Abstract
Abstract. Snow is a dominant water resource in high-mountain Asia (HMA) and crucial for mountain communities and downstream populations. 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 sensors' limitations, hamper the understanding of 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 d composite MOYDGL06* product was used as a training data for reducing the underestimation and overestimation of snow in daily products. The daily MODIS Terra and Aqua images were improved by implementing cloud removal algorithms followed by gap filling and reduction in overestimated snow beyond the respective 8 d composite snow extent of the MOYDGL06* product. The daily Terra and Aqua snow products were combined and merged with the Randolph Glacier Inventory version 6.0 (RGI 6.0) described as M*D10A1GL06 to make a more complete cryosphere product with 500 m spatial resolution. The pixel values in the daily combined product are preserved and reversible to the individual Terra and Aqua improved products. We suggest a weight of 0.5 and 1 to snow pixels in either or both Terra and Aqua products, respectively, for deriving snow cover statistics from our final snow product. The values 200, 242, and 252 indicate snow pixels in both Terra and Aqua and have a weight of 1, whereas pixels with snow in one of the Terra or Aqua products have a weight of 0.5. On average, the M*D10A1GL06 product reduces 39.1 % of uncertainty compared to the MOYDGL06* product. The uncertainties due to cloud cover (underestimation) and sensor limitations, mainly larger solar zenith angle (SZA) (overestimation) reduced in this product, are approximately 32.9 % and 6.2 %, respectively. The data in this paper are 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.org/10.1594/PANGAEA.918198 (Muhammad, 2020) and the algorithm source code at https://doi.org/10.5281/zenodo.3862058 (Thapa, 2020).
Highlights
Seasonal snow supplies a dominant runoff contribution to the major rivers in high-mountain Asia (HMA) sufficient for more than one-fifth of the global population (Armstrong et al, 2019)
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
The major issues of underestimation in MODIS data, which we highlighted in the previous paper (Muhammad and Thapa, 2020) because of clouds and overestimation caused by large sensor zenith angle (SZA), were reduced in this paper
Summary
Seasonal snow supplies a dominant runoff contribution to the major rivers in high-mountain Asia (HMA) sufficient for more than one-fifth of the global population (Armstrong et al, 2019). Snowmelt mostly dominates the spring runoff in HMA and plays a vital role in the water supplies for various sectors (Han et al, 2019). Glaciers contribute most of the meltwater in the peak summer season and are significantly losing mass due to climate change in the early 21st century (IPCC, 2019; Muhammad et al, 2019a). Climate change alters the snowmelt seasonality, onset timing, and water availability in the peak summer season (Hall et al, 2012; Ryberg et al, 2016). Thapa: M*D10A1GL06 for high-mountain Asia between 2002 and 2019
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