An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018-时空三极环境大数据平台

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).

数据使用方法

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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数据的引用

Sher Muhammad. (2020). An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018. 时空三极环境大数据平台, 2020. doi: https://doi.org/10.1594/PANGAEA.901821.
[Sher, M. (2020). An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018. A Big Earth Data Platform for Three Poles, 2020]

使用本数据时必须引用“文章的引用”中列出的文献，并进行数据的引用

参考文献

1.Muhammad, S., & Thapa, A. (2019). Improved MODIS TERRA/AQUA composite Snow and glacier (RGI6.0) data for High Mountain Asia (2002-2018). PANGAEA. https://doi.org/10.1594/PANGAEA.901821.( 查看 )

2.Muhammad, S., & Thapa, A. (2020). An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018. Earth Syst. Sci. Data, 12(1), 345–356. https://doi.org/10.5194/essd-12-345-2020.( 查看 | 下载 )

使用条款

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data. The users are kindly requested to cite the two associated references (data; research paper)

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)

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关键词

学科：冰冻圈
主题： MODIS 积雪冰川覆盖冰冻圈遥感产品冰冻圈遥感冰川编目 Randolph冰川目录积雪冰川（含冰盖） MOD10A12 MYD10A2 雪盖（积雪）
地点： High Mountain Asia
时间： 2002-2018

空间位置

East: 122.48	West: 58.22
South: 24.32	North: 49.19

数据细节

文件列表
/MOYDGL06_2002_2018_HMA.zip 大小：3.5 GB
时间分辨率：Daily
空间分辨率：100m - 1km
大小：3713.0 MB
浏览：4027 次
下载量：22 次
数据时间范围：2002-11-01 至 2019-01-16
共享方式： 开放获取
元数据更新时间：2021-04-19

附件信息

联系信息

Sher Muhammad

分发方：时空三极环境大数据平台

Email： poles@itpcas.ac.cn

导出元数据

Word JSON

时空三极环境大数据平台