Sensitivity to soil moisture by active and passive microwavesensors

The backscatter measured by radar and the emission measured by a radiometer are both very sensitive to the moisture content m_υ of bare-soil surfaces. Vegetation cover complicates the scattering and emission processes, and it has been presumed that the addition of vegetation masks the soil surface, thereby reducing the radiometric and radar soil-moisture sensitivities. Even though researchers working in the field of microwave remote sensing of soil moisture are all likely to agree with the preceding two statements, numerous claims and counterclaims have been voiced, primarily at symposia and workshops, espousing the superiority of the radiometric technique over the radar, or vice versa. The discussion is often reduced to disagreements over the answer to the following question “Which of the two sensing techniques is less impacted by vegetation cover?” This paper is an attempt to answer that question. Using realistic radiative-transfer models for the emission and backscatter, calculations were performed for three types of canopies, all at 1.5 GHz. The results lead to two major conclusions. First, the accepted presumption that vegetation cover reduces the soil-moisture sensitivity is not always true. Over certain ranges of the optical depth τ of the vegetation canopy and the roughness of the soil surface, vegetation cover can enhance, not reduce, the radar sensitivity to soil moisture. The second conclusion is that under most vegetation and soil-surface conditions, the radiometric and radar soil-moisture sensitivities decrease with increasing τ, and the rates are approximately the same for both sensors, suggesting that at least as far as vegetation effects are concerned, neither sensor can claim superiority over the other     

基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演

冬小麦是我国重要粮食作物之一,对冬小麦覆盖地表土壤水分进行监测有助于解决因土壤供水导致的冬小麦歉收和农业用水浪费等问题。为了降低冬小麦覆盖地表土壤水分微波遥感反演过程中冬小麦对雷达后向散射系数的影响,该文基于Sentinel-1携带的合成孔径雷达(SAR)数据和Sentinel-2携带的多光谱成像仪(MSI)数据,结合水云模型,开展冬小麦覆盖地表土壤水分协同反演研究。首先,基于MSI数据,该文定义了一种新的植被指数,即融合植被指数(FVI),用于冬小麦含水量反演;然后,该文发展了一种基于主被动遥感数据的冬小麦覆盖地表土壤水分反演半经验模型,校正冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;最后,以河南省某地冬小麦农田为研究区域,开展归一化水体指数(NDWI)和FVI两种指数与VV, VH, VV/VH 3种极化组合而成的6种反演方式下的土壤水分反演对比实验。结果表明:以FVI为植被指数,能够更好地去除冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;6种反演方式中,FVI与VV/VH组合下的反演效果最优,其决定系数为0.7642,均方根误差为0.0209 cm3/cm3,平均绝对误差为0.0174 cm3/cm3,展示了该文所提土壤水分反演模型的研究价值和应用潜力。     

The SIR-B Observations of Microwave Backscatter Dependence on Soil Moisture, Surface Roughness, and Vegetation Covers

An experiment was conducted from an L-band syntheticaperture perture radar aboard space shuttle Challenger in October 1984 to study the microwave backscatter dependence on soil moisture, surface roughness, and vegetation cover. The results based on the anlyses of an image obtained at 21° incidence angle show a positive correlation between scattering coefficient and soil moisture content, with a sensitivity comparable to that derived from the ground radar measurements [1]. The surface roughness strongly affects the microwave backscatter. A factor of 2 change in the standard deviation of surface roughness height gives a corresponding change of about 8 dB in the scattering coefficient. The microwave backscatter also depends on the vegetation types. Under the dry soil conditions, the scattering coefficient is observed to change from about -24 dB for an alfalfa or lettuce field to about -17 dB for a mature corn field. These results suggest that observations with a synthetic-aperture radar system of multiple frequencies ies and polarizations are required to unravel the effects of soil ture,oisre, surface roughness, and vegetation cover.     

