Data from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission have shown promise for the retrieval of soil moisture, and many soil moisture products using CYGNSS data have been developed. In this work, we present a merged product that combines several CYGNSS soil moisture products using a minimum variance estimator (MVE). The MVE identifies an optimal weighted averaging scheme based on the error covariance characteristics of the CYGNSS soil moisture products. The error covariance matrix is computed using two reference datasets: soil moisture data from the Soil Moisture Active Passive (SMAP) radiometer and in situ soil moisture data. The results from each of these provide insights into both the performance of the merged product and the individual input CYGNSS products. Overall, the merged product offers better performance than any individual CYGNSS product while also offering better temporal resolution than SMAP. The results of this work also demonstrate that the use of the MVE is a compelling technique for soil moisture applications.

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere (NH) is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network is presented. This framework defines the landscape FT-cycle retrieval as a time-series anomaly detection problem, considering the frozen states as normal and the thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is assessed over Alaska using in situ observations, demonstrating an 11% improvement in accuracy and reduced uncertainties compared to traditional methods that rely on thresholding the normalized polarization ratio (NPR).

Multimodal medical image fusion is a crucial task that combines complementary information from different imaging modalities into a unified representation, thereby enhancing diagnostic accuracy and treatment planning. While deep learning methods, particularly Convolutional Neural Networks (CNNs) and Transformers, have significantly advanced fusion performance, some of the existing CNN-based methods fall short in capturing fine-grained multiscale and edge features, leading to suboptimal feature integration. Transformer-based models, on the other hand, are computationally intensive in both the training and fusion stages, making them impractical for real-time clinical use. Moreover, the clinical application of fused images remains unexplored. This work proposes a novel CNN-based architecture that addresses these limitations by introducing a Dilated Residual Attention Network Module for effective multiscale feature extraction, coupled with a gradient operator to enhance edge detail learning. To ensure fast and efficient fusion, we present a parameter-free fusion strategy based on the nuclear norm of softmax weights, which requires no additional computations during training or inference. Extensive experiments demonstrate that our approach outperforms various baseline methods in visual quality and fusion speed, making it a possible practical solution for real-world clinical applications. Code will be released at https://github.com/simonZhou86/endran.

This study presents an investigation that involves comparing L-band Vegetation Optical Depth (L-VOD) obtained from Global Navigation Satellite System Transmissometry (GNSS-T) against metrics derived from airborne Light Detection and Ranging (LiDAR) data. Both data were collected during the SMAPVEX 2022 campaign in the temperate forests of the northeastern United States, covering Massachusetts and New York. From the LiDAR data, various parameters related to tree characteristics can be extracted, such as tree height, crown diameter and shape, vegetation area density, and woody volume. In this investigation, we initially computed LiDAR point cloud density as a proxy measure of vegetation structure for a given receiver position and the satellite's field of view, comparing it with L-VOD estimates at different positions within the studied forest. Our primary findings reveal a notable correlation between point density and L-VOD, despite the inherent errors in L-VOD estimates and the fact that the number of points may not be the optimal descriptor of the canopy architecture. In this paper, we will explore aforementioned LiDAR derived metrics against the GNSS-T L-VOD estimates to provide insights into the impact of canopy architecture on the L-VOD estimates, determining the specific vegetation layers that influence the measurement.

We reported on the progress of fast hybrid method (FHM) for full- wave simulations of propagation of L-band microwaves in forested environment. For L band, previously we performed full wave simulations of realistic trees initially at 8 meters [1], followed by 13 meters [2]. The progress in this work is at comparisons of the electromagnetic model simulations with SMAPVEX19-22 data: 1) The height of the trees have been extended to 17 meters with the multiple scattering effects of 91 trees in the spatial domain with simulated transmissivity at 0.57 2) the spatial patterns of electric field distribution are simulated with electric field as high as 1.6 that of the incident wave corresponding to 2.56 times the Poynying of the incident waves, and the patterns exhibits gaps and shadows 3) the effects of clustering of trees with gaps show different results from that of uniformly positioned trees and 4) tree structures are varied with examples of trees with two trunks branching out from the main trunk, and the case of tapering trunk radius.

