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).
Description of MOYDGL06* data The data associated to https://doi.org/10.1594/PANGAEA.901821 is Terra and Aqua combined 8-day composite snow–cover product generated from Terra MODIS 8-day composite Collection 6 (C6) (MOD10A2.006*), and Aqua MODIS 8-day composite (MYD10A2.006*), and merged with Randolph Glacier Inventory Version 6.0 (RGI6.0) for High Mountain Asia between 2000 and 2018. The data is described in Julian day and each year has 46 images in 8-day composites (Table 1) similar as in https://nsidc.org/data/MOD10A2/versions/6. The data is described by the values -200, 0, 200, 210, 240, and 250. Following is the description of the values in the improved combined snow product -200: Snow in either Terra or Aqua original product, consider it as no snow 0: Other classes than snow 200: Snow in the final product (snow in both Terra and Aqua original products) 210: No snow in the original products mainly due to cloud cover, converted to snow 240: Exposed debris-covered ice 250: Exposed debris-free ice Table 1: 8-day Composite Periods Period Days Period Days Period Days Period Days 1 1-8 13 97-104 25 193-200 37 289-296 2 9-16 14 105-112 26 201-208 38 297-304 3 17-24 15 113-120 27 209-216 39 305-312 4 25-32 16 121-128 28 217-224 40 313-320 5 33-40 17 129-136 29 225-232 41 321-328 6 41-48 18 137-144 30 233-240 42 329-336 7 49-56 19 145-152 31 241-248 43 337-344 8 57-64 20 153-160 32 249-256 44 345-352 9 65-72 21 161-168 33 257-264 45 353-360 10 73-80 22 169-176 34 265-272 46 361-368¹ 11 81-88 23 177-184 35 273-280 — — 12 89-96 24 185-192 36 281-288 — — ¹Includes 2 or 3 days from the next year.
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Resource Provider： Sher Muhammad