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.      

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.     

Measuring soil moisture with imaging radars

An empirical algorithm for the retrieval of soil moisture content and surface root mean square (RMS) height from remotely sensed radar data was developed using scatterometer data. The algorithm is optimized for bare surfaces and requires two copolarized channels at a frequency between 1.5 and 11 GHz. It gives best results for kh⩽2.5, μ_υ⩽35%, and &thetas;⩾30°. Omitting the usually weaker hv-polarized returns makes the algorithm less sensitive to system cross-talk and system noise, simplifies the calibration process and adds robustness to the algorithm in the presence of vegetation. However, inversion results indicate that significant amounts of vegetation (NDVI>0.4) cause the algorithm to underestimate soil moisture and overestimate RMS height. A simple criteria based on the σ_hv⁰/σ_vv⁰ ratio is developed to select the areas where the inversion is not impaired by the vegetation. The inversion accuracy is assessed on the original scatterometer data sets but also on several SAR data sets by comparing the derived soil moisture values with in-situ measurements collected over a variety of scenes between 1991 and 1994. Both spaceborne (SIR-C) and airborne (AIRSAR) data are used in the test. Over this large sample of conditions, the RMS error in the soil moisture estimate is found to be less than 4.2% soil moisture     

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.     

A sensitivity analysis of soil moisture retrieval from the tau-omega microwave emission model

The potential of the /spl tau/--/spl omega/ model for retrieving the volumetric moisture content of bare and vegetated soil from dual-polarization passive microwave data acquired at single and multiple angles is tested. Measurement error and several additional sources of uncertainty will affect the theoretical retrieval accuracy. These include uncertainty in the soil temperature, the vegetation structure, and consequently its microwave single-scattering albedo, and uncertainty in soil microwave emissivity based on its roughness. To test the effects of these uncertainties for simple homogeneous scenes, we attempt to retrieve soil moisture from a number of simulated microwave brightness temperature datasets generated using the /spl tau/--/spl omega/ model. The uncertainties for each influence are estimated and applied to curves generated for typical scenarios, and an inverse model used to retrieve the soil moisture content, vegetation optical depth, and soil temperature. The effect of each influence on the theoretical soil moisture retrieval limit is explored, the likelihood of each sensor configuration meeting user requirements is assessed, and the most effective means of improving moisture retrieval indicated.     

An observation system simulation experiment for the impact of landsurface heterogeneity on AMSR-E soil moisture retrieval

Using a high-resolution hydrologic model, a land surface microwave emission model (LSMEM), and an explicit simulation of the orbital and scanning characteristics for the advanced microwave sensing radiometer (AMSR-E), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity on large-scale retrieval and validation of soil moisture products over the U.S. Southern Great Plains using the 6.925 GHz channel on the AMSR-E sensor. Land surface heterogeneity impacts soil moisture products through the presence of nonlinearities in processes represented by the LSMEM, as well as the fundamental inconsistency in spatial scale between gridded soil moisture imagery derived from in situ point-scale sampling, numerical modeling, and microwave remote sensing sources. Results within the 575000 km² Red-Arkansas River basin show that, for surfaces with vegetation water contents below 0.75 kg/m², these two scale effects induce root mean squared errors (RMSEs) of 1.7% volumetric (0.017 cm_water³/cm_soil³ ) into daily 60 km AMSR-E soil moisture products and RMS differences of 3.0% (0.030 cm_water/3cm_soil³ ) into 60 km comparisons of AMSR-E soil moisture products and in situ field-scale measurements of soil moisture sampled on a fixed 25-km grid     

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     

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     

Ground-based passive microwave remote sensing observations of soilmoisture at S-band and L-band with insight into measurementaccuracy

A ground-based experiment in passive microwave remote sensing of soil moisture was conducted in Huntsville, AL, from July 1-14, 1996. The goal of the experiment was to evaluate the overall performance of an empirically-based retrieval algorithm at S-band and L-band under a different set of conditions and to characterize the site-specific accuracy inherent within the technique. With high temporal frequency observations at S-band and L-band, the authors were able to observe large scale moisture changes following irrigation and rainfall events, as well as diurnal behavior of surface moisture among three plots, one bare, one covered with short grass and another covered with alfalfa. The L-band emitting depth was determined to be on the order of 0-3 or 0-5 cm below 0.30 cm³/cm³ with an indication that it is less at higher moisture values. The S-band emitting depth was not readily distinguishable from L-band. The uncertainty in remotely sensed soil moisture observations due to surface heterogeneity and temporal variability in variables and parameters was characterized by imposing random errors on the most sensitive variables and parameters and computing the confidence limits on the observations. Discrepancies between remotely sensed and gravimetric soil moisture estimates appear to be larger than those expected from errors in variable and parameter estimation. This would suggest that a vegetation correction procedure based on more dynamic modeling may be required to improve the accuracy of remotely sensed soil moisture     

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.     

A semi-empirical backscattering model at L-band and C-band for asoybean canopy with soil moisture inversion

Radar backscatter measurements of a pair of adjacent soybean fields at L-band and C-band are reported. These measurements, which are fully polarimetric, took place over the entire growing season of 1996. To reduce the data acquisition burden, these measurements were restricted to 45° in elevation and to 45° in azimuth with respect to the row direction. Using the first order radiative transfer solution as a form for the model of the data, four parameters were extracted from the data for each frequency/polarization channel to provide a least squares fit to the model. For inversion, particular channel combinations were regressed against the soil moisture and area density of vegetation water mass. Using L-band cross-polarization and VV-polarization, the vegetation water mass can be regressed with an R ²=0.867 and a root mean square error (RMSE) of 0.0678 kg/m ². Similarly, while a number of channels, or combinations of channels, can be used to invert for soil moisture, the best combination observed, namely, L-band VV-polarization, C-band HV- and VV-polarizations, can achieve a regression coefficient of R²=0.898 and volumetric soil moisture RMSE of 1.75%     

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     

On current limits of soil moisture retrieval from ERS-SAR data

Assesses the feasibility of retrieving soil moisture content over smooth bare-soil fields using European Remote Sensing synthetic aperture radar (ERS-SAR) data. The roughness conditions considered in this study correspond to those observed in agricultural fields at the time of sowing. Within this context, the retrieval possibilities of a single-parameter ERS-SAR configuration is assessed using appropriately trained neural networks. Three sources of error affecting soil moisture retrieval (inversion, measurement, and model errors) are identified, and their relative influence on retrieval performance is assessed using synthetic datasets as well as a large pan-European database of ground and ERS-1 and ERS-2 measurements. The results from this study indicate that no more than two soil moisture classes can reliably be distinguished using the ERS configuration, even for the restricted roughness range considered.     

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.     

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。      

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。     

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.