Continuous and operational monitoring of forest canopy structure plays an important role in assessing the global carbon budget, mapping forest disturbance, planning restoration activities, and informing decision-making. Several studies have taken advantage of synthetic aperture radar (SAR) for forest mapping and monitoring because of its regular reliable acquisitions and high sensitivity to the structural and dielectric properties of the forest. This work utilizes the Senitnel-1 C- and ALOS-2 PALSAR-2 L-band interferometric coherence for canopy height estimation in savanna woodlands. A simplified physics-based Random Volume over Ground (RVoG) model is used for the height estimation. This study uses datasets collected over two test sites, one in Injune, Australia, and the second in Kruger National Park (KNP), South Africa. The proposed method achieved an overall RMSE of 2.39m for canopy height with a Pearson coefficient, r = 0.83 by simultaneous use of both C- and L-band coherence.

The NASA ISRO Synthetic Aperture Radar (NISAR) is scheduled for launch early in 2024 from the Satish Dhawan Space Centre (SDSC), at Sriharikota, near Chennai, India. This mission is the result of a collaboration between NASA and Indian Space Research Organization (ISRO), where NASA has contributed elements of the mission such as an L-band SAR, and ISRO has contributed other elements, such as an S-band SAR. After successful launch, the NISAR mission will collect left-looking L-band SAR data over most of the Earth’s land areas twice during every 12-day exact repeat orbit. (once while in an ascending orbit direction and once while in a descending orbit direction). NASA and ISRO have individual and joint requirements on the mission that include the performance of the imaging radars onboard the spacecraft. For example, NASA must demonstrate that this L-band SAR will achieve a set of identified science measurement accuracy requirements that span Ecosystem science, Solid Earth science, and Cryosphere science disciplines. Likewise, ISRO has several applications objectives on both the L-band and S-band data from NISAR that the ISRO science team and project will be developing and testing. Pre-launch and post-launch activities have been planned to validate that these requirements are met. Here, we will discuss how the NASA plans are being executed and will present any initial results at the conference.

In preparation for the NISAR launch and data collection in 2024, the NISAR Project Science Team is building workflows for each Science Team discipline (Ecosystems, Cryosphere, and Solid Earth). This abstract focuses on the Ecosystem disciplines and the development of on-demand cloud-processing workflows for wetlands inundation, forest biomass, agricultural active crop area, and forest disturbance. The workflow simulates NISAR data using UAVSAR or ALOS-2 Single Look Complex data, which are processed to Level 2 geocoded polarimetric covariance matrix products using InSAR Scientific Computing Environment 3.0 software and to Level 3 science products using the Algorithm Theoretical Basis Documents. In this presentation, we describe these workflows and efforts to improve efficiency and data accessibility by using a cloud processing system. We present preliminary sample products from each Ecosystem discipline: inundation, forest biomass, crop area, and forest disturbance..

This paper proposes a novel multivariate Gaussian Process Regression (GPR) approach for multi-class crop classification. We have trained and validated the proposed model utilising backscatter information from E-SAR C- and L-band dual-polarimetric data acquired during the AGRISAR 2006 campaign. Further, we use the Product of Experts (PoE) fusion strategy to combine decisions from the proposed Gaussian Process (GP) models trained and validated independently over C- and L-band data to analyze the changes in the classification performance. The synergistic C- and L- band information show an improved classification accuracy during various phenological stages of major crop types by (a) 4 to 37 % for VV-VH backscatter intensity channels and (b) 1 to 39 % for HH-HV backscatter intensity channels.

The NISAR mission which in its most recent round of launch preparations was set to launch in the spring of 2024, and now delayed until later in the fall or early spring of 2025, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The two frequency, L- and S-band will full-polarimetric capability over a 250 km wide swath using the SweepSAR technique [1] will collect reliable set of observations (60 per year; 30 each for ascending and descending passes) on a continuing basis that will allow for the modeling and observation of time-varying processes that are prevalent in the living environment broadly described as Ecosystems. Among the prime science goals of the NISAR Ecosystems disciplines are in the characterization of agriculture, disturbance, biomass, forest structure and water dynamics seen in the world’s rivers, coasts, and permafrost regions. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

In this paper we describe the development and motivation behind the development of a NASA-sponsored airborne instrument, SNOWWI (Snow Water-equivalent Wide Swath Interferometer) that is being developed for exploring the volume scattering and penetration depth characteristics of the snowpack at three different frequencies (5.4 GHz, C-band; 13.64 GHz known as Ku-low; 17.24 GHz known as Ku-high). The system, as it is being constructed is able to receive co- and cross-polarized (VV and VH) returns in an interferometric configuration. By implementing these components of the radar signature on the same platform, we will be able to explore the relationship between snow depth, density and snow water equivalent on the overall radar signature. This work is being done in conjunction with a strong modeling component being led by the University of Michigan, a ground campaign component supported by Boise State University and the US Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL), and a spaceborne concept development being led by Capella Space.

