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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (4): 457-480    DOI: 10.1007/s40333-025-0098-3    
Research article     
Accuracy assessment of cloud removal methods for Moderate-resolution Imaging Spectroradiometer (MODIS) snow data in the Tianshan Mountains, China
WANG Qingxue1,2, MA Yonggang2,3,4,5,*(), XU Zhonglin1,2,4, LI Junli6,7,8
1College of Ecology and Environment, Xinjiang University, Urumqi 830046, China
2Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
3College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
4Xinjiang Jinghe Observation and Research Station of Temperate Desert Ecosystem, Ministry of Education, Urumqi 830046, China
5Key Laboratory of Oasis Ecology of Education Ministry, Urumqi 830046, China
6Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
7University of Chinese Academy of Sciences, Beijing 100049, China
8Key Laboratory of GIS & RS Application, Xinjiang Uygur Autonomous Region, Urumqi 830011, China
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Abstract  

Snow cover plays a critical role in global climate regulation and hydrological processes. Accurate monitoring is essential for understanding snow distribution patterns, managing water resources, and assessing the impacts of climate change. Remote sensing has become a vital tool for snow monitoring, with the widely used Moderate-resolution Imaging Spectroradiometer (MODIS) snow products from the Terra and Aqua satellites. However, cloud cover often interferes with snow detection, making cloud removal techniques crucial for reliable snow product generation. This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms. Using real-time field camera observations from four stations in the Tianshan Mountains, China, this study assessed the performance of these datasets during three distinct snow periods: the snow accumulation period (September-November), snowmelt period (March-June), and stable snow period (December-February in the following year). The findings showed that cloud-free snow products generated using the Hidden Markov Random Field (HMRF) algorithm consistently outperformed the others, particularly under cloud cover, while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction (STAR) demonstrated varying performance depending on terrain complexity and cloud conditions. This study highlighted the importance of considering terrain features, land cover types, and snow dynamics when selecting cloud removal methods, particularly in areas with rapid snow accumulation and melting. The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning, multi-source data fusion, and advanced remote sensing technologies. By expanding validation efforts and refining cloud removal strategies, more accurate and reliable snow products can be developed, contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.

Key words： real time camera      cloud removal algorithm      snow cover      Moderate-resolution Imaging Spectroradiometer (MODIS) snow data      snow monitoring     
Received: 27 November 2024      Published: 30 April 2025
Corresponding Authors: *MA Yonggang (E-mail: mayg@xju.edu.cn)
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WANG Qingxue
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WANG Qingxue, MA Yonggang, XU Zhonglin, LI Junli. Accuracy assessment of cloud removal methods for Moderate-resolution Imaging Spectroradiometer (MODIS) snow data in the Tianshan Mountains, China. Journal of Arid Land, 2025, 17(4): 457-480.

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http://jal.xjegi.com/10.1007/s40333-025-0098-3     OR     http://jal.xjegi.com/Y2025/V17/I4/457

Fig. 1 Schematic diagram of the study area and locations of four real-time cameras observation stations. DEM, digital elevation model.
