X img





作者投稿 专家审稿 编辑办公 编委办公 主编办公

冰川冻土 ›› 2018, Vol. 40 ›› Issue (3): 511-527.doi: 10.7522/j.issn.1000-0240.2018.0056

• 冰冻圈与全球变化 • 上一篇    下一篇


韩涛1, 王大为1, 李丽丽2   

  1. 1. 西北区域气候中心, 甘肃 兰州 730020;
    2. 兰州大学 资源环境学院, 甘肃 兰州 730000
  • 收稿日期:2017-08-05 修回日期:2018-01-08 出版日期:2018-06-25 发布日期:2018-07-16
  • 通讯作者: 王大为,E-mail:giswang@163.com E-mail:giswang@163.com
  • 作者简介:韩涛(1972-),男,吉林省吉林市人,高级工程师,1992年在武汉大学获学士学位,从事生态环境遥感监测研究.E-mail:taohan72@126.com
  • 基金资助:

The establishment of NDSI and snow identification model for mapping regional snow cover using FY-3A/MERSI data:a case study in Qilian Mountains

HAN Tao1, WANG Dawei1, LI Lili2   

  1. 1. Northwest Regional Climate Center, Lanzhou 730020, China;
    2. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  • Received:2017-08-05 Revised:2018-01-08 Online:2018-06-25 Published:2018-07-16

摘要: 我国新型自主的极轨气象卫星风云3号A星(简称FY-3A)上搭载的中分辨率光谱成像仪(MERSI)为大面积雪监测提供了新的遥感数据源。以中国西北祁连山区为例,分析FY-3A/MERSI传感器积雪与其它地物的图谱特征差异,建立了适用于FY-3A/MERSI的归一化差分积雪指数(NDSI),以此为基础,构建了综合利用多光谱判别指标及土地覆盖类型(LULC)定类辅助的积雪判识模型,生成250 m分辨率的日积雪制图产品。模型通过逐步逼近的树状判别结构,去除了易和积雪混淆的部分乔木林、云、云阴影、水体、湖冰、沙(盐)地等地物,并提出应考虑积雪下覆地表特性的影响,调整设定不同LULC类型的积雪判别阈值约束,实时结合区域LULC影像进行积雪的最终判定与优化。对祁连山区2010-2011年积雪季FY-3A/MERSI影像的积雪制图应用结果表明,该资料能够客观精细地反映积雪的空间分布与动态发展过程。同时利用气象台站积雪观测记录及Terra/MODIS积雪判识结果进行对比验证,结果表明基于FY-3A/MERSI建立的积雪判识模型具有较高的精度和稳定性,特别是提高了云雪区分的效能。

关键词: 积雪, 归一化差分积雪指数NDSI, FY-3A/MERSI, 祁连山

Abstract: Medium Resolution Spectral Imager (MERSI) on board China's new generation polar orbit meteorological satellite FY-3A provides a new data source for snow monitoring in large area. As a case study, the typical snow cover of Qilian Mountains in northwest China was selected in this paper to develop the algorithm to map snow cover using FY-3A/MERSI. By analyzing the spectral response characteristics of snow and other surface elements, as well as each channel image quality on FY-3A/MERSI, the widely used Normalized Difference Snow Index (NDSI) was defined to be computed from channel 2 and channel 7 for this satellite data. Basing on NDSI, a tree-structure prototype version of snow identification model was proposed, including five newly-built multi-spectral indexes to remove those pixels such as forest, cloud shadow, water, lake ice, sand (salty land), or cloud that are usually confused with snow step by step, especially, a snow/cloud discrimination index was proposed to eliminate cloud, apart from use of cloud mask product in advance. Furthermore, land cover land use (LULC) image has been adopted as auxiliary dataset to adjust the corresponding LULC NDSI threshold constraints for snow final determination and optimization. This model is composed as the core of FY-3A/MERSI snow cover mapping flowchart, to produce daily snow map at 250m spatial resolution, and statistics can be generated on the extent and persistence of snow cover in each pixel for time series maps. Preliminary validation activities of our snow identification model have been undertaken. Comparisons of the 104 FY-3A/MERSI snow cover maps in 2010-2011 snow season with snow depth records from 16 meteorological stations in Qilian Mountains region, the sunny snow cover had an absolute accuracy of 92.8%. Results of the comparison with the snow cover identified from 6 Terra/MODIS scenes showed that they had consistent pixels about 85%. When the two satellite resultant snow cover maps compared with the 6 supervise-classified and expert-verified snow cover maps derived from integrated MERSI and MODIS images, we found FY-3A/MERSI has higher accuracy and stability not only for nearly cloud-free scenes but also the cloud scenes, namely, FY-3A/MERSI data can objectively reflect finer spatial distribution of snow and its dynamic development process, and the snow identification model perform better in snow/cloud discrimination. However, the ability of the FY-3A/MERSI model to discriminate thin snow and thin cloud need to be refined. And the limitation, error sources of FY-3A/MERSI snow products would be assessed based on the accumulation of large amounts of data in the future.

Key words: snow cover, NDSI, FY-3A/MERSI, Qilian Mountains


  • TP79