The beamfilling algorithm for retrieval of hydrometeor profileparameters from passive microwave measurements

The horizontal inhomogeneity of the atmosphere within a satellite microwave radiometer's field of view (FOV) has always been considered as a source of rainfall retrieval errors. The hydrometeor profile retrieval algorithm presented exploits it to obtain an approximation of a radiative transfer model, which allows relatively simple inversion. The atmosphere within the FOV is treated as a combination of horizontally homogeneous domains. Assuming that one of known “basic” hydrometeor profiles occurs in each domain, the inverse problem is reduced to a determination of “beamfilling coefficients.” The online procedure includes determination of beamfilling coefficients and a footprint-averaged hydrometeor profile as a linear combination of “basic” ones. Off-line procedures involve the selection of a minimum number of necessary “basic” brightness temperature vectors and correction of “basic” hydrometeor profiles to provide the best retrieval accuracy for a given cloud/radiative simulation. The performance of the algorithm is tested for both numerical simulations and TRMM/TMI data. Numerical simulation has allowed a comparison of the information content of radiometer measurements from SSM/I, TMI, and the future AMSR. The effectiveness of the algorithm is being tested for rain water integral and rain rate retrievals from TRMM TMI measurements     

An analysis of SeaWinds-based rain retrieval in severe weather events

The Ku-band SeaWinds scatterometer estimates near-surface ocean wind vectors by relating measured backscatter to a geophysical model function for the near-surface vector wind. The conventional wind retrieval algorithm does not explicitly account for SeaWinds' sensitivity to rain, resulting in rain-caused wind retrieval error. A new retrieval method, termed "simultaneous wind/rain retrieval," that estimates both wind and rain from rain-contaminated measurements has been previously proposed and validated with Tropical Rain Measuring Mission data. Here, the accuracy of rains retrieved by the new method is validated through comparison with the Next Generation Weather Radar (NEXRAD) in coastal storm events. The rains detected by both sensors are comparable, though SeaWinds-estimated rains exhibit greater variability. The performance of simultaneous wind/rain retrieval in flagging excessively rain-contaminated winds is discussed and compared to existing methods. A new rain-only retrieval algorithm for use in rain-backscatter-dominated areas is proposed and tested. A simple noise model for SeaWinds rain estimates is developed, and Monte Carlo simulation is employed to verify the model. The model shows that SeaWinds rain estimates have a standard deviation of 2.5 mm/h, which is higher than the NEXRAD measurements. Thresholding SeaWinds rain estimates at 2 mm/h yields a better rain flag than current rain flag algorithms.     

A soft computing approach for rainfall retrieval from the TRMM microwave imager

A neural network model for rainfall retrieval over ocean from remotely sensed microwave (MW) brightness temperature (BT) is proposed. BT data are obtained from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The BT values from different channels of TMI over the Pacific Ocean (163/spl deg/ to 177/spl deg/W and 18/spl deg/ to 34/spl deg/S) are the input features. The near-surface rainfall rate from the Precipitation Radar (PR) are considered as a target. The proposed model consists of a neural network with online feature selection (FS) and clustering techniques. A K-means clustering algorithm is applied to cluster the selected features. Different networks have been trained to give an instantaneous rainfall rate with all input features as well as with selected features obtained by applying the FS algorithm. It is found that the hybrid network utilizing FS and clustering techniques performs better. The developed network is also validated with two independent datasets on March 14, 2000 over the Atlantic Ocean having stratiform rain and on March 21, 2000 over the Pacific Ocean having both stratiform and convective rain. In both cases, the hybrid network performs well with correlation coefficient improving to 0.78 and 0.81, respectively, in contrast to 0.70 and 0.75 for the network with all features. The rainfall rate retrieved from the hybrid network is also compared with the TMI surface rain rate, and a correlation of 0.84 and 0.75 is found for the two events. The proposed hybrid model is validated with a Doppler Weather Radar, and correlation of 0.52 is observed.     

