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Sciences in Cold and Arid Regions ›› 2020, Vol. 12 ›› Issue (6): 418-429.doi: 10.3724/SP.J.1226.2020.00418

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Spatial-temporal variability of snow cover over the Amur River Basin inferred from MODIS daily snow products in recent decades

XiaoLin Lu1,WanChang Zhang1(),ShuHang Wang2,Bo Zhang2,QuanFu Niu3,JinPing Liu1,4,Hao Chen1,4,HuiRan Gao1,4   

  1. 1.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    2.National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Science, Beijing 100012, China
    3.Lanzhou University of Technology, Lanzhou, Gansu 730050, China
    4.University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-08-28 Accepted:2020-12-24 Online:2020-12-31 Published:2021-01-14
  • Contact: WanChang Zhang E-mail:zhangwc@radi.ac.cn
  • Supported by:
    the National Key Research and Development Program of China(2016YFA0602302)

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Abstract:

MODIS snow products MOD10A1\MYD10A1 provided us a unique chance to investigate snow cover as well as its spatial-temporal variability in response to global changes from regional and global perspectives. By means of MODIS snow products MOD10A1\MYD10A1 derived from an extensive area of the Amur River Basin, mainly located in the Northeast part of China, some part in far east area of the former USSR and a minor part in Republic of Mongolia, the reproduced snow datasets after removal of cloud effects covering the whole watershed of the Amur River Basin were generated by using 6 different cloud-effect-removing algorithms. The accuracy of the reproduced snow products was evaluated with the time series of snow depth data observed from 2002 to 2010 within the Chinese part of the basin, and the results suggested that the accuracies for the reproduced monthly mean snow depth datasets derived from 6 different cloud-effect-removing algorithms varied from 82% to 96%, the snow classification accuracies (the harmonic mean of Recall and Precision) was higher than 80%, close to the accuracy of the original snow product under clear sky conditions when snow cover was stably accumulated. By using the reproduced snow product dataset with the best validated cloud-effect-removing algorithm newly proposed, spatial-temporal variability of snow coverage fraction (SCF), the date when snow cover started to accumulate (SCS) as well as the date when being melted off (SCM) in the Amur River Basin from 2002 to 2016 were investigated. The results indicated that the SCF characterized the significant spatial heterogeneity tended to be higher towards East and North but lower toward West and South over the Amur River Basin. The inter-annual variations of SCF showed an insignificant increase in general with slight fluctuations in majority part of the basin. Both SCS and SCM tended to be slightly linear varied and the inter-annual differences were obvious. In addition, a clear decreasing trend in snow cover is observed in the region. Trend analysis (at 10% significance level) showed that 71% of areas between 2,000 and 2,380 m a.s.l. experienced a reduction in duration and coverage of annual snow cover. Moreover, a severe snow cover reduction during recent years with sharp fluctuations was investigated. Overall spatial-temporal variability of Both SCS and SCM tended to coincide with that of SCF over the basin in general.

Key words: MODIS, SCF, SCS, SCM, Amur River Basin, cloud effect removal

Figure 1

Geo-location map showing the overview of Amur River Basin, including topography, major rivers, lakes, geographic locations of 78 meteorological stations and MODIS tile grid information in the Amur River Basin"

Table 1

The code and significance of MODIS snow cover products as well as rules of recoding"

MODIS original codingRecoding
Pixel valueLand-cover classPixel valueLand-cover class
0Data missing
1No decision
50Cloud
11Darkness, terminator, night
254Detector saturated
50Cloud
25Land25Land

37

39

Lake or inland water

Open water

37Lake

100

200

Snow-covered lake

Snow-covered land

200Snow cover
255Fill255Fill

Figure 2

Flow chart of could-effects removing algorithm cited and improved from Andreas et al. (2013) and Liu et al. (2017)"

Figure 3

The sub-watersheds delineated with ArcHydro module for the Amur River Basin"

Figure 4

Monthly mean percentage of cloud covers of MOD10A1, MYD10A1 and the monthly mean Accuracy of the images after implementation of each step of the cloud effect removal algorithm from 2002 to 2010"

Figure 5

Monthly mean of three binary indexes-Precision (a), Recall (b), and F (c) for all steps in the algorithm"

Figure 6

(a) The mean SCF from 2002 to 2016, (b) The standard deviation of SCF from 2002 to 2016, and (c) The change rate of SCF 2002 to 2016 over the Amur River Basin"

Figure 7

Spatial distribution of annual mean SCS (a) and SCM (b) from 2002 to 2016 over the Amur River Basin"

Figure 8

The annual mean SCS (a) and SCM (b) from 2002 to 2016 over the Amur River Basin"

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