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.     

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     

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.     

Use of radar and optical remotely sensed data for soil moisture retrieval over vegetated areas

This work assesses the possibility of obtaining soil moisture maps of vegetated fields using information derived from radar and optical images. The sensor and field data were acquired during the SMEX'02 experiment. The retrieval was obtained by using a Bayesian approach, where the key point is the evaluation of probability density functions (pdfs) based on the knowledge of soil parameter measurements and of the corresponding remotely sensing data. The purpose is to determine a useful parameterization of vegetation backscattering effects through suitable pdfs to be later used in the inversion algorithm. The correlation coefficients between measured and extracted soil moisture values are R=0.68 for C-band and R=0.60 for L-band. The pdf parameters have been found to be correlated to the vegetation water content estimated from a Landsat image with correlation coefficients of R=0.65 and 0.91 for C- and L-bands, respectively. In consideration of these correlations, a second run of the Bayesian procedure has been performed where the pdf parameters are variable with vegetation water content. This second procedure allows the improvement of inversion results for the L-band. The results derived from the Bayesian approach have also been compared with a classical inversion method that is based on a linear relationship between soil moisture and the backscattering coefficients for horizontal and vertical polarizations.     

Radar measurement of soil moisture content

The effect of soil moisture on the radar backscattering coefficient was investigated by measuring the 4-8 GHz spectral response from two types of bare-soil fields: slightly rough and very rough, in terms of the wavelength. An FM-CW radar system mounted atop a 75-ft truck-mounted boom was used to measure the return at 10 frequency points across the 4-8 GHz band, at 8 different look angles (0degthrough70deg), and for all polarization combinations. A total of 17 sets of data were collected covering the range 4-36 percent soil moisture content by weight. The results indicate that the radar response to soil moisture content is highly dependent on the surface roughness, microwave frequency, and look angle. The response seems to be linear, however, over the range 15-30 percent moisture content for all angles, frequencies, polarizations, and surface conditions.     

雷达遥感农业应用综述

雷达遥感具有全天时、全天候监测的能力,对植被具有一定的穿透能力,对植被散射体形状、结构、介电常数敏感;这些特性使得其在农业应用中极具潜力。该文首先介绍了雷达遥感在农业中的应用领域,概略总结了目前在农作物识别与分类、农田土壤水分反演、农作物长势监测等多个领域研究的综述文献;然后分别阐述了雷达散射计和各类SAR特征(包括:SAR后向散射特征、极化特征、干涉特征、层析特征)在农业各领域中应用的现状和取得的研究成果,最后结合农业应用需求和SAR技术发展总结了目前研究中存在的问题和原因,并对未来的发展进行了展望。      

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.     

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.     

A Comparison between Active and Passive Sensing of Soil Moisture from Vegetated Terrains

A comparison between active and passive sensing of soil moisture over vegetated areas is studied via scattering models. In active sensing, three contributing terms to radar backscattering can be identified: 1a) the ground surface scatter term; 2a) the volume scatter term representing scattering from the vegetation layer; and 3a) the surfacevolume. scatter term accounting for scattering from both surface and volume. In emission, three sources of contribution can also be identified: 1b) surface emission, 2b) upward volume emission from the vegetation layer, and 3b) downward volume emission scattered upward by the ground surface. As ground moisture increases, terms 1a) and 3a) increase due to increase in permittivity in the active case. However, in passive sensing, term 1b) decreases but term 3b} increases for the same reason. This self-compensating effect produces a loss in sensitivity to change in ground moisture. Furthermore, emission from vegetation may be larger than that from the ground. Hence, the presence of vegetation layer causes a much greater loss of sensitivity to passive than active sensing of soil moisture.     

Seasonal dynamics of C-band backscatter of boreal forests withapplications to biomass and soil moisture estimation

The seasonal changes of the C-band backscattering properties of boreal forests are investigated by applying 1) a semiempirical forest backscattering model and 2) multitemporal ERS-1 SAR data from two test areas in Finland. The semiempirical modeling of forest canopy volume backscattering and extinction properties is based on high-resolution data from the authors' ranging scatterometer HUTSCAT. The response of ERS-1 SAR to forest stem volume (biomass) and other forest characteristics is investigated by employing the National Forest Inventory sample plots, stand-wise forest inventory data and LANDSAT- and SPOT-based digital land use maps. The results show that the correlation between the backscattering coefficient and forest stem volume (biomass) varies from positive to negative depending on canopy and soil moisture. Additionally, the seasonal snow cover and soil freezing/thawing effects cause drastic changes in the radar response. A novel method for the estimation of forest stem volume (biomass) is introduced. This technique is based on the use of: 1) multitemporal ERS-1 SAR data; 2) reference sample plot information; and 3) the semiempirical backscattering model. It is shown that the multitemporal ERS-1 SAR images can be successfully used for estimating the forest stem volume. The effects of soil moisture variations to ERS-1 SAR results have been analyzed using hydrological soil moisture model and in situ data. The results indicate that the semiempirical model can he used for predicting the soil and canopy moisture variations in ERS-1 images     