An Artificial-Neural-Network-Based Integrated Regional Model for Rain Retrieval Over Land and Ocean

An integrated regional model is proposed for rain-rate retrievals over land/ocean from the brightness temperature (Tb) values of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The polarization-corrected temperature calculated from the 85.5-GHz channels is also considered as one of the inputs along with the nine channel Tb values. This model is applicable over the region between and . For this purpose, an artificial neural network is utilized. The collocated precipitation radar (PR) near-surface rain rates as given by a 2A25 data product is considered as a target value. The methodology consists of the separation of land and ocean pixels, the separation of stratiform and convective pixels over land/ocean, and the selection of important features (inputs) for the multilayer perceptron network by the feature selection technique for each group. For the separation of land/ocean pixels, the Tb values of the 10.65-GHz vertical channel are utilized. The values are utilized to separate the stratiform and convective pixels both over land and ocean. The rain retrieval from the developed model is validated with TRMM PR. Overall result shows the better agreement of the model-retrieved rain rate with the PR observation compared to the TMI (2A12) rain rate particularly over land. The rain retrieved from the developed model is further validated with Doppler weather radar. A reasonably good agreement is observed between these two estimations.     

A nonlinear multispectral statistical CLEAN-based precipitationparameter-retrieval algorithm

An iterative algorithm incorporating CLEAN deconvolution concepts for precipitation parameter retrieval using passive microwave imagery is presented. The CLEAN algorithm was originally designed to deconvolve single-channel radio astronomy images. In order to use CLEAN to retrieve precipitation parameters from multispectral passive-microwave imagery, extensions of the algorithm to accommodate nonlinear, multispectral, and statistical data mere designed and implemented. The primary advantage of the nonlinear multispectral statistical (NMS) CLEAN retrieval algorithm relative to existing algorithms is the use of high-resolution (high-frequency) imagery to guide the retrievals of precipitation parameters from lower resolution (Low-frequency) imagery. The NMS-CLEAN retrieval algorithm was used to estimate rain rate (RR) and integrated ice content (IIC) using simulated imagery of oceanic convection as would be observed from six channels of the proposed Advanced Microwave-Scanning Radiometer. Both the accuracy and structural detail of the retrieved rain rate were improved relative to the retrievals from a single-step, nonlinear, statistical algorithm. Reduced error and improved spatial resolution of a more minor magnitude was also seen in the integrated ice-content retrievals. This study also showed that spatially-simple storm structures resulted in better performance of the NMS-CLEAN retrieval algorithm     

Intercomparison of inversion algorithms to retrieve rain rate from SSM/I by using an extended validation set over the Mediterranean area

The capability of some inversion algorithms to estimate surface rain rate at the midlatitude basin scale from the Special Sensor Microwave Imager (SSM/I) data is analyzed. For this purpose, an extended database has been derived from coincident SSM/I images and half-hourly rain rate data obtained from a rain gauge network, placed along the Tiber River basin in Central Italy, during nine years (from 1992 to 2000). The database has been divided in a training set, to calibrate the empirical algorithms, and in a validation one, to compare the results of the considered techniques. The proposed retrieval methods are based on both empirical and physical approaches. Among the empirical methods, a regression, an artificial feedforward neural network, and a Bayesian maximum a posteriori (MAP) inversion have been considered. Three algorithms available in the literature are also included as benchmarks. As physical algorithms, the MAP method and the minimum mean square estimator have been used. Moreover, in order to test the behavior of the algorithms with different kinds of precipitation, a classification of rainy events, based on some statistical parameters derived from rain gauge measurements, has been performed. From this classification, an attempt to identify the type of event from radiometric data has been carried out. The purposes of this paper are to determine whether the use of an extended training set, referred to a limited geographical area, can improve the SSM/I skill in rain detection and estimation and, mainly, to confirm the validity of the physical approach adopted in previous works. It will be shown that, among all the estimators, the neural network presents the best performances and that the physical techniques provide results only slightly worse than those given by empirical methods, but with the well-known advantage of an easy application to different geographical zones and different sensors.     