This paper presents a refined Active and Passive (AP) algorithm from the Soil Moisture Active Passive (SMAP) mission, highlighting the progressive enhancements made to the passive algorithm over the years. The primary focus centers on the process of disaggregating coarse brightness temperature (TB) directly measured from the radiometer to attain fine-resolution TB, subsequently enabling the retrieval of soil moisture and vegetation optical depth. Throughout the operational phase of the SMAP SAR instrument, approximately 2.5 months of global SAR backscattering data were acquired simultaneously with TB data. With the imminent launch of the NASA-ISRO Synthetic Aperture Radar (NISAR) mission, the availability of continuous L-band SAR data will see a significant boost. The original SMAP SAR data encompassed four polarizations (VV, HH, HV, and VH), which prompted an examination of three disaggregation combinations: 1) The original SMAP AP algorithm, which utilizes HH, VV, and cross-polarization (X-pol) data (averaged from cross-polarizations). 2) Sole reliance on HH and X-pol data, a configuration that aligns with the capabilities of the NISAR mission, offering global coverage. 3) VV and X-pol data, aiming to provide a more comprehensive analysis. Across these three combinations, similar accuracy was observed at the core study sites, affirming the feasibility of utilizing NISAR HH/HV data exclusively for the AP algorithm. Additionally, this paper also demonstrates both the snapshot method and time-series method for parameter determination and engages in the discussion of their respective advantages and disadvantages.

To quantify the forest effects on P-band radar remote sensing, this paper utilizes the fast hybrid method (FHM) combining fast multiple scattering theory (FMST) and a numerical electromagnetic solution. Considering estimation of the domain area, the required number of trees for P-band signal analysis is 121. For the efficient scattering solutions of a large number of trees, the FHM uses the triple FFT applied to the Foldy-Lax equation where two FFTs are applied to a 2-D spatial domain and one FFT to the order of cylindrical waves corresponding to translation addition theorem. This speeds up calculation of a translation addition matrix multiplied by a column vector. The accuracy of FHM is validated by FEKO using 25 trees, showing excellent agreement. Also, FHM provides faster solutions than commercial software. Using dielectric constants of winter and summer conditions, forest effects in P-band signals are validated by calculating transmissivity.

The SMAP product provides the soil moisture (SM) computed using three different retrieval algorithms: the single channel H and V algorithms (SCA-H and SCA-V), and the dual-channel algorithm (DCA) which in addition provides the vegetation optical depth (VOD). The roughness model and the roughness parameters play an important role in the determination of the soil moisture and the VOD. In this regard, the SMAP SCA and DCA utilize different approaches to incorporate the effect of roughness. In this work we will summarize those approaches and we will evaluate the effect of the DCA approach on the retrieval of VOD. We will compare the SMAP DCA roughness parameter$h$with topographic parameters such as DEM height, DEM slope, DEM height standard deviation and DEM slope standard deviation.

In this paper, an Analytical Kirchhoff Solution (AKS) and Numerical Kirchhoff approach (NKA) are used to study coherent and incoherent land surface near specular scattering at L and P bands. The AKS model includes both coherent and incoherent waves, and includes the effects of topographic slopes and elevations. The land profile is modelled as a summation of three scales of surface roughness corresponding to “microwave”, “fine topography”, and “coarse topography”, where the microwave roughness and fine topography are treated as random processes while the coarse topography is deterministic. An airborne lidar survey performed over the San Luis Valley, CO is used to obtain surface roughness information for the simulation results of$\mathrm{P}$and L-band scattering. Results using the lidar surface data show that coherent reflection can dominate returns from a 5 km by 5 km area at P band, while incoherent scattering dominates L band returns in the same scenario.

Stratification in terrestrial snow is a key factor in the retrieval of snow water equivalence (SWE) due to the different snow volume fractions and particle sizes. In studying the layered structure of snow, radar tomography has been used and multiple ground based experiments are performed. The conventional back projection method has been used to construct the image based on radar measurements at different incident angles and frequencies. However, the conventional back projection method based on Born's approximation would show deformation in the final snow image. In this paper, we use the asymmetrical distorted Born's approximation to correct the deformation in the image.

A spaceborne P-band signals of opportunity synthetic aperture radar concept is proposed for the remote sensing of terrestrial snow. We have completed a performance analysis assuming a formation flight of 3 to 5 SmallSats on one orbit plane. The spacing between the SmallSats is chosen so that their ground tracks will be separated by 50 to 100 m to allow the use of interferometric synthetic aperture radar processing technique to obtain a spatial resolution of a few hundred meters. A point system design has been completed to determine the antenna concept and to indicate the dependence of spatial resolution and signal to noise ratio on the number of receivers. The performance for range delay determination was analyzed to assess the impact of various error sources, including instrument receiver noise and ionospheric delay. The dominant error source is the ionospheric delay, which will be corrected using the split-spectrum algorithm. Our overall error budget analysis indicates that an accuracy of about 3 cm for the snow water equivalent in dry snow and 5 cm for the snow depth of wet snow can be achieved.

