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Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/decade) as Arctic summer sea ice. More than one-sixth of the world’s 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’s cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of snow stored 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 will not be 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. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. 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 millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, 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, densities, and layering. We describe radar interactions with snow-covered landscapes, 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 applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements.

For northern countries like Canada, seasonal snow cover is a key component of the water cycle and a commodity of high importance to public safety, economic sustainability, and ecosystem function. Despite this importance, snow water equivalent (SWE - the amount of water stored by snow) information from existing surface observing networks and satellite data does not adequately address most user needs. To address this gap, a new synthetic aperture radar (SAR) mission capable of providing information on terrestrial SWE at previously unrealized spatial resolution is currently under development. The Terrestrial Snow Mass Mission (‘TSMM’) will provide moderate resolution (500m) dual frequency (13.5/17.25 GHz) Ku-band radar measurements across all northern hemisphere snow covered areas every 7 days. Data from this mission will be used at Environment and Climate Change Canada to (1) provide a new level of information on the temporal/spatial variability in SWE in support of climate services, and (2) feed into environmental prediction and analysis systems to improve weather and hydrological forecasts.

Terrestrial biosphere carbon dynamics are the most uncertain elements of the global carbon budget. Carbon stocks estimated using spatially extensive remote sensing are crucial in reducing this uncertainty, and using these stocks as initial conditions to biosphere models can improve carbon flux predictions beyond the site level. Yet remote-sensing data are not always consistently available for large regions, so methods assessing carbon uncertainty using data sources in one location may not be transferable to another. This study assesses the use of multiple-source data from lidar, radar, imaging spectroscopy, and national forest inventories to derive forest structure and composition necessary to initialise the Ecosystem Demography model (ED2), and hence evaluate short-term carbon flux uncertainty over Harvard Forest, Massachusetts. ED2 was initialized using forest structure and composition derived from lidar and national forest inventories, radar and national forest inventories, lidar and imaging sp...

Current satellite observing systems lack the capability to derive terrestrial snow water equivalent (SWE, the amount of liquid water stored in solid form by snow) at the spatial resolution, synoptic sensitivity, global coverage, and accuracy required for operational environmental monitoring, services, and prediction. The required combination of revisit time, spatial coverage, measurement resolution, and sensitivity to the mass of snow on the ground necessitates a new spaceborne observing concept. To address this observing gap, Environment and Climate Change Canada (ECCC), the Canadian Space Agency, industrial partners at Airbus, and international scientific collaborators are developing a new dual frequency (Ku-band: 13.5 and 17.2 GHz), moderate resolution (250 m), wide swath (~500 km) radar mission concept. This paper provides an overview of the measurement concept, and ongoing science activities in support of the technical mission development.

Planned satellite launches will provide open access and operational L-band radar data streams at space-time resolutions not previously available. To further prepare, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) platform was used to observe cropland sites across the southern United States to support the development of L-band (24 cm) prototype science products. Time series flights flew over four independent areas during the growing season of 2019 while crop ground measurements were collected. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of machine learning (ML) algorithms (random forest, feedforward fully connected neural network, support vector machine), the recently developed Multi-temporal Binary Tree Classification (MBTC), and a phenology (Coefficient of Variation; CoV) approach to synergistically assess performance, scattering mechanisms, and limitations. Specific objectives of this research application included 1.) evaluation of L-band mapping performance across multiple independent agricultural production areas with field scale training data, and 2.) assessment of the CoV approach for the generation of prototype NISAR Level 2 science products. Collectively, SAR terms with sensitivity to volume scattering performed well and consistently across CoV mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Dynamic phenology classes, such as herbaceous wetlands, had some confusion with CoV agriculture requiring further regionalized training optimization. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and 0.85, respectively, for crop type by late growth stages for L-band observations. As expected, time series information was more valuable compared to any single ML technique, site, or crop schema. Ultimately, as more SAR platforms launch, the user community should leverage physical contributions of different wavelengths and polarizations along with growing open access time series for efficient and meaningful agricultural products.

