Land degradation is defined as the reduction of biological and economic productivity, which impedes the capacity of the land to provide ecosystem services. There is a need to move towards near-real-time monitoring of land degradation using new sensors to detect degraded landscapes. Recently launched Sentinel-2 sensor presents the opportunity to collect high-resolution data regularly. Multi-temporal datasets provide crucial information to isolate evidence of land degradation from temporal changes in vegetation cover incurred as a result of climatic and phenological variability. This study applied an integrated approach involving multi-date Sentinel-2 data with environmental variables (i.e. soil moisture, rainfall, slope, evapotranspiration, elevation, soil temperature, rainfall, soil temperature, aspect and albedo). A stratified random sampling approach based on dominant land cover types were used to assess land degradation. Field plots of 20 m × 20 m were setup with three 50 cm × 50 cm quadrants inside. In each quadrant, the percentage estimation of grass cover and Leaf Area Index was measured. The model training and validation was implemented using the Random Forest algorithm based on default parameters. The pooled model represents the dry and wet seasons combined. Results showed that pooled model had higher accuracies for Photosynthetic vegetation (PV) (R² of 0.89, RMSE-11.46%, relRMSE-8.7%), Non-Photosynthetic Vegetation (NPV) (R² of 0.93, RMSE-5.64%, relRMSE% 17.72) and Bare Soil (BS) (R² of 0.92, RMSE 8.7% and relRMSE 11.46%). The pooled environmental model achieved accuracy of PV (R² of 0.42, RMSE-20.67%, relRMSE 4.83%), NPV (R² of 0.90, RMSE 6.50% and relRMSE 15%) and (BS R² of 0.85, RMSE 8.64% and relRMSE% 11.5) in estimating grass cover.

土地退化的定义是生物和经济生产力的下降，这阻碍了土地提供生态系统服务的能力。需要使用新的传感器来检测退化的景观，以近实时地监测土地退化。最近推出的Sentinel-2传感器提供了定期收集高分辨率数据的机会。多时相数据集提供了至关重要的信息，以将土地退化的证据与由于气候和物候变化而引起的植被覆盖的时间变化隔离开来。这项研究采用了一种综合方法，该方法涉及具有环境变量（即土壤湿度，降雨量，坡度，蒸散量，海拔，土壤温度，降雨量，土壤温度，纵横比和反照率）的多日期Sentinel-2数据。基于主要土地覆盖类型的分层随机抽样方法用于评估土地退化。设置20 m×20 m的野外图，内部有三个50 cm×50 cm象限。在每个象限中，测量草皮覆盖率和叶面积指数的估计值。使用基于默认参数的随机森林算法来实施模型训练和验证。汇集的模型代表了干燥和潮湿的季节相结合。结果表明，合并模型具有较高的光合植被（PV）（R 使用基于默认参数的随机森林算法来实施模型训练和验证。汇集的模型代表了干燥和潮湿的季节相结合。结果表明，合并模型具有较高的光合植被（PV）（R 使用基于默认参数的随机森林算法来实施模型训练和验证。汇集的模型代表了干燥和潮湿的季节相结合。结果表明，合并模型具有较高的光合植被（PV）（R0.89中的²个，RMSE-11.46％，relRMSE-8.7％），非光合植被（NPV）（R ²为0.93，RMSE-5.64％，relRMSE％17.72）和裸土（BS）（R ²为0.92，RMSE 8.7％和relRMSE 11.46％）。合并的环境模型实现了PV（R ²为0.42，RMSE-20.67％，relRMSE 4.83％），NPV（R ²为0.90，RMSE 6.50％和relRMSE 15％）和（BS R ²为0.85，RMSE 8.64％）的精度和relRMSE％11.5）估算草地覆盖率。