Full-wave simulations of the effective permittivity of firn aquifer and wet snow at L-band are reported. The Monte Carlo simulations are carried out for bicontinuous media embedded in a sphere, and the scattering cross section and absorption cross section are calculated. By averaging over realizations, the effective permittivity is extracted by comparing it with Mie solutions. The simulations are validated by using three numerical methods of 3-D solutions of Maxwell equations: finite difference frequency domain (FDFD), finite element method (FEM), and the discrete dipole approximation (DDA). Full-wave simulation results are compared with those from the classical Maxwell–Garnett and Polder–van Santen mixing formulas. The results show large differences from mixing formulas when there are large permittivity contrasts between the background medium and the scatterers, which are scenarios in aquifer and wet snow. The significance of these results is examined for the L-band microwave remote sensing of terrestrial wet snow and aquifer in polar regions.

This study aims to discuss the use of L-band radar backscatter and phase information to analyze soil moisture (SM) and crop conditions, focusing on corn vegetation. While previous research has primarily utilized total backscatter magnitude to assess SM and crop conditions, this work explores the potential of phase information, which may be more sensitive to crop structure and suitable for monitoring crop dynamics. The methodology involves radar measurements using the University of Florida L-band Automated Radar System (UF-LARS), destructive vegetation sampling, and calibration techniques. Preliminary results demonstrate significant differences in CPD between bare soil and vegetated conditions, with phase variations associated with different growth stages of corn. This ongoing project indicates the potential of phase information to characterize vegetation growth stages for corn and similar crops.

Wideband microwave radiometry holds promise for new in-sights for passive remote sensing of snow packs. We report on a plot-scale, season-long field experiment using two wide-band microwave radiometers operating with about 1 GHz of bandwidth at L-band frequencies. Despite considerable ra-dio frequency interference (RFI), there remains many 100's of MHz of usable bandwdith for measurement. We report on the wideband signatures of a snowpack that varies in depth from no snow to more than 57 cm deep.

This letter presents a new hardware setup to suppress radio frequency interference (RFI) for a recently developed microwave radiometer technique, known as wideband autocorrelation radiometry (WiBAR). WiBAR is a method that can directly measure the thickness of a low-loss layer like lake ice or dry snow on the ground by finding the lag time that corresponds to the transit time of layer in the autocorrelation function (ACF) of the received signal. However, RFI increases the noise floor of the ACF and results in a decreased signal-to-noise ratio (SNR) of the WiBAR delay peak in the ACF. We enhanced a WiBAR instrument with a tunable comb filter having a frequency response with many evenly spaced alternating pass and stop bands. We show the RFI mitigation performance of a WiBAR set-up with a comb filter in the laboratory with a simulation circuit that creates a spectrum polluted with RFI.

In this article, a unified framework of vegetation scattering using radiative transfer (RT) theory for passive and active remote sensing of vegetated land surfaces, especially those associated with moderate-to-large vegetation water contents (VWCs), e.g., forest field, is presented. The framework allows for modeling passive and active microwave signatures of the vegetated field with the same physical parameters describing the vegetation structure. RT equations are solved by a numerical iterative approach for both passive and active configurations. This approach allows including higher order scattering, which represents multiple scattering. In fields such as forests with large VWCs, associated with large scattering albedo and optical thickness, multiple scattering effects are critical. In the active iterative approach, cyclical terms are identified and backscattering enhancement is included by doubling contributions from cyclical terms. The method is applied to aspen trees in forest fields to compute the brightness temperatures and backscattering coefficients for passive and active remote sensing configurations, respectively. In the passive configuration, for forest field with VWC of 15 kg/$\text{m}^{2}$, the deviation between the zeroth-order brightness temperature, i.e., the tau–omega model results, and multiple scattering results around 40° observation angle, can be as large as 50 K for vertical polarization and 35 K for horizontal polarization. In the active configuration, the deviation between first-order results, which is identical to the distorted Born approximation, and the multiple scattering results around 40° incidence angle, is about 1.6 dB for VV and 0.7 dB for HH polarization. Multiple scattering is shown to be crucial for accurate forward modeling, especially over forested areas. The proposed approach is thus suitable for vegetation scattering with large VWCs. Furthermore, the proposed model is validated with the passive and active L-band sensor (PALS) acquired in SMAPVEX12 measurements in 2012, which demonstrates the applicability of this model.

This paper presents a study of snowpack thermal emissions at long wavelengths and over a wide frequency band. Brightness temperature measurements of a snow layer are reported and used to estimate the travel time through the layer. The Ultra-Wideband Software-Defined Radiometer (UWBRAD) and the Wideband Autocorrelation Radiometer (WiBAR) were deployed at the Keweenaw Research Center (KRC) from February to April 2020 to demonstrate these techniques. Results on snowpack brightness temperature and thickness measurements are presented and discussed.

