地球科学进展

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).
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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。

关键词: 被动微波遥感, 积雪深度, 雪水当量, 积雪产品

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.

Key words: Passive microwave remote sensing, Snow depth, Snow water equivalent, Snow products.

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
传感器 卫星平台 运行时间 频率/GHz 极化方式(H,V) 瞬间视场/(km×km)
SMMR Nimbus 7 1978.10-1987.08 6.6 H,V 136×89
10.7 H,V 87×57
18.0 H,V 54×35
21.0 H,V 47×30
37.0 H,V 47×30
SSM/I DMSP 1987.07-2009.11 19.35 H,V 70×45
22.24 V 60×40
37.0 H,V 38×30
85.5 H,V 16×14
SSMIS DMSP 2006.12- 19.35 H,V 70×45
22.24 V 60×40
37.0 H,V 38×30
91.66 H,V 38×30
AMSR-E Aqua 2002.06-2011.10 6.93 H,V 75×43
10.65 H,V 51×39
18.7 H,V 27×16
23.8 H,V 32×18
36.5 H,V 14×8
89.0 H,V 6×3
AMSR2 GCOM-W1 2012.05- 6.93 H,V 62×35
7.3 H,V 62×35
10.65 H,V 42×24
18.7 H,V 22×14
23.8 H,V 26×15
36.5 H,V 12×7
89.0 H,V 5×3
MWRI FY-3B/3C 2010.11- 10.65 H,V 51×85
18.70 H,V 50×30
23.80 H,V 27×45
36.50 H,V 18×30
89.00 H,V 9×15
表1 常用被动微波传感器特征参数
Table 1 Parameters summary of passive microwave sensors
传感器 卫星平台 运行时间 频率/GHz 极化方式(H,V) 瞬间视场/(km×km)
SMMR Nimbus 7 1978.10-1987.08 6.6 H,V 136×89
10.7 H,V 87×57
18.0 H,V 54×35
21.0 H,V 47×30
37.0 H,V 47×30
SSM/I DMSP 1987.07-2009.11 19.35 H,V 70×45
22.24 V 60×40
37.0 H,V 38×30
85.5 H,V 16×14
SSMIS DMSP 2006.12- 19.35 H,V 70×45
22.24 V 60×40
37.0 H,V 38×30
91.66 H,V 38×30
AMSR-E Aqua 2002.06-2011.10 6.93 H,V 75×43
10.65 H,V 51×39
18.7 H,V 27×16
23.8 H,V 32×18
36.5 H,V 14×8
89.0 H,V 6×3
AMSR2 GCOM-W1 2012.05- 6.93 H,V 62×35
7.3 H,V 62×35
10.65 H,V 42×24
18.7 H,V 22×14
23.8 H,V 26×15
36.5 H,V 12×7
89.0 H,V 5×3
MWRI FY-3B/3C 2010.11- 10.65 H,V 51×85
18.70 H,V 50×30
23.80 H,V 27×45
36.50 H,V 18×30
89.00 H,V 9×15
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
算法名称 算法公式 参考文献 公式编号
Chang算法 SD=1.59×(Tb18h-Tb37h) [24] (3)
SPD算法 SPD=(Tb19v-Tb37v)+Tb19v-Tb19h)
SD=A0×SPD-A1		 [45] (4)
(5)
车涛1算法 SD=0.78×(Tb18h-Tb37h)+b
SD=0.66×(Tb19h-Tb37h)+b		 [49] (6)
(7)
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
算法名称 算法公式 参考文献 公式编号
Chang算法 SD=1.59×(Tb18h-Tb37h) [24] (3)
SPD算法 SPD=(Tb19v-Tb37v)+Tb19v-Tb19h)
SD=A0×SPD-A1		 [45] (4)
(5)
车涛1算法 SD=0.78×(Tb18h-Tb37h)+b
SD=0.66×(Tb19h-Tb37h)+b		 [49] (6)
(7)
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
算法名称 算法公式 参考文献 公式编号
Forest 算法 SD=0.78×(Tb18h-Tb37h)/(1-f) [50] (8)
AMSR-E 算法 SD=f×SDf+(1-f)×SD0 [41,52] (9)
车涛2算法 SD=(0.78×(Tb18h-Tb37h)+b)/(1-f)
SD=(0.66×(Tb19h-Tb37h)+b)/(1-f)		 [54] (10)
(11)
蒋玲梅算法 SD=fgrass×SDgrass+fbarren×SDbarren
+fforest×SDforest+ffarmland×SDfarmland		 [37] (12)
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
算法名称 算法公式 参考文献 公式编号
Forest 算法 SD=0.78×(Tb18h-Tb37h)/(1-f) [50] (8)
AMSR-E 算法 SD=f×SDf+(1-f)×SD0 [41,52] (9)
车涛2算法 SD=(0.78×(Tb18h-Tb37h)+b)/(1-f)
SD=(0.66×(Tb19h-Tb37h)+b)/(1-f)		 [54] (10)
(11)
蒋玲梅算法 SD=fgrass×SDgrass+fbarren×SDbarren
+fforest×SDforest+ffarmland×SDfarmland		 [37] (12)
表4 积雪产品数据集
Table 4 Snow product dataset
序号 数据名称 空间范围 空间分辨率 时间范围 时间
分辨率		 获取地址
1 中国雪深长时间序列
数据集		 中国 25 km(或0.25°) 1978.10.24-2016.12.31 逐日 http:∥westdc.westgis.ac.cn
2 FY3-MWRI雪深雪水
当量产品		 全球 25 km 2011.7.15- 逐日 http:∥www.nsmc.org.cn
3 AMSR-E积雪产品 全球 25 km 2002.6.19-2011.11.3 逐日 http:∥nsidc.org/
4 SMMR、SSM/I 和
SSMIS积雪产品		 全球 25 km 1978.11.11- 逐日 http:∥nsidc.org/
5 GlobSnow-2积雪产品 北半球 25 km 1979—2012年 逐日 http:∥www.globsnow.info/
6 加拿大气象中心逐日
积雪深度分析数据		 全球 24 km 1998.08.01-2016.12.31 逐日 http:∥nsidc.org/
7 兰德公司月平均积雪
深度数据集		 全球(不包含
非洲和南美)		 4°×5° 1950—1976年 逐月 http:∥nsidc.org/
表4 积雪产品数据集
Table 4 Snow product dataset
序号 数据名称 空间范围 空间分辨率 时间范围 时间
分辨率		 获取地址
1 中国雪深长时间序列
数据集		 中国 25 km(或0.25°) 1978.10.24-2016.12.31 逐日 http:∥westdc.westgis.ac.cn
2 FY3-MWRI雪深雪水
当量产品		 全球 25 km 2011.7.15- 逐日 http:∥www.nsmc.org.cn
3 AMSR-E积雪产品 全球 25 km 2002.6.19-2011.11.3 逐日 http:∥nsidc.org/
4 SMMR、SSM/I 和
SSMIS积雪产品		 全球 25 km 1978.11.11- 逐日 http:∥nsidc.org/
5 GlobSnow-2积雪产品 北半球 25 km 1979—2012年 逐日 http:∥www.globsnow.info/
6 加拿大气象中心逐日
积雪深度分析数据		 全球 24 km 1998.08.01-2016.12.31 逐日 http:∥nsidc.org/
7 兰德公司月平均积雪
深度数据集		 全球(不包含
非洲和南美)		 4°×5° 1950—1976年 逐月 http:∥nsidc.org/
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