The objectives of this research are to (1) address remote sensing strategies and requirements for estimating snow depth and snow water equivalent (SWE) using existing L-Band interferometric data sets in coordination with field-based observations and modeling frameworks and, with this information, (2) inform the Next Generation Cold Land Processes Experiment (SnowEx) toward articulating the appropriate science and research questions for a single motivating science plan. As proposed, SnowEx is a multi-year airborne snow campaign with a primary goal of exploring multimodal sensor observations in coordination with field campaigns to inform the next generation snow remote sensing satellite platform. Based on limitations of satellite-based optical and LiDAR instruments operating in regions of the globe with consistent cloud-cover, the fact that many snow-dominated regions are at more northerly latitudes (limited solar illumination in the middle of winter), and these snow-dominated regions often experience periods of prolonged cloud cover (due to synoptic precipitation events), a microwave remote sensing platform may be the most viable path to space for a dedicated snow remote sensing mission. Specifically, L-Band radar interferometry has shown some unique promise with an archive of historical and contemporary satellite collections from JAXA's PALSAR-1 and PALSAR-2 instruments, respectively. Moreover, with the expected NISAR (NASA-ISRO Synthetic Aperture Radar) mission launch in 2020 and the unprecedented availability of dedicated global interferometric L-Band products every 12-days, as well as what is in essence a NISAR airborne simulator in JPL's UAVSAR platform, the L-Band interferometric approach to estimating snow depth and snow water equivalent (SWE) requires further investigation within the context of in-situ observations and modeling frameworks.

This paper describes of a ground-based synthetic radar (SAR) which can take short- and long-term radar cross-section measurements that can be used for the monitoring of dynamic forest characteristics through the instrument's sensitivity to the dielectric constants for the soil and woody structures. The SAR image is generated through the successive transmission/reception of radar echoes from a tram-mounted sensor that sits 20 meters above the forest floor. Processing of the data into range and azimuthally resolved imagery is achieved through the backprojection algorithm.

A better understanding of ecosystem processes requires accurate estimates of forest biomass and structure on global scales. Recently, there have been demonstrations of the ability of remote sensing instruments, such as radar and lidar, for the estimation of forest parameters from spaceborne platforms in a consistent manner. These advances can be exploited for global forest biomass accounting and structure characterization, leading to a better understanding of the global carbon cycle. The popular techniques for the estimation of forest parameters from radar instruments, in particular, use backscatter intensity, interferometry, and polarimetric interferometry. This paper analyzes the uncertainty in biomass estimates derived from single-season L-band cross-polarized (HV) radar backscatter over temperate forests of the Northeastern United States. An empirical approach is adopted, relying on ground-truth data collected during field campaigns over the Harvard and Howland Forests in 2009. The accuracy of field biomass estimates, including the impact of the diameter-biomass allometry, is characterized for the field sites. A single-season radar data set from the National Aeronautics and Space Administration Jet Propulsion Laboratory's L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument is analyzed to assess the accuracy of the backscatter-biomass relationships with a theoretical radar error model.

For the past three years, the University of Massachusetts and Harvard Forest have been working on an automated system for collecting remote sensing data about a regrowing region in Petersham, Massachusetts. The system consists of a two 15 m towers separated by a 50 m span and connected to one another by a pair of taut cables which support a tram that can travel between the two towers. This paper will discuss the operation of the system, the suite of instruments on-board the tram, and a radar system that is being constructed to operate on the tram. It is anticipated that this system will be able to perform measurements on a periodic basis for the purpose of monitoring the biophysical characteristics of the surrounding area.