An empirical algorithm for cloud liquid water from MSMR and its utilization in rain identification

In this paper, an empirical method to estimate cloud liquid water from Indian Remote Sensing P4 (IRS-P4) Multi-frequency Scanning Microwave Radiometer (MSMR) measurements is presented. MSMR brightness temperatures are collocated with concurrent observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)-derived cloud liquid water. The multiple-correlation coefficient between TMI-derived cloud liquid water and logarithmic of MSMR-derived brightness temperatures, and their differences at 18- and 21-GHz channels, is found to be about 82.4%. The relationship thus obtained has an rms error of 8.75 mgcm/sup -2/ in the measurements of cloud liquid water from MSMR with respect to TMI measurements. Verification of the algorithm is carried out with another set of concurrent measurements from MSMR and TMI. Further, the MSMR-derived cloud liquid water over the global oceans and for extreme weather conditions (cyclone) are compared with that from TMI and the Special Sensor Microwave/Imager (SSM/I) for independent verification. The cloud liquid water from MSMR is further used to successfully delineate rain events for quantitative estimation of rain rate from MSMR.     

Simultaneous wind and rain retrieval using SeaWinds data

The SeaWinds scatterometers onboard the QuikSCAT and the Advanced Earth Observing Satellite 2 measure ocean winds on a global scale via the relationship between the normalized radar backscattering cross section of the ocean and the vector wind. The current wind retrieval method ignores scattering and attenuation of ocean rain, which alter backscatter measurements and corrupt retrieved winds. Using a simple rain backscatter and attenuation model, two methods of improving wind estimation in the presence of rain are evaluated. First, if no suitable prior knowledge of the rain rate is available, a maximum-likelihood estimation technique is used to simultaneously retrieve the wind velocity and rain rate. Second, when a suitable outside estimate of the rain rate is available, wind retrieval is performed by correcting the wind geophysical model function for the known rain via the rain backscatter model. The new retrieval techniques are evaluated via simulation and validation with data from the National Centers for Environmental Prediction and the Tropical Rainfall Measuring Mission Precipitation Radar. The simultaneous wind/rain estimation method yields most accurate winds in the "sweet spot" of SeaWinds' swath. On the outer-beam edges of the swath, simultaneous wind/rain estimation is not usable. Wind speeds from simultaneous wind/rain retrieval are nearly unbiased for all rain rates and wind speeds, while conventionally retrieved wind speeds become increasingly biased with rain rate. A synoptic example demonstrates that the new method is capable of reducing the rain-induced wind vector error while producing a consistent (yet noisy) estimate of the rain rate.     

Passive microwave remote sensing of snow constrained byhydrological simulations

This paper describes a snow parameter retrieval algorithm from passive microwave remote sensing measurements. The three components of the retrieval algorithm include a dense media radiative transfer (DMRT) model, which is based on the quasicrystalline approximation (QCA) with the sticky particle assumption, a physically-based snow hydrology model (SHM) that incorporates meteorological and topographical data, and a neural network (NN) for computational efficient inversions. The DMRT model relates physical snow parameters to brightness temperatures. The SHM simulates the mass and heat balance and provides initial guesses for the neural network. The NN is used to speed up the inversion of parameters. The retrieval algorithm can provide speedy parameter retrievals for desired temporal and spatial resolutions, Four channels of brightness temperature measurements: 19V, 19H, 37V, and 37H are used. The algorithm was applied to stations in the northern hemisphere. Two sets of results are shown. For these cases, the authors use ground-truth precipitation data, and estimates of snow water equivalent (SWE) from SHM give good results. For the second set, a weather forecast model is used to provide precipitation inputs for SHM. Additional constraints in grain size and density are used. They show that inversion results compare favorably with ground truth observations     

Beam-filling effect correction with subpixel cloud fraction using a neural network

