Abstract
Abstract. Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).
Highlights
Snow is a crucial component of the hydrological cycle because it acts as water storage with a short delay during the seasonal runoff (Colbeck, 1977)
We have not used Moderate Resolution Imaging Spectroradiometer (MODIS) snow data for the year 2000 in our final product, but it is worth mentioning that the snow data till 10 December 2000 contain data voids and strips and are not recommended for any applications or analysis
Comparison of the snow cover area estimated by Landsat and MODIS original MOD10A2.006 and MYD10A2.006 products and individual and combined final products showed that our methodology improved the accuracy by 10 % from 77 % to 87 % on average, reducing the inevitable overestimation for 20 well-distributed Landsat scenes
Summary
Snow is a crucial component of the hydrological cycle because it acts as water storage with a short delay during the seasonal runoff (Colbeck, 1977). More than 60 % of the annual discharge in the major rivers of highmountain Asia (HMA) depends on meltwater, with variable rates across the region (Armstrong et al, 2019) Both the mountain communities and downstream population rely on water stored as snow for their daily use mainly in the early melt season (Lutz et al, 2016). Rapid snowmelt may cause natural hazards such as floods, damaging agriculture, infrastructure, and human life (Haq et al, 2012; Memon et al, 2015) These factors make it essential to monitor snow for downstream water resources management and hazards and disasters preparedness (Clifton et al, 2018; Tian et al, 2017; Zhang et al, 2010). Remote-sensing data are widely used to assess the snow extent and variability at regional or global scales (Hall et al, 2010)
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