In this article, we investigate the potential of using single-pass InSAR model-based approaches to retrieve dry snow parameters. Two InSAR scattering models of dry snow are considered: the dense-medium random volume over ground (RVoG) model and the simple variant of the full penetration (FP) model. A quasi-crystalline approximation (QCA)-based extinction analysis confirms the negligible extinction dependence of the InSAR observables at L/C/X-band for fresh dry snow. The FP models the low-frequency (L/C/X-band) InSAR phase as a single constraint of snow depth and density, which can be supplemented by an extra observation (e.g., InSAR coherence orin situdepth/density). The single-pass InSAR models and inversion approaches were validated using X-band InSAR data collected from a tower-based three-frequency (X/Ku-low/Ku-high) fully polarimetric TomoSAR system, where a multi-frequency polarimetric InSAR analysis and ground-to-volume ratio-based snow condition analysis were conducted. We also analyzed the sensitivity and error propagation of the single-pass InSAR phase and coherence in measuring dry snow depth/density. It was found that the X-band HH-pol FP-modeled single-pass InSAR phase along with RVoG-modeled coherence orin situdepth is capable of measuring snow water equivalent (SWE) with a 23–26 mm uncertainty (13–15%) and a 20–26 mm bias (12–15%) for dry snow SWE of 0.2 m, and with an optimal perpendicular baseline on the order of a tenth of the snow depth (0.8 m) at our test site. This single-pass InSAR approach with the FP model is potentially useful and thus needs further investigation for large-scale dry snow retrieval with a wide range of snow conditions using ground-based/airborne/spaceborne low-frequency (L/C/X-band) InSAR observations.

Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission augmented with a physical surface scattering model were analyzed to develop a semiempirical model, which consists of three main modeling components for soil moisture, vegetation, and surface roughness. CYGNSS data collected from March 2017 to March 2020 were collocated with the soil moisture data from the Soil Moisture Active Passive (SMAP) mission and climatology vegetation water content (VWC) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. The matchup data were binned as a function of soil moisture, VWC, and incidence angle. The CYGNSS data were calibrated using a coherent reflection equation to obtain an effective reflectivity. The response of CYGNSS effective reflectivity to soil moisture changes is consistent with the change of the Fresnel reflectivity based on Mironov’s soil dielectric constant model used by the SMAP and Soil Moisture Ocean Salinity (SMOS) missions for soil moisture retrieval. The CYGNSS effective reflectivity decreases approximately linearly (in dB) with respect to the NDVI-VWC. The estimated values of vegetation attenuation parameter ($b$) agree with values published in the literature and are corroborated with the estimated values of$b$using the SMAP dual-polarized channel algorithm based on land cover types. A CYGNSS surface scattering map has been derived and reveals a mixed contribution of coherent and incoherent scattering effects and the effects of topography. The semiempirical model, leveraging two of the key modeling functions used by microwave radiometry, will pave the way for a synergistic use of reflectometry and radiometry data for multiparameter retrieval and development of consistent soil moisture products.

More than half of the global land area undergoes seasonal freeze/thaw (FT) transitions in spring. Spatial patterns and timing of spring thawing influence eco-hydrological processes and landscape moisture availability over arctic and boreal ecosystems. The seasonal progression of spring thawing coincides with warmer temperatures, snowmelt, and a rapid increase in soil moisture, which initiates the growing season for ecosystem productivity. In this study, we utilize complementary satellite observations to determine the pattern and order of occurrence in landscape thawing, soil moisture increase, and ecosystem productivity that collectively define the eco-hydrological spring onset across Alaska and Northern Canada. Satellite data utilized include landscape FT status from SMAP and AMSR-2, OCO-2 derived solar-induced chlorophyll fluorescence (GOSIF), and gross primary production (GPP) and soil moisture from SMAP. The resulting spring onset maps showed spring thawing as the precursor to growing season onset, indicated by a rapid rise in available soil moisture and GPP. Our results indicated an average spring transition period of$3\pm 2$(SD) weeks between initial landscape thawing and growing season onset. A rapid increase in soil moisture generally followed landscape thawing but occurred before the subsequent seasonal rise in GPP. Spring onset generally occurred earlier in boreal forest (DOY$102\pm 14$) than arctic tundra (DOY$124\pm 22$).

The Cyclone Global Navigation Satellite System (CYGNSS) observation received the Global Positioning System (GPS) signals with a revisit time in the range of 2.8 to 7.2 hours and have about a few kilometers of spatial resolution for coherent reflection and ∼25 km for incoherent reflection [1]. The CYGNSS datasets have a great potential to provide the vegetation optical depth (VOD) by combining the soil moisture data from SMAP. In this paper, we developed a physical-based model to retrieve the VOD. The retrieved VOD has been compared with SMAP VOD in both regional and global scale.

Recently, the Synthetic Aperture Radar(SAR) Tomography (TomoSAR) has been used in monitoring the snowpack from X-band to Ku-band. This technique provides unique access to the structure of the imaged scene, and in the case of snowpack, it enables the separation of multiple snow layers as well as the detection of and compensation for soil and vegetation layers. The addition of polarimetric capabilities brings in the ability to detect spatially varying shapes, sizes, and permittivities, to decompose the backscattered signal into volumetric and surface scattering components, and to distinguish between snow, soil, and vegetation. There are a few ground-based field experiments that demonstrate the focused image recover the layering structure of the snowpack with different densities. To better understand the measurement, this paper aims to provide physical-based forward modeling to reconstruct the TomoSAR images with realistic snow profiles. Without loss of generality, we perform the analysis on a two-layer snowpack.