This paper presents calibration procedure for time domain and frequency domain mode of wideband autocorrelation radiometry, (TD-WiBAR) and (FD-WiBAR), enhanced with a comb filter for RFI mitigation. WiBAR is a novel microwave method for measuring the thickness of low-loss layers, such as snow and ice packs. The time domain mode is faster than the FD-WiBAR, but requires its own calibration. We investigated a WiBAR enhanced with a comb filter in the laboratory with a data collected from a simulation circuit using a Keysight Spectrum Analyzer. The data are further post-processed for the time and frequency domain calibration. The comb filter is provided to mitigate radio frequency interference (RFI). RFI increases the noise floor and results in a decreased signal to noise ratio (SNR) delay peak in the autocorrelation function of the WiBAR from which the snow depth is extracted.

Wideband autocorrelation radiometry (WiBAR) is a newly developed microwave radiometric technique to remotely and directly measure the microwave propagation time difference of multipath microwave emission from low-loss layered surfaces, such as a dry snowpack and a freshwater lake icepack. The microwave propagation time difference through the pack yields a measure of its vertical extent. A major source of error in the measured thickness of the pack is due to the error in the measured time delay by WiBAR using the inverse Fourier Transform (IFFT) approach, which is an unbiased estimator of frequency. Using the Cramer-Rao lower bound (CRLB) on the variance of the estimator, it is shown that the variance of the estimated time delay would reach this minimum variance for signal to noise ratios (SNRs) higher than a threshold value, which depends on the detection scenario (lake icepack or snowpack), the window function, and the incident angle. It is also shown that the variance of the measured thickness is very high near the Brewster angle, as expected.

This paper presents a new method to suppress radio frequency interference (RFI) for a recently developed microwave radiometer technique, known as wideband autocorrelation radiometry (WiBAR). WiBAR is a method which can measure directly the thickness of a low-loss layer like lake ice or snow on the ground. However, the RFI in the received signal increases the noise floor and results in a decreased signal to noise ratio (SNR) of the WiBAR delay peak in the autocorrelation function. We propose a new filter which acts like a Fabry-Perot interferometer (FPI) in optics and has the frequency response of a comb filter with many evenly spaced alternating pass and stop bands. The response of an ideal comb filter applied to the polluted spectrum captured by WiBAR's receiver to mitigate the RFI in the received signal.

A new passive microwave remote sensing technique, wideband autocorrelation radiometry (WiBAR), directly measures the microwave propagation time difference of multipath microwave emission from low-loss layered surfaces such as a dry snowpack and a freshwater lake icepack. The microwave propagation time difference through the pack yields a measure of its vertical extent. However, the presence of variable pack thicknesses within a footprint of the radiometer's antenna will add complexity to the retrieved time delay. This issue is more severe for WiBAR on airborne and spaceborne platforms than WiBAR on ground-based platforms since the footprint for a given radiometer antenna is larger. From a simple forward model for a layer having distinct thickness values within one footprint (pixel), the system requirements for resolving these distinct thickness values are derived. A subpixel lake ice thickness distinction of 3.7 cm is demonstrated with an X-band WiBAR instrument at an incidence angle of 70°.

The concept of wideband autocorrelation radiometry has recently been proposed and applied for the remote sensing of snow and lake ice. Such instruments measure the microwave emission from a scene of snow and ice over a wide and low frequency band where volume scattering within the snow/ice layer is negligible. Experiments have demonstrated that such wideband brightness temperature spectra can show oscillatory features. These features arise from coherent interference among the direct upward emission and its replicas from multiple reflections, are related to the layer thickness of snow or ice, and can be affected by interface roughness or any above snow vegetation canopy. The latter is therefore an important factor affecting the application of wideband autocorrelation radiometry in terrestrial snow remote sensing. In this paper, we analyze the effects of an above snow vegetation layer on brightness temperature spectra, particularly the possible decay of wave coherence arising from volume scattering in the vegetation canopy. In our analysis, the snow layer is assumed to be flat, and its upward emission and surface reflectivities are modeled by a fully coherent model, while the volume scattering from the vegetation layer is described by an incoherent radiative transfer model. The solution to the radiative transfer equation is obtained through an interative approach accounting for multiple scattering effects. The angular and polarization coupling arising from volume scattering and the emission contributed by the vegetation layer all cause smoothing of oscillatory patterns in the observed brightness temperature spectra.

Wideband autocorrelation radiometry (WiBAR) is a recently developed microwave radiometric technique to measure the lake icepack or snowpack thickness. This technique offers a direct method to remotely measure the microwave propagation time difference of multipath microwave emission from low-loss layered surfaces such as dry snowpack and freshwater lake icepack. The microwave propagation time difference through the pack yields a measure of its vertical extent. However, the lake icepack thickness measurement can be affected by the presence of a dry snowpack, which introduces another multipath interference. We present a simple geophysical forward model considering this effect and derive the WiBAR system requirements needed to correctly measure the icepack thickness. An X-band instrument fabricated from commercial-off-the-shelf (COTS) components is used to measure the thickness of fresh water lake ice at the University of Michigan Biological Station. The WiBAR was able to directly measure the icepack of about 36 cm with a snowpack of about 4 cm on top at incidence angle of 69.4° with an accuracy of 2 cm.

