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自然资源遥感  2022, Vol. 34 Issue (3): 1-9    DOI: 10.6046/zrzyyg.2021322
  综述 本期目录 | 过刊浏览 | 高级检索 |
雪深和雪水当量被动微波反演及应用进展
王泽坤1,2(), 甘甫平3(), 闫柏琨3, 李贤庆1,2, 李和谋1,2
1.中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京 100083
2.中国矿业大学(北京) 地球科学与测绘工程学院,北京 100083
3.中国自然资源航空物探遥感中心,北京 100083
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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摘要 

雪深和雪水当量是积雪观测最主要且关键的要素,在冰冻圈、全球气候变化、水资源调查等领域具有重要意义。微波遥感相较于可见光和近红外遥感对积雪观测具有优势,为此,对被动微波遥感反演雪深和雪水当量的研究进展进行了系统的总结。梳理了野外现场实地测量、地面台站长期观测和卫星遥感区域观测等3种积雪观测方式及其主要观测积雪参数; 重点总结并评价了半经验、物理模型和机器学习等3种雪深和雪水当量反演算法。展示了青藏高原被动微波积雪监测的研究成果,展望了对未来积雪参数遥感反演的发展趋势,为雪深和雪水当量被动微波反演的深入开展提供了科学的参考建议。
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王泽坤
甘甫平
闫柏琨
李贤庆
李和谋
关键词 ： 雪深和雪水当量,  被动微波,  反演算法,  积雪观测,  青藏高原    
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.
Key words： snow depth and snow water equivalent    passive microwave    inversion algorithm    snow cover observation    Qinghai-Tibet Plateau
收稿日期: 2021-10-11      出版日期: 2022-09-21
ZTFLH:  TP79  
基金资助:中国地质调查局地质调查项目“典型流域水循环要素与自然资源遥感定量调查”(DD20221642-3)
通讯作者: 甘甫平
作者简介: 王泽坤(1997-),女,硕士研究生,主要从事水文地质遥感应用研究。Email: zkwang0324@163.com。
引用本文:   
王泽坤, 甘甫平, 闫柏琨, 李贤庆, 李和谋. 雪深和雪水当量被动微波反演及应用进展[J]. 自然资源遥感, 2022, 34(3): 1-9.
WANG Zekun, GAN Fuping, YAN Bokun, LI Xianqing, LI Hemou. Inversion of snow depth and snow water equivalent based on passive microwave remote sensing and its application progress. Remote Sensing for Natural Resources, 2022, 34(3): 1-9.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021322      或      https://www.gtzyyg.com/CN/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  积雪观测主要参数和相关仪器手段[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  相关被动微波传感器特征参数[29]
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