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Remote Sensing Technology and Application  2020, Vol. 35 Issue (6): 1337-1347    DOI: 10.11873/j.issn.1004-0323.2020.6.1337
    
Snow Cover Recognition for Xinjiang based on Fusion of FY-4A/AGRI Spatial and Temporal Characteristics
Yonghong Zhang1,3(),Haixiao Cao1,Xi Kan2()
1.College of Automation,Nanjing University of Information Science& Technology,Nanjing 210044,China
2.Bingjiang College,Nanjing University of Information Science& Technology,Wuxi 214105,China
3.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing University of Information Science &Technology,Nanjing 210044,China
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Abstract  

Snow cover recognition with high temporal resolution plays an important role in the development of agriculture and animal husbandry and snow disaster warning in Xinjiang pastoral areas. To solve the problem that existing snow cover products are susceptible to complex topography, landform, underlying surface type and cloud cover, which leads to the reduced accuracy of snow cover recognition, a deep learning method is proposed to use the data of Fengyun-4A Star Multichannel Radiation Scanner (AGRI) and the number of geographic information.Based on the method of multi-feature time series fusion, a new snow cover recognition model based on convolution neural network is constructed and trained, which takes the multitemporal FY-4A/AGRI multispectral remote sensing data, terrain topographic information such as elevation, aspect, slope, and surface cover type as the input of the model, and the high-resolution snow cover map extracted by Landsat 8-OLI as the "true value" label.Clouds, snow and snow-free surfaces in Xinjiang's complex terrain and underlying areas ultimately lead to hourly snow cover products. It is verified by the data set and the snow coverof meteorological station in 2019 the accuracy of this method is higher than that of MOD10A1 and MYD10A1, the main international MODIS snow products, which significantly reduces the misclassification rate of cloud and snow.

Key words:  Xinjiang      Deep learning      Snow cover      Fengyun-4A/AGRI      MOD10A1     
Received:  31 July 2020      Published:  26 January 2021
ZTFLH:  P237  
Corresponding Authors:  Xi Kan     E-mail:  zyh@nuist.edu.cn;kanxi@nuist.edu.cn
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Yonghong Zhang
Haixiao Cao
Xi Kan

Cite this article: 

Yonghong Zhang,Haixiao Cao,Xi Kan. Snow Cover Recognition for Xinjiang based on Fusion of FY-4A/AGRI Spatial and Temporal Characteristics. Remote Sensing Technology and Application, 2020, 35(6): 1337-1347.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.6.1337     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I6/1337

Fig.1  Flow chart of method
Fig.2  Xinjiang Digital Elevation Model and label data sources
Fig.3  AGRI Multispectral remote sensing data pre-processing
通道序号中心波长/μm位深通道分辨率
10.471210 992×10 992
20.651221 984×21 984
30.831210 992×10 992
41.37125 496×5 496
51.61125 496×5 496
62.22125 496×5 496
73.72125 496×5 496
Table 1  AGRI 2 km Channel Information
Fig.4  Snow extraction method of Landsat 8 OLI image with track number 035146 on October 2, 2018
Fig.5  2D convolution network structure
Fig.6  Deep network snow identification mode
操作步骤网络层名称参数输出特征大小
空间 特征 提取输入层\7×7×13
第一次卷积卷积核=3×3×16,步长=15×5×16
第二次卷积卷积核=3×3×32,步长=13×3×32
5时相输入1 440×1
时序 特征 提取卷积层C1卷积核=3×1×16,步长=11 438×16
激活函数Rule\
池化层P1窗口=3×1,步长=3479×16
卷积层C2卷积核=3×16×32,步长=1477×32
激活函数Rule\
池化层P2窗口=3×1,步长=3159×32
卷积层C3卷积核=3×32×64,步长=1157×64
激活函数Rule\
全连接层权值=10 048×1010
Softmax3
Tabel 2  The parameters of network structure
行列号过境时间行列号过境时间
03114813点27分03314813点28分
03214813点28分03414813点29分
03315013点41分03414713点22分
03314913分34分03514713点23分
Tabel 3  OLI transit time
实验

