Backscatter Measurements Over Vegetation by Ground-Based Microwave Radars

In the study of radar backscattering from vegetated terrain, it is important to understand how the electromagnetic wave interacts with vegetation and the underlying ground. In this paper, an expression of backscattering from a vegetation canopy in the case of spherical wave illumination is derived. Such an expression might apply to the practical case of a ground-based scatterometer overlooking vegetation. The relative importance of the beamwidth as well as the platform height on backscattering from vegetated terrain is studied. Preliminary results indicate that the discrepancy with plane wave illumination can be rather significant, and therefore should not be overlooked

Effective Albedo of Vegetated Terrain at L-Band

This paper derives an explicit expression for an effective albedo of vegetated terrain from the zero- and multiple- order radiative transfer (RT) model comparison. The formulation establishes a direct physical link between the effective vegetation parameterization and the theoretical description of absorption and scattering within the canopy. The paper will present an evaluation of the derived albedo for corn canopies with data taken during an experiment at Alabama A&M Winfield A. Thomas Agricultural Research Station near Huntsville, Alabama in June, 1998. The test site consisted of two 50-m x 60-m plots - one with a bare surface and the other with grass cover - and four 30-m x 50-m plots of corn at different planting densities. One corn field was planted at a full density of 9.5 plants/sq m while the others were planted at 1/3, 1/2 and 2/3 of the full density. The fields were observed with a truck-mounted L-band radiometer at incident angle of 15 degree for the period of two weeks. Soil moisture (SM) changed daily due to irrigation and natural rainfall. Variations in gravimetric SM from 18 % to 34 % were seen during this period. Ground truth data, including careful characterization of the corn size and orientation statistics, and its dielectric, was also collected and used to simulate the effective albedo for the vegetation. The single-scattering albedo is defined as the fractional power scattered from individual vegetation constituents with respect to canopy extinction. It represents single-scattering properties of vegetation elements only, and is independent of ground properties. The values of the albedo get higher when there is dense vegetation (i.e. forest, mature corn, etc.) with scatterers, such as branches and trunks (or stalks in the case of corn), which are large with respect to the wavelength. This large albedo leads to a reduction in brightness temperature in the zero-order RT solution (known as tau-omega model). Higher-order multiple-scattering RT solutions are required for proper representation of scattering within vegetation. In this paper, an expression for an effective albedo for the whole canopy including the ground is derived for use in the zero-order RT model-based SM retrieval. This effective albedo takes into account of all the processes taking place within the canopy, including multiple-scattering. This new formulation will be presented and its importance for microwave SM retrieval will be evaluated for corn canopies in conjunction with the detailed ground truth data obtained during the experiment at Alabama in 1998. Emphasis will be placed on examining how the radiometer response to SM is modified by the corn canopy scattering under different field conditions. A semi-empirical parameterization of the effective albedo will be investigated through analysis of SM and vegetation water content effects on the effective albedo

Development of a Coherent Bistatic Vegetation Model for Signal of Opportunity Applications at VHF UHF-Bands

A coherent bistatic vegetation scattering model, based on a Monte Carlo simulation, is being developed to simulate polarimetric bi-static reflectometry at VHF/UHF-bands (240-270 MHz). The model is aimed to assess the value of geostationary satellite signals of opportunity to enable estimation of the Earth's biomass and root-zone soil moisture. An expression for bistatic scattering from a vegetation canopy is derived for the practical case of a ground-based/low altitude platforms with passive receivers overlooking vegetation. Using analytical wave theory in conjunction with distorted Born approximation (DBA), the transmit and receive antennas effects (i.e., polarization, orientation, height, etc.) are explicitly accounted for. Both the coherency nature of the model (joint phase and amplitude information) and the explicit account of system parameters (antenna, altitude, polarization, etc) enable one to perform various beamforming techniques to evaluate realistic deployment configurations. In this paper, several test scenarios will be presented and the results will be evaluated for feasibility for future biomass and root-zone soil moisture application using geostationary communication satellite signals of opportunity at low frequencies

