土壤水分微波遥感反演算法及应用研究进展

doi:10.3785/j.issn.1008-9209.2021.04.281

浙江大学学报（农业与生命科学版）

2022, Vol. 48

Issue (3): 289-302 DOI: 10.3785/j.issn.1008-9209.2021.04.281

综述

土壤水分微波遥感反演算法及应用研究进展

邓小东^1,²(

),王宏全^1,³(

)

^1.浙江大学环境与资源学院农业遥感与信息技术应用研究所，杭州 310058
^2.贵州大学矿业学院，贵阳 550025
^3.浙江省农业遥感与信息技术重点研究实验室，杭州 310058

Recent advances on algorithms and applications of soil moisture retrieval from microwave remote sensing

Xiaodong DENG^1,²(

),Hongquan WANG^1,³(

)

^1.Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
^2.Mining College, Guizhou University, Guiyang 550025, China
^3.Key Laboratory of Agriculture Remote Sensing and Information System of Zhejiang Province, Hangzhou 310058, China

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摘要：

土壤水分是农作物物候期生长和气候、环境变化的敏感因子，在陆表水气循环过程中发挥着重要作用。本文首先梳理了基于主动微波遥感、被动微波遥感、全球卫星导航系统反射测量（Global Navigation Satellite System Reflectometry, GNSS-R）技术的土壤水分反演算法：1）基于主动微波遥感的裸露地表经验模型、半经验模型、物理散射模型、植被覆盖的水云模型（water cloud model, WCM）和密歇根微波植被散射（Michigan microwave canopy scattering, MIMICS）模型；2）基于被动微波遥感的裸露地表粗糙度模型Q/H、H_p 、Q_p 和植被覆盖的 $τ$ - $ω$ 模型；3）地基、星载GNSS-R土壤水分反演算法。其次，分析了近几十年来微波遥感反演土壤水分的研究进展，提出了进一步精确量化植被和地表粗糙度等土壤水分反演要素的时空变异性是提高反演精度的关键，尤其要考虑植被生长过程及由此导致的电磁波辐射传输机制的不确定性问题。最后，展望了土壤水分在农业生产、陆表水气循环中的应用前景，并指出全球尺度土壤水分对气候变化的响应及反馈机制将是未来的研究热点。

关键词： 土壤水分; 微波遥感; 反演算法

Abstract:

Soil moisture is a sensitive factor for crop phenological growth, climate and environment changes, and it plays an important role in the land surface water and atmospheric circulation. In this paper, the soil moisture retrieval algorithms based on active microwave remote sensing, passive microwave remote sensing and Global Navigation Satellite System Reflectometry (GNSS-R) technology were firstly sorted, including: 1) active microwave remote sensing-based empirical model, semiempirical model, physical scattering model for bare ground surface, and water cloud model (WCM), Michigan microwave canopy scattering (MIMICS) model for vegetation coverage; 2) passive microwave remote sensing-based Q/H, H_p, Q_p roughness models for bare ground surface and $τ$ - $ω$ model for vegetation coverage; 3) spaceborne and ground-based GNSS-R soil moisture retrieval algorithms. Secondly, the research and development of soil moisture retrieval from microwave remote sensing in recent decades were reviewed. It was proposed that the key to improve the accuracy of soil moisture retrieval was to quantify accurately the spatial and temporal variability of soil moisture retrieval factors such as vegetation and surface roughness, especially the uncertainty of vegetation growth process and the resulting electromagnetic wave radiation transmission mechanism. Finally, the application outlook of soil moisture in agricultural production and land-surface moisture circulation was prospected, and it was pointed out that the response and feedback mechanism of soil moisture on global scale to climate change would be a research hotspot in the future.

Key words: soil moisture microwave remote sensing retrieval algorithms

收稿日期: 2021-04-28 出版日期: 2022-07-07

CLC:

TP 79

基金资助: 国家自然科学基金面上科学基金项目(32171781);国家自然科学基金青年科学基金项目(41801232);中央高校基本科研业务费青年教师专项资金(2018QNA6011)

通讯作者: 王宏全 E-mail: xddeng@zju.edu.cn;Hongquan_Wang@zju.edu.cn

作者简介: 邓小东（https://orcid.org/0000-0003-3038-5047），E-mail：xddeng@zju.edu.cn

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引用本文:

邓小东,王宏全. 土壤水分微波遥感反演算法及应用研究进展[J]. 浙江大学学报（农业与生命科学版）, 2022, 48(3): 289-302.

Xiaodong DENG,Hongquan WANG. Recent advances on algorithms and applications of soil moisture retrieval from microwave remote sensing. Journal of Zhejiang University (Agriculture and Life Sciences), 2022, 48(3): 289-302.

链接本文:

https://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2021.04.281 或 https://www.zjujournals.com/agr/CN/Y2022/V48/I3/289

表1 土壤水分微波遥感反演技术对比

主动微波遥感地表散射模型 Active microwave remote sensing surface scattering model		适用范围 Application range	文献 Reference
半经验模型 Semiempirical model	Oh	$10 °$ ≤ $θ$ ≤ $70 °, 0.13 ＜ k s ＜ 6.98,0.04 ＜ M v$ ≤ $0.291$	[7]
	Dubois	$θ ＞$ $30 °, k s$ ≤ $2.5, M v$ ≤ $35 %, 1.5 ? G H z ＜ f b ＜ 11 ? G H z$	[8]
	Shi	$25 °$ ≤ $θ$ ≤ $70 °, 0.2 ＜ k s ＜ 3.6,2.5 ＜ k l ＜ 35,2 % ＜ M v ＜ 50 %$	[9]
物理散射模型 Physical scattering model	GOM	$(2 k s c o s θ) 2 ＞ 10, k l ＞ 6, l 2 ＞ 2.76 ? s λ$	[11]
	POM	$k l ＞ 6, s l ＜ 0.25$	[11]
	SPM	$k s ＜ 0.3, k l ＜ 3, s l ＜ 0.3$ 0	[11]
	IEM	$k s ＜ 3, s l ＜ 0.3$ 0	[10-11]

表2 主动微波遥感地表散射模型的适用范围

图1 水云模型散射示意图1：植被层的直接后向散射；2：经过植被层双程衰减之后的剩余散射。

图2 MIMICS模型散射示意图1～5分别对应σpq10～σpq50。

图3 τ-ω模型辐射示意图

图4 土壤水分GNSS-R反演技术A.地基单天线；B.地基多天线；C.机载；D.星载。

表3 全球主要微波遥感土壤水分产品特征

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