In this study, the effects of cloud inhomogeneity on microwave rain rate retrievals are investigated. A single-channel (85 GHz) empirically based algorithm using a neural network approach is presented. The objective is to correct the beam-filling error (BFE), that might occur because of the inherent variability within coarse microwave pixels, with subpixel information. To this aim, we used the Tropical Rainfall Measuring Mission passive microwave, thermal infrared and radar data. The integration of spatial information into the retrieval algorithm enables us to partially overcome the BFE. We use two parameters which characterize the horizontal cloud inhomogeneity within the microwave radiometer field of view, and we add them to simulated brightness temperatures as inputs of the neural network algorithm. The first one is the cloud fraction derived from infrared measurement, and the second corresponds to the fraction of the rainy area derived from radar measurements. The output rain rates were validated using the Precipitation Radar data. It was found that adding cloud fraction of microwave pixels, can lead to more accurate retrievals. Instantaneous precipitation estimates demonstrated correlations of /spl sim/0.6-0.7 and /spl sim/0.7-0.8 with radar-derived rain rates, for ocean and land retrievals respectively. In spite of the problem inherent in deriving the cloud (or rain) fraction, the initial validation results presented in this study are reasonably encouraging and show the advantage of utilizing the information from different sensors in order to optimize the retrieval of rainfall.     

A two-dimensional satellite rainfall error model

A two-dimensional satellite rainfall error model ( SREM2D) is developed for simulating ensembles of satellite rain fields on the basis of "reference" rain fields derived from higher accuracy sensor estimates. With this model we aim at characterizing the multidimensional stochastic error structure of satellite rainfall estimates as a function of scale. The pertinent error dimensions we seek to address are: 1) the joint probability density function for characterizing the spatial structure of the successful delineation of rainy and nonrainy areas; 2) the temporal dynamics of rain estimation bias; and 3) the spatial variability of rain rate estimation error. Ground radar rain fields in the Southern plains of the United States are used as reference to evaluate SREM2D error parameters at 0.25-deg and hourly spatiotemporal resolution for an infrared (IR) rain retrieval algorithm (IR-3B41RT) developed at NASA. Comparison of SREM2D simulated satellite rainfall with actual IR-3B41RT data showed that the error modeling technique can preserve the estimation error characteristics across scales with marginal deviations. The model performance is compared against two simpler, but widely used, approaches of error modeling that do not account for uncertainty in rainy/nonrainy area delineation. It is shown that both of these approaches fare poorly with regards to preserving the error structure across scales. They underestimated the sensor retrieval error standard deviation by more than 100% upon aggregation, which, for SREM2D, was found to be below 30%. SREM2D is modular in design-it can be applied for any satellite rainfall algorithm to consistently characterize its error structure.     

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.     

PIn. II. Comparative evaluation of SSM/I and TMI precipitable waterestimate for the Mediterranean Sea

For pt.I see ibid., vol.39, no.12, p.2566-74 (2001). To estimate integrated precipitable water vapor along with liquid water path and water vapor effective profile (i.e. standard atmospheric profile approximation), utilizing the Special Sensor Microwave/Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometers, an operative procedure was developed and assessed. This procedure is based on a fast nonlinear physical inversion algorithm (PIn) developed by the authors. A large data set of near-coincident TMI and SSM/I data acquisitions were collected and used to supply the procedure. Retrieved parameters were compared against retrievals achieved with well-accepted statistical algorithms, and consistency between TMI and SSM/I retrievals was confirmed. As far as TMI and SSM/I precipitable water retrieving consistency is concerned, this research revealed a linear relationship up to 20 kg/m² and a general overestimate of TMI retrieving, for higher values. A new algorithm for obtaining integrated precipitable water from TMI brightness temperatures was introduced and the goodness of its accuracy was reported. The procedure proved to be reliable and portable and its integrated precipitable water vapor retrieving was assessed to be as accurate as the best radiometric retrieving algorithms, reported in literature. For SSM/I data, developed-procedure liquid water path estimates seemed to be in good agreement with statistical retrievals. Eventually the procedure provided effective water vapor vertical profiles which belong to a deterministic distribution area characterized by an upper and lower limit; it was confirmed that SSM/I and TMI vertical profile distribution areas mainly overlap even if they are characterized by different sensitivities to profile parameters     

Spatial scales of tropical precipitation inferred from TRMM microwave imager data