This article presents the first results of the newly conceived airborne Synthetic Aperture Radar system, SNOWWI.SNOWWI is a dual Ku- and C-Band interferometric and dualpolarized (VV and VH) system operating at 13.64 GHz, 17.24 GHz, and 5.39 GHz. The system aims to deliver snowpack observations to quantify Snow Depth (SD) and Snow Water Equivalent (SWE), which have been included as Targeted Observables in the National Academies’ 2017 Decadal Strategy for Earth Observation from Space. This manuscript includes results from the system’s first deployment in Grand Mesa, CO, in January and March 2024.

This paper presents 30 m gridded mosaic of forest canopy height for the New England region of the United States, encompassing Maine, New Hampshire, Vermont, Massachusetts, Connecticut, and Rhode Island, covering a total area of 18 million hectares. The forest height estimates were derived based on ALOS repeat-pass SAR interferometry (InSAR) observations (100 InSAR pairs) and a semi-empirical physical model [1]. sd Further efforts were devoted to automating this approach for generating large-scale forest height products and evaluate the performance of the products at different sites (e.g., flat, hilly area, etc). As validated against NASA’s LVIS airborne LiDAR, this approach presents an accuracy of 3–4 m (RMSE) over flat area and an accuracy of 4-5 m per sub-hectare pixel over hilly area on the order of sub-hectare pixel (0.81 ha). This approach demonstrates promising values in the context of combining low-frequency InSAR observations and LiDAR measurements from existing and future spaceborne missions.

This study proposes an extended temporal correlation model for targets with a noticeable periodic seasonal trend. Several studies have explored the nature of decorrelation in synthetic aperture radar (SAR) interferograms. Specifically, providing a model the decay in interferometric correlation over time between two images remains a challenging task. Initially, it is necessary to assume that the contributions of distributed elements within the same pixel undergo a change, leading to a reduction in correlation with previous acquisitions. The exponential decay model is the simplest and most widely used in the scientific community by considering coherent and incoherent groups of scatterers within a resolution cell. However, the coherence over vegetation canopies with seasonal behavior does not exhibit a monotonic exponential decay with time. Hence, in this study, we introduce a periodic term to account for the nature of this seasonality. The performance of the proposed model is evaluated with a total of nearly 2000 Sentinel-1 interferometric SAR (InSAR) pairs acquired over two test sites located one in Nallamala, India, and the other in Injune, Australia. The proposed model performed significantly better than the exponential model with up to 83% improvement in RMSE in modeling the long-term coherence over vegetation with strong seasonal patterns.

L-band FMCW radar is implemented for monitoring forest dynamics. It took short-term and long-term measurements with an internal calibration system that guarantees stability and precision. The radar data is compared to in-situ measurement, which infers causal relationships between radar backscatter signal and forest physiology index such as tree dielectric. This paper explains the relationship between radar signals and environmental components such as precipitation based on the measurement. The radar demonstrates some interesting observations, for example, trees’ diurnal activity and freeze-thaw process.

In this work, we introduce a novel InSAR processing routine for handling the low-coherence data from long temporal baseline (~ 4 months) L-band ALOS-2 InSAR pairs in the 2016–2017 snow season over the mountainous regions of Colorado, mostly covered by sparse forest. A physical InSAR scattering model was exploited to simulate the InSAR sensitivity of SWE retrieval for forest-covered sites. InSAR-retrieved SWE results are compared with SNOTEL in-situ measurements with a correlation of 0.5 (p-value of 0.01), however, with a much shorter dynamic range of 80 mm versus the actual SWE range of 400 mm. This paper sheds light on using long temporal baseline repeat-pass L-band InSAR data for forest-covered SWE retrieval.

This paper describes a new method of measuring soil moisture via its dielectric properties using a parallel twin lead ladder line. Using a Vector Network Analyzer (VNA) to measure the reflection coefficient of the transmission line which is then used to find the true permittivity of the soil. An experiment is conducted by burying a 450 Ohm ladder line where in which the result is compared to the method derived by other method where it shows good correlation at moisture levels below 25%.

