自然资源遥感, 2024, 36(1): 1-13 doi: 10.6046/zrzyyg.2022408

综述

土壤水分多源卫星遥感联合反演研究进展

蒋瑞瑞,1,2,3, 甘甫平,1, 郭艺1, 闫柏琨1

1.中国自然资源航空物探遥感中心,北京 100083

2.中国地质大学(北京)中国地质科学院,北京 100083

3.中国地质科学院,北京 100083

Progress in research on the joint inversion for soil moisture using multi-source satellite remote sensing data

JIANG Ruirui,1,2,3, GAN Fuping,1, 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

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。

责任编辑: 李瑜

收稿日期: 2022-10-20   修回日期: 2022-12-11  

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)

Received: 2022-10-20   Revised: 2022-12-11  

作者简介 About authors

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。

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。

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 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

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本文引用格式

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 doi:10.6046/zrzyyg.2022408

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[J]. Remote Sensing for Land & Resources, 2024, 36(1): 1-13 doi:10.6046/zrzyyg.2022408

0 引言

土壤水分是地球系统的重要组成部分,反演其含量不仅有助于全球气候变化[1]、碳循环[2]、水循环[3]等研究,也对区域农业发展[4]、生态建设[5]、生态水文过程[6]等具有重要意义。传统的土壤水分测量主要包括烘干法、电阻探测法[7]、时域反射法[8]、电容法[9]等地面直接测量方法,但由于土壤水分时空变异大,且受制于地面测量点的空间分布、测量数量,仪器及后期维护成本[10]等因素,难以支持区域或全球尺度的监测。随着卫星遥感事业的蓬勃发展,卫星星座及系列卫星成功发射与在轨稳定运行,实现了对土壤水分的局域、区域和全球的调查和动态监测[11]。

不同频谱与观测方式的遥感数据的协同应用与尺度转换支撑了局地到全球不同尺度的应用,研究者们提出并发展了多源遥感数据演土壤水分的算法和模型,有效提升了土壤水分产品的时空分辨率及精度。如Leng等[12] 利用被动微波土壤水分产品弥补光学数据在多云条件下缺失的像元,得到了中国区域全天候高空间分辨率土壤水分产品; 美国喷气推进实验室的Das等[13]利用Sentinel-1A/B雷达数据对主、被动土壤水分观测任务(Soil Moisture Active Passive,SMAP)的微波辐射计数据进行降尺度,生产了空间分辨率为3 km和1 km的高空间分辨率的土壤水分产品; Wang等[14]基于ALOS2-ScanSAR(advanced land observing satellite-2)和MODIS(moderate resolution imaging spectroradiometer)数据,综合考虑了二次散射的水云模型反演青藏高原地区土壤水分,并讨论了植被茎干因子对于反演结果的影响,验证了微波与光学遥感观测在土壤水分反演中的潜在耦合能力。

本文从土壤水分遥感反演原理出发,从光学遥感、微波遥感以及光学和微波的联合应用3个方面,对土壤水分遥感联合反演的技术方法特点、优势、存在的问题进行了剖析,明晰了未来的发展趋势。旨在为土壤水分多源卫星遥感联合反演的进一步研究提供理论基础和技术支持。

1 光学遥感土壤水分反演

不同含水量土壤在水分子吸收光谱区间具有较明显的反射率差异[15],通过构建对土壤水分变化敏感的可见光/近红外特征光谱指数,并建立其和土壤水分的定量关系,可以反演土壤水分[16]。土壤水分也会影响土壤的热特性,从而可以利用热红外遥感数据进行土壤水分反演[17]。上述单一类型的数据、单一特征的反演算法针对简单的下垫面可以取得好的应用效果,但地表类型的复杂性及土壤水分时空的异质性使这些算法在精度、应用范围等方面存在一定的局限性。温度(Ts)-植被指数(vegetation index,VI)联合反演算法得到了广泛发展[18]。同时,为弥补光学遥感反演时间分辨率较低的不足,利用静止轨道卫星获取地表短波净辐射(net surface shortwave radiation,NSSR)建立长时间序列Ts-NSSR土壤水分反演算法也得到重视[19]。