Recently, the Signal of Opportunity (SoOp) has been used in monitoring the snow pack from P-band. This technology makes use of existing satellite transmissions and become a cost-effective alternative to existing active technologies. The theoretical principle is based on the phase change of the reflected P-band signal to change in SWE. The P-band radio signals come from geostationary Mobile Use Objective System (MUOS) communication satellites, operating with dual-frequency channels at P-band (360-380 MHz and 240-270 MHz). P-band frequencies have excellent capability in penetration through thick vegetation (a confounding factor in existing SWE retrievals), and will offer unprecedented capability to sense snowpack under forest canopy. This paper provides an end-to-end simulation through OSSEs and support the understanding of physical mechanizes of surface features that contributing to the received signals.

Previous experiments showed that the frequency dependence of snow volume scattering from 18 to 90GHz is power of 2.8, which is much weaker than the power of 4 of Rayleigh scattering. Recently monitoring snow using existing satellites (such as Sentinel-1, COSMO-SkyMed and QuickScat) have been widely studied. The radar signatures from C to Ku band are important for active remote sensing of snow. In this paper, we study the snow aggregation effects and its frequency dependence of volume scattering from C to Ku band (4-18GHz) with the bicontinuous model. The bicontinuous media model is applied to model the snow microstructure with aggregates. The integral equation of snow scattering volume is derived based on the Born approximation which then leads to the scattering coefficients. Scattering coefficients of snow are computed from C to Ku band giving a frequency dependence of ~2.6 power, weaker than that of Rayleigh scattering because of the aggregation effects. In addition, results are also in good agreement with the full wave numerical solutions from X to Ku band.

P-band Signals of Opportunity (SoOp) has great potential for remote sensing of root zone soil moisture (RZSM) from space. We have carried out a tower-based experiment with receivers to detect the reflected signals from the communications satellites at the Fraser Experimental Forests (FEFs), Colorado, in 2017. The measured reflectivity data at 260 MHz have a good correlation with in-soil moisture (SM) measurements. Retrieval of SM from the reflectivity data was also performed with results indicating accuracy of about 0.01 bias and 0.02 standard deviation (std) with respect to the average of SM in the upper 10 cm of soil. The experimental data and retrieval analyses lend support to the use of P-band SoOp for the remote sensing of SM. Our data also indicate the limitation of single frequency observations, suggesting the requirement of multiple frequencies to enable RZSM remote sensing because the surface SM plays a critical role on the change of reflectivity even at P-band frequencies.

This paper shows results from a proof-of-concept tower experiment that computed phase and reflectivity from a reflected P-band signal. The change in phase of the reflected signal is related to SWE for dry snow and the rate of change of phase is directly correlated to frequency of observation. The effect of vegetation was also evaluated by using measurements from two towers: one with bare soil and one surrounded by small trees with heights of up to 3 meters. SWE has been retrieved from two sites with RMSD of 1.15-1.6 cm for different frequencies and sites. In addition, sensitivity in reflectivity measurement at 260 MHz due to changes in soil moisture was observed in summer 2018 data.

Traditionally, a snow stratigraphy is characterized through a snow pit study. It only represents a snapshot view of the snow vertical properties and cannot capture the continually evolving snow process. This type of study is also destructive and time-consuming. Recent studies show that by using multi-baseline SAR configuration, the tomographic processing can provide the vertical image of the snowpack and monitor temporal variability. In this study, the full wave solution of the forward model will be used to reconstruct the tomograms and relate snow properties with the images.

This paper shows results from a proof-of-concept tower experiment that computed phase from a reflected P-band signal. The change in phase of the reflected signal is related to SWE for dry snow and snow depth for wet snow and the rate of change of phase is directly correlated to frequency of observation. The effect of vegetation was also evaluated by using measurements from two towers: one with no vegetation and one surrounded by small trees with heights of up to 3 meters. The phase measurement from the two sites had excellent correlation of 0.99 during the accumulation phase. The correlation between SWE and phase measurement was found to be between 0.95 and 0.98 during the accumulation phase. During the melt phase, negative correlation between 0.68 and 0.80 was found between snow depth and phase measurement.

