Spatiotemporal variability in the C-factor: An analysis using high resolution satellite imagery

Estimating the cover and management factor (C-factor) for Universal Soil Loss Equation (USLE) that varies spatially and temporally within a watershed is time-consuming and resource-intensive. The Normalized Difference Vegetation Index (NDVI) approach can offer a potential alternative for this process. The current study examines nine NDVI models to compare and evaluate their performance in estimating the C-factor values for an agricultural watershed in southwestern Ontario, Canada. Satellite imagery from 2013 to 2020 was used to analyze the models’ similarities and differences on a detailed spatial and temporal scale. The results showed different C-factor values for each model, reflecting that they were developed for different geographical areas and purposes. While the Karaburun model differed from all other models on an annual basis, a detailed combined analysis of different spatial and temporal scales revealed that it was similar to other models. Seasonal analysis was found to be adequate for the current study, as it reduced the resources required and provided an overall view of the vegetation situation. However, a detailed monthly analysis may be necessary when investigating a specific season. The current analysis found that the summer months of June, July, and August have similar trends when comparing different models for different land uses and individual months, which aligns with the seasonal analysis. In conclusion, the current study highlights the importance of incorporating spatial and temporal scales in hydrological modeling and provides valuable insight into the applicability of different NDVI models for estimating the C-factor for southwestern Ontario watersheds. These findings can help inform future research and aid in developing accurate models for estimating soil erosion in this region. The results also emphasize that the NDVI approach has the potential for estimating the USLE C-factor and improving the estimation of soil erosion from agricultural watersheds by incorporating a variable C-factor over time and space. However, further research is needed to validate each model and determine which model best suits the study area.