地球科学进展 ›› 2018, Vol. 33 ›› Issue (6): 590 -605. doi: 10.11867/j.issn.1001-8166.2018.06.0590

综述与评述 上一篇    下一篇

基于被动微波遥感的积雪深度和雪水当量反演研究进展
肖雄新( ), 张廷军 *( )   
  1. 兰州大学资源环境学院,西部环境教育部重点实验室,甘肃 兰州 730000
  • 收稿日期:2017-12-11 修回日期:2018-05-15 出版日期:2018-06-20
  • 通讯作者: 张廷军 E-mail:xiaoxiongxin5118@126.com;tjzhang@lzu.edu.cn
  • 基金资助:
    *国家重大科学研究计划项目“复杂地形积雪遥感及其多尺度积雪变化研究”(编号:2013CBA01802);国家自然科学基金重大研究计划项目“黑河流域上游多年冻土地表水、地下水过程及其效应研究”(编号: 91325202)资助.

Passive Microwave Remote Sensing of Snow Depth and Snow Water Equivalent: Overview

Xiongxin Xiao( ), Tingjun Zhang *( )   

  1. Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth Environmental Sciences, Lanzhou University, Lanzhou 730000,China
  • Received:2017-12-11 Revised:2018-05-15 Online:2018-06-20 Published:2018-07-23
  • Contact: Tingjun Zhang E-mail:xiaoxiongxin5118@126.com;tjzhang@lzu.edu.cn
  • About author:

    First author:Xiao Xiongxin (1991-), male, Fuping County, Shaanxi Province, Master student. Research areas include remote sensing of snow. E-mail:xiaoxiongxin5118@126.com

  • Supported by:
    Project supported by the National Key Scientific Research Program of China “Study on the remote sensing and multi-scale snow cover change of complex terrain”(No.2013CBA01802);The National Natural Science Foundation of China “Hydro(geo)logical processes and their impacts in permafrost regions in the upper reach of Heihe River in Northwest China”(No.91325202).

积雪是冰冻圈重要组成要素之一,也是对天气和气候响应最为敏感的自然要素。被动微波能够穿透云层、积雪和大气进行全天候、全天时地工作,在估算积雪深度、雪水当量等积雪参数上有很大优势。综述了国内外基于被动微波遥感的积雪参数反演研究的进展,首先介绍了被动微波遥感监测积雪的基本理论,以及被动微波遥感数据;然后将当前的积雪深度和雪水当量反演算法总结为4类:①基于统计的线性反演算法;②基于微波积雪模型的反演算法;③基于先验知识的非线性反演算法;④数据融合与数据同化。随后介绍了常用的7种积雪数据产品,并讨论了影响积雪深度和雪水当量反演精度的几个因素,最后对未来积雪参数反演研究方向做出了展望。

Snow cover is an informative indicator of climate change because it affects local and regional surface energy and water balance, hydrological processes and climate. Passive Microwave (PM) works all weather and round the clock and penetrates clouds and snow. Passive microwave remote sensing data have been widely applied to retrieving snow depth and snow water equivalent in the past few decades. Recently, the snow depth retrieval study has rapidly developed. This paper reviewed the research progress of snow depth and snow water equivalent inversion algorithm using PM data at home and abroad. Firstly, the basic theory of passive microwave remote sensing snow monitoring and passive microwave remote sensing data were introduced. Then, the current snow depth and snow water equivalent inversion algorithm were summarized into four categories: ① A statistically based linear inversion algorithm; ② An inversion algorithm based on microwave transmission snow model; ③ A nonlinear inversion algorithm based on prior knowledge; ④ Data fusion and data assimilation. Afterwards, the commonly used seven kinds of snow data products were introduced, and several factors affecting the snow depth and the snow water inversion accuracy were discussed. Finally, the possible direction of future snow parameter inversion research was prospected.

中图分类号: 

图1 积雪覆盖地表微波辐射
Fig.1 Microwave radiation of the surface with snow cover
图1 积雪覆盖地表微波辐射
Fig.1 Microwave radiation of the surface with snow cover
表1 常用被动微波传感器特征参数
Table 1 Parameters summary of passive microwave sensors
表1 常用被动微波传感器特征参数
Table 1 Parameters summary of passive microwave sensors
表2 基于亮温梯度的积雪深度静态反演算法
Table 2 The static snow depth retrieval algorithm based on brightness temperature gradient
表2 基于亮温梯度的积雪深度静态反演算法
Table 2 The static snow depth retrieval algorithm based on brightness temperature gradient
表3 基于森林覆盖率修订的积雪深度静态反演算法
Table 3 The static snow depth retrieval algorithm based on forest fraction
表3 基于森林覆盖率修订的积雪深度静态反演算法
Table 3 The static snow depth retrieval algorithm based on forest fraction
表4 积雪产品数据集
Table 4 Snow product dataset
表4 积雪产品数据集
Table 4 Snow product dataset
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