Machine learning-based estimation of fractional snow cover in the Hindukush Mountains using MODIS and Landsat data

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 trimmedmodel 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.