Seasonal snow cover plays a key role in freshwater resources, water security, natural hazards, and weather and climate. However, accurate estimation of snow-water equivalent (SWE) with remote sensing observations remains a significant challenge. NASA Terrestrial Hydrology Program launched its multi-year SnowEx mission whose primary goal is to develop and test techniques for estimating how much water is stored in some complex Earth's terrestrial snow-covered regions (e.g. forested areas). The first year of the 5-year campaign took place in Colorado during the winter 2016–2017, during which in situ measurements and ground-based remote sensing observations were collected by the scientific community. Throughout February 2017, about 100 people were deployed and over 30 remote sensing instruments were used. This required an exceptional coordination effort, which resulted in collocated in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements) with ground-based remote sensing observations (microwave radiometers, radar, scatterometers, lidars, etc.). The public release of all these datasets has started (nsidc.org/data/snowex).

Wideband autocorrelation radiometry (WiBAR) is a new method to remotely sense the microwave propagation time τdelayof multi-path microwave emission of low loss layered surfaces such as dry snowpack and freshwater lake icepack. The microwave propagation time τdelaythrough the pack yields a measure of its vertical extent; thus, this technique is a direct measurement of depth. However, the presence of a different low loss layer on the lake icepack such as dry snowpack introduces another multi-path interference, which can effect the lake icepack thickness measurement. We present a simple geophysical forward model for the multipath interference phenomenon and derive the WiBAR system requirements needed to correctly measure the icepack thickness. An X- band instrument fabricated from commercial-off-the-shelf (COTS) components are used to measure the thickness fresh water lake ice at the University of Michigan Biological Station. Ice thickness retrieval is demonstrated from nadir to 73.9°.

A novel microwave radiometric technique, wideband autocorrelation radiometry (WiBAR), is introduced. The radiometer offers a direct method to remotely measure the microwave propagation time difference of multipath microwave emission from low-loss layered surfaces, such as a dry snowpack and a freshwater lake icepack. The microwave propagation time difference through the pack yields a measure of its vertical extent; thus, this technique provides a direct measurement of depth. It is also a low-power sensing method, since there is no transmitter. We present a simple geophysical forward model for the multipath interference phenomenon and derive the system requirements needed to design a WiBAR instrument. An X-band instrument fabricated from commercial-off-the-shelf (COTS) components measured the thickness of the freshwater lake ice at the University of Michigan Biological Station. Ice thickness retrieval is demonstrated from nadir to 59°. The WiBAR was able to directly measure the lake icepack thickness of about 36 cm with an accuracy of 2 cm over this range of incidence angles.

NASA SnowEx's goal is estimating how much water is stored in Earth's terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016–2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex.

In this study, a data-fusion algorithm is developed for estimation of high-resolution brightness temperatures (TB) at 1km from Soil Moisture Active Passive (SMAP) fine-grid TBproduct at 9km. It uses image segmentation to spatio-temporally cluster the study region based on meteorological and land cover similarity, followed by a support vector machine based regression that computes the value of the high-resolution TBat all pixels. High resolution remote sensing products such as land surface temperature, normalized difference vegetation index, enhanced vegetation index, precipitation, soil texture, and land-cover were used for disaggregation. The algorithm was implemented in Iowa, United States, from May to September 2016, and compared with the field observations of TBfrom Microwave Water and Energy Balance Experiment conducted as a part of the Soil Moisture Active Passive Validation Experiment (SMAPVEX16-MicroWEX). Additionally, they were also compared with the Sentinel downscaled SMAP TBat 1km. High resolution soil moisture is subsequently derived from high resolution TBusing inverse models.

In this study, the impact of spatial variability due to the heterogeneity of vegetation in the agricultural region on passive microwave signatures available at various scales are explored using the brightness temperature (TB) observed from ground, air, and space. These observations were conducted during a growing season of corn and soybean in South Fork watershed, Iowa, as part of the NASA-Soil Moisture Active Passive Validation Experiment (SMAPVEX16). Both empirical and physically-based microwave emission models are used to understand the effects of vegetation on TBfor corn and soybean using ground-based TBobservations. The modeled TBwill be upscaled based upon the USDA crop layer map to compare with the TBobserved in the coarse scales.

Wideband autocorrelation radiometry (WiBAR) is a new method to remotely sense the microwave propagation time τdelayof multi-path microwave emission of low loss layered surfaces such as dry snowpack and freshwater lake icepack. The microwave propagation time τdelaythrough the pack yields a measure of its vertical extent; thus, this technique is a direct measurement of depth. This technique is inherently low-power since there is no transmitter in contrast to active remote sensing techniques. In this paper, the system design parameters and physics of operation of the WiBAR are discussed, and it is shown that the microwave propagation time can be readily measured for lake icepack at incidence angles away from nadir to at least 59.1°.

