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的绿洲区域尺度土壤水分微波建模

根据最新Sentinel-1雷达系统参数及研究区地表参数特点,采用AIEM模型进行数值模拟分析,建立稀疏植被覆被下地表微波散射特征数据库,并在此基础上构建干旱区土壤水分模型.结果表明,1)不同入射角和极化方式下,后向散射系数对土壤含水量(Mv)、组合地表粗糙度(Zs)的响应分别呈明显对数相关,VV极化对土壤水分响应更敏感,最优响应区间范围为Mv 0~30%、Zs 0~0.06 cm.2)初探Sentinel-1雷达数据预处理方法,Gamma MAP滤波去噪最优,模型用于土壤水分空间分布信息提取与研究区同期野外实况具有良好的一致性,符合四月渭-库地区春旱期土壤水分时空分布特征.3)对于0-10 cm表层土壤水分,模拟值同实测值相关系数达到0.76,即该模型对于干旱区绿洲区域尺度表层土壤水分监测具有适用性.     

Effects of Vegetation Cover on the Radar Sensitivity to Soil Moisture

Measurements of the backscattering coefficient ?°, made for bare and vegetation-covered fields, are used in conjunction with a simple backscattering model to evaluate the effects of vegetation cover on the estimation accuracy of soil moisture when derived from radar observations. The results indicate that for soil moisture values below 50 percent of field capacity, the backscatter contribution of the vegetation cover limits the radar's ability to predict soil moisture with an acceptable degree of accuracy. However, for moisture values in the range between 50 and 150 percent of field capacity, the measured ?° is dominated by the soil contribution and the effects of vegetation cover become secondary in importance. It is estimated that in this upper soil moisture range, which is the primary range of interest in hydrology and agriculture, a radar soil moisture prediction algorithm would predict soil moisture with an error of less than ±15 percent of field capacity in 90 percent of the cases.     

Volumetric Effects in Cross-Polarized Airborne Radar Data

In recent years the detection of soil moisture using remote sensing technology has become of interest to investigators and a variety of state and federal government agencies. The parameter of interest is important in agricultural, meteorological, biological, and hydrological applications. The use of active microwave devices has shown to provide capability for remote measurement from space. Problems do exist however, in isolating moisture information from the effects of other parameters such as roughness and vegetation. Of special concern is the suppression of the roughness effects in the radar return. This paper presents an analysis of airborne cross-polarized radar measurements of agricultural scenes. The relative responses of the system to moisture and surface roughness are presented and compared to predicted responses using radar backscatter models. It is shown that the depolarized model predictions are sensitive to soil moisture, but are much less sensitive to surface roughness effects.     

SAR observations and modeling of the C-band backscattervariability due to multiscale geometry and soil moisture

The effect of the multiscale surface geometry on the sensitivity of C band synthetic aperture radar (SAR) data to soil moisture is studied. The experimental data consist of C-band SAR images of an agricultural site, including fields with various combinations of three distinct roughness components from small to large scale. The backscatter variability due to surface roughness has been analyzed. The effect of random roughness associated with soil clods is never less than 2 dB, and the effect of a row pattern can be as strong as 10 dB. In addition, the periodic drainage topography induces a backscatter variability due to soil moisture variation and drainage relief. The results indicate that airborne C-band SAR data cannot be easily inverted into soil moisture data. However, with ERS-1 or Radarsat data at an incidence angle of about 20°, the effect of random and periodic roughness can be reduced to about 2 dB if the look angle is less than 50°     

Soil Moisture Retrieval During a Corn Growth Cycle Using L-Band (1.6 GHz) Radar Observations

This paper reports on the retrieval of soil moisture from dual-polarized L-band (1.6 GHz) radar observations acquired at view angles of 15$^{circ}$, 35 $^{circ}$, and 55$^{circ}$ , which were collected during a field campaign covering a corn growth cycle in 2002. The applied soil moisture retrieval algorithm includes a surface roughness and vegetation correction and could potentially be implemented as an operational global soil moisture retrieval algorithm. The surface roughness parameterization is obtained through inversion of the Integral Equation Method (IEM) from dual-polarized (HH and VV) radar observations acquired under nearly bare soil conditions. The vegetation correction is based on the relationship found between the ratio of modeled bare soil scattering contribution and observed backscatter coefficient $(sigma^{rm soil}/sigma^{rm obs})$ and vegetation water content $(W)$. Validation of the retrieval algorithm against ground measurements shows that the top 5-cm soil moisture can be estimated with an accuracy between 0.033 and 0.064 $hbox{cm}^{3}cdothbox{cm}^{-3}$, depending on the view angle and polarization.      

