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 (R² 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 (<20%), resulted in poor performance of NDVI for cover prediction and better performance of snow variables. Our temporal analysis provides evidence that snow variables are important climate proxies by showing highly significant correlations of spring snow data with MODIS-NDVI during 2001–2020 (Pearson’s r 0.68) and field measured vegetation during 2006, 2007, 2016 and 2018 (R 0.3). Strong spatial differences were visible with higher correlations in alpine grasslands (MODIS NDVI: 0.72, field data: 0.74) compared to other regions and lowest correlations in riparian grasslands. We thereby show new monitoring approaches to grassland dynamics that enable the development of sustainable management strategies, and the mitigation of threats affecting cold grasslands of Central Asia.

亚洲山区寒冷旱地的外围环境使得遥感工具对于各自的监测至关重要。然而，植被覆盖率低和气象站的缺乏导致植被建模的不确定性，并阻碍了驱动退化因素的揭示。因此，我们使用 Sentinel-2 和带有随机森林算法的现场数据分析了有前景的变量的重要性，包括土壤调整指数和高分辨率积雪指标，用于阿富汗瓦罕地区的植被量化和分类。为了增加对远程气候代理的洞察力，我们结合了 MODIS 雪数据 (NDSI) 与现场测量的植被和 MODIS-NDVI 异常的时间相关性分析。R²0.77–0.8，RMSE 11.23–12.85）。省略空间交叉验证方法导致分类整体准确度的正评价偏差为 0.1，覆盖模型的 RMSE 为 25%，这表明必须谨慎对待不考虑环境数据空间结构的研究。500 次重复的 Boruta 算法突出显示 MSACRI、MSAVI、NDVI 和短波红外波段 12 作为最重要的变量。这表明，作为传统指数的补充，土壤调整变量和短波红外区域对于寒冷草原的植被建模至关重要。雪变量也显示出很高的重要性，但它们并没有提高模型的整体性能。单变量模型，仅限于植被覆盖率非常低（<20%）的区域，导致 NDVI 在覆盖预测方面表现不佳，而降雪变量表现更好。我们的时间分析通过显示 2001-2020 年春季降雪数据与 MODIS-NDVI (Pearson's r 0.68) 和 2006、2007、2016 和 2018 年 (R 0.3) 实地测量植被的高度显着相关性，提供了雪变量是重要气候代理的证据. 与其他地区相比，高山草原的相关性更高（MODIS NDVI：0.72，野外数据：0.74），而河岸草原的相关性最低，空间差异明显。因此，我们展示了草原动态的新监测方法，可以制定可持续管理战略，并减轻影响中亚寒冷草原的威胁。我们的时间分析通过显示 2001-2020 年春季降雪数据与 MODIS-NDVI (Pearson's r 0.68) 和 2006、2007、2016 和 2018 年 (R 0.3) 实地测量植被的高度显着相关性，提供了雪变量是重要气候代理的证据. 与其他地区相比，高山草原的相关性更高（MODIS NDVI：0.72，野外数据：0.74），而河岸草原的相关性最低，空间差异明显。因此，我们展示了草原动态的新监测方法，可以制定可持续管理战略，并减轻影响中亚寒冷草原的威胁。我们的时间分析通过显示 2001-2020 年春季降雪数据与 MODIS-NDVI (Pearson's r 0.68) 和 2006、2007、2016 和 2018 年 (R 0.3) 实地测量植被的高度显着相关性，提供了雪变量是重要气候代理的证据. 与其他地区相比，高山草原的相关性更高（MODIS NDVI：0.72，野外数据：0.74），而河岸草原的相关性最低，空间差异明显。因此，我们展示了草原动态的新监测方法，可以制定可持续管理战略，并减轻影响中亚寒冷草原的威胁。