A useful means of calibrating remote sensing retrieval algorithms is by means of a time series of ground truth measurements at specific locations. For snow depth (SD) or snow water equivalent (SWE), ground truth regarding snow density and snow grain size stratigraphy is provided manually via snow pits. While the data is of high quality, this method is time consuming and labor intensive and thus precludes significant durations of time series data. A low power device is presented for logging snow temperature, density, wetness and specific surface area in a small region adjacent to the device. Low cost enables multiple devices to operate simultaneously to capture vertical stratigraphy or lateral variability. Operation, calibration, and initial measurements are presented.

A recently developed microwave radiometric technique, known as wideband autocorrelation radiometry (WiBAR), offers a deterministic method to remotely sense the propagation time τdelayof multi-path microwave emission of low-loss terrain covers and other layered surfaces. Terrestrial examples are the snow and lake ice packs. The microwave propagation time τdelaythrough the pack yields a measure of its vertical extent. We report measurements of the icepack on Lake Superior, and the snowpack at University of Michigan Biological Station (UMBS) in winter 2014 and 2015, respectively. The observations are done at frequencies from 7 to 10 GHz for icepack and 1 to 3 GHz for snowpack. At these frequencies, the volume and surface scattering are small in the packs. This technique is inherently low-power since there is no transmitter as opposed to active remote sensing techniques. In this paper the system design parameters of the WiBAR is discussed and it is shown that the microwave travel time within a dry snow pack and lake ice pack can be readily measured for a wide range of layer thicknesses observed during the experiment.

The purpose of NASA's Snow and Cold Land Processes (SCLP) mission is to quantify the snow cover around the world from space using radiometer and radar technology [1]. Both techniques are based on scattering: snow cover scatters radiation of different frequencies in different ways, depending on quantities such as how wet the snow is, the snow grain size and shape, its depth, etc. However, it is not robustly understood how these quantities affect snow's scattering properties [2]. A major impediment is that it is difficult to measure snow ground truth. We present a novel technique for autonomous collection of snow ground truth data with both high temporal and spatial resolution over areas as large as 1km2. The goal of this project is to provide convenient ground-truth data that can be used for SCLP calibration and validation.

The ground-based University of Florida L-band Automated Radar System (UF-LARS) was developed to obtain observations of normalized radar backscatter (\mmbσ0) at high temporal resolution for soil moisture applications. The system was mounted on a 25 m manlift with capabilities of antenna positioning for multi-angle data acquisition and ranging. The RF subsystem of UF-LARS was based upon the established designs for ground-based scatterometers employing a vector network analyzer with simultaneous acquisition of V- and H-polarized returns. System integration and automated data acquisition were enabled using a software control system. Fifteen-minute observations of \mmb σ0collected over a growing season of sweet-corn and bare soil conditions in North Central Florida, were used to study the sensitivity of \mmbσ0to growing vegetation and near-surface (0-5 cm) soil moisture (\mmbSM0 - 5). On average, \mmb σ\mmbVV0were observed to be 23% higher than \mmbσ\mmbHH0during the mid- and late-stages of crop growth due to the vertical structure of stems. The correlation between 3-day observations of \mmbSM0 - 5 and \mmbσ\mmbVV0reduced by 55% compared to those obtained for ≤ 30-min observations. These findings suggested that data set at high temporal frequencies can be used to develop more realistic and robust forward backscattering models.

In this study, sensitivity of active and passive (AP) observations at L-band to near-surface SM was analyzed for bare sandy soils. The complementarity of AP microwave observations was used to obtain realistic SM profile that matched well with both AP observations during dynamic moisture conditions. Active observations exhibit less sensitivity to SM changes and higher sensitivity to surface roughness than passive observations. Based upon these findings, the observed brightness temperatures (TBs) were used to estimate a SM profile using an emission model. The backscatter (σ°) observations were used to estimate root mean square height (s) and correlation length (cl) using a backscatter model. The estimated SM profile, s, and cl resulted in RMSDs of 4.55K and 0.81dB between the observed and modeled TBand σ° values, respectively, for the rough surface. This study demonstrates the integrated use of AP to improve SM estimates.

Recent airborne field campaigns making passive microwave measurements have observed some radio frequency interference (RFI) that remained undetected by the kurtosis RFI-detection algorithm. The current pulsed-sinusoidal model for RFI does not explain this anomalous behavior of the detection algorithm. In this paper, a new RFI model is developed that takes into account multiple RFI sources within an antenna footprint. The performance of the kurtosis algorithm with the new model is evaluated. The behavior of the kurtosis detection algorithm under central-limit conditions due to multiple sources is experimentally verified. The new RFI model offers a plausible explanation for the lack of detection by the kurtosis algorithm of the RFI otherwise observed.