Synthetic Aperture Radar (SAR) is a coherent radar tech system used in a wide range of applications. The UMass Point Target Simulator (UMass-PTS) can help identify such scenarios and provide simulated data, and evaluate the quality and performance of the simulations. The UMass-PTS under squinted geometries is evaluated, as well as evaluating the performance and quality of data. The results show that the UMass-PTS is capable of generating a large number of simulations under a variety of simulated scenarios.

This paper presents a promising fusion prototype for forest stand height inversion using L-band spaceborne repeat-pass SAR interferometry (InSAR) and spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR measurements. NASA’s GEDI mission provides sparsely but extensively distributed LiDAR measurements which could serve as ample calibration samples to improve the forest height estimates based on InSAR information and semi-empirical scattering model. Based on previous efforts, this paper further removed the assumptions that were made given by the limited availability of calibration samples at that time, and developed a new inversion approach based on a global-to-local two-stage fitting scheme. Making good use of local GEDI samples in this approach allows a finer characterization of temporal decorrelation pattern and thus higher accuracy of forest height inversion. This approach is validated at the Howland Forest in Maine, U.S. by using ALOS InSAR and GEDI LiDAR data, where the estimates achieve a RMSE of 3.8m at a sub-hectare spatial resolution (e.g., 0.81 ha). The above experimental results demonstrates a promising prototype towards a large-scale forest height mapping using existing and future spaceborne L-band InSAR missions (JAXA’s ALOS-1/2, China’s L-SAR, NASA-ISRO’s NISAR), as well as spaceborne LiDAR missions (e.g. NASA’s GEDI, JAXA’s MOLI and China’s TECIS).

In this paper, we refine a previously developed forest height inversion and mosaicking approach that fuses spaceborne repeat-pass L-band Interferometric Synthetic Aperture Radar (InSAR) and limited lidar data that serve as training samples. In particular, with the availability of spaceborne lidar training samples from NASA's GEDI mission, the previous inversion approach has been adapted to use the sparsely distributed but extensive GEDI lidar data as local training dataset, so that the inverted height estimates are better tuned to match the local lidar data without making assumptions such as constant scene-wide mean behavior of temporal changes. The refined inversion approach is validated at the Howland Forest in Central Maine, US by using JAXA's ALOS/ALOS-2 InSAR data combined with NASA's GEDI lidar data, where the inverted height estimates are compared against NASA's LVIS lidar dataset that serve as ground truth. This refined inversion approach is a potential fusion scheme of the future spaceborne repeat-pass L-band InSAR and lidar missions (e.g. NASA's NISAR and GEDI, JAXA's ALOS-4 and MOLI, and China's LT-1 and TECIS-1).

Snow water equivalent (SWE) is an important characteristic of a terrestrial hydrological cycle that needs to be retrieved in any global snow satellite mission. Many retrieval algorithms have been proposed based on microwave backscattering of snow packs. And X and Ku bands have been a focus on many of these past and future missions. In this paper we analyse the airborne X(9.6 GHz) and Ku (17.2 GHz) band data of the SnowSAR 2017 campaign and the University of Massachusetts InSAR Ku (13.3 GHz) band data using the bi-continuous dense media radiative transfer (DMRT) model. In-situ measurements of density, temperature and specific surface area (SSA) from the snow pits are used as physical parameters and are used in estimating the numerical parameters ($\zeta$) and$b$which characterizes the model. The background effects such as rough surface scattering are also removed from the airborne data and only the volume scattering is analyzed. Overcoming limitations in other models such as the sticky sphere model, the bi-continuous media model gives a more realistic representation of snow microstructure and has a weaker frequency dependence.

The retrieval of soil moisture (SM) under forest canopy has long been an important goal for low frequency remote sensing. The NASA Soil Moisture Active Passive (SMAP) mission is engaged at three separate experiment sites to improve its SM retrieval algorithm in forested areas. Two of the sites are located in the deciduous forest region in Massachusetts and New York, US and one is located in southern boreal forest zone in Saskatchewan, Canada. Each site has a SM measurement network of 20-25 stations spread out over an area of about 30 km, which covers the SMAP radiometer footprint. In 2022, intensive observations will be carried out at each site which involve deployments of an airborne instrument, which is similar to the SMAP instrument, and intensive manual measurements of SM, surface and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. Here we show some early results using the networks and SMAP measurements to analyze the sensitivity of the SMAP L-band measurements to SM changes in forested area and the impact of the vegetation to the signal. The results suggest an upper limit for vegetation attenuation accounting for surface roughness effect and relate that to the values used in the current SMAP SM products.