Effects of Varying Soil Moisture Contents And Vegetation Canopies on Microwave Emissions

This paper presents an analysis of radiometric data taken at 21, 2.8, and 1.67 cm during a NASA sponsored flight over agricultural fields in Phoenix, AZ. The objective of the mission was to provide comprehensive information concerning microwave responses due to a broad range of soil moisture contents. Generally, data taken over bare fields agree well with theoretical estimates from a combined multilayer radiative transfer model with simple roughness correction. With the surface moisture content ranging between <5 and >35 percent, the emissivity ranges between >0.9 and ~0.7. The response to soil moisture content at 21 cm is more senstive than that at either 2.8 or 1.67 cm. The vegetation model takes into account both the effect of dielectric coefficient and the volume scattering characteristics of the vegetation layer. At the longer wavelengths (e.g., 21 cm) radiation from soil penetrates through vegetation layers of wheat and alfalfa and provides surface moisture information. However, short wavelength radiation from soil cannot penetrate through vegetation canopies; the volume scattering characteristics of vegetation controls the overall microwave signatures.     

The effect of topography on radar scattering from vegetated areas

The ways in which radar scattering from vegetated areas is affected by the topography of the surface underneath the vegetation are discussed. It is shown, using a discrete scatterer model, that the dominant scattering mechanism may change drastically when the ground surface is tilted relative to the horizontal. For a horizontal ground surface, for example, the total scattering may be dominated by scattering off the tree trunks, followed by a reflection off the ground surface. For a relatively small tilt in the ground surface, the ground-trunk interaction term may be replaced by scattering from the branches alone as the dominant scattering mechanism. The effect of the topography is more pronounced for scattering by longer wavelengths, and the implications on algorithms designed to infer forest woody biomass and soil and vegetation moisture using polarimetric SAR data are discussed. The effect of the topography on the scattering behavior from forested areas is illustrated with images acquired by the NASA/JPL three-frequency polarimetric SAR over the Black Forest in Germany     

基于遗传BP神经网络算法的主被动遥感协同反演土壤水分

提出了一种基于遗传神经网络算法的主被动遥感协同反演地表土壤水分的方法.首先,建立一个BP神经网络,并采用遗传算法对BP网络的节点权值进行了优化.然后分别将TM数据(TM3,TM4,TM6)、不同极化和极化比的(VV,VH,VH/VV)ASAR数据作为神经网络的输入,土壤水分含量作为网络的输出,用部分实测数据对网络进行训练并反演得到研究区土壤水分布图.最后,利用地面实测数据分别对遗传神经网络优化算法的有效性和主被动遥感协同反演的效果进行了验证,结果表明,新优化算法是有效可行的,且TM和ASAR协同反演的结果比两者单独反演的结果明显要好,体现了主被动遥感协同反演土壤水分的优势与潜力.     

不同土壤类型的粗糙地面电磁散射特性及布儒斯特效应研究

土壤表面散射特性对大地遥感等问题有着重要应用,地面对雷达波束的镜面反射是造成镜像干扰的主要原因,利用粗糙地面的布儒斯特效应将有效削弱镜面反射。采用四成分土壤介电模型计算不同类型土壤的介电常数,以二维高斯粗糙面模拟实际地面,引入锥形入射波来克服粗糙面的边缘衍射;应用基于物理意义的双网格法(PBTG)结合稀疏矩阵规范网格法(SMCG)计算分析土壤类型和湿度对地面散射特性的影响,进一步探究了粗糙地面布儒斯特效应随土壤类型、湿度及入射波频率等的变化关系。分析表明:土壤类型和湿度等因素对地面散射特性及布儒斯特效应均会产生不同程度的影响。研究成果对于不同土壤类型的地面环境遥感探测以及削弱镜像干扰提供了理论支撑。     

Microwave emission of vegetation: sensitivity to leafcharacteristics

The effects of leaf characteristics on the microwave emission of land surfaces are analyzed. In order to simulate these effects, a radiative transfer model is presented. The medium consists of a vegetated layer containing randomly oriented leaves, modeled as elliptic-shaped scatterers, over the ground surface. Radiative transfer equations are solved with a discrete-ordinate-eigenanalysis method. The calculation of the phase matrix of the elliptic scatterers is based on the generalized Rayleigh-Gans approximation, which increases the frequency range of the modeling. The sensitivity of brightness temperature and polarization ratio to leaf characteristics, volume fraction, gravimetric moisture, size, shape, and inclination distribution is investigated at C-, and X-band. The behavior of the simulated emission of a soybean canopy versus frequency and incidence angle is studied for different soil moisture levels. Up to 10 GHz the microwave emission appears to contain significant information on underlying soil moisture     