NASA's Soil Moisture Active Passive satellite mission will use both an L-band radiometer and radar to produce global-scale measurements of soil moisture. L-band backscatter is also sensitive to the water content of vegetation. We found that a moderate dew increased the L-band backscatter of a soybean canopy by 1 dB. Dew thus has the potential to add error to satellite observations of soil moisture.

Dielectric measurements of an organic-rich permafrost soil over the range from 1.0 to 16 GHz and from -30 °C to +25 °C are presented. The measured shrub soil contains up to 90% organic matter and is the first soil of this composition for which the soil dielectric has been characterized. Using the dielectric data thus obtained, the process of freezing has been analyzed of unfrozen water contained in the shrub tundra sample.

A few of the issues faced by the kurtosis detection algorithm on recent field campaigns is discussed here. The performance of the kurtosis algorithm in detecting multiple-source Radio Frequency Interference (RFI) is characterized. A new RFI statistical model is presented in the paper to take into account the behavior of RFI sources under a large foot-print. Results indicate the behavior of the kurtosis ratio under central-limit conditions due to large number of RFI sources.

The kurtosis statistic is an effective detector of pulsed sinusoidal radio frequency interference (RFI) in a microwave radiometer, but it fails to detect RFI when the pulsed sinusoid is present for exactly half of the integration period. That is, the kurtosis is blind at an RFI duty cycle of 50%. In this letter, we explore the possibilities of using a higher order statistic to eliminate this detection blind spot in the kurtosis statistic and to improve RFI detection performance. The sixth-order statistic does have sensitivity at an RFI duty cycle of 50%, but sensitivity is relatively low. In addition, it has a sensitivity comparable with the kurtosis for short duty cycle RFI, under certain circumstances.

Dielectric measurements of an organic-rich permafrost soil over the range from 1.0 to 16 GHz and from -30°C to +25°C are presented. The measured shrub soil contains up to 90% organic matter and is the first soil of this composition for which the soil dielectric has been characterized. The measurements were fitted to the generalized refractive mixing dielectric model (GRMDM) recently proposed by Mironov et al., which combines the complex refractive indexes for the major components of the soil. These components were found to be the solid content, bound water, transient bound water, liquid capillary water, and moistened ice water. The dielectric properties of the frequency-dispersive components are each described by their own Debye relaxation spectrum. The GRMDM has been modified to incorporate the temperature dependence of the Debye parameters. The phase transformation of the soil water components at the freezing temperature is taken into account. As a result, a temperature-dependable GRMDM (TD GRMDM) has been developed, including model parameters which have a physical interpretation. This TD GRMDM predicts the dielectric for this soil in the whole range of moistures, frequencies, and temperatures measured. The model prediction errors are on the same order as that of dielectric measurements. The model proposed is the first of its kind to provide a physical basis for radar and radiothermal remote sensing algorithms that retrieve the freeze/thaw state and the volumetric moisture in the upper layer of an Arctic soil.

Microwave radiometers detecting geophysical parameters are susceptible to radio-frequency interference (RFI) from anthropogenic sources. The kurtosis statistic can be a powerful means of identifying some types of low-power RFI, as thermal noise has a distinct kurtosis value of three, while thermal noise contaminated even with low-power nonthermal RFI often has other values of kurtosis. This paper presents a method for calculating the kurtosis and brightness that significantly reduces the radiometer data download requirements.

The kurtosis statistic is an excellent detector of pulsed sinu-soidal RFI in a digital microwave radiometer. The kurtosis is the ratio of the 4th central moment to the 2nd central moment, squared. However, to create the kurtosis, accumulations of every power of the digitized voltage up to 4th order must be calculated in the digital back-end of the radiometer, and these accumulations are offloaded for postprocessing. This paper investigates the possibility of eliminating the accumulation of the 3rd power of the digitized voltage. The elimination of this operation liberates some resources in the back-end digital electronics, and reduces the burden of data offloading from the radiometer for each integration period. Without the 3rd power of digitized voltage, the kurtosis can still be calculated under the assumption of zero skew in the IF signal. The kurtosis so calculated has the same mean value as that calculated with all the powers of the voltage, and the covariance of the kurtosis with the variance remains zero, indicating that this simplified kurtosis remains an unbiased detector of RFI. However, the lack of the 3rd accumulation in the calculation of the kurtosis results in an increase in the variance of the kurtosis statistic which is proportional to the DC offset between the back-end digitizer and the analog ground, which can result in reduced sensitivity of the kurtosis statistic to RFI.

The absorption coefficient of moist snow at low microwave frequencies is investigated. One geometrically realistic form of the presence of liquid water in a metamorphosed snow pack is a pendular ring. These rings form around the contact points between rounded ice grains. When averaged over all possible orientations, this shape of liquid water in the Rayleigh regime produces significantly more loss than if the same volume of water were present in a physically unrealistic form of spherical droplets. The pendular ring shape also produces significantly less loss when compared to the same volume of water in the form of a uniform coating around the ice grains, a commonly used model for the form of liquid water in moist snow.