As a joint L- and S-band SAR satellite, the NISAR mission will be launched in the mid-2022 to early 2023 timeframe. While the majority of 12-day repeat cycle data collections on a global basis will be dual-polarized L-band only, over India, cal/val sites, and other regions around the world, a significant amount of two-frequency data will be collected as well. Limitations on the collection of two-frequency data are fundamentally due to thermal, power, and data rate constraints on the system. In order to support the co-development of algorithms for the dual-frequency data, during three data collection periods in 2019 and 2021, the Indian Space Research Organization's (ISRO) airborne instrument was flown by NASA within the United States. In this paper we summarize some of the analysis and interpretation of this data as collected by ASAR and processed by ISRO for Ecosystems and radio science applications.

The NASA ISRO Synthetic Aperture Radar (NISAR) Mission plans to generate >40TB of raw data daily to support open access and operational L-band science. This includes Ecosystems applications for agriculture. 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 prototype science products. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of strategic algorithms to synergistically assess performance, scattering mechanisms, and limitations. SAR terms with sensitivity to volume scattering performed well and consistently across mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and. 85, respectively, for crop type by late growth stages for both L-band observations.

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.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will be augmented with two intensive observation periods (IOP). The first IOP is planned for April 2022 and the other one for July 2022. The IOPs will entail a deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

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.

The Ka-band wind scatterometry is a relatively new methodology to retrieve ocean surface winds. Modeling the Ka-band ocean surface backscatter is challenging, especially because of the lack of in-situ measurements. In the framework of the NASA Earth Ventures Suborbital-3 Submesoscale Ocean Dynamics Experiment (S-MODE) mission, a new data set of ocean surface backscatter has been collected. These measurements were obtained from a Ka-band Doppler scatterometer (KaBODS) located on the Woods Hole Oceanographic Institution (WHOI) Air-Sea Interaction Tower (ASIT). In this work we present our analysis and findings on the KaBODS backscatter measurements based on the development of a wind empirical backscatter model. We show that the data are characterized by a large variability, which is mainly due to intrinsic geophysical effects. The source of this geophysical variability is currently under investigation.

In this paper we provide a brief review of the Earth observation requirements for a number key science applications for which spaceborne Synthetic Aperture Radar sensors can contribute with critical measurements. We outline the current state of the science and identify information gaps associated with each application, and subsequently, provide recommendations on how these gaps can be mitigated in the 2020's time-frame by coordination of current and already planned missions, and for the next decade, with a vision for a comprehensive constellation system that would address the outstanding scientific requirements.

The NISAR mission, an L-band and S-band 12-day repeat-pass InSAR, currently scheduled to be launched in January 2023, will serve as an unprecedented resource for the Remote Sensing of Ecosystems Science community. The reliable set of 30 ascending and 30 descending 250 km swath of observations on a continuing basis will allow for the modeling and observation of hydrologic processes that serves as a forcing function and mode of energy transport for most living things, as well as changes in the landcover that are associated with agriculture, river and coastal dynamics, and disturbance. In this paper we provide an overview of the Ecosystem science that will be enabled by the NISAR mission and give a status of the basic algorithms that are being used to provide a basic set of tools to the community to make use of the data that NISAR will provide.

In this paper we present processing and analysis results of an airborne Ku-band InSAR, constructed at the University of Massachusetts, and flown on a Cessna 208 Caravan over the Trail Valley Creek region in Canada's Northwest Territories during the 2018–19 snow season. In this paper, we describe the Ku-band InSAR, provide some intermediate results and discuss on how these data can be used for furthering the science in the remote sensing of snow.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will run through 2021 and they will be augmented with two intensive observation periods (IOP). The first IOP will be conducted in April 2021, and a second one in July 2021. The IOPs will see deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

SAR tomographic methods have proven extremely adept at measuring vegetation vertical structure at a variety of wavelengths including L and P-bands. The three dimensional structure of vegetation and its changes resulting from either natural or anthropogenic causes are key parameters in monitoring ecosystems. The NASA/JPL UAVSAR collected data at L and P-bands at Delta Junction, Alaska in September of 2017 whereas the NASA/JPL UAVSAR and DLR F-SAR acquired data at the BERMS site near Saskatoon, Canada on August 19 and 23 of 2018 respectively. Tomographic data sets were collected at L-band and P-band by the NASA/JPL UAVSAR at Delta Junction and at L-band at BERMS and DLR F-SAR acquired data at L-band and S-band. Ground truth data sets and lidar data from the NASA LVIS system were also acquired at BERMS. We compare L and P tomography at Delta Junction and L-band and S-band tomography from the two systems to each other and to the lidar data sets at BERMS. These data are then used to estimate biomass and assess spatial gradients in the canopy vertical structure. We also compare our data with simulated boreal forest data to assess the sensitivity to the data collection geometry and canopy parameters.