1.1 Ts-VI空间特征土壤水分反演

土壤水分与土壤质地、地表(冠层)温度、植被指数等存在很大的相关性。基于地表能量平衡原理,从土壤蒸发、植被蒸腾出发,可以构建Ts-VI特征空间。在土壤水分—地表温度—植被相互作用过程中,Ts-VI二维散点图的空间特征可以提供植被水分胁迫条件,并间接反演土壤水分信息[20]。受到干旱胁迫的像元构成特征空间的干边,表示不同植被覆盖度下对应的温度最高值,对应区域土壤水分最小值。最大蒸散发的像元构成特征空间的湿边,表示不同植被覆盖度下对应的温度最低值,对应区域土壤水分的最大值。利用Ts-VI特征空间反演土壤水分的优势是: 方法简洁,对土壤含水量时序变化及植被水分状态敏感,便于生态水文研究。局限是: ①因未考虑气象(辐射、大气湿度、风速等)、植被类型对Ts-VI关系的影响,难以实现大区域应用且反演精度难以保证; ②在干边、湿边的确定中存在较大的人为主观性。

研究者基于特征空间的几何结构和物理特征,构建了大量的土壤水分特征指数(表1)。表1中: min,max,norm和i分别为最小值、最大值、归一化值和像元i; Ts为地表温度; Tc为植被冠层温度; Ta为气温; NDVI为归一化植被指数(normalized difference vegetation index,NDVI); RT为上午温度的上升速率(mid-morning rising rate of temperature,RT); FVC为植被覆盖度(fraction vegetation cover,FVC); TIRDC为热红外原始数字计数(thermal infrared raw digital count,TIRDC); SAVI为土壤调节植被指数(soil-adjusted vegetation index,SAVI); M0为土壤水分有效值; Di为像元i到特征空间参考线的垂直距离(参考线为经过土壤水分最高点且斜率为-0.5的直线)。实际应用中,需要根据干边对应残留土壤含水量,湿边对应饱和土壤含水量的原则,将不同土壤含水量拉伸到特征空间中,建立指数与土壤水分的转换关系从而实现土壤水分反演,通常函数关系表示为:

SM=f(Index,SMmax,SMmin),

式中: SM为土壤水分; Index为土壤水分特征指数; SMmax和SMmin分别为土壤水分最大和最小值。

表1   基于Ts-VI特征空间构建的常用指数

Tab.1  Common indices based on Ts-VI feature space construction

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,NDVITVDI=Ts-Tsmin(NDVI)Tsmax(NDVI)-Tsmin(NDVI)
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,FVCTRVDI=RTmax(i)-RT(i)RTmax(i)-RTmin
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, NDVIVTCI=TsNDVIimax-TsNDVIiTsNDVIimax-TsNDVIiminTsNDVIimax=a+bNDVIi
TsNDVIimin=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, NDVISWI=Tsmax(i)-Ts(i)Tsmax(i)-TsminTsmax(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,SAVIWDI=(Ts-Ta)min-(Ts-Ta)r(Ts-Ta)min-(Ts-Ta)max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, FVCTGMI=TIRDCnorm,max,i-TIRDCnorm,iTIRDCnorm,max,i-TIRDCnorm,min
TIRDCnorm,max,i=FVCi+FVCdTIRDCnorm,d-1FVCdTIRDCnorm,d-1
TIRDCnorm,i=TIRDCi-TIRDCminTIRDCmax-TIRDCmin		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,FVCM0=Tdry-TsTdry-Twet
Tdry=(Tcmax-Tsmax)×FVC+Tsmax
Twet=(Tcmin-Tsmin)×FVC+Tsmin		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,NDVIPSMIi=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]

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由于土壤残留含水量与土壤饱和含水量需要实验室测量,有研究用土壤田间持水量代替特征空间中土壤水分最大值,土壤萎蔫点代替最小值[30],进而基于M0的等值线构建其与土壤水分的关系,土壤水分估算值表现为:

M0×(SMmax-SMmin)+SMmin 。

后来在与不同深度土壤水分实测值进行对比验证后,发现该方法与40 cm根区层土壤水分相关性最高,均方根误差最小[27]。蔡庆空等[31]从研究区实测值中提取出土壤水分最大值和最小值,以建立特征空间中TVDI与土壤水分的关系,验证结果表明其RMSE小于0.06 cm3/cm3。

