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https://doi.org/10.5194/essd-2020-141
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-141
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Aug 2020

12 Aug 2020

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This preprint is currently under review for the journal ESSD.

An improved Terra–Aqua MODIS daily cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019

Sher Muhammad and Amrit Thapa Sher Muhammad and Amrit Thapa
  • International Center for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal

Abstract. Snow is a dominant water resource in High Mountain Asia (HMA) and crucial for the mountain communities and downstream population. Snow cover monitoring is significant to understand regional climate change, managing meltwater, and associated hazards/disasters. The uncertainties in passive optical remote sensing snow products mainly underestimation caused by cloud-cover and overestimation associated with sensorsˈ limitations hamper the understand snow dynamics. We reduced the biases in Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily snow data and generated a combined daily snow product for High Mountain Asia between 2002 and 2019. An improved MODIS 8-day composite MOYDGL06* product was used as a base for reducing the underestimation and overestimation of snow in daily products. The daily MODIS Terra and Aqua images were improved by the corresponding 8-day composite image of the MOYDGL06* product by implementing cloud removal algorithms followed by gap filling and reduction in overestimated snow beyond the respective 8-day composite snow extent. The daily Terra and Aqua snow products were combined and merged with the Randolph Glacier Inventory (RGI) Version 6.0 to make a more complete cryosphere product. The pixel values in the daily combined product are preserved and reversible to the individual Terra and Aqua improved products. We suggest a probabilistic approach for deriving snow cover statistics from our final snow product. The pixels with values 200, 242, and 252 indicate snow in both Terra and Aqua and has a 100 % probability, whereas pixels with snow in one of the Terra or Aqua products have a 50 % probability. The data associated with this paper are available for the end-users mainly useful for observation and simulation of climate, hydro-glaciological forcings, calibration, validation, and other water-related studies. The data are available at https://doi.pangaea.de/10.1594/PANGAEA.918198 (Muhammad, 2020) and the algorithm source code at https://doi.org/10.5281/zenodo.3862058 (Thapa, 2020).

Sher Muhammad and Amrit Thapa

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Status: open (until 07 Oct 2020)
Status: open (until 07 Oct 2020)
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Sher Muhammad and Amrit Thapa

Data sets

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

Filter modis daily snow using 8-day improved snow, combine improved daily snow and merge with RGI glacier A. Thapa https://doi.org/10.5281/zenodo.3862058

Sher Muhammad and Amrit Thapa

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