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The Normalized Difference Vegetation Index (NDVI), has been increasingly used to capture spatiotemporal variations in cover factor (C) determination for erosion prediction on a larger landscape scale. However, NDVI-based C factor (Cₙdᵥᵢ) estimation per se is sensitive to various biophysical variables, such as soil condition, topographic features, and vegetation phenology. As a result, Cₙdᵥᵢ often results in incorrect values that affect the quality of soil erosion prediction. The aim of this study is to multi-temporally estimate Cₙdᵥᵢ values and compare the values with those of literature values (Cₗᵢₜ) in order to quantify discrepancies between C values obtained via NDVI and empirical-based methods. A further aim is to quantify the effect of biophysical variables such as slope shape, erodibility, and crop growth stage variation on Cₙdᵥᵢ and soil erosion prediction on an agricultural landscape scale. Multi-temporal Landsat 7, Landsat 8, and Sentinel 2 data, from 2013 to 2016, were used in combination with high resolution agricultural land use data of the Integrated Administrative and Control System, from the Uckermark district of north-eastern Germany. Correlations between Cₙdᵥᵢ and Cₗᵢₜ improved in data from spring and summer seasons (up to r = 0.93); nonetheless, the Cₙdᵥᵢ values were generally higher compared with Cₗᵢₜ values. Consequently, modelling erosion using Cₙdᵥᵢ resulted in two times higher rates than modelling with Cₗᵢₜ. The Cₙdᵥᵢ values were found to be sensitive to soil erodibility condition and slope shape of the landscape. Higher erodibility condition was associated with higher Cₙdᵥᵢ values. Spring and summer taken images showed significant sensitivity to heterogeneous soil condition. The Cₙdᵥᵢ estimation also showed varying sensitivity to slope shape variation; values on convex-shaped slopes were higher compared with flat slopes. Quantifying the sensitivity of Cₙdᵥᵢ values to biophysical variables may help improve capturing spatiotemporal variability of C factor values in similar landscapes and conditions.

Quantifying the Sensitivity of NDVI-Based C Factor Estimation and Potential Soil Erosion Prediction using Spaceborne Earth Observation Data