目前,基于特征空间反演土壤水分方法的发展主要致力于研究不同温度、温差及植被指数构建的相关指数对于反演精度的影响。在Ts-VI空间遥感信息综合反演中,特征空间构建所用的地表温度信息主要是通过热红外数据反演获取。根据MODIS,AVHRR(advanced very high resolution radiometer),ASTER(advanced spaceborne thermal emission and reflection radiometer),VIIRS(visible infrared imaging radiometer),FY(Fengyun),GOES(geostationary operational environmental satellite)及Landsat系列等热外数据的特点,采用温度发射率分离算法、劈窗算法等,获取不同空间分辨率和时间分辨率的温度信息。对于植被指数VI,最初NDVI因能够有效通过植被表面绿度及生长状态反映植被生长环境的干旱胁迫而被广泛用于特征空间构建。随着研究的深入和业务的需要,根据数据源、下垫面的差异等,一些新型植被指数,比如增强型植被指数(enhanced vegetation index,EVI)、比值植被指数(ratio vegetation index,RVI)、FVC、SAVI、修正土壤调整植被指数(modified soil-adjusted vegetation index,MSAVI)等也被引入到Ts-VI空间进行土壤水分的反演中。有的研究基于上午时间段地表温度的上升速率和植被覆盖度提出新的改进植被干旱指数(temperature rising rate vegetation dryness index,TRRVDI)反演土壤水分,结果经过验证相对误差仅为4%[32]; 基于该思路,Przeź等[33] 提出了用地表温度和离地2 m气温的温差代替地表温度的修正TDVI,并利用降水数据与经典TDVI进行比较,结果表明改进的TDVI能够更精确地反映地表土壤水分变化。最近有研究对多种土壤水分相关指数展开联合分析与应用,如刘子琪等[34]对多种土壤水分指数在不同植被覆盖度状况下的适用性进行了定量分析; 高琪等[35]通过偏最小二乘和机器学习方法,用TVDI,EVI,SAVI等26个光谱指数和温度、地形因子分别构建土壤水分反演模型,结果表明随机森林法效果最好。

1.2 Ts-NSSR时间特征土壤水分反演

基于极轨卫星反演土壤水分的方法受卫星重访周期、数据有效覆盖等影响,难以获取高频次的土壤水分信息。由于地球静止轨道卫星能够以高时间分辨率对特定的区域进行长时间序列观测,先后有研究者对地球静止轨道卫星估算土壤水分的潜力进行验证[36-37],在利用陆面模型对裸土多个物理参数与土壤水分的敏感性进行分析后,发现中午时段(日出后1.5~4 h)温度上升差值dT与NSSR差值dS之比TN对于土壤水分最敏感[38](图1),并基于此提出了与TN和最大温度出现时间(td)相关的多元线性模型来反演裸土土壤水分,公式为:

SM=c+dTN+etd,

式中: SM为地表土壤水分; c,d,e为拟合系数。

图1

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图1   晴空条件NSSR与Ts日变化状况

Fig.1   Diurnal variation of NSSR and Ts under clear sky conditions


但该方法仅针对于地表温度的变化进行简单线性拟合,忽略了地表温度与NSSR所包含的非线性关系。基于此,有的研究利用地表温度与NSSR的椭圆特征,通过逐步线性回归建立与土壤水分的关系[39],计算公式为:

SM=n1x0+n2y0+n3a+n4q+n0,

式中: a为椭圆长半轴; q为椭圆旋转角度; x0,y0为椭圆中心的横、纵坐标; ni(i=0,1,2,3,4)为模型系数,椭圆参数均由公共土地模型(community land model,CLM)确定。图2[40]展示了椭圆参数及椭圆关系,图中b为椭圆短半轴。

图2

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图2   NSSR与TS日变化椭圆关系及参数示意图

Fig.2   Schematic diagram of diurnal elliptic relationship between NSSR and TS and its parameters


最近,有的研究基于西班牙土壤水分监测网络(REMEDHUS)地表实测数据,针对上述2种反演土壤水分的方法进行比较验证[40],结果表明: 2种方法均能较好估算出裸土地表土壤水分,RMSE在0.04~0.05 cm3/cm3左右,反映出利用时间连续的地球同步轨道卫星数据估算区域乃至全球尺度土壤水分的巨大潜力。