Remote Sensing of Soil Moisture: Recent Advances

In the past few years there have been many advances in our understanding of microwave approaches for the remote sensing of soil moisture. These advances include a method for estimating the dependence of the soil's dielectric constant on its texture; the use of percent of field capacity to express soil moisture magnitudes independently of soil texture; experimental and theoretical estimates of the soil moisture sampling depth; models for describing the effect of surface roughness on the microwave response in terms of surface height variance and the horizontal correlation length; verification of the ability of radiative transfer models to predict the microwave emission from soils; and experimental and theoretical estimates of the effects of vegetation on the microwave response to soil moisture. This research has demonstrated that it is possible to remotely sense soil moisture in the surface layer of the soil (about 0-5 cm). In addition there have been simulation studies indicating how remotely sensed surface soil moisture may be used to estimate evapotranspiration rates and root-zone soil moisture.     

Electromagnetic Scattering From Multilayer Rough Surfaces With Arbitrary Dielectric Profiles for Remote Sensing of Subsurface Soil Moisture

Radar remote sensing of soil moisture content at low frequencies requires an accurate scattering model of realistic soils, which often involves multilayer rough surfaces and dielectric profiles. In this paper, a hybrid analytical/numerical solution to two-dimensional scattering from multilayer rough surfaces separated by arbitrary dielectric profiles based on the extended boundary condition method (EBCM) and scattering matrix technique is presented. The reflection and transmission matrices of rough interfaces are constructed using EBCM. The dielectric profiles are modeled as stacks of piecewise homogeneous dielectric thin layers, whose scattering matrices are computed by recursively cascading reflection and transmission matrices of individual dielectric interfaces. The interactions between the rough interfaces and stratified dielectric profiles are taken into account by applying the generalized scattering matrix technique. The scattering coefficients are obtained by combining the powers computed from the resulting Floquet modes of the overall system. The bistatic scattering coefficients are validated against existing analytical and numerical solutions. Field-collected soil moisture data are then used for numerical simulations to investigate the penetration capability at different frequencies and to address the potential of low-frequency radar systems in estimating deep soil moisture. In particular, soil moisture profiles during dry ground, wet ground, and wet subsurface layer conditions are examined. The results show that both backscattering coefficients and copolarized phase difference at low frequencies are sensitive to the roughness of subsurface interfaces and deep soil moisture. Also, much larger depth sensitivity can be achieved using copolarized phase difference than scattering coefficients     

Improvement of Moisture Estimation Accuracy of Vegetation-Covered Soil by Combined Active/Passive Microwave Remote Sensing

The measured effects of vegetation canopies on radar and radiometric sensitivity to soil moisture are compared to first-order emission and scattering models. The models are found to predict the measured emission and backscattering with reasonable accuracy for various crop canopies at frequencies between 1.4 and 5.0 GHz, especially at angles of incidence less than 30°. The vegetation loss factor L (?) increases with frequency and is found to be dependent upon canopy type and water content. In addition, the effective radiometric power absorption coefficient of a mature corn canopy is roughly 1.75 times that calculated for the radar at the same frequency. Comparison of an L-band radiometer with a C-band radar shows the two systems to be complementary in terms of accurate soil moisture sensing over the extreme range of naturally occurring soil-moisture conditions. The combination of both an L-band radiometer and a C-band radar is expected to yield soil-moisture estimates that are accurate to better than +/-30 percent of true soil moisture, even for a soil under a lossy crop canopy such as mature corn. This is true even without any other ancillary information.     

利用主被动遥感数据估算土壤湿度和粗糙度的新方法

土壤的散射和热辐射特性与土壤表面的粗糙度及含水量密切相关。利用本文给出的结果，可方便地由主被抽感的实验数据推断地表的湿度和粗糙度。     

A large-antenna microwave radiometer-scatterometer concept for ocean salinity and soil moisture sensing