Station Geographic
coordinate		 Elevation (m) Elevation type Monitoring period
(yyyy-mm-dd)		 Surface
Luotuobozi Station 42°36′12′′N, 84°39′11′′E 2395.19 Mid- to high-altitude 2016-09-06-
2020-07-01		 Grassland
Shuidian
Station		 43°06′38′′N, 83°58′45′′E 2955.58 Mid- to high-altitude 2016-09-06-
2020-07-01		 Grassland
Shenglidaoban Station 43°08′39′′N, 85°45′53′′E 3317.75 High-altitude 2016-09-06-
2018-05-05		 Grassland
Chahanwusu Station 42°22′48′′N, 85°28′07′′E 1962.19 Mid- to high-altitude 2016-09-06-
2020-07-01		 Barren or sparsely vegetated land
Table 1 Background information about the each real-time camera station
MODIS product
Snow Snow-free
Observation Snow TP FN
Snow-free FP TN
Table 2 Confusion matrix for precision validation
Station Dataset Type TP FN FP TN Accuracy Precision Recall f OE UE
Luotuobozi Station Dataset 1 Clear-sky 269 29 1 382 0.948 0.996 0.902 0.947 0.004 0.098
Cloudy 366 54 6 288 0.907 0.984 0.874 0.925 0.028 0.126
All sky 635 83 7 670 0.926 0.989 0.885 0.934 0.014 0.115
Dataset 2 Clear-sky 274 24 2 381 0.955 0.993 0.919 0.955 0.007 0.081
Cloudy 372 48 11 283 0.909 0.971 0.888 0.928 0.051 0.112
All sky 646 72 13 664 0.930 0.980 0.901 0.939 0.026 0.099
Dataset 3 Clear-sky 270 28 5 378 0.942 0.982 0.906 0.942 0.018 0.094
Cloudy 379 41 20 274 0.906 0.950 0.905 0.927 0.092 0.095
All sky 649 69 25 652 0.923 0.963 0.905 0.933 0.051 0.095
Dataset 4 Clear-sky 87 21 4 153 0.888 0.956 0.798 0.870 0.033 0.202
Cloudy 97 31 2 115 0.852 0.980 0.764 0.858 0.022 0.236
All sky 184 52 6 268 0.871 0.968 0.780 0.864 0.028 0.220
Shuidian Station Dataset 1 Clear-sky 257 58 4 136 0.864 0.985 0.816 0.892 0.029 0.184
Cloudy 482 93 9 169 0.865 0.982 0.838 0.904 0.051 0.162
All sky 739 151 13 305 0.864 0.983 0.830 0.900 0.041 0.170
Dataset 2 Clear-sky 271 44 6 134 0.890 0.978 0.860 0.916 0.043 0.140
Cloudy 536 39 13 165 0.931 0.976 0.932 0.954 0.073 0.068
All sky 807 83 19 299 0.916 0.977 0.907 0.941 0.060 0.093
Dataset 3 Clear-sky 262 53 13 127 0.855 0.953 0.832 0.888 0.093 0.168
Cloudy 477 98 28 150 0.833 0.945 0.830 0.883 0.157 0.170
All sky 739 151 41 277 0.841 0.947 0.830 0.885 0.129 0.170
Dataset 4 Clear-sky 100 32 0 54 0.828 1.000 0.758 0.862 0.000 0.242
Cloudy 121 23 2 46 0.870 0.984 0.840 0.906 0.042 0.160
All sky 221 55 2 100 0.849 0.991 0.801 0.886 0.020 0.199
Shenglidaoban Station Dataset 1 Clear-sky 95 22 0 103 0.882 1.000 0.812 0.896 0.000 0.188
Cloudy 291 17 0 79 0.953 1.000 0.945 0.972 0.000 0.055
All sky 386 39 0 182 0.928 1.000 0.908 0.952 0.000 0.092
Dataset 2 Clear-sky 97 20 0 103 0.893 1.000 0.829 0.907 0.000 0.171
Cloudy 300 8 4 75 0.966 0.987 0.974 0.980 0.080 0.026
All sky 397 28 4 178 0.941 0.990 0.934 0.961 0.033 0.066
Dataset 3 Clear-sky 94 23 0 103 0.877 1.000 0.803 0.891 0.000 0.197
Cloudy 297 11 6 73 0.953 0.980 0.964 0.972 0.120 0.036