2 微波遥感土壤水分反演

不同含水量的土壤介电特性不同,水含量越高,其介电常数越大,这种差异使微波辐射特性对地表土壤水分变化敏感,是直接利用微波遥感进行土壤水分反演的基础。而在真实应用场景中,植被理化参数、地表粗糙度和土壤含水量对微波辐射特性的影响严重混叠,对三者影响进行分离是核心问题,植被状态和地表粗糙度是土壤水分微波反演精度的主要影响因素。为了提高流域、区域以及全球尺度的土壤水分反演的精度,在微波遥感水分反演中,往往充分优选不同载荷、不同频率、不同极化、多角度的数据,以更好地消除植被效应、粗糙度对土壤水分反演的影响。

2.1 多参数联合的被动微波反演

被动微波尤其是低频微波传感器发展成熟、对于地表穿透力更强、对土壤水分更加敏感,被广泛应用于中高分辨率土壤水分产品的生产之中。这一生产过程中发展出许多基于辐射传输过程机理模拟的方法,如基于τ-ω辐射传输模型发展的L波段微波发射模型(L-band microwave emission of the biosphere,L-MEB)[41]、单通道算法(single channel algorithm,SCA)[42]、双通道算法(dual channel algorithm,DCA)[43]、地面参数反演模型(land parameter retrieval model,LPRM)[44]等多种模型。由于地表辐射传输过程中会受到植被层散射、大气衰减等过程的影响,机理模拟法使用建模方法将这些环境因素参数化[45]。

目前,SMOS(soil moisture and ocean salinity)卫星和SMAP卫星采取不同机理反演方法承担了全球土壤水分产品的生产任务,在这一生产过程中,参数化方案起到了关键作用。表2中整理了2种土壤水分产品算法的参数化方案。表2中: ω为单次散射反照率; τNAD为天顶角下的植被光学厚度; τini为植被光学厚度初始值; LAI为叶面积指数(leaf area index,LAI); VWC为植被含水量(vegetation water content,VWC); IGBP为国际地圈生物圈计划(international geosphere-biosphere program,IGBP),为模型提供土地覆盖类型数据; TG为地表等效温度; TC为植被冠层温度; Tsoil,surf为土壤层0~10 cm温度; Tsoil,deep为10~20 cm土壤层温度; εG为土壤介电常数; 欧洲中期天气预报中心(european centre for medium-range weather forecasts,ECMWF)为反演模型提供近实时地表温度数据。算法根据地表不同植被结构和土壤状况设计出不同的参数来量化植被效应和粗糙度效应,使模型更加接近真实辐射传输过程,从而提高模型反演精度。而算法设计初期的植被效应仅用植被光学厚度和单次散射反照率2个参数来量化[46],尔后研究者针对不同观测角下植被冠层结构的各向异性又引入植被结构校正参数,以修正辐射传输过程中冠层散射以及极化方式对植被光学厚度的影响[47]。还有的研究基于地基或机载实验,对不同下垫面进行反演模型的参数优化,其精度均较SMAP/SMOS土壤水分产品有所提高。如有的研究利用农田的实测土壤温度和冠层温度取代SMAP中由陆地表面模型深度校正同化得到的土壤温度和等效于土壤温度的植被冠层温度,将SMAP裸土和低矮植被区的土壤水分产品的RMSE由0.063 cm3/cm3提升至0.041 cm3/cm3,高植被覆盖区域由0.083 cm3/cm3提升至0.078 cm3/cm3[48]。靳梦杰[49]结合地面观测参数,针对阔叶林和针叶林分别建立了不同极化的植被参数化模型,该模型能反映出植被光学厚度和单次散射反照率的时变特性和极化特性,极大提高了SMOS和SMAP算法在林下的土壤水分反演精度。

表2   被动微波土壤水分反演方法参数化方案(修改自[50])

Tab.2  Parametric scheme of passive microwave soil moisture retrieval method

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=fSM,TG,% clay		Mironov模型



eG=f(SM,TG,% clay)		[60]

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2.2 基于模型的主动微波反演

土壤水分是影响土壤后向散射能力的重要参数,主动微波通过雷达观测的后向散射系数、构建与土壤水分的转化关系,实现土壤水分空间连续反演。目前已发展出Oh模型、Dubois模型、Shi模型等多种半经验模型,以及小扰动模型(small perturbation model,SPM)、几何光学模型(geometry optics model,GOM)、物理光学模型(physical optics model,POM)、积分方程模型(integral equation model,IEM)和高级积分方程模型(advanced integrated emission model,AIEM)等物理模型对裸土及植被稀疏地区进行土壤水分反演。