SnowEx is a multi-year airborne snow campaign led by NASA. The purpose of SnowEx is to figure out how much water is stored in Earth's terrestrial snow-covered regions. As part of the 2017 NASA SnowEx campaign, we deployed a portable triple-frequency (9.6GHz, 13.5GHz and 17.2GHz) and fully polarimetric frequency-modulated continuous-wave (FMCW) radar at Fraser, Colorado. The radar was installed on a 60cmx60cm frame to enable a full reconstruction of the three-dimensional variability per each radar channel. The tomography technique uses the radar echo from the multiple viewing positions and provides a unique access to the vertical structure of the snow layer. With current setup, the range resolution is 30cm. In this paper, we will review the radar design and signalprocessing algorithm - time domain back projection. The generated vertical images show the snow stratigraphy, which is consistent with ground snow pit measurement. The continuous operation demonstrates diurnal thawing and refreezing process. The snow density is retrieved by comparing to the snow free image.

The NASA Soil Moisture Active Passive (SMAP) mission has been successfully operated for almost three years. The SMAP freeze/thaw algorithm is based on a seasonal threshold approach. It's important to have a stable and self-consistent freeze and thaw reference that can be applied for multi-year dataset. The three-year long radiometer datasets allow us to reassess the criteria of reference setup and evaluate its stability. In this paper, we first refined the freeze reference requirements and compare three different methods of setting up the thaw reference to minimize the false flags. The original freeze/thaw products is in the polar grid and only cover the region north of 45° N latitude. The limitation is due to lack of enough freezing days in the lower latitude, where the freezing reference cannot be generated. To extend the freeze/thaw product to global region, we combine the single channel algorithm in the lower latitude and southern atmosphere. The global results have been validated through WMO air temperature.

The P-band Signals of Opportunity (SoOp) technique has significant potential for remote sensing of root zone soil moisture from space. We have conducted a proof-of-concept experiment to demonstrate the sensitivity of P-band reflectivity to soil moisture. The reflectivity data has a high correlation (~0.87) with the in situ soil moisture observations. Theoretical forward modeling and retrieval analyses have been carried out to assess the accuracy of using multifrequency SoOp data to retrieve the vertical soil moisture profile. Sensitivity analysis has also been carried out to determine the impact of ancillary data.

Landscape freeze/thaw (FT) state is a key variable in Earth's carbon cycle. NASA's Soil Moisture Active Passive (SMAP) satellite mission, launched in January 2015, provides global retrievals of FT state every two to three days. Validating SMAP FT observations with in-situ observations is difficult due to the substantial scale mismatch between a point estimate and a satellite footprint, inducing “representativeness errors” in the in-situ observations. Triple collocation (TC) is a validation technique that addresses this problem by combining estimates from in-situ, model and spaceborne estimates to obtain error estimates for all three products, without assuming that any product is error-free. Unfortunately, it fails when applied to binary or categorical variables, such as landscape FT state. In this study, we use a new variant of TC - categorical triple collocation (CTC) - that can be applied to binary variables, to validate the SMAP FT product across northern land regions (>45N).

Snow water equivalent (SWE) storage is critical parameters of the water cycle and may be important indicators of climate change. Despite their importance in the seasonal and regional terrestrial water cycle, SWE is currently poorly characterized in space and time. We develop a method for observations of these parameters using P-band signals of opportunity (SoOp) concept to measure the SWE. Effect of wet snow on the measurement is analyzed through modeling and it is found that for wet snow, the phase of the SoOp measurement becomes correlated to snow depth while for dry snow, phase is correlated to the Snow Water Equivalent (SWE). In addition, qualitative data analysis from two different site for a proof-of-concept experiment is shown in this paper.

The baseline science objective of the NASA Soil Moisture Active Passive (SMAP) mission is to produce a daily landscape freeze/thaw state for the region north of 45° N latitude with a mean spatial classification accuracy of 80% and 2-3 day average intervals separated by AM and PM overpasses [1]. Following the loss of the SMAP radar in July 2015, radiometer inputs were used to develop a standard freeze/thaw product (L3-FT-P) with relaxed spatial resolution from 3km to 36km. A 9km gridded product (L3-FT-P-E) has been developed by applying enhanced resolution radiometer inputs to the same algorithm. This paper provides an overview of the algorithm development as well as the validation and calibration using in situ observations from both selected core sites and sparse ground station networks.

This paper provides a theoretical basis for retrieving snow water equivalent (SWE) and root zone soil moisture (RZSM) by using the coherent reflected signal from the communication satellite at P-band signals of opportunity. Based on theoretical modeling, the wave propagation constant in the snow is proportional to the snow density. It is shown that the phase change of reflected signal from snowpack has a quasi-linear dependence on SWE. The model has been extended to multilayer to accommodate the various vertical snow profiles. In addition, the P-band reflectivity is also sensitive to the change of root zone soil moisture. In the paper, we also shown the reflectivity calculated using coherent wave approach has excellent sensitivity to the change of soil moisture for moderate range of incidence angles. In order to validate the theoretical results, a proof of concept ground-based experiment is conducted at Fraser, CO since 2015.

