土壤水分多源卫星遥感联合反演研究进展

doi:10.6046/zrzyyg.2022408

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摘要

土壤水分与全球气候变化、碳循环、水循环等过程,以及农业生产、生态保护修复等环节密切相关。土壤水分的探测经历了从地面测量到遥感探测的发展过程,实现了全球及区域尺度的调查监测。由于数据谱段不一、辐射传输机制不同、反演算法多样,需从算法机理、优势、局限等角度进行全面分析,为精度提升、算法改进提供基础。为此,该文从光学遥感、微波遥感、光学微波协同3个方面,系统梳理了光学遥感温度-植被指数空间特征、温度-地表短波净辐射时间特征反演土壤水分,被动、主动微波反演和主、被动微波遥感联合反演,以及基于精度改善和时空尺度转化的光学微波协同反演等技术方法的特点、存在的问题。目前,多源遥感数据联合反演土壤水分主要存在如下问题: ①数据存在缺失及时空尺度不匹配问题; ②不同数据源对地表穿透性不一致; ③联合反演模型依赖于经验参数和大量辅助参数。随着卫星监测网络的完善、数据源对地表探测深度研究的深入,以及联合反演物理机理的明确、辅助参数时空连续数据集的建立,上述问题会得到有效解决。

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	蒋瑞瑞
	甘甫平
	郭艺
	闫柏琨

关键词 ：土壤水分, 多源遥感, 光学遥感, 微波遥感, 联合反演

Abstract：

Soil moisture is closely associated with global climate change, the carbon cycle, and the water cycle, as well as agricultural production and ecological conservation and restoration. The detection of soil moisture has shifted from ground survey to remote sensing detection, achieving global- and regional-scale survey and monitoring. Given differences in data spectrum segments, radiative transfer mechanisms, and inversion algorithms, it is necessary to comprehensively analyze the mechanisms, advantages, and limitations of algorithms, with the purpose of laying a foundation for accuracy and algorithm improvement. From the aspects of optical remote sensing, microwave remote sensing, and optic-microwave cooperation, this study systematically analyzed the features and challenges of the following inversion techniques: inversion based on the T_s-VI spatial and T_s-NSSR temporal characteristics of optical remote sensing data, inversion using passive and active microwave data, joint inversion using active and passive microwave data and remote sensing data, and optical-microwave cooperative inversion based on accuracy improvement and spatio-temporal transformation. At present, the joint inversion of soil moisture using multi-source remote sensing data faces the following challenges: ① The data suffer missing and spatio-temporal mismatching; ② Different data sources exhibit varying degrees of surface penetration; ③ The joint inversion model relies on empirical parameters and numerous auxiliary parameters. These challenges can be addressed with the improvement in the satellite monitoring network, the increase in the surface detection depths of data sources, the clarification of the physical mechanisms of joint inversion, and the establishment of spatio-temporal continuous datasets of auxiliary parameters.

Key words： soil moisture multi-source remote sensing optical remote sensing microwave remote sensing joint inversion

收稿日期: 2022-10-20 出版日期: 2024-03-13

ZTFLH:

TP79

基金资助:国家重点研发计划课题“长江和黄河三角洲生态环境演化过程和机制研究”(2019YFE0127200-4);中国地质调查局项目“流域水循环要素与自然资源遥感调查监测”(DD20221642);“高分遥感地质环境综合应用示范”(300012000000194286)

通讯作者: 甘甫平(1971-),男,博士,研究员,主要从事遥感技术方法及地学应用研究。Email: fpgan@aliyun.com。

作者简介: 蒋瑞瑞(1999-),女,硕士研究生,主要从事微波遥感反演土壤水分研究。Email: jjrr3636@163.com。

引用本文:

蒋瑞瑞, 甘甫平, 郭艺, 闫柏琨. 土壤水分多源卫星遥感联合反演研究进展[J]. 自然资源遥感, 2024, 36(1): 1-13.
JIANG Ruirui, GAN Fuping, GUO Yi, YAN Bokun. Progress in research on the joint inversion for soil moisture using multi-source satellite remote sensing data. Remote Sensing for Natural Resources, 2024, 36(1): 1-13.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022408 或 https://www.gtzyyg.com/CN/Y2024/V36/I1/1

