Monitoring the temporal dimension of soil erosion in Mayurakshi Basin, India: A novel approach integrating RUSLE, Shannon’s entropy and landscape ecological metrics

Roy, Subhasis; Das, Souvik; Sengupta, Somasis; Mistry, Sukhendu; Chatterjee, Jaya

doi:10.1007/s12040-022-02006-9

Cited by 4 publications

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feng et al found that the C-factor is sensitive to yellow VIs, such as the normalized difference tillage index (NDTI) and the normalized difference senescent vegetation index (NDSVI) [21]. The normalized bare soil index (NBIL) and the modified soil-adjusted vegetation index (MSAVI) have also been used to calculate the C-factor [76,77].…”

Section: C-factor Estimated By Vegetation Indices (M3)mentioning

confidence: 99%

Global Analysis of the Cover-Management Factor for Soil Erosion Modeling

2023

View full text Add to dashboard Cite

Land use and management practices (LUMPs) play a critical role in regulating soil loss. The cover-management factor (C-factor) in Universal Soil Loss Equation (USLE)-type models is an important parameter for quantifying the effects of LUMPs on soil erosion. However, accurately determining the C-factor, particularly for large-scale assessments using USLE-type models, remains challenging. This study aims to address this gap by analyzing and comparing the methods used for C-factor quantification in 946 published articles, providing insights into their strengths and weaknesses. Through our analysis, we identified six main categories of methods for C-factor quantification in USLE-type modeling. Many studies have relied on empirical C-factor values for different land-use types or calculated C-factor values based on vegetation indices (VIs) in large study areas (>100 km2). However, we found that no single method could robustly estimate C-factor values for large-scale studies. For small-scale investigations, conducting experiments or consulting the existing literature proved to be more feasible. In the context of large-scale studies, employing methods based on VIs for C-factor quantification can enhance our understanding of the relationship between vegetation changes and soil erosion potential, particularly when considering spatial and spatiotemporal variations. For the global scale, we recommend the combined use of different equations. We suggest further efforts to develop C-factor datasets at large scales by synthesizing field-level experiment data and combining high-resolution satellite imagery. These efforts will facilitate the development of effective soil conservation practices, ensuring sustainable land use and environmental protection.

show abstract

Section: C-factor Estimated By Vegetation Indices (M3)mentioning

confidence: 99%