Glacier Area and Snow Cover Changes in the Range System Surrounding Tarim from 2000 to 2020 Using Google Earth Engine

Abstract

Glacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We studied the evolution of snow cover to understand whether it could explain the evolution of glacier area. In this study, we used the thresholding of the NDSI (Normalized Difference Snow Index) retrieved with MODIS data to extract annual glacier area and snow cover. We evaluated how the glacier trends related to snow cover area in five subregions in the Tarim Basin. The uncertainty in our retrievals was assessed by comparing MODIS results with the Landsat-5 TM in 2000 and Landsat-8 OLI in 2020 glacier delineation in five subregions. The glacier area in the Tarim Basin decreased by 1.32%/a during 2000–2020. The fastest reductions were in the East Tien Shan region, while the slowest relative reduction rate was observed in the West Tien Shan and Pamir, i.e., 0.69%/a and 1.08%/a, respectively, during 2000–2020. The relative glacier stability in Pamir may be related to the westerlies weather system, which dominates climate in this region. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. During the summer months, the sequence of multiple snowfall and snowmelt events leads to intermittent snow cover, which was the key feature applied to discriminate snow and glacier.

Measuring glacier changes in the Tianshan Mountains over the past 20 years using Google Earth Engine and machine learning

“…In terms of sub-region scale, it was found that glaciers in the Eastern Tianshan and Dzhungarsky Alatau have most significant negative mass balance rate (Barandun et al, 2021). In contrast, the annual mass balance variability was lower in the Central Tianshan (Barandun et al, 2021), and the glacier area shrinkage rate in the Northern/Western Tianshan had spatial variability ranging from 0.11%/a to 3.7%/a (Zhang et al, 2021), which was more consistent with our results. Huang et al (2021) suggested that the glacier area shrinkage rate in the Eastern Tianshan was 0.8%/a between 1990 and 2018, but our results show that the glacier loss rate in the Eastern Tianshan consistently exceeded 2%/a.…”

A New Automatic Extraction Method for Glaciers on the Tibetan Plateau under Clouds, Shadows and Snow Cover

“…The overlapping data at each pixel position formed a pixel set. Usually, the minimum or median of pixel sets is used to synthesize images [40,41,52]. The image synthesis process is shown in Figure 3, followed by image collection, cloud filtering, and image synthesis.…”

“…The band ratio method [8], NDSI method [6], and RF method [9,10] are commonly used in glacier extraction. In this study, the three methods were used for the widely used median composite image and minimum composite image [40,41,52]. There were six methods in total: the band ratio method based on median composite image (Median_Band) (B4/B6 > 5.0), the band ratio method based on minimum composite image (B4/B6 > 5.0), the NDSI method based on median composite image (Median_NDSI) (NDSI > 0.4, NDWI < 0.3), the NDSI method based on minimum composite image (Min_NDSI) (NDSI > 0.4, NDWI < 0.6), the RF method based on median composite image (Median_RF), and the RF method based on minimum composite image (Min_RF).…”

“…This cloud-based platform enables us to synthesize cloud-free images pixel by pixel as much as possible. Presently, the Google Earth Engine cloud platform has been widely used to monitor global forest changes [27][28][29], global surface water changes [30,31], urban changes [32][33][34], climate change [35][36][37], land use changes [38,39], and snow and ice changes [40][41][42][43][44][45][46]. Many GEE-based glacier studies can weaken the influence of clouds to some extent, but are not able to reduce the effects of debris [41], shadow, snow [42], and glacial lakes [43].…”

Study of snow cover/depth evolution characteristics in Tianshan region of China based on geographical partition

“…Baba et al 14 combined MERRA-2 and ERA5 satellite data to analyze the spatial distribution of snow water equivalent in the High Atlas region, and the results show that the snow water equivalent distribution obtained from the combination of the two satellites performs well in the snowpack area during most of the hydrological year. Zhang et al 15 extracted glacier and snow cover areas from MODIS data and studied the relationship between glacier evolution trends and snow cover evolution in the Tarim Basin.…”