特征指数		构成特征空间变量		算法		与土壤水分关系及适用条件		参考文献
温度植被干旱指数(temperature-vegetation dryness index,TVDI)		T_s,NDVI		TVDI= $T s - T s m i n (N D V I) T s m a x (N D V I) - T s m i n (N D V I)$ T_smin=b_min+a_minNDVI T_smax=b_max+a_maxNDVI		与土壤水分呈显著负相关,适用于覆盖度较好区域,不适用于稀疏植被覆盖区域		[21]
温度变化速率-植被干旱指数(temperature rate-vegetation dryness index,TRVDI)		RT,FVC		TRVDI= $R T m a x (i) - R T (i) R T m a x (i) - R T m i n$ RT_max=a+bFVC_i		与土壤水分呈显著负相关,适用于所有植被覆盖情况。RT基于地球同步轨道数据计算,适合用于监测地表土壤水分的时间变化情况		[22]
条件温度植被指数(temperature vegetation difference index,VTCI)		T_s, NDVI		VTCI= $T s N D V I i m a x - T s N D V I i T s N D V I i m a x - T s N D V I i m i n$ $T s N D V I i m a x$ =a+bNDVI_i $T s N D V I i m i n$ =a^'+b^'NDVI_i		与土壤水分呈显著正相关,适用于中高植被覆盖区域		[23]
土壤水分指数(soil water index,SWI)		T_s, NDVI		SWI= $T s m a x (i) - T s (i) T s m a x (i) - T s m i n$ $T s m a x (i)$ =a+bNDVI_i		与土壤水分呈显著正相关,适用于中等植被覆盖度区域		[24]
土壤水分亏缺指数(water deficit index,WDI)		T_s -T_a,SAVI		WDI= $(T s - T a) m i n - (T s - T a) r (T s - T a) m i n - (T s - T a) m a x$		与土壤水分呈显著负相关,适用于所有植被覆盖区域		[25]
热地表覆盖水分指数(thermal ground cover moisture index,TGMI)		TIRDC, FVC		TGMI= $T I R D C n o r m, m a x, i - T I R D C n o r m, i T I R D C n o r m, m a x, i - T I R D C n o r m, m i n$ TIRDC_norm,max,_i= $F V C i + F V C d T I R D C n o r m, d - 1 F V C d T I R D C n o r m, d - 1$ TIRDC_norm,_i= $T I R D C i - T I R D C m i n T I R D C m a x - T I R D C m i n$		与土壤水分呈显著正相关,适用于农业干旱监测,能较好表现出旱地和灌溉区域的土壤水分空间差异		[26]
土壤水分有效值(M₀)	T_s,FVC		M₀= $T d r y - T s T d r y - T w e t$ T_dry=( $T c m a x$ - $T s m a x$ )×FVC+ $T s m a x$ T_wet=( $T c m i n$ - $T s m i n$ )×FVC+ $T s m i n$		与土壤水分呈显著正相关,适用于所有植被覆盖区域		[27]
垂直土壤水分指数(perpendicular soil moisture index,PSMI)	T_s,NDVI		PSMI_i=D_i/(1+NDVI_i) D_i=(TIR_i_,norm+NDVI_i)/ $2$		与土壤水分呈显著负相关,适用于估算农田土壤水分		[28-29]

Tab.1 基于T_s-VI特征空间构建的常用指数

Fig.1 晴空条件NSSR与T_s日变化状况

Fig.2 NSSR与T_S日变化椭圆关系及参数示意图

效应	参数化方案	L-MEB算法	SCA算法	参考文献
植被效应	植被模型	τ-ω模型	τ-ω模型	[51]
	等效反照率	ω_V=ω_H=ω ω=0.06北方森林 ω=0.08(亚)热带森林	ω_V=ω_H=ω ω=0.05森林	[52]
	植被光学厚度	τ_NAD作为位置参数,和土壤水分同时反演 τ_NAD初始值 τⁱⁿⁱ=f(LAI)	τ_NAD=bVWC b=f(IGBP) b=0.10-0.12 VWC=f(NDVI,IGBP)	[53-54]
	植被结构对τ影响	结构校正参数tt_P tt_V=tt_h=1	τ_v=τ_h (观测角=40°)	[55-56]
	等效地表温度	T_G=f(T_soil,surf,T_s_oil,deep)	T_G=f(T_soil,surf,T_soil,deep)	[57-58]
	植被温度	ECMWF表层土壤温度	T_C=T_G	[50]
粗糙度效应	地表粗糙度	H-Q-N模型 H=0.3森林 Q=0; N_v=0,N_h=2	H-Q-N模型 H=0.16森林 Q=0; N_v=N_h=2	[57?-59]
计算土壤水分	土壤介电模型	2012年4月前 Dobson模型 2012年4月后 Mironov模型 e_G=f $S M, T G, % ? c l a y$	Mironov模型 e_G=f(SM,T_G,% clay)	[60]

Tab.2 被动微波土壤水分反演方法参数化方案(修改自[50])

Tab.3 主动微波反演土壤水分模型(修改自[65])

Fig.3 光学与微波联合反演土壤水分流程

方法	原理	公式/模型	参考文献
多元回归统计法	将土壤水分分解成多种高分辨率特征变量的多项式	SM= $∑ i = 0 2 ∑ j = 0 2 ∑ k = 0 2$ a_ijkNDVI_iT_jA_k	[87]
物理模型法	建立高分辨率环境参数(常用土壤蒸发效率)与土壤水分的转化关系	SM=SM_CR+ $? S E E ? S M C R - 1$ ×(SEE_HR-SEE_CR)	[88]
权重分解法	通过土壤水分相关的高、低分辨率辅助数据计算降尺度的权重因子	SM=SWI_CR $S W I H R S W I C R$	[89]
机器学习	挖掘高分辨率辅助数据与低分辨率数据的非线性关系和内在关联特征	神经网络、支持向量机、随机森林	[90?-92]

Tab.4 光学-微波协同降尺度主要方法

Tab.5 光学-微波协同降尺度应用举例

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