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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (3) : 1-9     DOI: 10.6046/zrzyyg.2021322
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Inversion of snow depth and snow water equivalent based on passive microwave remote sensing and its application progress
WANG Zekun1,2(), GAN Fuping3(), YAN Bokun3, LI Xianqing1,2, LI Hemou1,2
1. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology(Beijing), Beijing 100083, China
2. College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China
3. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
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Abstract  

Snow depth and snow water equivalent are critical elements for snow cover observation and are greatly significant in fields such as cryosphere, global climate change, and water resource surveys. Microwave remote sensing is superior to both visible-light and near-infrared remote sensing in snow cover observation. This study systematically summarized the research results of the passive microwave remote sensing in the inversion of snow depth and snow water equivalent. It organized three types of snow cover observation methods, i.e., field surveys, long-term observations at ground stations, and regional observations based on satellite remote sensing, as well as major snow cover parameters to be observed. Furthermore, it summarized and evaluated three inversion algorithms, i.e., semi-empirical method, physical model, and machine learning. Finally, this study presented the results of the snow cover in the Qinghai-Tibet Plateau observed using passive microwave remote sensing, predicted the future development trend of remote sensing-based inversion of snow cover parameters, and put forward scientific suggestions for the in-depth implementation of the inversion of snow depth and snow water equivalent passive microwave remote sensing.
Keywords snow depth and snow water equivalent      passive microwave      inversion algorithm      snow cover observation      Qinghai-Tibet Plateau     
ZTFLH:  TP79  
Corresponding Authors: GAN Fuping     E-mail: zkwang0324@163.com;fpgan@aliyun.com
Issue Date: 21 September 2022
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Zekun WANG
Fuping GAN
Bokun YAN
Xianqing LI
Hemou LI
Cite this article:   
Zekun WANG,Fuping GAN,Bokun YAN, et al. Inversion of snow depth and snow water equivalent based on passive microwave remote sensing and its application progress[J]. Remote Sensing for Natural Resources, 2022, 34(3): 1-9.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021322     OR     https://www.gtzyyg.com/EN/Y2022/V34/I3/1
观测方式 观测参数 仪器及手段
野外现场
实地测量
(人工)		 积雪深度(分层) 直尺
雪水当量 雪秤
雪层温度(分层)、雪表面温度、雪土界面温度 针式温度计、手持式红外温度计
雪颗粒形态 形状卡片
雪粒径(分层) 数码显微镜
积雪含水量(分层)、积雪介电常数(分层) 雪特性分析仪(Snow Fork)
积雪密度(分层) 雪铲、雪特性分析仪(Snow Fork)
积雪硬度 指针式推拉力计
地面台站
长期观测
(自动化、
人工)		 积雪深度 SR50A超声波
雪水当量 GMON3
雪反照率 四分量表
雪层温度、雪表面温度、雪土界面温度 红外温度计
积雪含水量、介电常数、积雪密度 积雪分析系统(SPA)
雪压 雪压计
风速、风向、大气压、空气温度、相对湿度 风温湿压传感器
卫星遥感
区域观测		 积雪面积 光学遥感反演
雪反照率
雪层温度
积雪深度 微波遥感反演
雪水当量
积雪含水量
Tab.1  Main parameters and related instruments of snow observation[6,23-24]
传感器 卫星平台 运行时间 频率/GHz
和极化方
式(H/V)		 瞬时视
场/km		 幅宽/
km
SMMR Nimbus-7 1978年10月—1987年8月 6.6 H,V
10.7 H,V
18 H,V
21 H,V
37 H,V		 148×95
91×59
55×41
46×30
27×18		 780
SSM/I DMSP 1987年6月—2009年11月 19.3 H,V
22.2 V
37 H,V
85.5 H,V		 70×45
60×40
38×30
16×14		 1 394
SSMIS DMSP 2006年11月至今 19.3 H,V
22.2 V
37 H,V
91.7 H,V		 70×45
60×40
38×30
16×13		 1 700
AMSR-E Aqua 2002年6月—2011年10月 6.9 H,V
10.7 H,V
18.7 H,V
23.8 H,V
36.5 H,V
89 H,V		 75×43
51×29
27×16
31×18
14×8
6×4		 1 445
AMSR-2 GCOM-
W1		 2012年8月至今 6.9 H,V
7.3 H,V
10.65 H,V
18.7 H,V
23.8 H,V
36.5 H,V
89 H,V		 62×35
62×35
42×24
22×14
19×11
12×7
5×3		 1 450
MWRI FY-3A/
B/C/D		 2010年11月至今 10.65 H,V
18.7 H,V
23.8 H,V
36.5 H,V
89 H,V		 85×51
50×30
45×27
30×18
5×9		 1 400
Tab.2  Related passive microwave sensor characteristic parameters[29]
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