All sky 391 34 6 176 0.927 0.985 0.920 0.951 0.050 0.080
Dataset 4 Clear-sky 85 22 0 98 0.878 1.000 0.802 0.890 0.000 0.198
Cloudy 218 14 0 77 0.954 1.000 0.944 0.971 0.000 0.056
All sky 303 36 0 175 0.925 1.000 0.899 0.947 0.000 0.101
Chahanwusu Station Dataset 1 Clear-sky 17 1 7 813 0.990 0.739 0.944 0.829 0.009 0.056
Cloudy 28 16 22 490 0.926 0.583 0.636 0.609 0.045 0.364
All sky 45 17 29 1303 0.964 0.634 0.726 0.677 0.023 0.274
Chahanwusu Station Dataset 2 Clear-sky 13 5 5 815 0.990 0.722 0.722 0.722 0.005 0.278
Cloudy 35 9 28 484 0.924 0.556 0.795 0.654 0.063 0.205
All sky 48 14 33 1299 0.968 0.593 0.774 0.671 0.024 0.226
Dataset 3 Clear-sky 14 4 9 811 0.987 0.609 0.778 0.683 0.009 0.222
Cloudy 27 17 18 494 0.928 0.600 0.614 0.607 0.041 0.386
All sky 41 21 27 1305 0.967 0.603 0.661 0.631 0.019 0.339
Dataset 4 Clear-sky 4 3 2 363 0.986 0.667 0.571 0.615 0.006 0.429
Cloudy 3 13 1 218 0.934 0.600 0.188 0.286 0.010 0.813
All sky 7 16 3 581 0.965 0.636 0.304 0.412 0.007 0.696
Table S1 Accuracy assessment of the four cloud removal methods
Station Dataset Type TP FN FP TN Accuracy Precision Recall f OE UE
Luotuobozi Station Dataset 1 Clear-sky 61 13 1 136 0.934 0.984 0.824 0.897 0.007 0.176
Cloudy 59 16 3 70 0.872 0.952 0.787 0.861 0.041 0.213
All sky 120 29 4 206 0.908 0.968 0.805 0.879 0.019 0.195
Dataset 2 Clear-sky 64 10 1 136 0.948 0.985 0.865 0.921 0.007 0.135
Cloudy 63 12 6 67 0.878 0.913 0.840 0.875 0.082 0.160
All sky 127 22 7 203 0.919 0.948 0.852 0.898 0.033 0.148
Dataset 3 Clear-sky 61 13 4 133 0.919 0.938 0.824 0.878 0.029 0.176
Cloudy 64 11 9 64 0.865 0.877 0.853 0.865 0.123 0.147
All sky 125 24 13 197 0.897 0.906 0.839 0.871 0.062 0.161
Dataset 4 Clear-sky 18 9 1 64 0.891 0.947 0.667 0.783 0.015 0.333
Cloudy 14 6 2 30 0.846 0.875 0.700 0.778 0.063 0.300
All sky 32 15 3 94 0.875 0.914 0.681 0.780 0.031 0.319
Shuidian Station Dataset 1 Clear-sky 58 25 3 86 0.837 0.951 0.699 0.806 0.034 0.301
Cloudy 87 41 7 52 0.743 0.926 0.680 0.784 0.119 0.320
All sky 145 66 10 138 0.788 0.935 0.687 0.792 0.068 0.313
Dataset 2 Clear-sky 61 22 5 84 0.843 0.924 0.735 0.819 0.056 0.265
Cloudy 111 17 5 54 0.882 0.957 0.867 0.910 0.085 0.133
All sky 172 39 10 138 0.864 0.945 0.815 0.875 0.068 0.185
Dataset 3 Clear-sky 59 24 10 79 0.802 0.855 0.711 0.776 0.112 0.289
Cloudy 97 31 15 44 0.754 0.866 0.758 0.808 0.254 0.242
All sky 156 55 25 123 0.777 0.862 0.739 0.796 0.169 0.261
Dataset 4 Clear-sky 27 17 0 38 0.793 1.000 0.614 0.761 0.000 0.386
Cloudy 20 1 0 1 0.955 1.000 0.952 0.976 0.000 0.048
All sky 47 18 0 39 0.827 1.000 0.723 0.839 0.000 0.277
Shenglidaoban Station Dataset 1 Clear-sky 29 3 0 53 0.965 1.000 0.906 0.951 0.000 0.094
Cloudy 57 5 0 30 0.946 1.000 0.919 0.958 0.000 0.081
All sky 86 8 0 83 0.955 1.000 0.915 0.956 0.000 0.085
Shenglidaoban Station Dataset 2 Clear-sky 30 2 0 53 0.976 1.000 0.938 0.968 0.000 0.063
