Estimating the CSLE Biological Conservation Measures’ B-Factor Using Google Earth’s Engine

Abstract

The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of the B-factor at the regional scale is fundamental in predicting regional soil erosion and dynamic changes. In this study, we developed an optimal computational procedure for estimating and mapping the B-factor in the Google Earth Engine (GEE) cloud computing environment using multiple data sources through data suitability assessment and image fusion. Taking the Yanhe River Basin in the Loess Plateau of China as an example, we evaluated the availability of daily precipitation data (CHIRPS, ERA5, and PERSIANN-CDR data) against the data at national meteorological stations. We estimated the B-factor from Sentinel-2 data and proposed a new method, namely the trend migration method, to patch the missing values in Sentinel-2 data using three other remote sensing data (MOD09GA, Landsat 7, and Landsat 8). We then calculated and mapped the B-factor in the Yanhe River Basin based on rainfall erosivity, vegetation coverage, and land use types. The results show that the ERA5 precipitation dataset outperforms the CHIRPS and PERSIANN-CDR data in estimating rainfall and rainfall erosivity, and it can be utilized as an alternative data source for meteorological stations in soil erosion modeling. Compared to the harmonic analysis of time series (HANTS), the trend migration method proposed in this study is more suitable for patching the missing parts of Sentinel-2 data. The restored high-resolution Sentinel-2 data fit nicely with the 10 m resolution land use data, enhancing the B-factor calculation accuracy at local and region scales. The B-factor computation procedure developed in this study is applicable to various river basin and regional scales for soil erosion monitoring.