Accurate estimation of snow-covered area (SCA) is vital for effective water resource management, especially in snowmelt-dependent regions like the Kabul River Basin (KRB). It serves as a reference point for comparing expected variations in water availability driven by climate change, particularly in arid and semi-arid regions like the KRB. In this study, fractional snow cover (FSC) was estimated across the KRB using Landsat and moderate resolution imaging spectroradiometer (MODIS) datasets. For this purpose, 34 Landsat-8 and MODIS image-pairs were acquired covering the snowfall period from 01 October 2018 and 31 March 2021. The training dataset consisted of 31 image-pairs, while the remaining three were used as an independent test dataset. Sample sizes and training strategies (i.e., full, semi, and trimmed-models), three each, were evaluated to understand the relevance of the predictor variables. The full-model incorporated MODIS surface reflectance bands (SRB) 1–7 spectral indices, topography and landcover while the semi-model included MODIS SRB 1–7 and indices. The trimmed-model only utilized SRB 1–7. Random Forests (RF) facilitated FSC mapping with Landsat-8 data as a reference. The findings indicated comparable performance between full and semi models, whereas the trimmed-model exhibited weaker performance. The correlation coefficient (R) of the full, semi and trimmed models ranged from 0.83 to 0.92, 0.83–0.92 and 0.82–0.87 respectively. The models performed strongly in grassland regions (R = 0.89–0.90), but moderately in forested areas (R = 0.43–0.53). This approach results in improved MODIS-based SCA-mapping in the Hindukush Mountains, facilitating better water resource management in the region.

中文翻译：

---

使用 MODIS 和 Landsat 数据基于机器学习估算兴都库什山脉积雪覆盖率


准确估计积雪面积 (SCA) 对于有效的水资源管理至关重要，特别是在喀布尔河流域 (KRB) 等依赖融雪的地区。它可以作为比较气候变化导致的可用水资源预期变化的参考点，特别是在 KRB 等干旱和半干旱地区。在这项研究中，使用 Landsat 和中分辨率成像光谱仪 (MODIS) 数据集估算了整个 KRB 的积雪分数 (FSC)。为此，采集了 2018 年 10 月 1 日至 2021 年 3 月 31 日降雪期间的 34 个 Landsat-8 和 MODIS 图像对。训练数据集由 31 个图像对组成，其余 3 个图像对用作独立测试数据集。评估样本大小和训练策略（即完整模型、半模型和修剪模型），各三个，以了解预测变量的相关性。全模型包含 MODIS 表面反射带 (SRB) 1-7 光谱指数、地形和土地覆盖，而半模型包含 MODIS SRB 1-7 和指数。修剪后的模型仅使用 SRB 1-7。随机森林 (RF) 以 Landsat-8 数据作为参考，促进 FSC 制图。研究结果表明，完整模型和半模型的性能相当，而修剪模型的性能较差。全模型、半模型和修整模型的相关系数（R）分别为0.83-0.92、0.83-0.92和0.82-0.87。这些模型在草原地区表现强劲（R = 0.89–0.90），但在森林地区表现一般（R = 0.43–0.53）。这种方法改进了兴都库什山脉基于 MODIS 的 SCA 制图，促进了该地区更好的水资源管理。