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.     

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.     

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.     

Estimating the Effective Soil Temperature at L-Band as a Function of Soil Properties

To retrieve soil moisture from L-band microwave radiometry, it is necessary to account for the effects of temperature within both vegetation and soil media. To compute the effective soil temperature TG, several simple formulations accounting for soil temperatures at the surface and at depth and surface soil moisture have been developed. However, the effects of the soil physical properties in terms of texture, density, or structure, which all may be important variables in the modeling of TG, have never been investigated. In this paper, several simple formulations of TG at L-band, accounting for or ignoring the effects of soil texture and density, were developed and compared based on a very large simulated data set. The best configurations and parameterizations of these simple formulations were computed and could be directly used for operational applications in future soil moisture retrieval studies. For instance, we showed that the use of the surface temperature in the estimation of TG can be significantly improved by using additional information on the soil temperature at depth (the average error in the estimation of TG decreased from ~ 4 to ~ 1.8 K). On the contrary, almost no improvement was obtained if air temperature was used instead of surface temperature. Also, it is shown that the use of additional information on the soil properties, mainly the soil clay content and density, led to improved results by about 0.2 K in the estimation of TG. The improvement was found to be larger for sandy and dry soils: simplified formulations accounting for soil properties are able to represent the fact that TG is closer to the soil temperature at depth for these soil conditions.     

L-Band Radar Sensing of Soil Moisture

The objectives of this experiment were to assess the performance of an L-band, 25-cm wavelength imaging synthetic aperture radar (SAR) for soil moisture determination, and to study the temporal variability of radar returns from a number of agricultural fields. A series of three overflights was accomplished during March 1977 over an agricultural test site in Kern County, CA. Soil moisture samples were collected from bare fields at nine sites at depths of 0-2, 2-5, 5-15, and 15-30 cm. These gravimetric measurements were converted to percent of field capacity for correlation to the radar return signal. The initial signal film was optically correlated and scanned to produce image data numbers. These numbers were then converted to relative return power by linear interpolation of the noise power wedge which was introduced in 5-dB steps into the original signal film before and after each data run. Results of correlations between the relative return power and percent of field capacity (%FC) demonstrate that the relative return power from this imaging radar system is responsive to the amount of soil moisture in bare fields. The signal returned from dry (15%FC) and wet (130%FC) fields where furrowing is parallel to the radar beam differs by about 15 dB. Problems remain to be resolved before this technique can be operationally employed. First, adequate calibration of the radar system is required to insure comparability of data both from area to area within a single flight and between different flights.     

A Method for Soil Moisture Estimation in Western Africa Based on the ERS Scatterometer

The analysis of feedback phenomena, which occur between continental surfaces and the atmosphere, is one of the keys to an improved understanding of African monsoon dynamics. For this reason, the monitoring of surface parameters, particularly soil moisture, is very important. This paper presents a new methodology for the estimation of surface soil moisture over Western Africa based on the data provided by the European Remote Sensing wind scatterometer instrument, in which an empirical model is used to estimate volumetric soil moisture. This approach takes into account the effects of vegetation and soil roughness in the soil moisture estimation process. The proposed estimations have been validated using different methods, and a good degree of coherence has been observed between satellite estimations and ground truth measurements over the Banizambou site in Niger. Moisture and rainfall estimations for the same site are shown to be strongly correlated. Comparison with the multimodel analysis product provided by the Global Soil Wetness Project, Phase 2, indicates that their estimations are well correlated, although land surface models provide slightly overestimated levels of soil moisture.     

Microwave Bistatic Reflectivity Dependence on the Moisture Content And Matric Potential of Bare Soil

Results are presented of an experimental program to determine the functional dependence of the microwave reflectivity of nonvegetated soil surfaces upon volumetric soil moisture and matric potential. A combination evaporation-drainage field experiment was conducted on a bare Captina silt loam with reflectivity, soil moisture content, and matric potential monitored for extended time periods. Results show that for a restricted pressure range (approximately -0.05 to -0.75 bar) there is excellent linear correlation between the log of bistatic reflectivity and both volumetric moisture content and matric potential. Layering effects due to steep moisture content (and matric potential) gradients in the profile are demonstrated to have two distinct and significant effects on the reflectivity response. At near saturation of rough surfaces a very thin dry surface layer appears to modify the effective roughness. This leads to a saturation of reflectivity at high moisture contents. As the surface proceeds to dry further, deeper layers produce coherent interference patterns in the reflectivity response, particularly at the higher frequencies.     