The Effects of Soil Moisture, Surface Roughness, and Vegetation on L-Band Emission and Backscatter

Measurements with Shuttle Imaging Radar B (SIR-B) at 1.28 GHz and an airborne multiple-beam push-broom radiometer at 1.4 GHz were made over a number of agricultural fields near Fresno, California during October 7-10, 1984. These measurements provided a unique data set for studies of microwave emission and backscatter from surfaces of various characteristics. The effects of surface roughness and vegetation (alfalfa and lettuce) were analyzed with respect to the responses of microwave emission and backscatter to soil-moisture variations. A theoretical model (Kirchhoff approximation) was employed to assess these effects. It was found that for microwave emission, the effect of surface roughness is less significant compared to that of vegetation. On the other hand, the surface roughness was shown to play a dominant role compared to the vegetation cover in the microwave backscatterve backscatter. The two roughness parameters in the theoretical model calculations were the surface correlation length and the standard deviation of surface height. These parameters were found to be affected strongly by the soil-texture effect in the emissivity calculations. A disagreement was found between the calculated and the observed scattering coefficients if the measured surface correlation length and standard deviation of surface height were input to the model. Either one of these two parameters had to be modified appreciably to bring a comparability between the measured and calculated scattering coefficients.     

Laboratory backscatter measurements over urea ice with a snow coverat Ku band

Indoor laboratory facilities were used to measure radar backscatter at Ku band (13.9 GHz) over urea ice, which has been shown to be structurally similar to sea ice. Data were collected at angles of incidence from normal to 55°, over very thin (0 to 9 cm) ice, snow-covered ice, and ice with a hooded snow cover. The laboratory proved to be useful in creating and controlling specific physical properties of ice while keeping all other variables constant, a difficulty with measurements collected in the field. It was found that surface scattering and the dielectric constant are the dominant factors that cause variations (up to 15 dB) in the measured backscatter. The addition of a snow cover increased the surface roughness of the smooth ice, increasing the backscatter at 20° incidence angle by about 11 dB and decreasing the backscatter at normal incidence by about 6 dB. The subsequent flooding of this snow layer increased the backscatter at all angles of incidence due to the increased dielectric constant of the wet slush layer. These results indicate the importance of the snow layer in influencing the surface characteristics of the ice sheet, which in turn modifies the backscattered signal     

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.     

A methodology for surface soil moisture and vegetation opticaldepth retrieval using the microwave polarization difference index

A methodology for retrieving surface soil moisture and vegetation optical depth from satellite microwave radiometer data is presented. The procedure is tested with historical 6.6 GHz H and V polarized brightness temperature observations from the scanning multichannel microwave radiometer (SMMR) over several test sites in Illinois. Results using only nighttime data are presented at this time due to the greater stability of nighttime surface temperature estimation. The methodology uses a radiative transfer model to solve for surface soil moisture and vegetation optical depth simultaneously using a nonlinear iterative optimization procedure. It assumes known constant values for the scattering albedo and roughness, and that vegetation optical depth for H-polarization is the same as for V-polarization. Surface temperature is derived by a procedure using high frequency V-polarized brightness temperatures. The methodology does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes and may be applied to other wavelengths. Results compare well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors     

Statistical characteristics of simulated radar imagery from baresoil surfaces: effects of surface roughness and soil moisturevariability

The potential of high-resolution radar imagery to estimate various hydrological parameters, such as soil moisture, has long been recognized. Image simulation is one approach to study the interrelationships between the radar response and the underlying ground parameters. In order to perform realistic simulations, the authors incorporated the effects of naturally occurring spatial variability and spatial correlations of those ground parameters that affect the radar response, primarily surface roughness and soil moisture. Surface roughness and soil moisture images were generated for a hypothetical 100×100 m bare soil surface area at 1 m resolution using valid probability distributions and correlation lengths. These values were then used to obtain copolarized radar scattering coefficients at 2 GHz (L band) and 10 GHz (X band) frequencies using appropriate backscatter models, which were then converted to a digital number within 0-255 gray scale in order to generate radar images. The effect of surface roughness variability causes variability in the radar image, which is more apparent under smooth soil conditions. On the other hand, the inherent spatial pattern in soil moisture tends to cause similar patterns in the radar image under rougher soil conditions. The maximum difference between contrast-enhanced mean values of the radar image digital number due to moisture variations occurs at surface roughness values in the 1.5-2.0 cm range     

A semiempirical model for interpreting microwave emission fromsemiarid land surfaces as seen from space

A radiative transfer model for simulating microwave brightness temperatures over land surfaces is described. The model takes into account sensor viewing conditions (spacecraft altitude, viewing angle, frequency, polarization) and atmospheric parameters over a soil surface characterized by its moisture, roughness, and temperature and covered with a layer of vegetation characterized by its temperature, water content, single scattering albedo, structure and percent coverage. In order to reduce the influence of atmospheric and surface temperature effects, the brightness temperatures are expressed as polarization ratios that depend primarily on the soil moisture and roughness, canopy water content, and percentage of cover. The approach used is described, and the sensitivity of the polarization ratio to these parameters is investigated. Simulation of the temporal evolution of the microwave signal over semiarid areas in the African Sahel is presented and compared to actual satellite data from the SMMR instrument on Nimbus-7     

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。     

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.     