半经验模型通常基于不同波段、入射角、极化方式的雷达观测数据及一定条件下的地表参数实测数据而建立,对各个参数的适用范围有不同的要求。Oh模型是基于偏振雷达对不同入射角、粗糙度和土壤水分条件下的观测数据及粗糙度、土壤水分实测数据建立起来的[61],具有较宽的应用范围,但对于高频微波数据和粗糙地表并不适用[62]。Dubois模型针对裸土和植被稀疏地区而设计,在参数适用范围内效果较好,总体误差可以控制在0.04 cm3/cm3之内[63]。Shi模型模拟的地表参数范围较广且不依赖于试验区,能够较好估算裸土和稀疏植被区域的粗糙度参数及土壤水分[64]。但半经验模型只在适用范围内能取得较好效果; 对于地表状况复杂的区域,通常使用反演机理更加完善的物理模型。表3对这些模型的原理和参数适用范围进行了总结,表中: θ为入射角; k为自由空间波数; s和l分别为均方根高度和相关长度,是量化地表粗糙度的参数; SM为土壤水分; f为频率; λ为波长。

表3   主动微波反演土壤水分模型(修改自[65])

Tab.3  Model of active microwave retrieval of soil moisture

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]

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在物理模型中, GOM适用于非常粗糙的地表, POM适用于中等粗糙地表, SPM适用于相关长度较小的地表,这些表面散射模型在描述地表粗糙度时各有局限性[72]。为了模拟连续的不同粗糙度地表状况,研究者在基尔霍夫模型的基础上引入补偿场,提出了IEM模型[73],该模型能够模拟较宽粗糙度范围下的后向散射系数。但积分方程模型对于补偿场系数进行了简化从而在模拟时会造成误差,有的研究在此基础上对补偿场系数进行重新推导,提出了适用性更广、模拟精度更高的AIEM模型[74]。多项研究证明,AIEM具有模拟多波段、多角度、多极化等不同雷达数据在不同粗糙度和土壤水分条件下的后向散射系数的能力[75⇓-77],是目前反演土壤水分的主流方法。有的研究利用AIEM提出了一种利用双入射角和双偏振L波段雷达数据估算裸地土壤水分的新型反演模型,反演精度在0.08~0.32 cm3/cm3之间[78]。最近,有研究利用AIEM验证了不同极化组合反演土壤水分的潜力,结果表明HH+VV极化组合反演结果最好,与实测数据的RMSE达到0.044 cm3/cm3[79]。耿德源等[80]对多种地表微波散射模型进行对比分析,结果表明用AIEM和Oh联合模拟交叉极化通道的方法优于其他模型。

2.3 主被动微波遥感联合反演

辐射计所接收的地表辐射亮温对土壤水分十分敏感,但因空间分辨率低不能表达小尺度区域土壤水分的空间差异,且容易受到射频干扰造成局部数据缺失。而主动微波的合成孔径雷达(synthetic aperture Radar,SAR)具有高空间分辨率,且相比于土壤水分,其后向散射系数对表层植被冠层结构和地表粗糙度更敏感。所以研究者用主、被动微波联合反演土壤水分以提高反演结果的精度和时空分辨率。其主要思路是利用被动微波数据反演植被参数或土壤水分作为主动微波反演模型的输入参数,以得到高精度土壤水分产品。利用主动微波数据反演植被信息和地表粗糙度参数输入被动反演模型,也能得到同样效果。

如O’Neill等[81]用主动微波计算植被的透射率和散射率,并将植被参数输入被动微波数据的辐射传输模型中计算土壤水分,经过与实测值的验证,平均绝对误差达到0.02 cm3/cm3; 武胜利[82]将被动微波反演的土壤水分输入主动微波后向散射系数模型计算均方根斜度,在假设土壤粗糙度在稳定的前提下,获取的均方根斜度可用于后续雷达数据计算土壤水分,将该方法应用于青藏高原地区土壤水分反演,验证结果均方根误差达到0.046 cm3/cm3。此外,人工智能的发展也为主、被动参数互补提供了新的思路,Santi等[83]基于人工神经网络法分析主、被动微波数据之间的协同作用,并利用SMAP,AMSR2和Sentinel-1协同反演土壤水分。验证结果表明,该方法的均方根误差为0.024 cm3/cm3,误差明显小于SMAP官方产品精度0.04 cm3/cm3。

