Contributions to Satellite-Based Land Cover Classification, Vegetation Quantification and Grassland Monitoring in Central Asian Highlands Using Sentinel-2 and MODIS Data

The peripheral setting of cold drylands in Asian mountains makes remote sensing tools essential for respective monitoring. However, low vegetation cover and a lack of meteorological stations lead to uncertainties in vegetation modeling, and obstruct uncovering of driving degradation factors. We therefore analyzed the importance of promising variables, including soil-adjusted indices and high-resolution snow metrics, for vegetation quantification and classification in Afghanistan’s Wakhan region using Sentinel-2 and field data with a random forest algorithm. To increase insights on remotely derived climate proxies, we incorporated a temporal correlation analysis of MODIS snow data (NDSI) compared to field measured vegetation and MODIS-NDVI anomalies. Repeated spatial cross-validation showed good performance of the classification (80–81% overall accuracy) and foliar vegetation model ( R 2 0.77–0.8, RMSE 11.23–12.85). Omitting the spatial cross-validation approach led to a positive evaluation bias of 0.1 in the overall accuracy of the classification and 25% in RMSE of the cover models, demonstrating that studies not considering the spatial structure of environmental data must be treated with caution. The 500-repeated Boruta-algorithm highlighted MSACRI, MSAVI, NDVI and the short-wave infrared Band-12 as the most important variables. This indicates that, complementary to traditional indices, soil-adjusted variables and the short-wave infrared region are essential for vegetation modeling in cold grasslands. Snow variables also showed high importance but they did not improve the overall performance of the models. Single-variable models, which were restricted to areas with very low vegetation cover (