The kurtosis statistic is an effective detector of pulsed sinusoidal RFI in a microwave radiometer, but it fails to detect RFI when the pulsed sinusoid is present for exactly half of the integration period. That is, the kurtosis is blind at an RFI duty cycle of 50%. In this paper, we explore the possibilities of using a higher order statistic to eliminate this detection blind spot in the kurtosis statistic. The higher order statistic does have sensitivity at an RFI duty cycle of 50%, but sensitivity is relatively low.

Microwave radiometers detecting geophysical parameters are very susceptible to radio-frequency interference (RFI) from anthropogenic sources. RFI is always additive to a brightness observation, and so the presence of RFI can bias geophysical parameter retrieval. As microwave radiometers typically have the most sensitive receivers operating in their band, low-level RFI is both significant and difficult to identify. The kurtosis statistic can be a powerful means of identifying some types of low-level RFI, as thermal noise has a distinct kurtosis value of three, whereas thermal noise contaminated even with low-level nonthermal RFI often has other values of kurtosis. This paper derives some benign distortions of the kurtosis statistic due to digitization effects and demonstrates these effects with a laboratory experiment in which a known amount of low-level RFI is injected into a digital microwave radiometer.

Radio frequency interference (RFI) from anthropogenic sources in microwave radiometers detecting geophysical parameters is both common and insidious. As this RFI is always additive to the brightness, the presence of undetected RFI can bias the geophysical parameter retrieval. As radiometers have the most sensitive receivers operating in their band, low levels of RFI are both significant and difficult to identify. The kurtosis statistic is a tool being explored as a means of detecting low level RFI in microwave receivers. The performance of the kurtosis statistic as a detector of RFI is introduced.

A microwave radiometer specifically designed to detect and mitigate many types of Radio Frequency Interference (RFI) is described. The L-band RFI Detection and Mitigation Testbed (DetMit Testbed) will not be optimized for radiometric observation as much as it is optimized for flexibility in the presence of RFI. While the DetMit Testbed will be a fully functional polarimetric L-band radiometer, the ultimate application of this instrument is not so much brightness measurements as it will be validation of RFI mitigation strategies for employment in future L-band (and other frequency) radiometers. The design approaches for the L-band RFI Detection and Mitigation Testbed are expected to apply to C- band and X-band, and presumably also to other frequencies of interest that experience RFI.

We quantify the performance of three commonly used techniques to calibrate ground-based microwave radiometers for soil moisture studies, external (EC), tipping-curve (TC), and internal (IC). We describe two ground-based C-band radiometer systems with similar design and the calibration experiments conducted in Florida and Alaska using these two systems. We compare the consistency of the calibration curves during the experiments among the three techniques and evaluate our calibration by comparing the measured brightness temperatures (TBs) to those estimated from a lake emission model (LEM). The mean absolute difference among the TBs calibrated using the three techniques over the observed range of output voltages during the experiments was 1.14 K. Even though IC produced the most consistent calibration curves, the differences among the three calibration techniques were not significant. The mean absolute errors (MAEs) between the observed and LEM TBs were about 2-4 K. As expected, the utility of TC at C-band was significantly reduced due to transparency of the atmosphere at these frequencies. Because IC was found to have a MAE of about 2 K that is suitable for soil moisture applications and was consistent during our experiments under different environmental conditions, it could augment less frequent calibrations obtained using the EC or TC techniques

The University of Michigan Microwave Geophysics Group participated in the Cold Lands Processes Experiment from February to April 2003 by deploying its Truck Mounted Radiometer System-3 (TMRS-3) to perform temporal monitoring of the snow pack at the local scale observation site (LSOS). The LSOS was located at the Fraser Experimental Forest headquarters in the mountains near Fraser, CO. The small clearing in which the TMRS-3 was deployed was adjacent to tall evergreen trees. To quantify the amount of the downwelling brightness from these trees onto the snow pack, the TMRS-3 periodically observed these trees. Microwave brightness data were collected from the trees every 15 from horizontal incidence to 45 from zenith. Both polarizations were observed for 6.7, 19, and 37 GHz. A rapid decrease in brightness is evident as the radiometers were pointed progressively upward. The next May, an upward-looking hemispherical (ldquofish-eyerdquo) photograph was taken from the center of the clearing, and it reveals a significant sky background through the incomplete canopy. By superimposing Gaussian approximations to the microwave antenna gain pattern of the individual TMRS-3 radiometers onto the photograph, we estimated the amount that the main beams were filled with canopy and with sky. Comparison of the measured data to that expected for a partially filled main beam indicates that the needle-leaf canopy is roughly an isotropic emitter having emissivities at frequencies between 6.7 and 37 GHz of between 0.93 and 0.97 with air temperature as a proxy for tree temperature.