Microwave radiometry and scatterometry are established techniques for surface remote sensing applications. Some applications, such as measurement of sea surface salinity (SSS), sea surface temperature (SST), and soil moisture, require low frequency observations (/spl sim/6 GHZ and below) for good sensitivity, and sensors with large antennas to achieve adequate spatial resolution. Potentially, benefits can be obtained by observing simultaneously with passive and active channels, at similar frequencies, viewing angles, and spatial resolutions, making use of the complementary information contained in the emissivity and backscattering signatures of land and ocean targets. In this study, the authors investigate a concept for combined passive and active multichannel sensing with high spatial resolution, high measurement sensitivity, and wide swath for frequent global coverage. The system consists of a lightweight, relating, deployable mesh antenna with offset feeds. The system specifications are designed primarily for the measurement of sea surface salinity, since this application drives the precision and calibration requirements and, like soil moisture, is a science measurement for which no spaceborne capability currently exists. Demonstration of a capability for sea surface salinity will enhance the potential of this large antenna concept for other applications such as soil moisture and, by including higher frequencies, high resolution measurements of ocean winds, precipitation, sea-surface temperature, and sea-ice.     

Radar Scattering from a Diffuse Vegetation Layer over a Smooth Surface

A simple model is presented for the oblique backscatter and bistatic scatter from a smooth surface overlain by a diffuse layer. Only single scattering in the diffuse layer is taken into account. The model analysis shows that the combination of volume scattering and oblique reflection at the surface may increase appreciably the waves scattering. The scattering strongly depends on the properties of the smooth surface. These results support some of the observations made with the Seasat spaceborne imaging radar over flooded regions with heavy vegetation cover.     

A parameterized surface reflectivity model and estimation of bare-surface soil moisture with L-band radiometer

Soil moisture is an important parameter for hydrological and climatic investigations. Future satellite missions with L-band passive microwave radiometers will significantly increase the capability of monitoring Earth's soil moisture globally. Understanding the effects of surface roughness on microwave emission and developing quantitative bare-surface soil moisture retrieval algorithms is one of the essential components in many applications of geophysical properties in the complex Earth terrain by microwave remote sensing. We explore the use of the integral equation model (IEM) for modeling microwave emission. This model was validated using a three-dimensional Monte Carlo model. The results indicate that the IEM model can be used to simulate the surface emission quite well for a wide range of surface roughness conditions with high confidence. Several important characteristics of the effects of surface roughness on radiometer emission signals at L-band 1.4 GHz that have not been adequately addressed in the current semiempirical surface effective reflectivity models are demonstrated by using IEM-simulated data. Using an IEM-simulated database for a wide range of surface soil moisture and roughness properties, we developed a parameterized surface effective reflectivity model with three typically used correlation functions and an inversion model that puts different weights on the polarization measurements to minimize surface roughness effects and to estimate the surface dielectric properties directly from dual-polarization measurements. The inversion technique was validated with four years (1979-1982) of ground microwave radiometer experiment data over several bare-surface test sites at Beltsville, Maryland. The accuracies in random-mean-square error are within or about 3% for incidence angles from 20/spl deg/ to 50/spl deg/.     

基于Radarsat2与Landsat8协同反演植被覆盖地表土壤水分的一种新方法

主动微波遥感与被动光学遥感在反演地表土壤水分方面分别具有各自的优缺点,为了将这两者的优势结合弥补缺点,提出了一种基于Radarsat 2与Landsat 8数据协同反演植被覆盖地表土壤水分的半经验耦合模型.该模型基于水云模型,将光学遥感反演得到的植被冠层含水量作为水云模型的关键输入参数,并同时考虑植被冠层与土壤以及其之间的部分对雷达后向散射系数的影响,以此来去除雷达回波中的植被部分.最后选用内蒙古呼伦贝尔市额尔古纳市大兴安岭西侧研究区的Radarsat 2与Landsat 8遥感数据,利用新的耦合模型反演得到植被覆盖区土壤水分含量,并利用地面测量数据对模型进行验证.结果表明:利用Landsat 8数据反演植被含水量算法精度较高(R2=0.89),论文提出的耦合模型反演植被覆盖地表土壤水分精度比之前算法也有了较大的提高,其中HH极化效果最好,R2由0.27提高至0.65.这表明该耦合模型具有较好的反演精度,可以应用于植被覆盖区土壤水分含量的反演.     