3 光学遥感与微波遥感协同反演

在上文中,分别论述了可见光-近红外-热红外等光学波段和主、被动微波遥感土壤水分反演的方法、模型等。但土壤水分是水文过程、生物作用、土壤质变等综合作用的产物。不同土壤类型以及颗粒大小、不同植被类型与覆被情况等都将影响遥感反演的效果。为了更好地消除或减少这些影响因素,提高土壤水分遥感反演的精度,改善土壤水分时空异质性影响,相关协同的模型和算法应运而生。

3.1 基于精度改善的光学与微波反演模型协同

主、被动微波数据接收的电磁波信号受到地表植被冠层结构的影响,遥感反演土壤水分存在不确定性、区域性精度不高或精度存在区域差异等; 而光学数据包含植被敏感光谱,能有效提取地表植被层的物理信息。所以光学与微波协同提高植被覆盖区域的土壤水分反演精度的关键是利用光学数据量化植被的影响,目前研究的趋势是针对植被效应模型进行植被含水量、粗糙度等参数的改进。图3描述了光学与微波联合反演土壤水分的流程,其关键是通过光学数据提供的植被信息去除辐射传输信号中的植被效应。

图3

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图3   光学与微波联合反演土壤水分流程

Fig.3   Flow chart of combined optical and microwave retrieval of soil moisture


上文(2.1节)中介绍的被动微波反演土壤水分的核心τ-ω模型中,用于去除植被效应的植被光学厚度最终被参数化为关于NDVI和LAI的函数,而光学数据能够精细有效地提供这些植被指数,辅助去除辐射传输中植被效应的影响。

与被动微波相似,光学-主动微波协同反演土壤水分需要先构建光学数据与植被含水量的关系以量化后向散射信号中植被层的贡献,通过植被散射模型得到土壤后向散射系数,进而利用2.2节中介绍的主动微波模型反演土壤水分。目前考虑植被效应的散射模型有水云模型[84]和密歇根模型(Michigan microwave canopy scattering model,MIMICS)[85]。但MIMICS涉及参数复杂(需要植被叶片和枝干参数),难以得到广泛应用。而水云模型将植被层简化为均匀的水滴层,散射过程简化分解为植被层散射和到达地表经植被层二次衰减的散射,将总的后向散射系数(σ0)分解为植被后向散射(σveg0)和土壤后向散射(σsoil0)的总和。水云模型中的VWC通常由光学数据构建的不同植被参数构成,但由于水云模型中将地表粗糙度作为常量,忽视了植被冠层结构和土壤粗糙度对于后向散射系数的影响,所以有的研究尝试将粗糙度效应引入水云模型中。如马腾等[86]利用Oh模型的交叉极化比和变换土壤调节植被指数估算地表粗糙度参数,进而构建改进的水云模型; 并比较不同植被覆盖条件下改进前后水云模型的均方根误差。结果表明,在高植被覆盖条件下,改进的水云模型反演结果均优于经典水云模型反演精度。

3.2 多源数据时空尺度联合反演

光学遥感、微波辐射计及SAR土壤水分产品由于空间分辨率不同而被应用于不同空间尺度。辐射计由于空间分辨率较低一般为全球尺度产品,而光学及雷达由于空间分辨率较高一般为区域或流域尺度产品。由于单一产品存在不同的不确定性等问题,通过光学和微波遥感时空降尺度的方式,能够有效提升土壤水分产品的应用效益。光学-微波数据实现降尺度的核心是建立低空间分辨率被动微波土壤水分产品与中高空间分辨率光学数据辅助变量之间的相关关系或物理模型。目前,光学-微波协同降尺度方法有多元统计回归法、物理模型法、权重分解法、机器学习等,表4对这些方法的原理和基本公式或者模型进行了整理。表中: T为归一化地表温度; aijk为回归系数; A为地表反照率; SEE为土壤蒸发效率(soil evaporative efficiency,SEE); CR为低空间分辨率; HR为高空间分辨率。

表4   光学-微波协同降尺度主要方法

Tab.4  Main methods of optical-microwave cooperative downscaling

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=02∑j=02∑k=02aijkNDVIiTjAk[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+∂SEE∂SMCR-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=SWICRSWIHRSWICR[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]