In Summer/Fall 2019, the UAVSAR platform was used to collect dense time series over several agricultural and biomass sites in the southeastern US. This data is to be used for developing ecosystem science algorithms for the upcoming NASA-ISRO SAR (NISAR) mission. Development and testing of these algorithms require routine SAR processing steps such as image co-registration and terrain correction. Because the NISAR mission will use the Interferometric Synthetic Aperture Radar (InSAR) Scientific Computing Environment (ISCE) for data processing from Level 0 through Level 2, we focused on developing a new ISCE workflow to facilitate UAVSAR Multi-Looked-Cross Products (MLC) data processing. The workflows are python scripts and can be readily modified according to user needs. They operate in conjunction with a Docker image of ISCE, which allows data processing on any system that supports Docker (https://www.docker.com/). A defining feature of this workflow is that it usually only requires minimal interaction by the user: the user only needs to provide the desired UAVSAR MLC data and run one docker command to initiate the data processing.

The ecosystem science requirements for the NASA ISRO Synthetic Aperture Radar (NISAR) will need to be validated after its launch in 2021 [1]. Out of all disciplines that are encompassed by the NISAR mission, ecosystems are the one in most need of pre-launch proxy data, consisting of repeated L-band observations over an extended period of time. The solid earth, cryosphere, and hazards research communities have been able to use historical and contemporary spaceborne data available from ERS-1/2, Radarsat, TerraSAR, Sentinel-1, and others for developing and evaluating products similar to what NISAR would be able to provide. This has been possible, in part, because of the focus of these disciplines on sparsely vegetated surfaces and the fairly straight-forward correspondence of surface scattering properties at both L- and C-band (wavelength of 24 cm and 5 cm respectively). Time series data from L-band sensors of value for ecosystem science disciplines, in contrast, have been sporadic and irregular. Ecosystems targets are almost always vegetated, with the scattering components and volume scattering nature of the target giving different scattering responses at the different wavelength regimes. For this reason, the use of C-band observations as a proxy for NISAR's L-band, as is often done for other disciplines, is not possible for the development and testing of NISAR algorithms.In 2018, a plan was developed for a field/airborne/spaceborne campaign to acquire data in 2019 for NISAR pre-launch ecosystem algorithm development and for evaluation of NISAR ecosystem Cal/Val protocols. This plan includes the acquisition of not just L-band SAR data from the NASA/JPL UAVSAR airborne SAR, but also the acquisition of spaceborne data, airborne data, and field measurements to fully exercise the NISAR protocols for validation of its ecosystem science measurement requirements. Included in the plan is the processing of the field, airborne, and spaceborne data into validation products and the generation of NISAR-like level 3 science products.

Cloudy conditions impede and reduce the utility of optical imagery. With the launch of Sentinel-1A and B, the ongoing availability of RADARSAT-2 imagery, and the expected launch of the RADARSAT Constellation Mission (RCM), dense time series of C-band Synthetic Aperture Radar (SAR) data will now be readily available. For crop classification and mapping, SAR imagery has yet to be used to its full potential and has generally been combined with optical imagery. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for SAR-based crop monitoring and inventory. Sets of dense time-series SAR imagery which include RADARSAT-2 and Sentinel-1 data were prepared for this experiment. AAFC's operational Decision Tree (DT) and newly implemented Random Forest (RF) classification methodologies were applied to these SAR only data-stacks, and to optimized, traditional data-stacks of optical/SAR combinations. This paper outlines the results of these dense time-series classifications and how these results were affected by changing numbers of agriculture classes, numbers of available SAR imagery and numbers of training and validation data points for individual crop types. In general, for the dense time-series SAR stacks, overall accuracies of greater than 85%, a typical operational goal, were obtained for 6 of 12 sites. These results have important operational implications for particularly cloudy regions where the availability of optical imagery is limited.