A proof-of-concept experiment was carried out to demonstrate the feasibility of retrieving snow water equivalent (SWE) using P-band signals of opportunity. The fundamental observation is the change in the phase of the reflected waveforms as related to the change in SWE. Through theoretical modeling it was found that the change in SWE was approximately linearly dependent on the change in phase. This was verified by retrieving SWE data collected and processed from a tower-based experiment at Fraser, CO, USA. A linear regression was performed on measured phase and in situ SWE. The correlation was found to be 0.94 and root mean square deviation was found to be 7.5 mm.

This paper talks about retrieval of Snow Water Equivalent (SWE) using P-band Signals of Opportunity (SoOp). Modeling is done to show that the phase change in the observed signal is primarily due to change in SWE and is independent of snow density, soil moisture, snow grain size. In order to compare theory to experiment, experiment is conducted at Fraser, CO. Some preliminary data analysis from 1 week of data show that the phase changed when SWE changed.

Snowpack consists of ice grains that are densely packed in the wavelength scale at microwave frequencies so that the coherent microwave interactions among the ice grains are important in microwave signatures. We have used Numerical Maxwell Model of 3D simulations (NMM3D) of random media / discrete scatterer to study such interactions. In the partial coherent model of Dense Media Radiative Transfer (DMRT), we use NMM3D to calculate the effective propagation constants, the extinction coefficients and the phase matrices. These are then used in radiative transfer equations to calculate the emission and backscattering signatures. In the fully coherent model, we use NMM3D to calculate the bistatic scattering and emissivity for a layer of snow pack over the ground. Using the fully coherent approach, we calculate the complex scattering amplitudes from the snowpack, including both magnitude and phase. In microstructure characterization of snow, we have used 2 models a) densely packed scatters of sticky particles or multiple sizes, and b) computer generated bicontinuous media. Both models can be characterized by correlation functions. In this paper, we also describe the recent simulated results for tomography and co-polarization phase differences of anisotropic dense media.

The NASA Soil Moisture Active Passive (SMAP) mission produced a daily landscape freeze/thaw product (L3_FT_A) at 3-km spatial resolution derived from ascending and descending orbits of SMAP high-resolution L-band (1.4 GHz) radar measurements. Following the loss of the SMAP radar in July 2015, coarser (36-km) footprint passive microwave retrievals from the SMAP radiometer were used to derive an alternative daily freeze/thaw product (L3_FT_P). This presentation will provide an overview of the development of both L3_FT products. Validation using in situ observations from core validation sites is used to illustrate differences in the sensitivity of the 3 km radar versus the 36 km radiometer measurements to the landscape freeze/thaw state.

In this letter, we generate anisotropic bicontinuous media with different vertical and horizontal correlation functions. With the computer-generated bicontinuous medium, we then use numerical solutions of Maxwell equations in 3-dimensions (NMM3D) to calculate the anisotropic effective permittivities and the effective propagation constants of V and H polarizations. The copolarization phase difference (CPD) of VV and HH is then derived. The CPDs have recently been applied to the retrieval of snow water equivalent, snow depth, and anisotropy. The NMM3D simulation results are also compared with the results of the strong permittivity fluctuations in the low frequency limit and compared against the Maxwell-Garnett mixing formula.

Comparison studies are made between the QCA-Mie model and the bicontinuous model in microwave scattering from terrestrial snow. Both the scattering properties and the medium characterization are compared. For QCA, we use the multisize and the sticky particle models. For the bicontinuous model, we use the probability distribution function for the wavenumber. We compare the scattering rate and the angular distribution of scattering using the mean cosine of scattering and show that the two models have similar properties. In medium characterization, we use the pair distribution functions used in QCA to derive the correlation functions. We show that both the Percus-Yevick pair functions and the bicontinuous model have tails in the correlation functions that are distinctly different from the traditional exponential correlation functions. The methodologies of using ground measurements of grain size distributions and correlation functions to obtain model parameters are addressed.

We use the coherent model for L band radar remote sensing of soya bean fields. Analytic methods and Monte Carlo simulations are used. The novel feature of the analytic model consists of introducing mutual exclusion functions to eliminate the overlap effects of branches in the former branching model. To validate the new results, Monte Caro simulations are also used by generating samples of soya bean fields and calculating the scattered field for each sample. The Monte Carlo simulations are in good agreement with proposed analytic model. Backscattering coefficients are illustrated for a variety of scenarios with varying VWC and soil moisture conditions. The results show that HH are significantly different based on the coherent model versus that of the distorted Born approximation or first order radiative transfer model. The results are compared with experimental data measured at SMAPVEX 12 field campaign in both the absolute values of backscattering as well as the polarization ratio between VV and HH.