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多元统计回归法的基本原理是将土壤水分分解成多种特征变量的多项式,这些变量通常是由光学数据计算的地表温度、植被指数、地表反照率等构成[93]。但由于多元统计回归法缺乏物理基础,有的研究利用地表温度和植被覆盖度的特征空间估算土壤蒸散效率,将土壤蒸散效率作为表征土壤水分的指标对土壤水分产品降尺度[94]。而权重分解法的基本原理是将基于光学数据计算的土壤湿度指数[95]、条件温度植被指数[96]等作为权重因子对土壤水分产品实现降尺度。研究结果表明,这些降尺度方法均能较好地反映被动微波像元内土壤水分空间差异[97]。近年来,机器学习法在降尺度方面也取得了显著的成果,该方法可以根据训练样本建立精确的关系模型。但是机器学习模型的机理不清晰,并且模型的训练需要输入大量样本,训练样本的代表性、相关性都影响着模型的整体精度,不同输入变量的组合也会增加模型的不确定性。目前主流降尺度方案是将光学数据MODIS和SMOS,SMAP产品用不同方法进行耦合,以得到高空间分辨率土壤水分产品。表5整理了上述方法的应用案例,可以看出SMOS,SMAP产品不同降尺度方法得到的500 m,1 km,3 km产品精度,基本上能保持产品原有目标精度0.04 cm3/cm3。基于FY-3B数据用权重分解降尺度到1 km,结果精度仍有待提高。

表5   光学-微波协同降尺度应用举例

Tab.5  Examples of optical-microwave collaborative downscaling applications

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 m0.04[98]
SMAP
36 km		MODIS(VI)1 km
Sentinel-3(Ts)1 km		物理模型法1 km0.04~0.11[99]
FY-3B
25 km		MODIS(Ts,VI)
1 km		权重分解法1 km0.11[100]
SMAP
9 km		MODIS(Ts)1 km
MODIS(反射率)500 m		机器学习3 km0.04[101]

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4 结论

通过对光学遥感、主被动微波以及光学和微波联合反演土壤水分方法的总结,能够看出多源遥感数据联合应用可以充分发挥不同数据的优点,但是也因为不同数据自身的限制,使联合反演土壤水分仍面临一些亟待解决的问题。

1)土壤水分反演中,数据源受制于传感器物理特性,在一定条件下存在数据缺失、时空尺度不匹配等问题。随着越来越多传感器和各种专项卫星监测网络的建成与应用,这些问题会得到改善。

2)不同频段遥感数据对“地表-植被”系统的穿透深度不同,反映的信息各有侧重,开展联合反演是实现“地表-植被”系统“层析成像”、提高反演精度的重要途径。目前联合反演土壤水分的关键是,根据不同土壤类型和下垫面,分别构建统一的地表粗糙度模型与植被传输模型。

3)多源遥感数据联合反演土壤水分算法为了描述复杂的辐射传输过程,对许多未知参数进行了假设,且参与反演过程的植被参数往往基于经验得到,因而为反演结果增加了不确定性。随着多角度、多时相、多极化数据的产生,更多地面信息可以被提取出来应用于反演模型,减少模型的未知参数,许多模型病态反演的问题会得到解决。此外,建立辅助参数(如植被含水量)的时空连续数据集对于反演模型精度的提高有着重要作用。

多源遥感数据联合反演土壤水分是土壤水分反演方法的大势所趋。随着各种新型传感器的开发与应用,土壤水分反演模型也将得到进一步的创新和改进,加上气象、地形等越来越多数据的融合,土壤水分时空分辨率和精度将得到全面提升。

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土壤水分是连接地表水循环和能量循环的关键参量,精确获取该参量对于理解气候变化、地表水文过程、地气间能量交换机理等具有重要意义。微波遥感由于其较为合适的探测深度和坚实的理论基础在观测地表浅层土壤水分上具有很大优势,结合反演方法可以获取空间连续的土壤水分含量,有助于更加客观认知土壤水分的时空演变机理。随着微波遥感数据的不断丰富,多种微波遥感土壤水分反演方法相继涌现,为了更好地了解其发展和趋势,本文总结了当前土壤水分微波反演常用的卫星遥感数据并分析其发展趋势,后从主动微波反演、被动微波反演和多源协同反演3个方面梳理了各类土壤水分微波反演方法的原理、发展和优缺点,最终总结出目前微波遥感土壤水分反演方法的发展趋势：即土壤水分微波反演方法的时空普适性逐渐增强、面向高时空分辨率的土壤水分微波协同反演方法快速发展以及土壤水分微波反演方法的智能化水平不断提高。