A new type of microwave radiometer detector that is capable of identifying low-level pulsed radio frequency interference (RFI) has been developed. The Agile Digital Detector can discriminate between RFI and natural thermal emission signals by directly measuring other moments of the signal than the variance that is traditionally measured. The kurtosis is the ratio of the fourth central moment of the predetected voltage to the square of the second central moment. It can be an excellent indicator of the presence of RFI. A number of issues that are related to the proper calculation of the kurtosis are addressed. The mean and standard deviation of the kurtosis, in both the absence and the presence of pulsed sinusoidal RFI, are derived. The kurtosis is much more sensitive to short-pulsed RFI-such as from radars-than to continuous-wave RFI. The minimum detectable power for pulsed sinusoidal RFI is found to be proportional to (M3N)-1/4, whereNis the number of independent samples andMis the number of frequency subbands in the receiver.

Advanced microwave scanning radiometer - EOS (AMSR-E) brightness temperature observations of the North Slope, Alaska are synthesized using an improved Backus-Gilbert algorithm and compared to the standard brightness temperature product for AMSR-E. This algorithm improves the spatial resolution for the 6 GHz and 10 GHz channels by taking advantage of the oversampling of these two frequencies, which allows a tradeoff between noise and spatial resolution. Nearly circular synthesized footprints for all channels are achieved by using a circular Gaussian reference footprint. Our synthetic observations differed from the standard AMSR-E products at 19 and 37 GHz where the scales of surface heterogeneities become important. The synthesized AMSR-E observations are compared with ground-based data collected during the Tenth Radiobrightness Energy Balance Experiment (REBEX10) conducted from May to June of 2004 near Toolik Lake on the North Slope. Plot-scale and satellite resolution scale observations differed at both 6 and 19 GHz caused by sensitivity to scale- dependent hydrologic processes.

A new type of microwave radiometer detector has been developed that is capable of identifying low level Radio Frequency Interference (RFI) and of reducing or eliminating its effect on the measured brightness temperature. The Agile Digital Detector (ADD) can discriminate between RFI and natural thermal emission signals by directly measuring higher order moments of the signal than the variance that is traditionally measured. After detection, the ADD then uses spectral and temporal filtering methods to selectively remove the RFI. ADD performance has been experimentally verified and its performance characterized while connected to an airborne C-Band radiometer (the NOAA/ETL PSR) installed on a NASA WB-57 flying over major urban centers. Index Terms—microwave radiometer, radio frequency interference

The ability of electromagnetic models to accurately predict microwave emission of a snowpack is complicated by the need to account for, among other things, nonindependent scattering by closely packed snow grains, stratigraphic variations, and the occurrence of wet snow. A multilayer dense medium model can account for the first two effects. While microwave remote sensing is well known to be capable of binary wet/dry discrimination, the ability to model brightness as a function of wetness opens up the possibility of ultimately retrieving a percentage wetness value during such hydrologically significant melting conditions. In this paper, the first application of a multilayer dense medium radiative transfer theory (DMRT) model is proposed to simulate emission from both wet and dry snow during melting and refreezing cycles. Wet snow is modeled as a mixture of ice particles surrounded by a thin film of water embedded in an air background. Melting/refreezing cycles are studied by means of brightness temperatures at 6.7, 19, and 37 GHz recorded by the University of Michigan Truck-Mounted Radiometer System at the Local Scale Observation Site during the Cold Land Processes Experiment-1 in March 2003. Input parameters to the DMRT model are obtained from snow pit measurements carried out in conjunction with the microwave observations. The comparisons between simulated and measured brightness temperatures show that the electromagnetic model is able to reproduce the brightness temperatures with an average percentage error of 3% (~8 K) and a maximum relative percentage error of around 8% (~20 K)

In this paper we compare three techniques typically used for calibrating a microwave radiometer and understanding its design stability. The first calibration technique utilizes cold and hot load measurements to construct calibration curves. For our case, we used sky as the cold load and microwave absorber at ambient temperature as hot load. The second calibration technique utilizes measurements of the brightness temperature of the sky at various zenith angles to determine the atmospheric opacity needed for the calibration curves. The third calibration technique utilizes measurements of internal reference load and micro-controller inside the radiometer at both polarizations to estimate the system gain fluctuations and the receiver noise temperatures. We calibrated our University of Florida C-band Microwave Radiometer (UFCMR) every two weeks during the first Microwave Water and Energy Balance Experiment (MicroWEX-1). We found that, the first and third techniques produced similar calibration results with 1 Kelvin/volt standard deviation. However, the third technique was the most stable during the entire experiment with the smallest standard deviations which were 2.41 Kelvin/volt for the slope of the calibration curves at H-pol and 7.46 Kelvin/volt for the slope of the calibration curves at V-pol during MicroWEX-1

