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A synthesized approach for estimating the C-factor of RUSLE for a mixed-landscape watershed: A case study in the Gongshui watershed, southern China
Agriculture, Ecosystems & Environment ( IF 6.0 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.agee.2020.107009 H. Yan , L. Wang , T.W. Wang , Z. Wang , Z.H. Shi
Agriculture, Ecosystems & Environment ( IF 6.0 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.agee.2020.107009 H. Yan , L. Wang , T.W. Wang , Z. Wang , Z.H. Shi
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Abstract The cover-management factor (C-factor) in the revised universal soil loss equation (RUSLE) indicates the effects of vegetation cover and management practices on water erosion. The conditions governing the C-factor can be managed by farmers and managers to affect soil loss and soil carbon cycle. Currently, the most common approach for estimating the C-factor on large scales is using the normalized difference vegetation index (NDVI) individually, which cannot precisely characterize the differences in the C-factor in various land-use types with the same degree of vegetation coverage. We developed a multiple-land-use synthesized C-factor (MLUS-C) model to capture the dynamics of the C-factor with a high spatio-temporal resolution for mixed-landscape watersheds. In this model, space-time fusion was executed based on multi-source remotely sensed data to estimate the phenological succession of vegetation cover, and the impacts of farmland management on large-scale evaluation of the C-factor were considered. A case study was conducted in the Gongshui watershed, southern China, where the dataset of precipitation and sediment yield covered a period of 11 years. We validated the simulation from the MLUS-C model and compared it with the results from NDVI-only approaches via conversion from the C-factor to sediment yield. The validation results showed that the MLUS-C model significantly improved the simulation accuracy and model adaptability. The root mean square error of our model was 71.5-95.5% lower than those of NDVI-only approaches. Our model has an advantage in estimating the C-factor in heterogeneous landscapes and provides a basis for implementing measurements to efficiently reduce soil loss.
更新日期：2020-10-01
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