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/spl les/2.5, /spl mu//sub /spl upsi///spl les/35%, and /spl theta//spl ges/30/spl deg/. 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 /spl sigma//sub hv//sup 0///spl sigma//sub vv//sup 0/ 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. >

/pdf/measuring-soil-moisture-with-imaging-radars-2o55u7791b.pdf

Measuring soil moisture with imaging radars

Proposes a new model for the inversion of surface roughness and soil moisture from polarimetric synthetic aperture radar (SAR) data, based on the eigenvalues and eigenvectors of the polarimetric coherency matrix. It demonstrates how three polarimetric parameters, namely the scattering entropy (H), the scattering anisotropy (A), and the alpha angle (/spl alpha/) may be used in order to decouple surface roughness from moisture content estimation offering the possibility of a straightforward inversion of these two surface parameters. The potential of the proposed inversion algorithm is investigated using fully polarimetric laboratory measurements as well as airborne L-band SAR data and ground measurements from two different test sites in Germany, the Elbe-Auen site and the Weiherbach site.

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Inversion of surface parameters from polarimetric SAR

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.

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

Microwave remote sensing of soil moisture has been an active area of research since the 1970s but has yet found little use in operational applications Given recent advances in retrieval algorithms and the approval of a dedicated soil moisture satellite, it is time to re-assess the potential of various satellite systems to provide soil moisture information for hydrologic applications in an operational fashion This paper reviews recent progress made with retrieving surface soil moisture from three types of microwave sensors - radiometers, Synthetic Aperture Radars (SARs), and scatterometers The discussion focuses on the operational readiness of the different techniques, considering requirements that are typical for hydrological applications It is concluded that operational coarse-resolution (25-50 km) soil moisture products can be expected within the next few years from radiometer and scatterometer systems, while scientific and technological breakthroughs are still needed for operational soil moisture retrieval at finer scales (< 1 km) from SAR Also, further research on data assimilation methods is needed to make best use of the coarse-resolution surface soil moisture data provided by radiometer and scatterometer systems in a hydrologic context and to fully assess the value of these data for hydrological predictions

Operational readiness of microwave remote sensing of soil moisture for hydrologic applications

The scatterometers onboard the European Remote Sensing satellites (ERS-1 & ERS-2) and the METeorological OPerational satellite (METOP) have been shown to be useful for surface soil moisture retrieval using the so-called TU-Wien change detection method. This paper presents an improved soil moisture retrieval algorithm based on the existing TU-Wien method but with new parameterization as well as a series of modifications. The new algorithm, WAter Retrieval Package 5 (WARP5), copes with some limitations identified in the earlier method WARP4 and provides the possibility of migrating soil moisture retrieval from ERS-SCAT to METOP-ASCAT data. The WARP5 algorithm results in a more robust and spatially uniform soil moisture product, thanks to its new processing elements, including a method for the correction of azimuthal anisotropy of backscatter, a comprehensive noise model, and new techniques for calculation of the model parameters. Cross-comparisons of WARP4 and WARP5 data sets with the Oklahoma Mesonet in situ observations and also with European Centre of Medium Range Weather Forecast (ECMWF) ReAnalysis (ERA-Interim) global modeled data show that the new algorithm has a better performance and effectively corrects retrieval errors in certain areas.

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An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations

Polarimetric radar measurements were conducted for bare soil surfaces under a variety of roughness and moisture conditions at L-, C-, and X-band frequencies at incidence angles ranging from 10 degrees to 70 degrees . Using a laser profiler and dielectric probes, a complete and accurate set of ground truth data was collected for each surface condition, from which accurate measurements were made of the rms height, correlation length, and dielectric constant. Based on knowledge of the scattering behavior in limiting cases and the experimental observations, an empirical model was developed for sigma degrees /sub hh/, sigma degrees /sub vv/, and sigma degrees /sub hv/ in terms of ks (where k=2 pi / lambda is the wave number and s is the rms height) and the relative dielectric constant of the soil surface. The model, which was found to yield very good agreement with the backscattering measurements of the present study as well as with measurements reported in other investigations, was used to develop an inversion technique for predicting the rms height of the surface and its moisture content from multipolarized radar observations. >

An empirical model and an inversion technique for radar scattering from 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.