The local spatial scales of tropical precipitating systems were studied using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rate imagery from the TRMM satellite. Rain rates were determined from TMI data using the Goddard Profiling (GPROF) Version 5 algorithm. Following the analysis of Ricciardulli and Sardeshmukh (RS), who studied local spatial scales of tropical deep convection using global cloud imagery (GCI) data, active precipitating months were defined alternatively as those having greater than either 0.1 mm/h or 1 mm/h of rain for more than 5% of the time. Spatial autocorrelation values of rain rate were subsequently computed on a 55/spl times/55 km grid for convectively active months from 1998 to 2002. The results were fitted to an exponential correlation model using a nonlinear least squares routine to estimate a spatial correlation length at each grid cell. The mean spatial scale over land was 90.5 km and over oceans was 122.3 km for a threshold of 0.1 mm/h of rain with slightly higher values for a threshold of 1 mm/h of rain. An error analysis was performed which showed that the error in these determinations was of order 2% to 10%. The results of this study should be useful in the design of convective schemes for general circulation models and for precipitation error covariance models for use in numerical weather prediction and associated data assimilation schemes. The results of the TMI study also largely concur with those of RS, although the more direct relationship between the TMI data and rain rate relative to the GCI imagery provide more accurate correlation length estimates. The results also confirm the strong impact of land in producing short spatial scale convective rain.     

Retrieving ocean surface wind speed from the TRMM Precipitation Radar measurements

Spaceborne scatterometery has been used for many years now to retrieve the ocean surface wind field from normalized radar cross-section measurements of the ocean surface. Though designed specifically for the measurement of precipitation profiles in the atmosphere, the Precipitation Radar (PR) of the Tropical Rainfall Measuring Mission (TRMM) also acquires surface backscattering measurements of the global oceans. As such, this instrument provides an interesting opportunity to explore the benefits and pitfalls of alternative radar configurations in the satellite remote sensing of ocean winds. In this paper, a technique was developed for retrieving ocean surface winds using surface backscattering measurements from the TRMM PR. The wind retrieval algorithm developed for TRMM PR makes use of a maximum-likelihood estimation technique to compensate for the low backscattering associated with the PR configuration. The high vertical resolution of the PR serves to filter-out rain-contaminated cells normally integrated into Ku-band scatterometer measurements. The algorithm was validated through comparisons of ocean surface wind speeds derived from PR with remotely measured winds from TMI and QuikSCAT, as well as in situ observations from oceanographic buoys, revealing good agreements in wind speed estimations.     

Tests of sequential data assimilation for retrieving profile soilmoisture and temperature from observed L-band radiobrightness

Sequential data assimilation (Kalman filter optimal estimation) techniques are applied to the problem of retrieving near-surface soil moisture and temperature state from periodic terrestrial radiobrightness observations that update soil heat and moisture diffusion models. The retrieval procedure uses a time-explicit numerical model to continuously propagate the soil state profile, its error of estimation, and its interdepth covariances through time. The model's coupled soil moisture and heat fluxes are constrained by micrometeorology boundary conditions drawn from observations or atmospheric modeling. When radiometer data are available, the Kalman filter updates the state profile estimate by weighing the propagated state, error, and covariance estimates against an a priori estimate of radiometric measurement error. The Kalman filter compares predicted and observed radiobrightnesses directly, so no inverse algorithm relating brightness to physical parameters is required. The authors demonstrate Kalman filter model effectiveness using field observations and a simulation study. An observed 1 m soil state profile is recovered over an eight-day period from daily L-band observations following an intentionally poor initial state estimate. In a four-month simulation study, they gauge the longer term behavior of the soil state retrieval and Kalman gain through multiple rain events, soil dry-downs, and updates from radiobrightnesses     

Global Millimeter-Wave Precipitation Retrievals Trained With A Cloud-Resolving Numerical Weather-Prediction Model, Part II: Performance Evaluation

