The Effects of Seasonality in Estimating the C‐Factor of Soil Erosion Studies

Abstract

Monitoring soil erosion risk is an important part of soil conservation practices. It is usually estimated with the Universal Soil Loss Equation, and the C‐factor (vegetation cover) is derived from optical satellite images. However, because of lack of data and resources, or in rapid assessments, C‐factor is estimated using one or a few satellite observations, despite being temporally variable according to plants' phenology. The aim of this work was to study the effect of seasonality in estimating C‐factor. This was achieved by demonstrating first that there is a difference when estimating soil erosion with Universal Soil Loss Equation at variable time steps in a year, namely once, seasonally and monthly. Using Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index images and statistical analysis at subcatchment scale, it was shown that there is a significant difference when estimating mean annual soil loss with the aforementioned temporal options. The highest differences were observed between monthly and annual time steps. The second objective was to identify which is the optimum time to estimate C‐factor in a year. The results show that November, October and March are the optimum months for single image estimation of annual soil erosion. Statistical analysis with a random point dataset suggested that the spatial variability of the results was influenced by the land cover type, especially in areas with variable leaf cover where a single date estimation of C‐factor was not representative of the whole year, such as annual crops and deciduous trees. Copyright © 2013 John Wiley & Sons, Ltd.

Extent of Cropland and Related Soil Erosion Risk in Rwanda

“…C " expp´aˆN DV I b´NDV I˙ ( 9) Previous studies indicated that seasonal variations of the Normalized Difference Vegetation Index (NDVI) could strongly influence the estimated soil erosion rate [18,71]. However, the heavy cloudiness problem in the tropical rainforests and mountain areas of the country [18,26] made it nearly impossible to retrieve the dynamic of NDVI and land cover change with satellite data for a whole year.…”

Changes in Erosion and Runoff due to Replacement of Pasture Land with Sugarcane Crops

“…For a better understanding of soil erosion processes caused by crop changes and their effects in larger areas, it is important to increase the experimental monitoring areas in both spatial and temporal scales [45][46][47][48].…”

Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

“…Therefore, from NDVI values, some methods have been developed to estimate the Rusle C factor (Knijff et al, 1999;Asis & Omasa, 2007;Durigon et al, 2014). These methods are developed from regression models which correlate C factor values, either measured in the field or obtained from other studies, to NDVI values generally obtained from TM sensor on Landsat 5, or by the moderate resolution imaging spectroradiometer (Modis) data (Schönbrodt et al, 2010;Le et al, 2012;Alexandridis et al, 2013;Bargiel et al, 2013). However, under tropical climate conditions, the C factor tends to be higher than that calculated by these methods for the same vegetation cover.…”