Snow cover estimation from MODIS and Sentinel-1 SAR data using machine learning algorithms in the western part of the Tianshan Mountains
Snow cover estimation from MODIS and Sentinel-1 SAR data using machine learning algorithms in the western part of the Tianshan Mountains
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使用机器学习算法根据 MODIS 和 Sentinel-1 SAR 数据估算天山西部地区的积雪
DOI：
10.1007/s11629-019-5723-1
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发表时间：
2020-04
影响因子：
2.5
通讯作者：
Li Lan-hai
中科院分区：
文献类型：
--
作者：
Liu Yang;Chen Xi;Hao Jian-Sheng;Li Lan-hai
由于云雾天气的影响，利用遥感技术的光学图像获取山区积雪的空间分布比较困难。结合中分辨率成像光谱仪(MODIS)积雪产品和哨兵一号合成孔径雷达(SAR)的散射特性，提出了基于对象的主成分分析-支持向量机(PCA-SVM)积雪制图方法。首先，从Sentinel-1ASAR图像中提取特征参数，包括VV/VH后向散射、散射熵和散射α来描述积雪和非积雪的变化。其次，利用主成分分析(PCA)算法对特征参数进行提取，得到最优的积雪覆盖特征组合。最后，利用最优特征组合，利用基于对象的支持向量机分类器提取了空间分辨率为20m的积雪覆盖图。将该方法应用于新疆天山西部的新源县中国研究区。根据不同试验点的观测数据对该方法的精度进行了分析。结果表明，提取的雪盖像素数小于实际雪盖像素数(fb1=93.86，fb2=59.78)。两阶段合成孔径雷达图像对积雪覆盖像素的威胁评分(TS)、检测概率(POD)和虚警率(FAR)不同(TS1=86.84，POD1=90.10，FAR1=4.01；TS2=56.40，POD2=57.62，FAR2=3.62)。发现了积雪覆盖和非积雪覆盖像素的错误和错误分类。尽管分类不是很准确，但该方法显示了整合不同来源以恢复稳定时期积雪的空间分布的潜力。
Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component analysis-support vector machine (PCA-SVM) method is proposed for snow cover mapping through the integration of moderateresolution imaging spectroradiometer (MODIS) snow cover products and the Sentinel-1 synthetic aperture radar (SAR) scattering characteristics. First, derived from the Sentinel-1A SAR images, the feature parameters, including VV/VH backscatter, scattering entropy, and scattering alpha, were used to describe the variations of snow and non-snow covers. Second, the optimum feature combinations of snow cover were formed from the feature parameters using the principle component analysis (PCA) algorithm. Finally, using the optimum feature combinations, a snow cover map with a 20 m spatial resolution was extracted by means of an object-based SVM classifier. This method was applied in the study area of the Xinyuan County, which is located in the western part of the Tianshan Mountains in Xinjiang, China. The accuracies in this method were analyzed according to the data observed at different experimental sites. Results showed that the snow cover pixels of the extraction were less than those in the actual situation (FB1=93.86, FB2=59.78). The evaluation of the threat score (TS), probability of detection (POD), and false alarm ratio (FAR) for the snow-covered pixels obtained from the two-stage SAR images were different (TS1=86.84, POD1=90.10, FAR1=4.01; TS2=56.40, POD2=57.62, FAR2=3.62). False and misclassifications of the snow cover and non-snow cover pixels were found. Although the classifications were not highly accurate, the approach showed potential for integrating different sources to retrieve the spatial distribution of snow covers during a stable period.
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影响因子：
13.5
作者：
Czyzowska-Wisniewski, Elzbieta H.;van Leeuwen, Willem J. D.;Wisniewski, Wit T.
通讯作者：
Wisniewski, Wit T.
影响因子：
1.6
作者：
A. McNair
通讯作者：
A. McNair
基于机器学习技术的非局部时空过滤 MODIS 分数积雪产品的间隙填充
影响因子：
5
作者：
Hou Jinliang;Huang Chunlin;Zhang Ying;Guo Jifu;Gu Juan
通讯作者：
Gu Juan
影响因子：
2.5
作者：
Yang Liu;Lan-hai Li;Xi Chen;Jinming Yang;Jian-sheng Hao
通讯作者：
Yang Liu;Lan-hai Li;Xi Chen;Jinming Yang;Jian-sheng Hao
影响因子：
25.2
作者：
Tingjun Zhang
通讯作者：
Tingjun Zhang