This paper provides an overview of the scattering model, inversion approach, and validation of the application results for creating large-scale moderate-resolution (hectare-level) mosaics of forest height through using spaceborne repeat-pass SAR interferometry and lidar. By incorporating several improvements to the forest height inversion and mosaicking approach, the height estimation accuracy along with the robustness of this approach have been considerably enhanced from its originally reported accuracy of RMSE of 3-4 m at a 20-hectare aggregated pixel size to RMSE of 3-4 m on the order of 3-6 hectares. Furthermore, practical data processing schemes are provided in detail. Extensive validation results are demonstrated which include: 1) a forest height mosaic (total area of 11.6 million hectares) is generated for the U.S. states of Maine and New Hampshire using Japanese Aerospace Exploration Agency's (JAXA) ALOS-1 InSAR correlation data and a small airborne lidar strip (44 000 hectares); 2) the mosaic height estimates are further compared with the available airborne lidar data and field measurements over both flat and mountainous areas; and 3) feasibility of using modern repeat-pass InSAR satellites with short repeat interval is also examined by using JAXA's ALOS-2 data. This simple and efficient approach is a potential observational prototype with much smaller error budget for the future spaceborne repeat-pass L-band InSAR systems with small spatial baseline and moderate/large temporal baseline (such as NISAR) in combination with lidar (such as GEDI) on the application of large-scale forest height/biomass mapping. It also serves as a complementary tool to the spaceborne single-pass InSAR systems using InSAR/PolInSAR methods when full-pol data are not available and/or when the underlying topography slope causes problems for these approaches.

We present an error model for forest cover mapping and change detection with L-band synthetic aperture radar (SAR), which considers measurement noise, forest height, number of images available, and imaging conditions. When applied to a multiseasonal set of Advanced Land Observing Satellite Phased-Array type L-band SAR images acquired over a forest site in southern Sweden, the error model, which is founded on a semiempirical model, suggests that a bitemporal set of cross-polarized L-band backscatter observations is sufficient to detect a forest cover loss of 50% at hectare scale for mature forests. The error probability increases when using co-polarization images, images acquired under adverse imaging conditions, or when detecting forest cover change in a forest of low height. The availability of multitemporal L-band observations is expected to improve forest cover retrieval and change detection, albeit highly correlated forest cover retrieval errors between images acquired within narrow time intervals (e.g., months) pose a limit on the improvements that can be achieved.

Accurate spatio-temporal information about rice growth is an important factor for agronomic management and regional grain yield estimation. In this letter, a unified framework for monitoring and mapping of rice using dense time-series of Sentinel-1 synthetic aperture radar (SAR) images is proposed. A processing chain for such dense time-series Sentinel-1 images is developed with the Google Earth Engine's cloud computing platform. A dense time-series analysis of backscatter response of rice with different management practices is analyzed. Subsequently, the early and late transplanted rice is classified using a clustering algorithm within this platform. The proposed approach is used to monitor different cultivars of rice in three districts in the state of West Bengal, which is one of the major rice growing regions in India. The classification accuracy is assessed across 150 validation points spanning multiple blocks for the 2017 monsoon season. The Sentinel-1 SAR images acquired up to the early vegetative stage for rice have provided satisfactory classification accuracy with an overall accuracy >85% with κ ~ 0.86 across different management practices throughout the region.

Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance information as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete deforestation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross-pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency's ALOS-1/2 and NASA-ISRO's NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring.

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.

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.

The next World Radio Conference (WRC) will be held in November 2019 in Geneva, Switzerland. This paper discusses WRC-19 agenda items that could impact scientific uses in Earth satellite remote sensing and radio astronomy.

Spaceborne SAR interferometry (InSAR) has the potential of mapping the forest height on a global scale and a monthly/weekly basis, which can improve our understanding of the global carbon dynamics. In previous work, repeat-pass SAR interferometry from spaceborne sensors is utilized to create large-scale forest height maps based on a newly developed approach. This paper thus serves as a summary paper and also sheds light on the future directions with improved results. In particular, it will be shown that repeat-pass SAR interferometry is able to create a large-scale (11.6 million hectares) forest height mosaic product with RMSE ≤ 4 m for forest stands on the order of 6 hectares over both the flat and mountainous areas in New England, US through using the past and current spaceborne repeat-pass InSAR observations (i.e. JAXA's ALOS-1 and ALOS-2) combined with sparse airborne lidar training samples (44,000 hectares). Moreover, the results and performance of this approach can be remarkably improved with several enhancement techniques that can be easily satisfied with the use of future spaceborne repeat-pass InSAR and lidar missions (e.g. NASA-ISRO's NISAR and NASA's GEDI). The methodology described in this paper can be considered as a complimentary tool to the existing PolInSAR technique when single-pass full/dual-pol data are not available and/or the underlying topography is complicated.

