Critical oversights of remote sensing-based RUSLE cover and management (C) factor estimation in Ethiopia: A review

Abstract

The Revised Universal Soil Loss Equation (RUSLE) is the most widely used empirical model to estimate the rate of soil loss. The cover and management (C) factor, among the RUSLE model factors, is the most challenging factor to estimate mainly because of its high sensitivity to spatiotemporal change, complexity as it is a product of multiple sub-factors, and expensiveness and time-consuming as it requires reliable long-term and up-to-date field data on vegetation cover, land use practices, and management practices. Nowadays, remote sensing approaches are used as an alternative method to compute the C-factor in countries like Ethiopia where C-factor estimation is difficult using the original Soil Loss Ratio (SLR) approach. Hence, the objective of this review is to view the remote sensing approaches employed and identify the critical aspects overlooked by previous studies in Ethiopia while estimating the RUSLE C-factor value using remote sensing approaches. The assignment of uniform C-factor value from literature after the classification of remote sensing imagery into different land use and land cover categories and the Normalized Difference Vegetation Index (NDVI) approaches are the two widely used approaches by previous studies in Ethiopia to estimate the RUSLE C-factor. Inadequate consideration of the seasonal variation of cover and management factor (91.89%), underestimation of the effect of management practices (89.19%), underestimation of spatial variation of cover and management factor (81.08%), and inadequate consideration of the effect of vegetation and crop canopy cover on soil erosion (76.67%) were identified as the critical oversights made by the previous RUSLE-based studies in Ethiopia during the estimation of cover and management (C) factor using remote sensing approaches which may induce inaccurate soil loss prediction in the country. Future research works in Ethiopia should pay more attention to the use of time-series multi-date satellite imagery acquired in different seasons of the year instead of relaying single-date imagery acquired during the dry season while estimating the C-factor using remote sensing approaches. Furthermore, future researchers should pay more attention to the use of a spatially explicit approach and the development of local C-factor values based on field measurements, remote sensing data, and other data sources that should consider the spatiotemporal variation of crop type and management practices for accurate estimates of C-factor, which will improve the accuracy of RUSLE-based soil loss prediction in the country.