Novel Climate-Specific Models for C-Factor Assessment: Insights from the Five-Parameter Logistic Function, the De Martonne Index, and NDVI

Abstract

Abstract Soil is a delicate natural resource that is vulnerable to erosional forces. Cover management, representing factor C, is one of the most important factors in mitigating soil and water losses. However, conventional methods for estimating factor C are often resource-intensive and slow to track rapid changes. Remote sensing-based models for estimating factor C exist, but they are not always adaptable to different climates. This study addresses two key gaps in soil erosion research: (i) introducing novel models to estimate Factor C across diverse climate zones and (ii) evaluating Factor C values across Morocco. This study proposes a novel approach to estimating C-factor values across humid, semi-humid/semi-arid, and arid/hyperarid zones. Three climate-specific models, each tailored to a distinct climate zone, were developed using the Five-Parameter Logistic (5PL) function, the De Martonne Aridity Index, and NDVI. The curve_fit() routine in Python is used to perform nonlinear regression curve fitting. These models offer several advantages over traditional methods, including: swift temporal resolution, enhanced cost-effectiveness, and precise spatial resolution. The findings of the study show that the proposed models are in good agreement with literature-based factor C values across all climatic zones. These models provide valuable tools for researchers studying soil loss and can help decision-makers to formulate and update effective land management strategies.