Forest canopy height is essential information for many forest management activities and is a critical parameter in models of ecosystem processes. Several methods are available to measure canopy height from single-tree to regional and global scales, but the methods vary widely in their sensitivities, leading to different height estimates even for identical stands. We compare four technologies for estimating canopy height in pine and hardwood forests of the Piedmont region of North Carolina, USA: (1) digital elevation data from the global Shuttle Radar Topography Mission (SRTM) C-band radar interferometry, (2) X- and P-band radar interferometry from the recently developed airborne Geographic Synthetic Aperture Radar (GeoSAR) sensor, (3) small footprint lidar measurements (in pine only), and (4) field measurements acquired by in situ forest mensuration. Differences between measurements were smaller in pine than in hardwood forests, with biases ranging from 5.13 to 12.17 m in pine (1.60–13.77 m for lidar) compared to 6.60–15.28 m in hardwoods and RMSE from 8.40 to 14.21 m in pine (4.73–14.92 m for lidar) compared to 9.54–16.84 in hardwood. GeoSAR measurements of canopy height were among the most comparable measurements overall and showed potential for successful calibration, with R

In this paper, we present a time-domain method for estimating the phase, amplitude, and timing of a test signal, referenced either to a theoretical version of the signal, or to another signal undergoing a similar transformation as the measured signal. The use of time-domain basis for this method allows for its direct application to data samples collected by a digital oscilloscope or a dedicated A/D converter, and precludes the need for conversion into in-phase and quadrature components. The estimation process described in this paper is based on a maximum likelihood formulation, which allows for the estimate performance to be related to errors associated with sampling and is shown to achieve the Cramer-Rao lower bound for variance. Measurements such as the ones described in this paper are important for characterizing interferometric and polarimetric radar systems, as well as for determining the number of observations necessary for achieving a given degree of accuracy in the measurement. By including a statistical description of the estimation process, we enable the ability for using the technique for evaluating hypotheses describing the measurement error model. This last point is critical because it creates a mechanism for accepting or rejecting system model scenarios based on the signal-to-noise ratio and the number of digitized samples.

In this paper, an airborne Ka-band (35 GHz) FMCW scatterometer and its preliminary results are presented. The paper describes the system, the calibration and the data processing needed to study the response of different targets to the scatterometer. The results obtained in the first flights include normalized radar cross-section measurements of inland water bodies and a tree height estimation algorithm.

Vertical structure and biomass are key characteristics of the forest random medium. This paper calculates the power and interferometric synthetic aperture radar (InSAR) sensitivity to tree height and vegetation density as it manifests in extinction, using a homogeneous, random-volume model of the forest medium, and accounting for speckle and thermal noise. Tree height and extinction are both related to biomass within the context of this simple model. Signal and noise calculations show that InSAR coherence and phase are more sensitive than radar power to structure and biomass to 10% variations in structure parameters over a wide range of medium to high density forests. For example, for extinctions of 0.2 db m−1 and other parameters as noted in the text, the sensitivity of InSAR coherence to 10% changes in tree height exceeds observation errors for trees shorter than about 37 m, as opposed to 14 m for radar power. InSAR phase sensitivity to 10% structural and associated biomass changes exceeds obse...

In this paper, an airborne Ka-band (35GHz) FMCW interferometric synthetic aperture radar (InSAR) and its preliminary processing results are presented. Among the issues that had to be addressed in the processing of the SAR imaginer was the correction of range ambiguities through the use of a phase gradient algorithm. In this paper, the results of the 35 GHz InSAR are presented to demonstrate the height mapping capability of the UMass 35 GHz radar system.

This paper describes the estimation of vertical-structure parameters from combined interferometric and polarimetric radar data.

In remote sensing, the propagation of electromagnetic fields through random media is often of concern. We may wish to characterize the effects of clouds or water droplets along the line of sight between an airplane and a radar installation, or we may be interested in using radar to probe the random medium itself, such as in determining snow depth and particle size. In all of these problems it is necessary to predict the propagation constant in the "random" medium. The purpose of this paper is to characterize the accuracy of the quasi-crystalline approximation and other associated methods of determining the effective permittivity for two-dimensional (2-D) random media. A numerical method based on the method of moments is used as a gauge for comparison with the theoretical methods. After deriving the 2-D quasi-crystalline approximation and presenting the numerical method, the behavior of the effective permittivity is analyzed for a range of particle sizes, volume fractions and dielectric losses. From this analysis, regions of validity for the theoretical methods are determined. An investigation is also given which explores the effect of particle arrangement methods on the pair distribution function which, in turn, is shown to have a significant effect on the imaginary component of the effective permittivity.