Quantitative analysis of RADARSAT SAR data over a sparse forest canopy

This article studies the behavior of the backscattering coefficient of a sparse forest canopy composed of relatively short black spruce trees. Qualitative analysis of the multiangular data measured by the RADARSAT synthetic aperture radar (SAR) sensor shows a good agreement with surface and vegetation volume scattering fundamental behaviors. For a quantitative analysis, allometric equations and measurements of tree components collected within the framework of the Extended Collaboration to Link Ecophysiology and Forest Productivity (ECOLEAP) project are used, in an existing multilayer radiative transfer model for forest canopies, to simulate the RADARSAT SAR data. In our approach, the fractional cover of trees estimated from aerial photographs is used as a weighting parameter to adapt the closed-canopy backscattering model to the sparse forest under study. Our objective is to analyze the sensitivity of the backscattering coefficient as a function of sensor configuration, soil wetness, forest cover, and forest structural properties in order to determine the suitable soil, vegetation, and sensor parameters for a given thematic application. For the entire incidence angle domain (20/spl deg/ to 50/spl deg/) of the sensor, simulations show that over a sparse forest composed of mature trees the monitoring of the ground surface is possible only under very wet soil conditions. Therefore, this article informs about the ability of the RADARSAT SAR sensor in monitoring wetlands.     

High-resolution change estimation of soil moisture using L-band radiometer and Radar observations made during the SMEX02 experiments

The soil moisture experiments held during June-July 2002 (SMEX02) at Iowa demonstrated the potential of the L-band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L-band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L-band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving subpixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at a much finer scale. This paper presents a simple approach for estimation of change in soil moisture at a higher (radar) spatial resolution by combining L-band copolarized radar backscattering coefficients and L-band radiometric brightness temperatures. Sensitivity of AIRSAR L-band copolarized channels has been demonstrated by comparison with in situ soil moisture measurements as well as PALS brightness temperatures. The change estimation algorithm has been applied to coincident PALS and AIRSAR datasets acquired during the SMEX02 campaign. Using AIRSAR data aggregated to a 100-m resolution, PALS radiometer estimates of soil moisture change at a 400-m resolution have been disaggregated to 100-m resolution. The effect of surface roughness variability on the change estimation algorithm has been explained using integral equation model (IEM) simulations. A simulation experiment using synthetic data has been performed to analyze the performance of the algorithm over a region undergoing gradual wetting and dry down.     

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.     

Inferring Vegetation Water Content From C- and L-Band SAR Images

This paper addresses the capability of synthetic aperture radar and optical images in combination with theoretical models to detect the vegetation water content (VWC) at field level. In this paper, a retrieval algorithm for the estimation of VWC from AirSAR acquired on vegetated fields during the SMEX'02 experiment is addressed. The aforementioned campaign has been chosen because, along with sensor observations, extensive ground truth measurements were acquired. The retrieval procedure, which is based on a Bayesian approach, has been initially developed for soil moisture extraction. It consists of two modules: one is pertinent to bare soils and the other one has been modified for vegetated fields. The last one uses the synergy with optical images to correct for the contribution of VWC. The VWC, a variable in the inversion procedure, as well as soil moisture can be estimated. The results indicate a good correlation with both ground measurements and VWC calculated from Landsat images through the use of normalized difference water index (NDWI). Furthermore, in the inversion procedure, the introduction of the dependence on roughness improves the estimates. This indicates that, even for dense vegetation, the contribution from bare soil greatly influences the radar signal. Three main levels of VWC are discriminated in the inversion procedure: values below 1 kg/m², values between 1 and 3 kg/m², and values greater than 3 kg/m².     

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。     

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.      

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。     

Estimation of bare surface soil moisture and surface roughnessparameter using L-band SAR image data

An algorithm based on a fit of the single-scattering integral equation method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L-band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively     

Robust inversion technique for retrieving soil moisture from multi-polarised backscatter of bare surface

A robust inversion technique using a genetic algorithm (GA) for retrieving soil moisture content from the multi-polarised radar data of bare soil surfaces is presented. This inversion technique employs a semi-empirical polarimetric backscattering model as a cost function for the GA. Good agreement is found between the values estimated by this inversion technique and those measured in situ.     

Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces

A semiempirical polarimetric backscattering model for bare soil surfaces is inverted directly to retrieve both the volumetric soil moisture content M/sub v/ and the rms surface height s from multipolarized radar observations. The rms surface height s and the moisture content M/sub v/ can be read from inversion diagrams using the measurements of the cross-polarized backscattering coefficient /spl sigma//sub vh//sup 0/ and the copolarized ratio p(=/spl sigma//sub hh//sup 0///spl sigma//sub vv//sup 0/). Otherwise, the surface parameters can be estimated simply by solving two equations (/spl sigma//sub vh//sup 0/ and p) in two unknowns (M/sub v/ and s). The inversion technique has been applied to the polarimetric backscattering coefficients measured by ground-based polarimetric scatterometers and the Jet Propulsion Laboratory airborne synthetic aperture radar. A good agreement was observed between the values of surface parameters (the rms height s, roughness parameter ks, and the volumetric soil moisture content M/sub v/) estimated by the inversion technique and those measured in situ.