Cloudy 60 2 3 27 0.946 0.952 0.968 0.960 0.100 0.032
All sky 90 4 3 80 0.960 0.968 0.957 0.963 0.036 0.043
Dataset 3 Clear-sky 29 3 0 53 0.965 1.000 0.906 0.951 0.000 0.094
Cloudy 60 2 4 26 0.935 0.938 0.968 0.952 0.133 0.032
All sky 89 5 4 79 0.949 0.957 0.947 0.952 0.048 0.053
Dataset 4 Clear-sky 26 3 0 51 0.963 1.000 0.897 0.945 0.000 0.103
Cloudy 53 3 0 29 0.965 1.000 0.946 0.972 0.000 0.054
All sky 79 6 0 80 0.964 1.000 0.929 0.963 0.000 0.071
Chahanwusu Station Dataset 1 Clear-sky 4 0 0 251 1.000 1.000 1.000 1.000 0.000 0.000
Cloudy 6 7 3 88 0.904 0.667 0.462 0.545 0.033 0.538
All sky 10 7 3 339 0.972 0.769 0.588 0.667 0.009 0.412
Dataset 2 Clear-sky 3 1 0 502 0.998 1.000 0.750 0.857 0.000 0.250
Cloudy 8 5 4 87 0.913 0.667 0.615 0.640 0.044 0.385
All sky 11 6 4 589 0.984 0.733 0.647 0.688 0.007 0.353
Dataset 3 Clear-sky 4 0 3 499 0.994 0.571 1.000 0.727 0.006 0.000
Cloudy 5 8 7 84 0.856 0.417 0.385 0.400 0.077 0.615
All sky 9 8 10 583 0.970 0.474 0.529 0.500 0.017 0.471
Dataset 4 Clear-sky 0 1 1 122 0.984 0.000 0.000 0.000 0.008 1.000
Cloudy 1 7 1 35 0.818 0.500 0.125 0.200 0.028 0.875
All sky 1 8 2 157 0.940 0.333 0.111 0.167 0.013 0.889
Table S2 Accuracy assessment of the four cloud removal methods during the snow accumulation period (September-November)
Station Dataset Type TP FN FP TN Accuracy Precision Recall f OE UE
Luotuobozi Station Dataset 1 Clear-sky 47 15 0 138 0.925 1.000 0.758 0.862 0.000 0.242
Cloudy 112 32 3 141 0.878 0.974 0.778 0.865 0.021 0.222
All sky 159 47 3 279 0.898 0.981 0.772 0.864 0.011 0.228
Dataset 2 Clear-sky 49 13 1 137 0.930 0.980 0.790 0.875 0.007 0.210
Cloudy 120 24 5 139 0.899 0.960 0.833 0.892 0.035 0.167
All sky 169 37 6 276 0.912 0.966 0.820 0.887 0.021 0.180
Dataset 3 Clear-sky 50 12 1 137 0.935 0.980 0.806 0.885 0.007 0.194
Cloudy 120 24 11 133 0.878 0.916 0.833 0.873 0.076 0.167
All sky 170 36 12 270 0.902 0.934 0.825 0.876 0.043 0.175
Dataset 4 Clear-sky 13 11 3 56 0.831 0.813 0.542 0.650 0.051 0.458
Cloudy 30 17 0 56 0.835 1.000 0.638 0.779 0.000 0.362
All sky 43 28 3 112 0.833 0.935 0.606 0.735 0.026 0.394
Shuidian Station Dataset 1 Clear-sky 57 33 1 50 0.759 0.983 0.633 0.770 0.020 0.367
Cloudy 176 52 2 117 0.844 0.989 0.772 0.867 0.017 0.228
All sky 233 85 3 167 0.820 0.987 0.733 0.841 0.018 0.267
Dataset 2 Clear-sky 68 22 1 50 0.837 0.986 0.756 0.855 0.020 0.244
Cloudy 206 22 8 111 0.914 0.963 0.904 0.932 0.067 0.096
All sky 274 44 9 161 0.891 0.968 0.862 0.912 0.053 0.138
Shuidian Station Dataset 3 Clear-sky 61 29 3 48 0.773 0.953 0.678 0.792 0.059 0.322
Cloudy 162 66 13 106 0.772 0.926 0.711 0.804 0.109 0.289
All sky 223 95 16 154 0.773 0.933 0.701 0.801 0.094 0.299
Dataset 4 Clear-sky 25 15 0 16 0.732 1.000 0.625 0.769 0.000 0.375
Cloudy 27 22 2 45 0.750 0.931 0.551 0.692 0.043 0.449
