| 基于混合像元分解的MODIS积雪面积信息提取及其精度评价——以天山中段为例 |
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| 引用本文： | 陈晓娜,包安明,张红利,柳梅英.基于混合像元分解的MODIS积雪面积信息提取及其精度评价——以天山中段为例[J].资源科学,2010,32(9):1761-1768. |
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| 作者姓名： | 陈晓娜 包安明 张红利 柳梅英 |
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| 作者单位： | 1. 中国科学院新疆生态与地理研究所,乌鲁木齐,830011;中国科学院研究生院,北京,100049
2. 中国科学院新疆生态与地理研究所,乌鲁木齐,830011 |
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| 基金项目： | 国家973重点研究项目: “气候变化对天山中段山区积雪资源和融雪径流过程的影响”(编号:2009CB421301);中国科学院知识创新重要方向项目: “新疆玛纳斯绿洲水盐迁移转化规律与演变趋势研究”(编号：KZCX2-YW-BR-12)。 |
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| 摘 要： | MODIS(Moderate Resolution Imaging Spectroradiometer)是现阶段积雪遥感监测及积雪水文学研究中积
雪面积信息获取的重要平台，但其空间分辨率相对较低，影像中混合像元现象普遍存在。本文以MOD02 HKM数
据为基础，通过线性光谱混合模型(LSMM，Linear Spectral Mixing Model)对研究区MODIS影像进行像元分解，从中
提取积雪面积信息，并进行精度评价。将线性光谱混合模型得到的积雪面积信息与美国国家冰雪数据中心提供的
MOD10A1日积雪覆盖数据影像进行对比分析。结果表明：利用线性光谱混合模型可以较好的分解出像元中积雪
面积信息，其分类精度达0.88;相同位置上MOD10A1的积雪分类精度为0.80。说明，对MODIS影像上积雪信息提
取来说，线性光谱混合模型的分类精度较高，具有较强的适用性。
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| 关 键 词： | 混合像元 MOD02 HKM MOD10A1 线性光谱混合模型 精度评价 |
A Study on Methods and Accuracy Assessment for Extracting Snow Covered Areas from MODIS Images Based on Pixel Unmixing:A Case on the Middle of the Tianshan Mountain |
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| CHEN Xiaon,BAO Anming,ZHANG Hongli and LIU Meiying.A Study on Methods and Accuracy Assessment for Extracting Snow Covered Areas from MODIS Images Based on Pixel Unmixing:A Case on the Middle of the Tianshan Mountain[J].Resources Science,2010,32(9):1761-1768. |
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| Authors: | CHEN Xiaon BAO Anming ZHANG Hongli and LIU Meiying |
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| Institution: | Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urmqi 830011, China;Graduate University of the Chinese Academy of Science, Beijing 100049, China;Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urmqi 830011, China;Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urmqi 830011, China;Graduate University of the Chinese Academy of Science, Beijing 100049, China;Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urmqi 830011, China;Graduate University of the Chinese Academy of Science, Beijing 100049, China |
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| Abstract: | Moderate Resolution Imaging Spectroradiometer (MODIS) is a critical remote sensing
data source in snow monitoring and snow hydrology study. However, mixed pixel is a common
problem encountered in using satellite data with moderate or low spatial resolution. Relatively low
spatial resolutions (i.e., 250 m, 500 m, and 1000 m) have limited widespread applications of
MODIS data in research, such as snow area extraction, snow water evaluation and snowmelt runoff
simulation. In this paper, the author extracted the snow area from MOD02 HKM image, one of the
three MODIS L1B products (MOD02 QKM, MOD02 HKM, MOD02 1KM) at 500 m spatial
resolution acquired from U.S. National Aeronautics and Space Administration (NASA), aiming to
extract snow area at sub pixel scale on the basis of the line spectral mixture model (LSMM). In
addition, the authors compared the classification accuracy of extracted snow area with the snow
cover map derived from MOD10A1 grey level snow and ice products of the same image
acquisition time and spatial resolution provided by the U.S. National Snow & Ice Data Center
(NSIDC) , and subsequently estimated their classification accuracies with the binary snow-covered
area derived from Landsat 5 TM data at 30 m spatial resolution based on the SNOMAP algorithm
using the quality accuracy assessment method. For better running LSMM and eliminating cloud
effects on snow area extraction, cloud-free days in May 15, 2007, were selected for this study.
Results indicated that the use of the line spectral mixture model in snow area extraction can
provide better snow classification accuracy, showing the quantity accuracy assessment result of
0.88 and a standard deviation of 0.087 at a 3×3 pixel scale, while the classification accuracy of
MOD10A1 grey level snow and ice product was found to be 0.80 and 0.135, respectively, at the
same locations and statistical scales. This suggests that the line spectral mixture model could be
effective in snow area extraction from MODIS data of relatively low spatial resolutions. |
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| Keywords: | Mixed pixel MOD02 HKM MOD10A1 Quantity accuracy assessment Middle of the Tianshan Mountain |
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