The Topographic Ice Mapping Mission (TIMMi) instrument is a unique millimeter-wave interferometric radar system operating at 35 GHz (Ka-band). It was constructed, in part, to advance the technology readiness level of NASA's Surface Water and Ocean Topography (SWOT) mission, a spaceborne platform that will globally map the altimetry of Earth's water to gain insight into surface water interactions and dynamics. Previous ground deployments of TIMMi, while successful in demonstrating the abilities of the system, suffered from poor SNR due to high incidence angles. TIMMi was most recently deployed on an airborne platform to take advantage of the lower angle of incidence and to prove its capability in measuring large swaths of topography at high resolution. This paper outlines some of the challenges and considerations for adapting the smaller Ka-band system to an airborne platform, as well as some preliminary results from these datasets.

This paper investigates the ability of radar image segmentation to produce meaningful, structurally homogenous objects with respect to lidar-derived forest metrics. A comparative approach is taken to determine if radar-derived segments perform better in this respect than arbitrary, square segments or landcover-derived segments. It is found that segmentation of UAVSAR co- and cross-polarization backscatter magnitudes results in increased lidar homogeneity on the segment level relative to the arbitrary and landcover segmentations.

Quantification of the various components of the carbon cycle budget is key to improved climate modeling and projecting anthropogenic affects on climate in the future. Estimating the levels of above ground biomass contained in the world's forests that comprise 86% of the planet's above ground carbon and monitoring the rate of change to these standing stocks resulting from both natural and anthropogenic disturbances is necessary to solving the carbon cycle sink. Remote sensing is the only viable means of obtaining a global inventory of forest biomass at the hectare scale. The most promising means of obtaining remotely sensed biomass measurements involve using either lidar or radar measurements of vegetation structure coupled with allometric relationships. We have collected repeat-pass L-band fully polarimetric radar data at multiple spatial and temporal baselines to investigate the tree height and structure measurements using polarimetric interferometry techniques. This paper will discuss this experiment and comparison with lidar data.

The National Research Council's decadal survey recommended DESDynI as one of the high priority missions for NASA. The mission envisions an InSAR/Lidar instrument for observing ecosystem structures on global scales with high spatial resolutions. Consistent and highly resolved global maps of biomass and carbon stocks require highly accurate observations of vegetation, in fact it is expected that such accuracies would require a combination of the high vertical precision of Lidar observations and the large spatial extent of SAR/InSAR measurements. Here we analyze radar backscatter data along with biomass estimates from a field campaign conducted in the Harvard forest in Massachusetts, USA.

To help optimize the real time scanning of an adaptive radar network, it is necessary to define a performance metric for weighing the competing scanning tasks. A point volume vector velocity estimation methodology can be used in this context to derive the confidence bounds of the vector velocity estimator that can, in turn, be utilized as a quality indicator in devising scanning strategies. This document presents the basic derivation of this estimation process and provides examples of processed weather moment data.

In this paper the calibration of the University of Massachusetts Advanced Multi-Frequency Radar (AMFR) is discussed in detail. The calibration is performed primarily through the use of an internal calibration path, and is confirmed through cross-calibration with another calibrated radar. Additionally, the performance of AMFR during the 2007 Canadian CloudSat/CALIPSO Validation Project (C3VP) is demonstrated with respect to calibration and instrument stability. It is shown that the calibration during the C3VP experiment was accurate to within 1.5 dB when compared with a C-band weather radar operated by Environment Canada.