All sky 52 37 2 61 0.743 0.963 0.584 0.727 0.032 0.416
Shenglidaoban Station Dataset 1 Clear-sky 33 19 0 17 0.725 1.000 0.635 0.776 0.000 0.365
Cloudy 87 12 0 20 0.899 1.000 0.879 0.935 0.000 0.121
All sky 120 31 0 37 0.835 1.000 0.795 0.886 0.000 0.205
Dataset 2 Clear-sky 34 18 0 17 0.739 1.000 0.654 0.791 0.000 0.346
Cloudy 93 6 1 19 0.941 0.989 0.939 0.964 0.050 0.061
All sky 127 24 1 36 0.867 0.992 0.841 0.910 0.027 0.159
Dataset 3 Clear-sky 32 20 0 17 0.710 1.000 0.615 0.762 0.000 0.385
Cloudy 90 9 2 18 0.908 0.978 0.909 0.942 0.100 0.091
All sky 122 29 2 35 0.835 0.984 0.808 0.887 0.054 0.192
Dataset 4 Clear-sky 28 18 0 15 0.705 1.000 0.609 0.757 0.000 0.391
Cloudy 56 10 0 20 0.884 1.000 0.848 0.918 0.000 0.152
All sky 84 28 0 35 0.810 1.000 0.750 0.857 0.000 0.250
Chahanwusu Station Dataset 1 Clear-sky 0 0 1 279 0.996 0.000 0.000 0.000 0.004 -
Cloudy 0 0 1 207 0.995 0.000 0.000 0.000 0.005 -
All sky 0 0 2 486 0.996 0.000 0.000 0.000 0.004 -
Dataset 2 Clear-sky 0 0 1 279 0.996 0.000 0.000 0.000 0.004 -
Cloudy 0 0 7 201 0.966 0.000 0.000 0.000 0.034 -
All sky 0 0 8 480 0.984 0.000 0.000 0.000 0.016 -
Dataset 3 Clear-sky 0 0 0 280 1.000 0.000 0.000 0.000 0.000 -
Cloudy 0 0 1 207 0.995 0.000 0.000 0.000 0.005 -
All sky 0 0 1 487 0.998 0.000 0.000 0.000 0.002 -
Dataset 4 Clear-sky 0 0 1 133 0.993 0.000 0.000 0.000 0.007 -
Cloudy 0 0 1 102 0.990 0.000 0.000 0.000 0.010 -
All sky 0 0 2 235 0.992 0.000 0.000 0.000 0.008 -
Table S3 Accuracy assessment of the four cloud removal methods during the snowmelt period (March-June)
Station Dataset Type TP FN FP TN Accuracy Precision Recall f OE UE
Luotuobozi Station Dataset 1 Clear-sky 160 1 0 0 0.994 1.000 0.994 0.997 - 0.006
Cloudy 195 5 0 0 0.975 1.000 0.975 0.987 - 0.025
All sky 355 6 0 0 0.983 1.000 0.983 0.992 - 0.017
Dataset 2 Clear-sky 160 1 0 0 0.994 1.000 0.994 0.997 - 0.006
Cloudy 189 11 0 0 0.945 1.000 0.945 0.972 - 0.055
All sky 349 12 0 0 0.967 1.000 0.967 0.983 - 0.033
Luotuobozi Station Dataset 3 Clear-sky 158 3 0 0 0.981 1.000 0.981 0.991 - 0.019
Cloudy 195 5 0 0 0.975 1.000 0.975 0.987 - 0.025
All sky 353 8 0 0 0.978 1.000 0.978 0.989 - 0.022
Dataset 4 Clear-sky 56 1 0 0 0.982 1.000 0.982 0.991 - 0.018
Cloudy 53 8 0 0 0.869 1.000 0.869 0.930 - 0.131
All sky 109 9 0 0 0.924 1.000 0.924 0.960 - 0.076
Shuidian Station Dataset 1 Clear-sky 142 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Cloudy 219 0 0 0 1.000 1.000 1.000 1.000 - 0.000
All sky 361 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Dataset 2 Clear-sky 142 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Cloudy 219 0 0 0 1.000 1.000 1.000 1.000 - 0.000
All sky 361 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Dataset 3 Clear-sky 142 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Cloudy 218 1 0 0 0.995 1.000 0.995 0.998 - 0.005