Spaceborne SAR interferometry (InSAR) has the potential of detecting forest change on a global scale with fine (meter-level) spatial resolution as well as on a monthly/weekly basis regardless of day or night. This is significant to characterize the land-use change and its impact on climate change. In this paper, both single-pass and repeat-pass SAR interferometry from spaceborne sensors are combined in order to detect and quantify (with Normalized RMSE ≤ 30%) forest disturbance at a large scale (dozens of kilometers) however with a fine spatial resolution (< 1 hectare) based on two newly developed approaches. The single-pass InSAR approach is not only able to detect forest disturbance but also capable of characterizing meter (or even sub-meter) level change of forest phase-center (mean) height due to forest growth and/or degradation. These methods are extensively validated with the past and current spaceborne single-pass and repeat-pass InSAR missions (i.e. JAXA's ALOS-1, ALOS-2 and DLR's TanDEM-X) over subtropical forests in Australia as well as tropical forests in Brazil. Such techniques also serve as observing prototypes for the fusion of the future spaceborne InSAR missions (such as NASA-ISRO's NISAR and DLR's TanDEM-L).

In this paper, an agricultural land cover classification algorithm is presented that uses L-band SAR observations over time to create a crop/non-crop classification. A statistical measurement known as the coefficient of variation (CV) is introduced, which as a measurement of change is able to differentiate between changing agricultural fields and relatively static non-crop areas. Images from the airborne AgriSAR 2006 campaign in northern Germany and from the ALOS PALSAR satellite over Minnesota, USA are used to demonstrate the algorithm. The small region covered by the AgriSAR campaign is used to highlight the differences in behavior of L- and C-band, and in this case the better results of L-band data when using the CV algorithm. Results in both regions show promise for future use of the CV algorithm.

This paper applies the forest height inversion approach developed in [1] along with the automatic mosaicking algorithm described in [2], and generates a state mosaic map for the US state of New Hampshire (NH) by utilizing the spaceborne repeat-pass ALOS/PALSAR InSAR correlation magnitude data and the airborne lidar data from NH GRANIT database over the White Mountain National Forest (WMNF). Since the forest height map for the US state of Maine (ME) has already been generated using ALOS/PALSAR InSAR data and a small strip of LVIS lidar data over the Howland forest in central Maine, a two-state mosaic, i.e., ME+NH mosaic, can be generated using the LVIS lidar strip with the GRANIT lidar data serving as a separate validation site so that the quality (e.g. error propagation) of the ME+NH mosaic map can be determined. In addition to providing the mosaic map of forest height, this forest height inversion approach is also modified to generate a forest disturbance map over the ground validation site at WMNF where ground truth (such as GRANIT lidar) data is available. The approaches along with the analysis described in this paper will serve as observing prototypes for the data fusion of future spaceborne repeat-pass InSAR missions (e.g. NASA's NISAR [3]) and lidar missions (e.g. NASA's GEDI [4]) in characterizing the large-scale forest height as well as disturbance events (e.g. selective logging and/or forest degradation).

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.

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.

Snow characteristics, such as Snow Water Equivalent (SWE) and snow grain size, are essential to monitor the global hydrological cycle and thus an indicator of climate change. This paper demonstrates an InSAR scattering model for dense medium such as snow considering the multiple scattering effect through the use of Quasi-Crystalline Approximation (QCA) and Percus-Yevick pair distribution function. Based on the simplified versions of the model, simulated results are shown for the problem of snow characteristics retrieval. First, it is noticed that the Ka-band InSAR phase has better sensitivity to the snow grain size, while the L-band InSAR phase has better sensitivity to the snow depth. Then, the Ka- and L-band InSAR correlation measurements are utilized to retrieve the snow volume parameters along with the ground parameters simultaneously. The InSAR scattering model and the retrieval approach proposed in this paper can be a complimentary tool for other techniques of retrieving snow characteristics.