Characterization of Forest Opacity Using Multi-Angular Emission and Backscatter Data

This paper discusses the results from a series of field experiments using ground-based L-band microwave active/passive sensors. Three independent approaches are employed to the microwave data to determine vegetation opacity of coniferous trees. First, a zero-order radiative transfer model is fitted to multi-angular microwave emissivity data in a least-square sense to provide "effective" vegetation optical depth. Second, a ratio between radar backscatter measurements with the corner reflector under trees and in an open area is calculated to obtain "measured" tree propagation characteristics. Finally, the "theoretical" propagation constant is determined by forward scattering theorem using detailed measurements of size/angle distributions and dielectric constants of the tree constituents (trunk, branches, and needles). The results indicate that "effective" values underestimate attenuation values compared to both "theoretical" and "measured" values

Impact of Conifer Forest Litter on Microwave Emission at L-Band

This study reports on the utilization of microwave modeling, together with ground truth, and L-band (1.4-GHz) brightness temperatures to investigate the passive microwave characteristics of a conifer forest floor. The microwave data were acquired over a natural Virginia Pine forest in Maryland by a ground-based microwave active/passive instrument system in 2008/2009. Ground measurements of the tree biophysical parameters and forest floor characteristics were obtained during the field campaign. The test site consisted of medium-sized evergreen conifers with an average height of 12 m and average diameters at breast height of 12.6 cm. The site is a typical pine forest site in that there is a surface layer of loose debris/needles and an organic transition layer above the mineral soil. In an effort to characterize and model the impact of the surface litter layer, an experiment was conducted on a day with wet soil conditions, which involved removal of the surface litter layer from one half of the test site while keeping the other half undisturbed. The observations showed detectable decrease in emissivity for both polarizations after the surface litter layer was removed. A first-order radiative transfer model of the forest stands including the multilayer nature of the forest floor in conjunction with the ground truth data are used to compute forest emission. The model calculations reproduced the major features of the experimental data over the entire duration, which included the effects of surface litter and ground moisture content on overall emission. Both theory and experimental results confirm that the litter layer increases the observed canopy brightness temperature and obscure the soil emission

Effective Tree Scattering at L-Band

For routine microwave Soil Moisture (SM) retrieval through vegetation, the tau-omega [1] model [zero-order Radiative Transfer (RT) solution] is attractive due to its simplicity and eases of inversion and implementation. It is the model used in baseline retrieval algorithms for several planned microwave space missions, such as ESA's Soil Moisture Ocean Salinity (SMOS) mission (launched November 2009) and NASA's Soil Moisture Active Passive (SMAP) mission (to be launched 2014/2015) [2 and 3]. These approaches are adapted for vegetated landscapes with effective vegetation parameters tau and omega by fitting experimental data or simulation outputs of a multiple scattering model [4-7]. The model has been validated over grasslands, agricultural crops, and generally light to moderate vegetation. As the density of vegetation increases, sensitivity to the underlying SM begins to degrade significantly and errors in the retrieved SM increase accordingly. The zero-order model also loses its validity when dense vegetation (i.e. forest, mature corn, etc.) includes scatterers, such as branches and trunks (or stalks in the case of corn), which are large with respect to the wavelength. The tau-omega model (when applied over moderately to densely vegetated landscapes) will need modification (in terms of form or effective parameterization) to enable accurate characterization of vegetation parameters with respect to specific tree types, anisotropic canopy structure, presence of leaves and/or understory. More scattering terms (at least up to first-order at L-band) should be included in the RT solutions for forest canopies [8]. Although not really suitable to forests, a zero-order tau-omega model might be applied to such vegetation canopies with large scatterers, but that equivalent or effective parameters would have to be used [4]. This requires that the effective values (vegetation opacity and single scattering albedo) need to be evaluated (compared) with theoretical definitions of these parameters. In a recent study [9], effective vegetation opacity of coniferous trees was compared with two independent estimates of the same parameter. First, a zero-order RT model was fitted to multiangular microwave emissivity data in a least-square sense to provide effective vegetation optical depth as done in spaceborne retrieval algorithms. Second, a ratio between radar backscatter measurements with a corner reflector under trees and in an open area was calculated to obtain measured tree propagation characteristics. Finally, the theoretical propagation constant was determined by forward scattering theorem using detailed measurements of size/angle distributions and dielectric constants of the tree constituents (trunk, branches, and needles). Results indicated that the effective attenuation values are smaller than but of similar magnitude to both the theoretical and measured values. This study will complement the previous work [9] and will focus on characterization of effective scattering albedo by assuming that effective vegetation opacity is same as theoretical opacity. The resultant effective albedo will not be the albedo of single forest canopy element anymore, but it becomes a global parameter, which depends on all the processes taking place within the canopy including multiple scattering as described

