Review Article: Global Monitoring of Snow Water Equivalent Using High-Frequency Radar Remote Sensing

Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 × 106 km2 of Earth\u27s surface (31 % of the land area) each year, and is thus an important expression and driver of the Earth\u27s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (∼ −13 % per decade) as Arctic summer sea ice. More than one-sixth of the world\u27s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth\u27s cold regions\u27 ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of water stored as snow on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations are not able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high-socio-economic-value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-band synthetic aperture radar (SAR) for global monitoring of SWE. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimeter-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modeling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, density, and layering. We describe radar interactions with snow-covered landscapes, the small but rapidly growing number of field datasets used to evaluate retrieval algorithms, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and application communities on progress made in recent decades and sets the stage for a new era in SWE remote sensing from SAR measurements

Exploring bistatic scattering modeling for land surface applications using radio spectrum recycling in the Signal of Opportunity Coherent Bistatic Simulator

The potential for high spatio-temporal resolution microwave measurements has urged the adoption of the signals of opportunity (SoOp) passive radar technique for use in remote sensing. Recent trends in particular target highly complex remote sensing problems such as root-zone soil moisture and snow water equivalent. This dissertation explores the continued open-sourcing of the SoOp coherent bistatic scattering model (SCoBi) and its use in soil moisture sensing applications. Starting from ground-based applications, the feasibility of root-zone soil moisture remote sensing is assessed using available SoOp resources below L-band. A modularized, spaceborne model is then developed to simulate land-surface scattering and delay-Doppler maps over the available spectrum of SoOp resources. The simulation tools are intended to provide insights for future spaceborne modeling pursuits

Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method

Computational tools for characterizing electromagnetic scattering from objects with uncertain shapes are needed in various applications ranging from remote sensing at microwave frequencies to Raman spectroscopy at optical frequencies. Often, such computational tools use the Monte Carlo (MC) method to sample a parametric space describing geometric uncertainties. For each sample, which corresponds to a realization of the geometry, a deterministic electromagnetic solver computes the scattered fields. However, for an accurate statistical characterization the number of MC samples has to be large. In this work, to address this challenge, the continuation multilevel Monte Carlo (CMLMC) method is used together with a surface integral equation solver. The CMLMC method optimally balances statistical errors due to sampling of the parametric space, and numerical errors due to the discretization of the geometry using a hierarchy of discretizations, from coarse to fine. The number of realizations of finer discretizations can be kept low, with most samples computed on coarser discretizations to minimize computational cost. Consequently, the total execution time is significantly reduced, in comparison to the standard MC scheme.Comment: 25 pages, 10 Figure

Numerical Analysis of Microwave Scattering from Layered Sea Ice Based on the Finite Element Method

Source at https://doi.org/10.3390/rs10091332.A two-dimensional scattering model based on the Finite Element Method (FEM) is built for simulating the microwave scattering of sea ice, which is a layered medium. The scattering problem solved by the FEM is formulated following a total- and scattered-field decomposition strategy. The model set-up is first validated with good agreements by comparing the results of the FEM with those of the small perturbation method and the method of moment. Subsequently, the model is applied to two cases of layered sea ice to study the effect of subsurface scattering. The first case is newly formed sea ice which has scattering from both air–ice and ice–water interfaces. It is found that the backscattering has a strong oscillation with the variation of sea ice thickness. The found oscillation effects can increase the difficulty of retrieving the thickness of newly formed sea ice from the backscattering data. The second case is first-year sea ice with C-shaped salinity profiles. The scattering model accounts for the variations in the salinity profile by approximating the profile as consisting of a number of homogeneous layers. It is found that the salinity profile variations have very little influence on the backscattering for both C- and L-bands. The results show that the sea ice can be considered to be homogeneous with a constant salinity value in modelling the backscattering and it is difficult to sense the salinity profile of sea ice from the backscattering data, because the backscattering is insensitive to the salinity profile

Three-Dimensional Electromagnetic Scattering from Layered Media with Rough Interfaces for Subsurface Radar Remote Sensing