STAR-Light is a 10 element, 1.4 GHz aperture synthesis radiometer being developed at the University of Michigan. It features 3 new technologies: 1) 2-dimensional aperture synthesis using 3-bit correlation, 2) Direct Sampling Digital Receiver (DSDR) architecture, and 3) band definition in the digital definition in the digital domain. The instrument successfully completed Critical Design Review in the spring of 2002 and is being fabricated as funds become available. As a test of robustness of the receiver design, we are using four STAR-Light receivers in a dual-polarized 1.4 GHz radiometer, and two are used as IF amplifiers in a dual-polarized 6.7 GHz radiometer. Both radiometers will eventually be fully polarimetric. The system is currently deployed near Fraser, Colorado, as part of the NASA Cold Lands Processes Experiment (CLPX) to occur in February and April, 2003. We will report on the performance of these receivers in this relatively harsh environment.

We investigate anisotropy in 1.4-GHz brightness induced by a field corn vegetation canopy. We find that both polarizations of brightness are isotropic in azimuth during most of the growing season. When the canopy is senescent, the brightness is a strong function of row direction. On the other hand, the 1.4-GHz brightness is anisotropic in elevation: an isotropic zero-order radiative transfer model could not reproduce the observed change in brightness with incidence angle. Significant scatter darkening was found. The consequence of unanticipated scatter darkening would be a wet bias in soil moisture retrievals through a combination of underestimation of soil brightness (at H-pol) and underestimation of vegetation biomass (at V-pol). A new zero-order parameterization was formulated by allowing the volume scattering coefficient to be a function of incidence angle and polarization. The small magnitude of the scattering coefficients allows the zero-order model to retain its limited physical significance.

With the advent of high-frequency radio frequency (RF) circuits and components technology, millimeter-wave (MMW) radars are being proposed for a large number of military and civilian applications. Accurate and high-resolution characterization of the polarimetric radar backscatter responses of both clutter and man-made targets at MMW frequencies is essential for the development of radar systems and optimal detection and tracking algorithms. Toward this end, a new design is developed for ultrafast, wide-band, polarimetric, instrumentation radars that operate at 35 and 95 GHz. With this new design, the complete scattering matrix of a target (magnitude and phase) can be measured over a bandwidth of 500 MHz in less than 2 /spl mu/s. In this paper, the design concepts and procedures for the construction and calibration of these radars are described. In addition, the signal processing algorithm and data-acquisition procedure used with the new radars are presented. To demonstrate the accuracy and applicability of the new radars, backscatter measurements of certain points and distributed targets are compared with their analytical radar cross section (RCS) and previously measured /spl sigma//spl deg/ values, respectively, and good agreements are shown. These systems, which can be mounted on a precision gimbal assembly that facilitates their application as high-resolution imaging radar systems, are used to determine the MMW two-way propagation loss of a corn field for different plant moisture conditions.

The presence of trees in a given scene can hamper detection of nearby targets by millimeter-wave (MMW) radars especially at near grazing incidence. Proper characterization of scattering and attenuation in tree canopies is important for optimal detection algorithms. In this paper, a new technique for determining the extinction and volume backscattering coefficients in tree canopies using the measured radar backscatter response is proposed and verified experimentally. The technique, which can be applied to already available wideband radar backscatter data, is used to compute the extinction and volume backscattering coefficients of different tree canopies under various physical conditions. The dynamic range of these coefficients are presented and results at 35 GHz are compared with results at 95 GHz.

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/spl deg/ in elevation and to 45/spl deg/ 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/sup 2/=0.867 and a root mean square error (RMSE) of 0.0678 kg/m/sup 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 R/sup 2/=0.898 and volumetric soil moisture RMSE of 1.75%.

In preparation for the Shuttle Imaging Radar-C/XSAR (SIR-C/XSAR) flights, the University of Michigan has been involved in the development of calibration procedures and precision calibration devices to quantify the complex radar images with an accuracy of 0.5 dB in magnitude and 5 degrees in phase. In this paper, the preliminary results of the SIR-C calibration and a summary of the University of Michigan's activity in the Raco calibration super-site is presented. In this calibration campaign an array of point calibration targets including trihedral corner reflectors and polarimetric active radar calibrators (PARCs) in addition to a uniform distributed target were used for characterizing the radiometric calibration constant and the distortion parameters of the C-band SAR. Two different calibration methods, one based on the application of point targets and the other based on the application of the distributed target, are used to calibrate the SIR-C data and the results are compared with calibrated images provided by JPL. The distributed target used in this experiment was a field of grass, sometimes covered with snow, whose differential Mueller matrix was measured immediately after the SIR-C overpass using The University of Michigan polarimetric scatterometer systems. The scatterometers were calibrated against a precision metallic sphere and measured 100 independent spatial samples for characterizing the differential Mueller matrix of the distributed target to achieve the desired calibration accuracy. The L-band SAR has not yet been adequately calibrated for inclusion here.>