Microwave remote sensing of soil moisture: elimination of textureeffect

In view of the influence of soil texture on microwave radiation, an attempt is made to eliminate the textural effects on the microwave reflectivity/emission. To determine the appropriate soil moisture parameter that minimizes the textural influences on microwave radiation from soils, soil moisture is expressed in terms of gravimetric and volumetric units and percentage of field capacity (M_fc ) and is plotted individually against the microwave reflectivity of soils. Only when soil moisture is in volumetric units are the textural influences significantly reduced. Therefore, a parameter, termed the critical water content (W_c), that takes into account the bound-water content of soils is used. An empirical relation between the interpolated values of W_c and the wilting point of soils has been developed. The soil water content above the W_c of each soil is considered as effective water M_eff     

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。     

Impact of Multiresolution Active and Passive Microwave Measurements on Soil Moisture Estimation Using the Ensemble Kalman Smoother

An observing system simulation experiment is developed to test tradeoffs in resolution and accuracy for soil moisture estimation using active and passive L-band remote sensing. Concepts for combined radar and radiometer missions include designs that will provide multiresolution measurements. In this paper, the scientific impacts of instrument performance are analyzed to determine the measurement requirements for the mission concept. The ensemble Kalman smoother (EnKS) is used to merge these multiresolution observations with modeled soil moisture from a land surface model to estimate surface and subsurface soil moisture at 6-km resolution. The model used for assimilation is different from that used to generate "truth." Consequently, this experiment simulates how data assimilation performs in real applications when the model is not a perfect representation of reality. The EnKS is an extension of the ensemble Kalman filter (EnKF) in which observations are used to update states at previous times. Previous work demonstrated that it provides a computationally inexpensive means to improve the results from the EnKF, and that the limited memory in soil moisture can be exploited by employing it as a fixed lag smoother. Here, it is shown that the EnKS can be used in large problems with spatially distributed state vectors and spatially distributed multiresolution observations. The EnKS-based data assimilation framework is used to study the synergy between passive and active observations that have different resolutions and measurement error distributions. The extent to which the design parameters of the EnKS vary depending on the combination of observations assimilated is investigated     

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.     

Microwave Emission from Smooth Bare Fields and Soil Moisture Sampling Depth

This paper studies the depth to which soil moisture can be directly estimated with microwave measurements over smooth bare fields. The analyses are based on both theoretical and experimental considerations at the frequencies of 1.4, 5.0, and 10.7 GHz. Radiative transfer calculations of microwave emissivities at these frequencies are performed with a number of moisture profiles measured for two soils. The calculated emissivities are compared with those derived from the Fresnel equation to deduce the microwave sampling depth in soils. The data acquired from the ground-level radiometric measurements during the summers of 1979-1981 are examined and compared with the theoretical retical analysis. Both theoretical and experimental analyses lead to the conclusion that the microwave sampling depth in soils is about one tenth of the wavelength of observation. It is shown that the moisture content at any depth near the surface of a smooth soil can be estimated, in principle, by a combination of a radiometric measurement and a curve generated by the Fresnel equation at an appropriate frequency, provided that the texture of the soil is known.     

Multifrequency Measurements of the Effects of Soil Moisture, Soil Texture, And Surface Roughness

An experiment on remote sensing of soil moisture content was conducted over bare fields with microwave radiometers at the frequencies of 1.4, 5, and 10.7 GHz, during July-September of 1981. Three bare fields with different surface roughnesses and soil textures were prepared for the experiment. Ground-truth acquisition of soil temperatures and moisture contents for 5 layers down to the depths of 15 cm was made concurrently with radiometric measurements. The experimental results show that the effect of surface roughness is to increase the soil's brightness temperature and to reduce the slope of regression between brightness temperature and moisture content. The slopes of regression for soils with different textures are found to be comparable and the effect of soil texture is reflected in the difference of regression line intercepts at brightness-temperature axis. The result is consistent with laboratory measurement of soil's dielectric permittivity. Measurements on wet smooth bare fields give lower brightness temperatures at 5 than at 1.4 GHz. This phenomenon is not expected from current radiative transfer theory, using laboratory measurements of the relationship between dielectric permittivity and moisture content for different soil-water mixtures at frequencies of <5 GHz.     

Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach

It is widely recognized that Synthetic Aperture Radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such as, for example, hydraulic conductivity values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing and land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure based on the Extended Kalman Filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing.      

Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations

This is the first paper in a two-part sequence that evaluates the microwave dielectric behavior of soil-water mixtures as a function of water content, temperature, and soil textural composition. Part I presents the results of dielectric constant measurements conducted for five different soil types at frequencies between 1.4 and 18 GHz. Soil texture is shown to have an effect on dielectric behavior over the entire frequency range and is most pronounced at frequencies below 5 GHz. In addition, the dielectric properties of frozen soils suggest that a fraction of the soil water component remains liquid even at temperatures of -24° C. The dielectric data as measured at room temperature are summarized at each frequency by polynomial expressions dependent upon both the volumetric moisture content m and the percentage of sand and clay contained in the soil; separate polynomial expressions are given for the real and imaginary parts of the dielectric constant. In Part II, two dielectric mixing models will be presented to account for the observed behavior: 1) a semiempirical refractive mixing model that accurately describes the data and requires only volumetric moisture and soil texture as inputs, and 2) a theoretical four-component mixing model that explicitly accounts for the presence of bound water.     

Penetration depth as a DInSAR observable and proxy for soil moisture

We use prior theory and experimental results to construct a quantitative relationship between soil moisture and the penetration depth of synthetic aperture radar (SAR) microwaves at L-, C-, and X-bands. This relationship is nonlinear and indicates that a change of 5% volumetric water content (VWC) can cause between 1 and 50 mm of change in C-band penetration depth depending on initial VWC. Because these depths are within the range of differential interferogram SAR (DInSAR) measurement capability, penetration depth may be a viable proxy for measuring soil moisture. DInSAR is unlikely to detect a measurable change in penetration depth above 30% VWC, though certain clay rich soils may continue to cause surface deformation above that level. The possibility of using clay swelling as a proxy for soil moisture was found to be less feasible than penetration depth. Soil moisture may also be a significant, and previously unrecognized, source of noise in the measurement of subtle deformation signals or the creation of digital elevation models using repeat-pass DInSAR.     

Comparison of Three in Situ Surface Soil Moisture Measurements And Application to C-Band Scatterometer Calibration

C-band scatterometers can be used to measure the surface soil moisture. This technique does not directly give the water content and a signal calibration is necessary. This is done by comparing the scatterometer signal (expressed as a scattering cross section per unit area) to gravimetric samples. The gravimetric sample calibration takes a lot of time and people, hence it is not adapted to airborne or satellite remote-sensing measurements. In this paper, new automatic equipment based on the measurement of the real part of the complex permittivity of moist soil is presented. The results of a one-month experiment show that this technique is well adapted to the automatic monitoring of soil moisture in general. In particular, it can be used for the calibration of microwave remote-sensing equipment.     

Potential of Estimating Soil Moisture Under Vegetation Cover by Means of PolSAR

In this paper, the potential of using polarimetric SAR (PolSAR) acquisitions for the estimation of volumetric soil moisture under agricultural vegetation is investigated. Soil-moisture estimation by means of SAR is a topic that is intensively investigated but yet not solved satisfactorily. The key problem is the presence of vegetation cover which biases soil-moisture estimates. In this paper, we discuss the problem of soil-moisture estimation in the presence of agricultural vegetation by means of L-band PolSAR images. SAR polarimetry allows the decomposition of the scattering signature into canonical scattering components and their quantification. We discuss simple canonical models for surface, dihedral, and vegetation scattering and use them to model and interpret scattering processes. The performance and modifications of the individual scattering components are discussed. The obtained surface and dihedral components are then used to retrieve surface soil moisture. The investigations cover, for the first time, the whole vegetation-growing period for three crop types using SAR data and ground measurements acquired in the frame of the AgriSAR campaign.      

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.     

Fault simulation on massively parallel SIMD machines algorithms,implementations and results

Two algorithms for fault simulation of combinational networks on massively parallel SIMD machines are presented. One algorithm uses a variant of the PPSFP [1] approach, while the other uses a mixture of parallel fault simulation [2] and PPSFP [1]. The algorithms have been implemented on the [Thinking Machines Corporation's] Connection Machine [3]. The second algorithm compares very favorably with published results for well known serial algorithms on the ISCAS benchmark circuits [4]. The results indicate that parallel processing could be a valuable tool for accelerating VLSI CAD applications.