vegetation indices (ExRmG, MGRVI, ViGREEN), digital surface models (DSM), and digital terrain model (DTM) to determine the canopy cover (CC), surface cover (SC), and soil roughness (SR), accessing the soil loss ratio (SLR) per phenological stage. Late on, we estimate the C-factor weighting the SLR by the rainfall erosivity. Our annual C-factor aligns with the most values applied to sugarcane studies and ranged from 0.0241 to 0.2938. Our results pointed out that using the proposed methods can access suitable annual C-factor for sugarcane areas. Furthermore, we highlighted the ViGREEN because it presented a significant performance in orthomosaics classification and has a potential already reported in other studies on C-factor at different scales.