All sky 360 1 0 0 0.997 1.000 0.997 0.999 - 0.003
Dataset 4 Clear-sky 48 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Cloudy 74 0 0 0 1.000 1.000 1.000 1.000 - 0.000
All sky 122 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Shenglidaoban Station Dataset 1 Clear-sky 33 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Cloudy 147 0 0 0 1.000 1.000 1.000 1.000 - 0.000
All sky 180 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Dataset 2 Clear-sky 33 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Cloudy 147 0 0 0 1.000 1.000 1.000 1.000 - 0.000
All sky 180 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Dataset 3 Clear-sky 33 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Cloudy 147 0 0 0 1.000 1.000 1.000 1.000 - 0.000
All sky 180 0 0 0 1.000 1.000 1.000 1.000 - 0.000
Dataset 4 Clear-sky 31 0 0 0 1.000 1.000 0.939 0.969 - 0.061
Cloudy 109 0 0 0 1.000 1.000 0.741 0.852 - 0.259
All sky 140 0 0 0 1.000 1.000 0.778 0.875 - 0.222
Chahanwusu Station Dataset 1 Clear-sky 13 1 5 168 0.968 0.722 0.929 0.813 0.029 0.071
Cloudy 22 9 16 127 0.856 0.579 0.710 0.638 0.112 0.290
All sky 35 10 21 295 0.914 0.625 0.778 0.693 0.066 0.222
Dataset 2 Clear-sky 10 4 4 169 0.957 0.714 0.714 0.714 0.023 0.286
Cloudy 27 4 17 126 0.879 0.614 0.871 0.720 0.119 0.129
All sky 37 8 21 295 0.920 0.638 0.822 0.718 0.066 0.178
Dataset 3 Clear-sky 10 4 6 167 0.947 0.625 0.714 0.667 0.035 0.286
Cloudy 22 9 10 133 0.891 0.688 0.710 0.698 0.070 0.290
All sky 32 13 16 300 0.920 0.667 0.711 0.688 0.051 0.289
Dataset 4 Clear-sky 4 2 0 84 0.978 1.000 0.667 0.800 0.000 0.333
Cloudy 2 6 0 71 0.924 1.000 0.250 0.400 0.000 0.750
All sky 6 8 0 155 0.953 1.000 0.429 0.600 0.000 0.571
Table S4 Accuracy assessment of the four cloud removal methods during the stable snow period (December-February of the following year)
Fig. 2 Time series of snow data at Luotuobozi (a), Shuidian (b), Shenglidaoban (c), and Chahanwusu (d) stations located in the Tianshan Mountains, central Xinjiang. The white areas represent snow-free.
Fig. 3 Real-time camera image of Luotuobozi Station on 5 November 2017
Fig. 4 Diagram illustrating snow cover classification results of different datasets on 5 November 2017. (a), Dataset 1; (b), Dataset 2; (c), Dataset 3; (d), Dataset 4.
Fig. 5 Real-time camera image of Luotuobozi Station on 19 April 2018
Fig. 6 Diagram illustrating snow cover classification results of different datasets on 19 April 2018. (a), Dataset 1; (b), Dataset 2; (c), Dataset 3; (d), Dataset 4.
Fig. 7 Real-time camera image of Chahanwusu Station on 21 December 2016
Fig. 8 Diagram illustrating snow cover classification results of different datasets on 21 December 2016. (a), Dataset 1; (b), Dataset 2; (c), Dataset 3; (d), Dataset 4.
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