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

A semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces is presented. Based on existing scattering models and data sets measured by polarimetric scatterometers and the JPL AirSAR, the parameters of the co-polarized phase-difference probability density function, namely the degree of correlation /spl alpha/ and the co-polarized phase-difference /spl sigmav/, in addition to the backscattering coefficients /spl sigma//sub /spl nu//spl nu///sup 0/,/spl sigma//sub hh//sup 0/ and /spl sigma//sub /spl nu/h//sup 0/, are modeled empirically in terms of the volumetric soil moisture content m/sub /spl nu// and the surface roughness parameters ks and kl, where k=2/spl pi/f/c, s is the rms height and l is the correlation length. Consequently, the ensemble-averaged differential Mueller matrix (or the differential Stokes scattering operator) is specified completely by /spl sigma//sub /spl nu//spl nu///sup 0/,/spl sigma//sub hh//sup 0/,/spl sigma//sub /spl nu/h//sup 0/,/spl alpha/, and /spl zeta/.

/pdf/semi-empirical-model-of-the-ensemble-averaged-differential-59cdfedz8m.pdf

Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces

Whereas it is well known that electromagnetic scattering by a randomly rough surface is strongly influenced by the surface-height correlation function, it is not clear as to how long a surface-height profile is needed and at what interval it should be sampled to experimentally quantify the correlation function of a real surface. This paper presents the results of a Monte Carlo simulation conducted to answer these questions. It was determined that, in order to measure the rms height and the correlation length with a precision of /spl plusmn/10%, the surface segment should be at least 40l long and 200l long, respectively, where l is the mean (or true) value of the surface correlation length. Shorter segment lengths can be used if multiple segments are measured and then the estimated values are averaged. The second part of the study focused on the relationship between sampling interval and measurement precision. It was found that, in order to estimate the surface roughness parameters with a precision of /spl plusmn/5%, it is necessary that the surface be sampled at a spacing no longer than 0.2 of the correlation length.

Condition for precise measurement of soil surface roughness

A semi-empirical polarimetric scattering model for bare soil surfaces is developed. This scattering model is constructed based on the existing theoretical models in conjunction with an extensive experimental data collected with polarimetric scatterometer systems at microwave frequencies. The backscattering coefficients as well as parameters of phase difference statistics, degree of correlation (/spl alpha/) and polarized phase difference (/spl zeta/), are expressed in terms of both surface parameters (rms height, correlation length, and dielectric constant) and radar parameters (frequency and incidence angle). The semi-empirical model is used as a basis for an inversion algorithm to estimate the surface parameters from the polarimetric backscatter response of a surface when the radar parameters are known. By performing a sensitivity analysis, a set of optimum parameters are chosen for the inversion algorithm. It is shown that the co-polarized ratio (/spl sigma//sup 0/ /sub hv///spl sigma//sup 0/ /sub vv/) the cross-polarized ratio (/spl sigma//sup 0/ /sub hv///spl sigma//sup 0/ /sub vv/), and the degree of correlation for co-polarized phase difference are most sensitive to the surface parameters and least affected by the measurement errors. >

http://mwal.hongik.ac.kr/HomeEng/pdf/An%20inversion%20algorithm%20for%20retrieving%20soil%20moisture%20and%20surface%20roughness%20from%20polarimetric%20radar%20observation.pdf

Yisok Oh

Papers

An empirical model and an inversion technique for radar scattering from bare soil surfaces

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

Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces

Condition for precise measurement of soil surface roughness

An inversion algorithm for retrieving soil moisture and surface roughness from polarimetric radar observation