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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 07 Jun 2019

Submitted as: data description paper | 07 Jun 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Earth System Science Data (ESSD).

A combined Terra/Aqua MODIS snow-cover and RGI6.0 glacier product (MOYDGL06*) for the High Mountain Asia between 2002 and 2018

Sher Muhammad1,2 and Amrit Thapa1 Sher Muhammad and Amrit Thapa
  • 1International Center for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal
  • 2Institute of International Rivers and Eco-security, Yunnan University, 650500 Kunming, China

Abstract. Snow is a significant component of the ecosystem and water resources in the High Mountain Asia (HMA). Accurate, continuous and long-term snow monitoring is necessary for water resources management and economic development. In this study, we improved Moderate-resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua snow–cover for HMA by a multi-step approach. The primary purpose of this study was to reduce uncertainty in MODIS snow cover. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensor, we combined MODIS Terra and Aqua snow-cover products considering snow only if a pixel is snow in both the products otherwise no snow, unlike some previous studies considering snow if any of the Terra or Aqua product is snow. Our methodology generates a new product which removes a significant amount of uncertainty in raw MODIS 8-day composite product comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data as ground truth, both for winter and summer at twenty well-distributed sites in the study area. Our validation results show that the adopted methodology improved accuracy on average by 10 %, mainly reducing the snow overestimation. The final product covers the period from 2002 to 2018, as a combination of snow and glaciers created by merging RGI6.0 glacier boundaries separately debris-covered and debris-free to the final snow product namely MOYDGL06*. Each of the Terra and Aqua datasets contains seven hundred and forty-six image files derived initially from approximately one hundred thousand satellite individual images. The data is available for researchers to use for various climate and water-related studies. The data is available at (Muhammad and Thapa, 2019).

Sher Muhammad and Amrit Thapa
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Status: final response (author comments only)
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Sher Muhammad and Amrit Thapa
Data sets

Snow (MODIS-TERRA/AQUA) and glacier (RGI6.0) composite data for High Mountain Asia S. Muhammad and A. Thapa

Sher Muhammad and Amrit Thapa
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Short summary
Snow is the major water resource in High Mountain Asia therefore, it is crucial to continuously monitor it. Currently, remote sensing and mainly MODIS is used for snow monitoring. However, the available MODIS snow product is not useful for various applications without postprocessing and improvement. This study reduces uncertainty in the MODIS snow data. We found that approximately 50 % of the snow is overestimated by MODIS TERRA and AQUA products separately which were improved in this study.
Snow is the major water resource in High Mountain Asia therefore, it is crucial to continuously...