This paper evaluates the performance of the global precipitation rate retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) that was described in Part I of this paper. AMSU is in polar orbit on several National Ocean and Atmospheric Administration (NOAA) operational weather satellites. Predicted rms retrieval errors based on a 15-km resolution 0.5-1.0-mm/h MM5 truth were 0.88, 0.83, 1.13, and 3.04 for stratiform, warm rain, ice-free rain, and convective rain, respectively, which were averaged over all view angles for land and sea up to 73deg latitude. For MM5 rates of 4-8 mm/h, these rms errors increased to 2.8, 3.4, 3.9, and 4.9 mm/h, respectively. The corresponding rms retrieval accuracies for MM5 hydrometeor water paths between 0.125 and 0.25 mm for rainwater, snow, and graupel were 0.19, 0.10, and 0.22 mm, respectively. The rms retrieval accuracy for the 0.125-0.25-m/s peak vertical wind was 0.08 m/s. Biases are small for cumulative precipitation estimates, although an upward correction factor of 1.37 is derived for convective precipitation rate probability distributions. Differences between these retrievals and those from the conically scanned Advanced Microwave Scanning Radiometer for the Earth Observing System instrument and an alternate NOAA AMSU algorithm are also characterized.     

The GSMaP Precipitation Retrieval Algorithm for Microwave Sounders—Part I: Over-Ocean Algorithm

We develop an over-ocean rainfall retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) based on the Global Satellite Mapping of Precipitation (GSMaP) microwave radiometer algorithm. This algorithm combines an emission-based estimate from brightness temperature (Tb) at 23 GHz and a scattering-based estimate from Tb at 89 GHz, depending on a scattering index (SI) computed from Tb at both 89 and 150 GHz. Precipitation inhomogeneities are also taken into account. The GSMaP-retrieved rainfall from the AMSU (GSMaP_AMSU) is compared with the National Oceanic and Atmospheric Administration (NOAA) standard algorithm (NOAA_AMSU)-retrieved data using Tropical Rainfall Measuring Mission (TRMM) data as a reference. Rain rates retrieved by GSMaP_AMSU have better agreement with TRMM estimates over midlatitudes during winter. Better estimates over multitudes over winter are given by the use of Tb at 23 GHz in the GSMaP_AMSU algorithm. It was also shown that GSMaP_AMSU has higher rain detection than NOAA_AMSU.      

Modeling and measurement of rainfall by ground-based multispectral microwave radiometry

The potential of ground-based multispectral microwave radiometers in retrieving rainfall parameters is investigated by coupling physically oriented models and retrieval methods with a large set of experimental data. Measured data come from rain events that occurred in the USA at Boulder, Colorado, and at the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains (SGP) site in Lamont, OK. Rain cloud models are specified to characterize both nonraining clouds, stratiform and convective rainfall. Brightness temperature numerical simulations are performed for a set of frequencies from 20 to 60 GHz at zenith angle, representing the channels currently deployed on a commercially available ground-based radiometric system. Results are illustrated in terms of comparisons between measurements and model data in order to show that the observed radiometric signatures can be attributed to rainfall scattering and absorption. A new statistical inversion algorithm, trained by synthetic data and based on principal component analysis is also developed to classify the meteorological background, to identify the rain regime, and to retrieve rain rate from passive radiometric observations. Rain rate estimate comparisons with simultaneous rain gauge data and rain effect mitigation methods are also discussed.     

Retrieval of biophysical parameters of agricultural crops using polarimetric SAR interferometry

An existing two-layer model for forest height estimation is adapted for agricultural crops in order to develop a retrieval algorithm based on polarimetric synthetic aperture radar interferometry. This new inversion scheme is specifically tailored for vertically oriented agricultural crops, with extinction coefficients dependent on the wave polarization. Physical parameters of the vegetation scene are estimated from the location of the measured coherences in the complex plane. The proposed inversion scheme is validated experimentally with indoor wide-band polarimetric measurements on samples of corn and rice fields. Results show that the estimates of the thickness of the vegetation layer and the ground topography are reasonably accurate for a wide range of frequencies and baselines. Moreover, some interesting results are also obtained when using only dual-polarized data, which brings up new applications for present and future spaceborne missions.