| Summary: | 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 (C<sub>ndvi</sub>) estimation per se is sensitive to various biophysical variables, such as soil condition, topographic features, and vegetation phenology. As a result, C<sub>ndvi</sub> often results in incorrect values that affect the quality of soil erosion prediction. The aim of this study is to multi-temporally estimate C<sub>ndvi</sub> values and compare the values with those of literature values (C<sub>lit</sub>) 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 C<sub>ndvi</sub> 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 C<sub>ndvi</sub> and C<sub>lit</sub> improved in data from spring and summer seasons (up to <i>r</i> = 0.93); nonetheless, the C<sub>ndvi</sub> values were generally higher compared with C<sub>lit</sub> values. Consequently, modelling erosion using C<sub>ndvi</sub> resulted in two times higher rates than modelling with C<sub>lit</sub>. The C<sub>ndvi</sub> values were found to be sensitive to soil erodibility condition and slope shape of the landscape. Higher erodibility condition was associated with higher C<sub>ndvi</sub> values. Spring and summer taken images showed significant sensitivity to heterogeneous soil condition. The C<sub>ndvi</sub> estimation also showed varying sensitivity to slope shape variation; values on convex-shaped slopes were higher compared with flat slopes. Quantifying the sensitivity of C<sub>ndvi</sub> values to biophysical variables may help improve capturing spatiotemporal variability of C factor values in similar landscapes and conditions.
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