The objective of this dissertation is to develop forward scattering models for active microwave remote sensing of natural features represented by layered media with rough interfaces. In particular, soil profiles are considered, for which a model of electromagnetic scattering from multilayer rough surfaces with/without buried random media is constructed. Starting from a single rough surface, radar scattering is modeled using the stabilized extended boundary condition method (SEBCM). This method solves the long-standing instability issue of the classical EBCM, and gives three-dimensional full wave solutions over large ranges of surface roughnesses with higher computational e±ciency than pure numerical solutions, e.g., method of moments (MoM). Based on this single surface solution, multilayer rough surface scattering is modeled using the scattering matrix approach and the model is used for a comprehensive sensitivity analysis of the total ground scattering as a function of layer separation, subsurface statistics, and sublayer dielectric properties. The buried inhomogeneities such as rocks and vegetation roots are considered for the first time in the forward scattering model. Radar scattering from buried random media is modeled by the aggregate transition matrix using either the recursive transition matrix approach for spherical or short-length cylindrical scatterers, or the generalized iterative extended boundary condition method we developed for long cylinders or root-like cylindrical clusters. These approaches take the field interactions among scatterers into account with high computational efficiency. The aggregate transition matrix is transformed to a scattering matrix for the full solution to the layered-medium problem. This step is based on the near-to-far field transformation of the numerical plane wave expansion of the spherical harmonics and the multipole expansion of plane waves. This transformation consolidates volume scattering from the buried random medium with the scattering from layered structure in general. Combined with scattering from multilayer rough surfaces, scattering contributions from subsurfaces and vegetation roots can be then simulated. Solutions of both the rough surface scattering and random media scattering are validated numerically, experimentally, or both. The experimental validations have been carried out using a laboratory-based transmit-receive system for scattering from random media and a new bistatic tower-mounted radar system for field-based surface scattering measurements.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91459/1/xduan_1.pd

Improvement of microwave emissivity parameterization of frozen Arctic soils using roughness measurements derived from photogrammetry

Soil emissivity of Arctic regions is a key parameter for assessing surface properties from microwave brightness temperature (Tb) measurements. Particularly in winter, frozen soil permittivity and roughness are two poorly characterized unknowns that must be considered. Here, we show that after removing snow, the 3D soil roughness can be accurately inferred from in-situ photogrammetry using Structure from Motion (SfM). We focus on using SfM techniques to provide accurate roughness measurements and improve emissivity models parametrization of frozen arctic soil for microwave applications. Validation was performed from ground-based radiometric measurements at 19 and 37 GHz using three different soil emission models: the Wegmüller and Mätzler [1999, TGRS] model (Weg99), the Wang and Choudhury [1981, JGR] model (QNH), and a geometrical optics model (Geo Optics). Measured and simulated brightness temperatures over different tundra and rock sites in the Canadian High Arctic show that Weg99, parametrized with SfM-based roughness and optimized permittivity (ε), yielded an RMSE of 3.1 K (R2=0.71) for all frequencies and polarizations. Our SfM based approach allowed us to measure roughness with 0.1 mm accuracy at 55 locations of different land cover type using a digital camera and metal plates of know dimensions

Multispectral Resource Sampler: Proof of concept. Literature survey of bidirectional reflectance

A bibliography compiled in order to give a comprehensive review of previous work in scene bidirectional reflectance, particularly those studies relevant to the Multispectral Resource Sampler (MRS) is presented. The bibliography contains 124 abstracts. In addition a synthesis of the literature results is given along with background information concerning MRS