Characterization of the Earth's oceanic and cryospheric topography is among NASA's strategic goals for the next decade. An effective technique for achieving such measurements is radar interferometry. For this technology, use of mm-wave frequencies (Ku- and Ka-band) is appealing due to the proportional size of the space borne structure to the observing wavelength and minimal snow penetration. At such high frequencies, achieving stability in instrument performance and the mechanical deployment structure for accurate measurements is an engineering challenge. To address this, the Microwave Remote Sensing Laboratory at the University of Massachusetts is developing a high performance Ku- and Ka-band interferometer as a prototype for a space borne instrument. The interferometer will be used in a scaled-down ground-based configuration. This paper presents an instrument description of the Ka-band interferometer (the down-converter in particular) and first results from its performance evaluation.

This paper presents an algorithm that derives the vector velocity estimate in a radar network environment considering the velocities that exceed the maximum unambiguous velocity of its observing sensor nodes. At first, an analytical derivation with underlying assumptions is presented and then simulation results are shown and discussed. This unfolding vector velocity algorithm is based on maximum likelihood theory operating on the first three weather radar spectral moments and can potentially provide a real time velocity detection of broader range of velocities than given by the nodal maximum unambiguous velocity.

This paper demonstrates the feasibility of using multialtitude, fixed-baseline INSAR to estimate vertical-structure parameters of forests. Two model scenarios applied to C-band INSAR data collected at 8-km and 4-km altitudes over central Oregon with TOPSAR suggest the need for either a ground surface contribution with a single vegetation layer or a two-layer vegetation model. The two layer model parameters seem more reasonable and qualitatively reflect the field-measured characteristics of the forest studied. The parameters produced from the single set of INSAR cross-correlations in this paper do not constitute a proof that multialtitude INSAR will reliably produce vertical structure parameters, but they do prompt future, more complete demonstrations. A complete set of amplitudes and phases will be used in parameter estimation scenarios, augmenting those shown in (2) and (3), from 2-, 4-, and 8-km altitude INSAR. Including polarimetry in the observation vector will constrain ground/volume power fractions and terrain slope parameters.

Aside from providing the correlation and pair distribution functions directly, numerical simulation can be used in conjunction with numerical scattering methods to simulate radar scattering behavior of dense random media. A packing algorithm is described that is capable of simulating the distribution of particles in very dense media. The size and shape of the particles is specified by the user and is not restricted to simple shapes and discrete sizes. The algorithm may be used to validate existing theories of particle distributions or may be used as the first step in a Monte-Carlo simulation of fields within a random dense media.

Radar backscatter experiments were conducted at 35 and 94 GHz to measure the response of snow-covered ground to snow depth, liquid water content and ice crystal size. The measurements included observations over a wide angular range extending between normal incidence and 60/spl deg/ for all linear polarization combinations. A numerical radiative transfer model was developed and adapted to fit the experimental observations. Next, the radiative transfer model was exercised for a wide range of conditions and the data that was generated was used to develop relatively simple semi-empirical expressions that relate the backscattering coefficient (for each linear polarization) to incidence angle, snow depth, crystal size, and liquid water content.

GeoSAR is a program to develop a dual frequency airborne radar interferometric mapping instrument designed to meet the mapping needs of a variety of users in government and private industry. Program participants are the Jet Propulsion Laboratory (JPL), Calgis, Inc., and the California Department of Conservation with funding provided initially by DARPA and currently by the National Imagery and Mapping Agency. Begun to address the critical mapping needs of the California Department of Conservation to map seismic and landslide hazards throughout the state, GeoSAR is currently undergoing tests of the X-band and P-band radars designed to measure the terrain elevation at the top and bottom of the vegetation canopy. Maps created with the GeoSAR data will be used to assess potential geologic/seismic hazard (such as landslides), classify land cover, map farmlands and urbanization, and manage forest harvests. This system is expected to be fully operational in 2002. In this paper we describe an experiment conducted at California's Latour State Demonstration Forest located near the city of Redding. This experiment marks the first operation of the-P-band radar in a vegetated area.