Parameter sensitivity of soil moisture retrievals from airborne L-band radiometer measurements in SMEX02

Over the past two decades, successful estimation of soil moisture has been accomplished using L-band microwave radiometer data. However, remaining uncertainties related to surface roughness and the absorption, scattering, and emission by vegetation must be resolved before soil moisture retrieval algorithms can be applied with known and acceptable accuracy using satellite observations. Surface characteristics are highly variable in space and time, and there has been little effort made to determine the parameter estimation accuracies required to meet a given soil moisture retrieval accuracy specification. This study quantifies the sensitivities of soil moisture retrieved using an L-band single-polarization algorithm to three land surface parameters for corn and soybean sites in Iowa, United States. Model sensitivity to the input parameters was found to be much greater when soil moisture is high. For even moderately wet soils, extremely high sensitivity of retrieved soil moisture to some model parameters for corn and soybeans caused the retrievals to be unstable. Parameter accuracies required for consistent estimation of soil moisture in mixed agricultural areas within retrieval algorithm specifications are estimated. Given the spatial and temporal variability of vegetation and soil conditions for agricultural regions it seems unlikely that, for the single-frequency, single-polarization retrieval algorithm used in this analysis, the parameter accuracy requirements can be met with current satellite-based land surface products. We conclude that for regions with substantial vegetation, particularly where the vegetation is changing rapidly, any soil moisture retrieval algorithm that is based on the physics and parameterizations used in this study will require multiple frequencies, polarizations, or look angles to produce stable, reliable soil moisture estimates.     

Microwave Emission and Scattering From Deserts: Theory Compared With Satellite Measurements

The emission and scattering from desert surfaces are analyzed using simulations and measurements from the Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU) microwave satellite instruments. Deserts are virtually free of vegetation, so the satellite radiometers are able to observe the emissivities of different minerals, such as limestone and quartz. Moreover, since deserts contain little moisture, the thermal emission originates below the surface at a depth of many wavelengths. At high frequencies, where the penetration depth of radiation is smallest, the radiometric measurements display the large diurnal variation in surface temperature, which reaches its maximum at around 1 P.M. Conversely, at low frequencies, where the penetration depth is largest, the radiation measurements display the small diurnal variation of subsurface temperature, which reaches a minimum at around 6 A.M. In addition to these emission signals, sand particles also scatter microwave radiation. Volume scattering causes the measurements to decrease as the frequency increases; although compared to other scattering media (snow cover and precipitation), the larger absorption and fractional volume (i.e., solidity) of sand reduce the scattering. Although the scattering effect is small, SSM/I measurements between 19 and 85 GHz show that deserts scatter the upwelling microwave radiation in a manner similar to light precipitation, which makes it difficult to uniquely identify precipitation over arid regions. Interestingly, the higher frequency AMSU measurement at 150 GHz is nearly the same as at 89 GHz for deserts, whereas the 150-GHz measurement is much lower than at 89 GHz for precipitation. These different spectral features at high frequencies can provide a means of separating the scattering from desert surfaces from that of precipitation.     

基于可见光红外与被动微波遥感的土壤水分协同反演

利用MODIS传感器的可见光、红外波段数据反演土壤水分在一定时段内的基准值,用被动微波传感器AMSR-E数据反演其变化量,提出将被动微波遥感数据与热红外遥感数据在模型级别协同反演大范围地表土壤水分的方法,这样每天可输出1 km×1 km的升、降轨土壤水分反演结果.以新疆为研究区,对上述方法进行了土壤水分协同反演实验,以地面实测数据为参考的验证结果表明,所提模型得到的土壤水分值与地面实测值之间相关性较高,均方根误差较小,优于单一传感器数据的反演结果,可更好地满足新疆土壤水分监测的需求.     

Sensitivity to microwave measurements to vegetation biomass andsoil moisture content: a case study

A comparative evaluation of the potential of active and passive microwave sensors in estimating vegetation biomass and soil moisture content is carried out. For this purpose, experimental data collected on an agricultural area by airborne scatterometers and radiometers during the AGRISCATT and AGRIRAD 1988 campaigns have been used. The results show that both microwave backscattering and emission are sensitive to vegetation biomass over a wide frequency range. Multifrequency observations seem to offer good probabilities for separating wide leaf from small leaf herbaceous crops, and for detecting different growth stages. Low frequency data (L band) at a steep incidence angle (10°) confirm that both the backscattering coefficient and the normalized temperature are correlated and sensitive to soil moisture content