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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (1) : 1-13     DOI: 10.6046/zrzyyg.2022408
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Progress in research on the joint inversion for soil moisture using multi-source satellite remote sensing data
JIANG Ruirui1,2,3(), GAN Fuping1(), GUO Yi1, YAN Bokun1
1. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
2. Chinese Academy of Geological Sciences, China University of Geosciences(Beijing), Beijing 100083, China
3. Chinese Academy of Geological Sciences, Beijing 100083, China
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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 Ts-VI spatial and Ts-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.
Keywords soil moisture      multi-source remote sensing      optical remote sensing      microwave remote sensing      joint inversion     
ZTFLH:  TP79  
Issue Date: 13 March 2024
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Ruirui JIANG
Fuping GAN
Yi GUO
Bokun YAN
Cite this article:   
Ruirui JIANG,Fuping GAN,Yi GUO, et al. Progress in research on the joint inversion for soil moisture using multi-source satellite remote sensing data[J]. Remote Sensing for Natural Resources, 2024, 36(1): 1-13.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022408     OR     https://www.gtzyyg.com/EN/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) Ts,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 )
Tsmin=bmin+aminNDVI
Tsmax=bmax+amaxNDVI		 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
RTmax=a+bFVCi		 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) Ts, 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+bNDVIi
T s N D V I i m i n=a'+b'NDVIi		 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) Ts, 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+bNDVIi 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) Ts -Ta,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
TIRDCnorm,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
TIRDCnorm,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) Ts,FVC M0= T d r y - T s T d r y - T w e t
Tdry=( T c m a x- T s m a x)×FVC+ T s m a x
Twet=( 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) Ts,NDVI PSMIi=Di/(1+NDVIi)
Di=(TIRi,norm+NDVIi)/ 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  Common indices based on Ts-VI feature space construction
Fig.1  Diurnal variation of NSSR and Ts under clear sky conditions
Fig.2  Schematic diagram of diurnal elliptic relationship between NSSR and TS and its parameters
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 ωV=ωH=ω
ω=0.06北方森林
ω=0.08(亚)热带森林		 ωV=ωH=ω
ω=0.05森林		 [52]
植被光学厚度 τNAD作为位置参数,和土壤水分同时
反演
τNAD初始值
τini=f(LAI)		 τNAD=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ttP
ttV=tth=1		 τv=τh (观测角=40°) [55-56]
等效地表温度 TG=f(Tsoil,surf ,Tsoil,deep ) TG=f(Tsoil,surf,Tsoil,deep) [57-58]
植被温度 ECMWF表层土壤温度 TC=TG [50]
粗糙度效应 地表粗糙度 H-Q-N模型
H=0.3森林
Q=0; Nv=0,Nh=2		 H-Q-N模型
H=0.16森林
Q=0; Nv=Nh=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
eG=f S M , T G , % ? c l a y		 Mironov模型



eG=f(SM,TG,% clay)		 [60]
Tab.2  Parametric scheme of passive microwave soil moisture retrieval method
主动微波地
表散射模型		 基本原理 参数适用范围 参考
文献
半经验模型 Oh 根据散射理论建立后向散射系数、极化比、交叉极化比与土壤水分、地表粗糙度参数的经验公式 10°≤θ≤70°
0.13<ks<6.98
0.04<SM≤0.291		 [66]
Dubois 根据散射理论建立同极化后向散射系数与介电常数、粗糙度参数、雷达入射角之间的半经验关系,结合介电模型得到土壤水分 kl>6
s/l<0.25
SM≤0.35
1.5 GHz<f
<11 GHz		 [63]
Shi 根据散射理论建立同极化后向散射系数与介电常数、粗糙度谱、极化幅度之间的半经验关系,结合介电模型得到土壤水分 25°≤θ≤70°
0.2<ks<3.6
2.5<kl<35
0.02<SM<0.5		 [64]
物理散射模型 GOM 基于基尔霍夫模型的驻留相位近似法 (2kscosθ)2>10
kl>6
l2>2.76sλ		 [67]
POM 基于基尔霍夫模型的标量近似法 kl>6
s/l<0.25		 [68]
SPM 微扰理论 ks<0.3
kl<3
s/l<0.3		 [69]
IEM 基尔霍夫切面场上引入补偿场 ks<3
s/l<0.3		 [70]
AIEM 在IEM基础上增加更加完整的多次散射项 ks<3
0.05<s/l<0.5		 [71]
Tab.3  Model of active microwave retrieval of soil moisture
Fig.3  Flow chart of combined optical and microwave retrieval of soil moisture
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 2aijkNDVIiTjAk [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=SMCR+ ? S E E ? S M C R - 1×(SEEHR-SEECR) [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=SWICR 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  Main methods of optical-microwave cooperative downscaling
微波数据
及分辨率		 光学数据
及分辨率		 所用方法 降尺度后
分辨率		 RMSE/
(cm3·cm-3)		 参考
文献
SMOS
40 km		 MODIS(Ts,VI)500 m 多元回归统计法 500 m 0.04 [98]
SMAP
36 km		 MODIS(VI)1 km
Sentinel-3(Ts)1 km		 物理模型法 1 km 0.04~0.11 [99]
FY-3B
25 km		 MODIS(Ts,VI)
1 km		 权重分解法 1 km 0.11 [100]
SMAP
9 km		 MODIS(Ts)1 km
MODIS(反射率)500 m		 机器学习 3 km 0.04 [101]
Tab.5  Examples of optical-microwave collaborative downscaling applications
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