Vegetation/Forest Effects in Microwave Remote Sensing of Soil Moisture

This thesis includes (1) the distorted Born approximation (DBA) and an improved coherent model for vegetation-covered surfaces at L-band for data-cube based soil moisture retrieval; (2) a unified approach for combined active and passive remote sensing of vegetation-covered surfaces with the same input physical parameters; (3) Numerical Maxwell Model in 3D (NMM3D) simulations of a vegetation canopy comprising randomly distributed dielectric cylinders; and (4) a hybrid method based on the generalized T matrix of single objects and Foldy-Lax equations for NMM3D full-wave simulations of the realistic vegetation/forest with vector spherical, spheroidal and cylindrical wave expansoins. The main contributions and novelty of this thesis are NMM3D full-wave simulations of vegetation/forest canopy using the generalized T matrix of the single object and Foldy-Lax equations of multiple scattering among many objects. Before this work, the large-scale full-wave simulations of vegetation/forst such as many tree trunks were deemed very difficult. The NMM3D full-wave simulation results showed that the results of past models significantly overestimate attenuation in a vegetation/forest canopy. The NMM3D full-wave models predict transmissions that are several times greater than that of past models. A much greater microwave transmission means the microwave can better penetrate a vegetation/forest canopy and thus it can be used to retrieve soil moisture. The thesis starts with the DBA to compute the backscattering coefficients for various kinds of vegetation-covered surfaces such as pasture, wheat and canola fields. For the soybean fields, an improved coherent branching model is used. The novel feature of the analytic coherent model consists of conditional probability functions to eliminate the overlapping effects of branches in the former branching models. In order to make use of complex physical models for real time retrieval for satellite missions, the outputs of the physical model are provided as lookup-tables (data-cubes). By inverting the lookup-tables, time-series retrieval of soil moisture is performed. Next, the DBA is extended to calculate the bistatic scattering coefficients. Emissivities are calculated by integrating the bistatic scattering coefficients over the hemispherical solid angle. The backscattering coefficients and emissivities calculated using this approach form a consistent model for combined active and passive microwave remote sensing. In the analytical physical models mentioned above, as well as in another commonly used approach of the radiative transfer equation (RTE), the attenuation of the wave is accounted for by the attenuation rate per unit distance, which originates from the concept of an “effective medium”. Such a model is unsuitable for a vegetation canopy. Because of these issues, NMM3D full-wave simulations of vegetation are pursued. Firstly, the scattering of a vegetation canopy consisting of cylindrical scatterers is calculated. The approach for solving Maxwell’s equations is based on the Foldy-Lax multiple scattering equations (FL) combined with the body of revolution (BOR). For a layer of extended-cylinders distributed in clusters, the NMM3D simulations at C-band show very different results from DBA/RTE. The method FL-BOR is limited for rotationally symmetric objects such as cylinders and circular disks. To perform NMM3D full-wave simulations for realistic vegetation/forests, a hybrid method is used, which is a hybrid of the off-the-shelf techniques and newly developed techniques. The newly developed techniques are the three key steps of the hybrid method: (1) extracting the generalized T matrix of each single object using vector spheroidal/cylindrical waves, (2) vector wave transformations, and (3) solving FL for all the objects.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153347/1/huanght_1.pd

Electromagnetic Modeling for Radar Remote Sensing of Snow-Covered Terrain

This thesis investigates the radar remote sensing of snow-covered terrain for estimation of snow equivalent water on global scale. The importance and impact of this research stems from the fact that water from snowmelt is the major source of water for inland cities and agriculture during summer. This effort is focused on developing a physics-based model for snow and a fully coherent polarimetric scattering model for snow above ground. Both the physical model and the forward polarimetric scattering model present a significant improvement compared to the existing models for snowpack. Computer-generated snow media are constructed using 3-D spatial exponential correlation functions, along with Lineal-Path functions that serve to preserve the connectivity of the snow particles. A fully-coherent model is presented through the use of the Statistical S-matrix Wave Propagation in Spectral-Domain (SSWaP-SD) technique. The SSWaP-SD depends on the discretization of the medium into thin slabs. Several realizations of a thin snow slab are solved numerically to form the statistics of the scattering matrix representing such a thin snow layer. For each thin slab of the snow-pack, a corresponding polarimetric N-port (representing different directions of scattering) S-matrix is generated. These S-matrices are cascaded using the SSWaP-SD method to calculate the total forward and backward bistatic scattered fields in a fully coherent way. The SSWaP-SD, in conjunction with a Method of Moments (MoM) code based on the Discrete-Dipole Approximation (DDA), is chosen to leverage both the time-efficient computations of the DDA and the full-coherency of the SSWaP-SD method, simultaneously. In addition to the MoM-DDA, a Finite Element Method (FEM) based on commercial software is used for cross-comparison and validation. The simulation results of the backscattering from an arbitrary thick snow layer are presented and validated with measurements. The underlying rough ground surface response is then estimated through both an analytical technique based on the Physical Optics (PO) method and a numerical solver based on MoM using a commercial full-wave solver. Finally, the complete response is then calculated by cascading the S-matrices representing the snow and the rough surface responses. The simulation results of the backscattering are presented using a Monte-Carlo process, which show very good agreement with measurements.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167972/1/mzaky_1.pd