Monitoring seasonal and phenological variability of cover management factor for wheat cropping systems under...

In modeling studies, the use of spatial data derived from geographic information systems and remote sensing applications to simulate the impact of phenological and seasonal changes on soil loss has a promising effect on the accuracy of predictions. The objective of this work was to estimate the C-factor (cover management) as a dynamic-factor RUSLE (revised universal soil loss equation) model based on an NDVI (Normalized Difference Vegetation Index) approach derived from high-resolution Landsat 8 and Landsat ETM7 satellite images for 140 different rain-fed wheat parcels in terms of seasonal and phenological-based by the integrated use of remote sensing and GIS. Overall, it was found that the highest C values, an average of 0.70, were estimated for the emergence period of the wheat, while the lowest value of 0.06 was found in the booting period. Seasonally, the estimated average C values in these parcels were 0.69, 0.63, 0.13, and 0.44 for the autumn, winter, spring, and summer, respectively. Corresponding soil losses for those seasons were 1.70, 1.55, 0.28, and 1.13 t ha^-1 year^-1 respectively. Comparatively, without considering the phenological growing periods of wheat, the annual predicted soil loss rate was 11.5% higher than the conditions considered. The present study concluded that an assessment of seasonal and phenological changes in the C-factor for fragile ecosystems with weak crop-cover development could significantly improve the accuracy of the RUSLE model predictions and effectively manage limited soil and water resources.