L-band estimation of forest canopy attenuation by a time-domain analysis of radar backscatter response

In radiometric sensing of soil moisture through a forest canopy, knowledge of canopy attenuation is required. Active sensors have the potential of providing this information since the backscatter signals are more sensitive to forest structure. In this dissertation, a new radar technique is presented for estimating canopy attenuation. The technique employs details found in a transient solution where the canopy (volume scattering) and the tree-ground (double interaction) effects appear at different times in the return signal. The influence that these effects have on the expected time-domain response of a forest stand is characterized through numerical simulations. A coherent forest scattering model, based on a Monte Carlo simulation, is developed to calculate transient response from distributed scatterers over a rough surface. The forest transient response model for linear co-polarized cases is validated with the microwave deciduous tree data acquired by the ComRAD (Combined Radar/Radiometer) system. The attenuation algorithm is applicable when the forest height is sufficient to separate components of the radar backscatter transient response. The frequency correlation functions (FCF) of the volume scatter and the double interaction terms are computed by the distorted Born approximation. A ratio of these FCF's is formed and compared with data at a set of frequencies over the decorrelation bandwidth of the returns. The resulting system of equations only depends on the canopy thickness, the canopy attenuation and a combined parameter involving the forest scattering coefficients and the ground reflectance. A least square method is used to solve for the attenuation and the combined parameter by assuming the canopy thickness is estimated a priori from the transient response. Finally, the technique is used with ComRAD L-band stepped frequency data to evaluate its performance under various physical conditions

Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features

The accuracy of geophysical retrievals from radiometers relies on calibration quality, encompassing both absolute radiometric accuracy and spectral consistency. Radiometers have employed various calibration techniques, including external targets, vicarious sources, and internal calibrators like noise diodes or matched reference loads. Calibration techniques face challenges like frequency dependence, instrumental effects, environmental influences, drift, aging, and radio frequency interference. Recent hardware advancements enable radiometers to collect raw samples containing both temporal and spectral information. Leveraging advanced modeling techniques like deep learning (DL) enables detecting subtle correlations, non-linear dependencies, and higher-order interactions within the data extracting valuable information that may have been challenging with conventional methods. This study utilizes NASA's Soil Moisture Active Passive (SMAP) satellite's level 1A and level 1B data products to develop a DL-based radiometer calibrator to estimate antenna temperature. Spectrograms of second raw moments equivalent to power carrying the 2-D spectral features serve as primary input in a supervised convolutional neural network-based architecture. DL-based calibrator has demonstrated high correlation and low root mean square error when incorporating spectral information from both reference and noise diodes and when not considering this information. Findings suggest that the ancillary features such as internal thermistor temperature and loss elements exhibit sufficient accuracy in estimating antenna temperature to compensate for variations in receiver noise temperature and short-term gain fluctuations in the absence of the reference load and noise diode power. The proposed calibration technique with reduced reference information might enable radiometers for a higher number of antenna scene observations within a footprint