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微波遥感土壤湿度产品是目前在大尺度水资源或气候变化研究中比较常用的地表土壤湿度数据，但其空间分辨率一般都较粗，不能满足区域或流域尺度相关研究要求.因而，在这些尺度的相关研究中需要对土壤湿度产品进行空间降尺度.UCLA法是一种土壤湿度降尺度方法，该方法使用地表温度和植被指数特征空间指数（Ts/VI指数）作为降尺度因子.本文以AMSRE土壤湿度产品作为土壤湿度粗分辨率数据，使用MODIS地表温度产品（MYD11A1）和植被指数产品（MYD13A2）计算3种指数&mdash;&mdash;土壤湿度指数（SW）、温度植被干旱指数（TVDI）和条件温度植被指数（VTCI），对比了3种Ts/VI指数分别作为UCLA法降尺度因子的效果.这3种指数均能得出合理的降尺度结果，但使用TVDI和VTCI的降尺度结果稍优于SW，说明TVDI和VTCI更适合作为UCLA法的降尺度因子.最后讨论了UCLA法的误差来源，如粗分辨率土壤湿度产品的测量误差、降尺度因子的计算误差以及UCLA法自身的误差，并对未来的研究做出展望.&nbsp;

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DOI:10.3389/fenvs.2021.555216      URL     [本文引用: 1]

DISPATCH is a disaggregation algorithm of the low-resolution soil moisture (SM) estimates derived from passive microwave observations. It provides disaggregated SM data at typically 1 km resolution by using the soil evaporative efficiency (SEE) estimated from optical/thermal data collected around solar noon. DISPATCH is based on the relationship between the evapo-transpiration rate and the surface SM under non-energy-limited conditions and hence is well adapted for semi-arid regions with generally low cloud cover and sparse vegetation. The objective of this paper is to extend the spatio-temporal coverage of DISPATCH data by 1) including more densely vegetated areas and 2) assessing the usefulness of thermal data collected earlier in the morning. Especially, we evaluate the performance of the Temperature Vegetation Dryness Index (TVDI) instead of SEE in the DISPATCH algorithm over vegetated areas (called vegetation-extended DISPATCH) and we quantify the increase in coverage using Sentinel-3 (overpass at around 09:30 am) instead of MODIS (overpass at around 10:30 am and 1:30 pm for Terra and Aqua, respectively) data. In this study, DISPATCH is applied to 36 km resolution Soil Moisture Active and Passive SM data over three 50 km by 50 km areas in Spain and France to assess the effectiveness of the approach over temperate and semi-arid regions. The use of TVDI within DISPATCH increases the coverage of disaggregated images by 9 and 14% over the temperate and semi-arid sites, respectively. Moreover, including the vegetated pixels in the validation areas increases the overall correlation between satellite and in situ SM from 0.36 to 0.43 and from 0.41 to 0.79 for the temperate and semi-arid regions, respectively. The use of Sentinel-3 can increase the spatio-temporal coverage by up to 44% over the considered MODIS tile, while the overlapping disaggregated data sets derived from Sentinel-3 and MODIS land surface temperature data are strongly correlated (around 0.7). Additionally, the correlation between satellite and in situ SM is significantly better for DISPATCH (0.39–0.80) than for the Copernicus Sentinel-1-based (−0.03 to 0.69) and SMAP/S1 (0.37–0.74) product over the three studies (temperate and semi-arid) areas, with an increase in yearly valid retrievals for the vegetation-extended DISPATCH algorithm.

宋承运, 王艳丽.

风云三号土壤水分遥感产品降尺度方法对比分析

[J]. 黑龙江工程学院学报, 2021, 35(3):13-16,32.

[本文引用: 1]

Song C Y, Wang Y L.

A comparison study of FY-3B soil moisture downscaling methods

[J]. Journal of Heilongjiang Institute of Technology, 2021, 35(3):13-16,32.

[本文引用: 1]

Xu W, Zhang Z, Long Z, et al.

Downscaling SMAP soil moisture products with convolutional neural network

[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:4051-4062.

DOI:10.1109/JSTARS.2021.3069774      URL     [本文引用: 1]

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