Physical models for radar backscattering coefficients are developed for the global land surface at L-band (1.26 GHz) and 40°incidence angle to apply to the soil moisture retrieval from the upcoming soil moisture active passive mission data. The simulation of land surface classes includes 12 vegetation types defined by the International Geosphere-Biosphere Programme scheme, and four major crops (wheat, corn, rice, and soybean). Backscattering coefficients for four polarizations (HH/VV/HV/350611873VH) are produced. In the physical models, three terms are considered within the framework of distorted Born approximation: surface scattering, double-bounce volume-surface interaction, and volume scattering. Numerical solutions of Maxwell equations as well as theoretical models are used for surface scattering, double-bounce reflectivity, and volume scattering of a single scatterer. To facilitate fast, real-time, and accurate inversion of soil moisture, the outputs of physical model are provided as lookup tables (with three axes; therefore called datacube). The three axes are the real part of the dielectric constant of soil, soil surface root mean square (RMS) height, and vegetation water content (VWC), each of, which covers the wide range of natural conditions. Datacubes for most of the classes are simulated using input parameters from in situ and airborne observations. This simulation results are found accurate to the co-pol RMS errors of to 3.4 dB (six woody vegetation types), 1.8 dB (grass), and 2.9 dB (corn) when compared with airborne data. Validated with independent spaceborne phased array type L-band synthetic aperture radars and field-based radar data, the datacube errors for the co-pols are within 3.4 dB (woody savanna and shrub) and 1.5 dB (bare surface). Assessed with spaceborne Aquarius scatterometer data, the mean differences range from ~ 1.5 to 2 dB. The datacubes allow direct inversion of sophisticated forward models without empirical parameters or formulae. This capability is evaluated using the time-series inversion algorithm over grass fields.

A normalized residual scattering index is introduced. This index is based on the relationship between coincident microwave-backscatter and brightness-temperature observations. In this letter, the L-band NRSI is shown to correlate with vegetation cover conditions on a global scale. The interpretation of global observations from the Aquarius satellite is based on Passive Active L-band System airborne data collected during field experiments together with ground truth. The benefit of the method is that it is sensitive to land-cover features affecting both active and passive measurements while being insensitive to surface effects such as soil moisture.

Active and passive microwave remote sensing has been used for monitoring the soil moisture and snow water equivalent. In the interactions of microwaves with bare soil, the effects are determined by scattering of electromagnetic waves by random rough surfaces. In the interactions of microwaves with terrestrial snow, the effects are determined by volume scattering of dense media characterized by densely packed particles. In this paper, we review the electromagnetic full-wave simulations that we have conducted for such problems. In volume scattering problems, one needs many densely packed scatterers in a random medium sample to simulate the physical solutions. In random rough surface scattering problems, one needs many valleys and peaks in the sample surface. In random media and rough surface problems, the geometric characterizations of the media and computer generations of statistical samples of the media are also challenges besides electromagnetic computations. In the scattering of waves by soil surfaces, we consider the soil to be a lossy dielectric medium. The random rough surface is characterized by Gaussian random processes with exponential correlation functions. Surfaces of exponential correlation functions have fine-scale structures that cause significant radar backscattering in active microwave remote sensing. Fine-scale features also cause increase in emission in passive microwave remote sensing. We apply Monte Carlo simulations of solving full 3-D Maxwell's equations for such a problem. A hybrid UV/PBTG/SMCG method is developed to accelerate method of moment solutions. The results are illustrated for coherent waves and incoherent waves. We also illustrate bistatic scattering, backscattering, and emissivity which are signatures measured in microwave remote sensing. For the case of scattering by terrestrial snow, snow is a dense medium with densely packed ice grains. We have used two models: densely packed particles and bicontinuous media. For the case of densely packed particles, we used the Metropolis shuffling method to simulate the positions of particles. The particles are also allowed to have adhesive properties. The Foldy–Lax equations of multiple scattering are used to study scattering from the densely packed spherical particles. The results are illustrated for the coherent waves and incoherent waves. For the case of bicontinuous media, the method developed by Cahn is applied to construct the interfaces from a large number of stochastic sinusoidal waves with random phases and directions. The volume scattering problem is then solved by using CGS–FFT. We illustrate the results of frequency and polarization dependence of such dense media scattering.