裸地散射特性分析

本文详细地研究了裸地（农用耕地）的散射特性。根据实验现象提出了新的散射系数与入射角关系模型，与实验数据获得了很好的吻合性。通过分析裸地散射系数的雷达参数（入射角、极化、频率）和地面参数（粗糙度、土壤湿度）的响应特性，得到微波遥感土壤湿度时的最佳工作参数。     

Discrete scatter model for microwave radar and radiometer responseto corn: comparison of theory and data

As part of the Multisensor Aircraft Campaign, MACHYDRO, two microwave sensors, NASA's Airborne Synthetic Aperture Radar (AIRSAR) and Pushbroom Microwave Radiometer (PBMR) collected data over the same corn fields during the summer of 1990. During these flights, measurements were made on the ground of soil moisture and plant parameters. In this paper the measured canopy and soil parameters are used in a discrete scatter model to predict the response of both sensors (radar and radiometer). A distorted Born approximation is used to compute the scattering coefficient for the corn canopy. The backscatter coefficient gives the radar response and the radiometer response is obtained by integrating the bistatic coefficient over all scattering angles above ground. The objective of this analysis is to test the model and, in particular, to determine how well a single set of plant parameters and single model can yield agreement with both the radar and radiometer measurements. The model values are in reasonably good agreement with the measurements at horizontal polarization and reflect observed changes in soil moisture     

Radar return from a continuous vegetation canopy

The radar backscatter coefficientsigmadegof alfalfa was investigated as a function of both radar parameters and the physical characteristics of the alfalfa canopy. Measurements were acquired with an 8-18 GHz FM-CW mobile radar over an angular range of0deg-70degas measured from nadir. The experimental data indicate that the excursions ofsigmadegat nadir cover a range of nearly 18 dB during one complete growing cycle. An empirical model forsigmadegwas developed, which accounts for its variability in terms of soil moisture, plant moisture, and plant height.     

Soil moisture estimation from ERS/SAR data: toward an operational methodology

Previous studies have shown the possibility of using European Remote Sensing/synthetic aperture radar (ERS/SAR) data to monitor surface soil moisture from space. The linear relationships between soil moisture and the SAR signal have been derived empirically and, thus, were a priori specific to the considered watershed. In order to overcome this limit, this study focused on two objectives. The first one was to validate over two years of data the empirical sensitivity of the radar signal to soil moisture, in the case of three agricultural watersheds with different soil compositions and land cover uses. The slope of the observed relationship was very consistent. Conversely, the offset could change, making the soil moisture retrieval only relative (and not absolute). The second one was to propose an "operational" methodology for soil moisture monitoring based on ERS/SAR data. The implementation of this methodology is based on two steps: the calibration period and the operational period. During the calibration period, ground truth campaigns are performed to measure vegetation parameters (to correct the SAR signal from the vegetation effect), and the ERS/SAR data is processed only once a field land cover map is established. In contrast, during the operational period, no vegetation field campaigns are performed, and the images are processed as soon as they are available. The results confirm the relevance of this operational methodology, since no loss of performance (in soil moisture retrieval) is observed between the calibration and operational periods.     

Improved Spatial Mapping of Rainfall Events with Spaceborne SAR Imagery

The Seasat satellite acquired the first spaceborne synthetic-aperture radar (SAR) images of the earth's surface, in 1978, at a frequency of 1.275 GHz (L-band) in a like-polarization mode at incidence angles of 23 ± 30. Although this may not be the optimum system configuration for radar remote sensing of soil moisture, interpretation of two Seasat images of Iowa demonstrates the sensitivity of microwave backscatter to soil moisture content. In both scenes, increased image brightness, which represents more radar backscatter, can be related to previous rainfall activity in the two areas. Comparison of these images with ground-based rainfall observations illustrates the increased spatial coverage of the rainfall event that can be obtained from the satellite SAR data. These data can then be color-enhanced by a digital computer to produce aesthetically pleasing output products for the user community. When the methodology for extracting accurate information about soil moisture status from radar data is developed, it will prove useful in a wide variety of agronomic and hydrological investigations.     

A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations

NASA's Earth System Science Pathfinder Hydrospheric States (Hydros) mission will provide the first global scale space-borne observations of Earth's soil moisture using both L-band microwave radiometer and radar technologies. In preparation for the Hydros mission, an observation system simulation experiment (OSSE) has been conducted. As a part of this OSSE, the potential for retrieving useful surface soil moisture at spatial resolutions of 9 and 3 km was explored. The approach involved optimally merging relatively accurate 36-km radiometer brightness temperature and relatively noisy 3-km radar backscatter cross section observations using a Bayesian method. Based on the Hydros OSSE data sets with low and high noises added to the simulated observations or model parameters, the Bayesian method performed better than direct inversion of either the brightness temperature or radar backscatter observations alone. The root-mean-square errors of 9-km soil moisture retrievals from the Bayesian merging method were reduced by 0.5 %vol/vol and 1.4 %vol/vol from the errors of direct radar inversions for the entire OSSE domain of all 34 consecutive days for the low and high noise data sets, respectively. Improvement in soil moisture estimates using the Bayesian merging method over the direct inversions of radar or radiometer data were even more significant for soil moisture retrieval at 3-km resolution. However, to address the representativeness of these results at the global and multiyear scales, further performance comparison studies are needed, particularly with actual field data.