截取的像

素块大小

空间特征提取网络/(卷积核数)时序特征提取网络/(卷积核数)模型输入数据
一层二层三层一层二层三层四层池化步长
A7×716//16///3AGRI 7通道+地理信息数据
B7×71632/1632//3AGRI 7通道+地理信息数据
C7×71632/163264/3AGRI 7通道+地理信息数据
D7×71632/1632641283AGRI 7通道+地理信息数据
E7×71632641632641283AGRI 7通道+地理信息数据
F7×71632/163264/2AGRI 7通道+地理信息数据
G7×71632/163264/4AGRI 7通道+地理信息数据
H5×51632/163264/3AGRI 7通道+地理信息数据
I9×91632/163264/3AGRI 7通道+地理信息数据
J7×71632/163264/3AGRI 7通道
K7×71632/163264/3AGRI 7通道+NDSI
L7×71632/163264/3AGRI 7通道+NDVI
M7×71632/163264/3AGRI 7通道+DEM
N7×71632/163264/3AGRI 7通道+坡向
O7×71632/163264/3AGRI 7通道+坡度
P7×71632/163264/3AGRI 7通道+地表覆盖类型
Tabel 4  The parameters of network structure
实验测试集准确度训练集准确度
裸地综合裸地综合
MOD10A10.931 00.940 30.949 60.940 3////
MYD10A10.929 80.941 30.948 30.939 8////
A0.746 80.669 70.769 80.728 80.766 90.689 50.789 80.748 9
B0.905 60.897 40.918 60.901 20.924 50.914 50.938 60.924 3
C0.941 90.936 90.959 60.946 80.954 50.956 60.974 60.961 9
D0.908 90.927 80.924 50.920 80.918 70.924 50.934 80.926 4
E0.919 70.921 50.929 80.929 40.910 90.917 80.931 00.929 1
F0.895 40.896 70.901 90.898 60.901 80.908 70.908 90.908 6
G0.895 40.896 70.901 90.898 60.901 80.908 70.908 90.908 6
H0.935 80.938 80.949 80.935 60.947 80.948 90.939 80.945 6
I0.935 60.931 20.941 20.936 40.945 80.945 50.938 90.943 8
J0.925 40.928 70.946 70.933 60.935 40.937 80.963 60.945 6
K0.927 70.928 90.946 90.934 50.942 10.948 20.968 40.952 9
L0.933 20.928 70.939 80.933 90.957 90.934 60.956 30.949 6
M0.933 20.936 90.953 50.941 20.936 40.948 60.968 90.951 3
N0.930 90.931 20.942 30.934 80.950 70.946 30.963 80.953 6
O0.933 90.937 20.942 30.937 80.946 90.938 50.961 30.948 9
P0.934 30.934 60.946 30.938 40.948 00.946 30.966 50.953 6
Tabel 5  Experimental results of the Multi-method
数据编号过境时间轨道行号轨道列号积雪分类精度总精度
M12018/09/250351450.938 90.958 7
M22018/10/020351460.934 60.938 9
M32018/11/240341490.956 30.954 6
M42019/11/190301410.926 40.934 6
M52019/12/050291410.926 80.936 4
M62020/02/070291410.945 80.948 2
Tabel 6  Landsat8-OLI Related information
Fig.7  Comparison of OLI color image and snow extraction method in this paper
Fig.8  Comparison of snow extraction results by various methods at 13:45 on December 6, 2018 in Xinjiang
Fig.9  Comparison of local effects of different methods for snow extraction
Fig.10  Accuracy comparison of weekly snow cover over Xinjiang of snow period in 2019
周号积雪分类精度/(%)
MYD10A1MOD10A1本文方法
1~9、51、5293.7693.1994.15
45~5070.9669.5671.36
Tabel 7  Accuracy comparison of snow cover over Xinjiang of snow period in 2019
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