For electromagnetic (EM) scattering by dense media, the traditional approach is to use particles of spheres or ellipsoids that are densely and randomly packed in a background medium. The particles have discrete permittivities that are different from the background medium. The dense-medium model has been applied to the microwave remote sensing of terrestrial snow. In this paper, we propose a different approach of using a bicontinuous medium with discrete permittivities and study the EM scattering properties using analytical and numerical methods. The bicontinuous medium is a continuous representation of interfaces between inhomogeneities within the medium. Discrete permittivities are then assigned to the inhomogeneities of the structure. The analytical approach is based on the Born approximation using the derived analytical correlation functions. The numerical method is based on the numerical Maxwell model of 3-D (NMM3D) approach. In particular, the discrete-dipole approximation and the conjugate gradient-squared method accelerated by the fast Fourier transform technique are used in solving the volume integral equation. Scattering results of analytical and numerical approaches are compared. Numerical results are illustrated using parameters in microwave remote sensing of terrestrial snow. In the NMM3D simulations, three kinds of convergence tests are conducted, viz., convergence with respect to the discretization size, convergence with respect to the sample size, and convergence with respect to the number of realization. The NMM3D results indicate that the scattering by the bicontinuous medium with a broader size distribution has a weaker frequency dependence than that by the medium with a more narrow size distribution. The frequency-dependence power law index can be lower than two, which is very much lower than the power of four in Rayleigh scattering. The NMM3D results also exhibit fairly large cross-polarization returns which account for the local nonisotropic microstructures of bicontinuous media, although the medium is statistically isotropic.

In this paper, we considered a physical-based model which use numerical solution of Maxwell Equations in three-dimensional simulations and apply into Dense Media Radiative Theory (DMRT). The model is validated in two specific dataset from the second Cold Land Processes Experiment (CLPX II) at Alaska and Colorado. The data were all obtain by the Ku-band (13.95 GHz) observations using airborne imaging polarimetric scatterometer (POLSCAT). Snow is a densely packed media. To take into account the collective scattering and incoherent scattering, analytical Quasi-Crystalline Approximation (QCA) and Numerical Maxwell Equation Method of 3-D simulation (NMM3D) are used to calculate the extinction coefficient and phase matrix. DMRT equations were solved by iterative solution up to 2ndorder for the case of small optical thickness and full multiple scattering solution by decomposing the diffuse intensities into Fourier series was used when optical thickness exceed unity. It was shown that the model predictions agree with the field experiment not only co-polarization but also cross-polarization. For Alaska region, the input snow structure data was obtain by the in situ ground observations, while for Colorado region, we combined the VIC model to get the snow profile.

In this paper, we model the backscattering coefficients of multi-layer dry snowpacks, based on Dense Media Radiative Transfer theory (DMRT) with the Quasicrystalline Approximation (QCA). The DMRT model accounts for adhesive aggregate effects, which leads to dense media Mie scattering by using a Sticky particle model. The same set of DMRT equations are used for modeling both active and passive remote sensing. The model is validated by using the Cold-Land Processes Field Experiment CLPX ground based polarimetric scatterometry observation at local-scale observation site (LSOS) and airborne polarimetric Ku-band scatterometer (POLSCAT) data at Fool-Creek, Fraser. The snow density profiles are from ground observation and grain sizes are fitting parameters. It shows that the co-polarization simulations are in good agreement with the data, the cross-polarization simulations are around 2 dB lower than ground based observation and 5 dB lower than airborne observation. With the same set of multi-layer snowpack profile, the QCA/DMRT model matched co-polarization backscattering coefficients and all 4 channels of brightness temperature observations simultaneously at LSOS. The cross-polarization simulation can be improved by 3-dimensional numerical solutions of Maxwell equations (NMM3D). Study at Fool-Creek shows that NMM3D/DMRT simulations can match both co-polarization and cross-polarization observations simultaneously.

A model for the microwave emissions of multilayer dry snowpacks, based on dense media radiative transfer (DMRT) theory with the quasicrystalline approximation (QCA), provides more accurate results when compared to emissions determined by a homogeneous snowpack and other scattering models. The DMRT model accounts for adhesive aggregate effects, which leads to dense media Mie scattering by using a sticky particle model. With the multilayer model, we examined both the frequency and polarization dependence of brightness temperatures (Tb's) from representative snowpacks and compared them to results from a single-layer model and found that the multilayer model predicts higher polarization differences, twice as much, and weaker frequency dependence. We also studied the temporal evolution of Tb from multilayer snowpacks. The difference between Tb's at 18.7 and 36.5 GHz can be 5 K lower than the single-layer model prediction in this paper. By using the snowpack observations from the Cold Land Processes Field Experiment as input for both multi- and single-layer models, it shows that the multilayer Tb's are in better agreement with the data than the single-layer model. With one set of physical parameters, the multilayer QCA/DMRT model matched all four channels of Tb observations simultaneously, whereas the single-layer model could only reproduce vertically polarized Tb's. Also, the polarization difference and frequency dependence were accurately matched by the multilayer model using the same set of physical parameters. Hence, algorithms for the retrieval of snowpack depth or water equivalent should be based on multilayer scattering models to achieve greater accuracy.

The Dense Media Radiative Transfer theory (DMRT) of Quasicrystalline Approximation of Mie scattering by sticky particles is used to study the multiple scattering effects in layered snow in microwave remote sensing. Results are illustrated for various snow profile characteristics. Polarization differences and frequency dependences of multilayer snow model are significantly different from that of the single-layer snow model. Comparisons are also made with CLPX data using snow parameters as given by the VIC model.