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 algorithm and results for an automatic mosaicking method in generating a large-scale forest stand height (FSH) map utilizing spaceborne repeat-pass HV-pol InSAR coherence data. This technique demonstrates the capability for creating FSH metrics that can cover large areas. By using repeat-pass InSAR correlation measurements that are dominated by temporal decorrelation and scene-wide fitting parameters (two) that depend on the mean random motion and dielectric changes of the volume scatterers within the scene, it can be shown that a height sensitive measure can be created and validated over the whole scene. In order to combine these single-scene results into a mosaic, a matrix formulation is used with nonlinear least squares (NLS) and observations in adjacent-scene overlap areas to create a self-consistent estimate of FSH over the larger region. This mosaicking algorithm is validated over the US state of Maine by comparing the inverted FSH with Laser Vegetation Imaging Sensor (LVIS) height and National Biomass and Carbon Dataset (NBCD) Basal Area Weighted (BAW) height.

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.

In this demonstration, we present an open-source Serial ATA core designed for Virtex-4 FPGAs. This core utilizes the RocketIO Multi-Gigabit Transceiver (MGT) of the Virtex-4 to interface with hard drives at SATA Generation 1 (SATA I, 1.5 Gb/s) and Generation 2 (SATA II, 3.0 Gb/s) speeds. A full design hierarchy from host software to the physical layer is provided with the distribution to facilitate design use. A simple, FIFO interface allows for easy integration with other FPGA modules. The demonstration illustrates the correct write and read behavior of the core using a Xilinx ML405 board and a solid state disk. The peak transfer rate of the core for SATA I (130 MB/s) is demonstrated. Our goal for the demonstration is to educate the reconfigurable computing community regarding the availability of the core and to illustrate its capabilities.

The ability to discern malignant from benign tissue in excised human breast specimens in Breast Conservation Surgery (BCS) was evaluated using single frequency terahertz radiation. Terahertz (THz) images of the specimens in reflection mode were obtained by employing a gas laser source and mechanical scanning. The images were correlated with optical histological micrographs of the same specimens, and a mean discrimination of 73% was found for five out of six samples using Receiver Operating Characteristic (ROC) analysis. The system design and characterization is discussed in detail. The initial results are encouraging but further development of the technology and clinical evaluation is needed to evaluate its feasibility in the clinical environment.

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.

Knowledge of the global biomass distribution is essential in monitoring the carbon cycle budget and the climate change. In addition to limited field inventory data, researchers have been developing remote sensing techniques (e.g., LiDAR, SAR backscatter, InSAR phase and/or correlation magnitude) to derive biomass maps. Most of the techniques seek for empirical relationships between biomass and remote sensing measures; however, lack of a direct physical interpretation of the measurement constrains the utility and understanding of remote sensing data sensitivity to the forest characteristics of interest. In this paper, we explore the use of InSAR correlation magnitude to invert for the tree height through use of a physical scattering model [1]. The inversion algorithm, along with the estimates of the tree heights, will be cross-compared with itself over our test area: the Injune region (ILCP) in Australia.

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.

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.

In this paper, a vector velocity estimation approach based on the maximum likelihood technique operating on moment data within the overlapping area of a network of Doppler radars is presented. The relationships between the estimated vector velocity, the statistics of the measured signal, the characteristics of the observing geometry and volume, and the hardware and signal processing parameters are all derived. The most relevant error sources to the network measurements are derived and incorporated into the overall estimation process. The relationship between the measurement and estimation errors is identified, and exploited, so that estimation performance can be measured and, if necessary, improved through the means of error norm minimization. Techniques for mitigating errors in the synthesized reflectivity and velocity folding are presented as well. Results with error metrics are shown for several typical weather observation scenarios that include error sources and simulated data. Finally, it is shown how the technique may be used to provide useful information for the design and intercomparison of various Doppler radar network geometries.

In this paper, a real-time algorithm that operates on data provided by a network of weather radars in the form of moments is presented. The algorithm implementation, performance, and abilities are presented and discussed. As part of this paper, a methodology for mapping from native spherical coordinates to a common network grid is developed and demonstrated. Metrics which gauge the quality of mapping and quality of data are derived, and their use from a network point of view is demonstrated. The main product of the algorithm, which is an implementation of a vector velocity estimation technique based on a maximum likelihood approach, is shown, and its results are displayed, compared, and discussed. The application of a methodology for velocity unfolding and reflectivity display enhancement is presented as well.

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.

In the following, we present first results obtained from the design, construction and deployment of a 35 GHz fixed-point radar interferometer, that uses the principles of FMCW for generating a range resolving signal. Using a single transmit antenna and two receive, separated by an interferometric baseline, it is possible to determine phase differences between the two receive channels, and given the viewing geometry, to solve for the target topography. To test the system described in this short paper, it was deployed on a local mountain top, some 270m above the local terrain. With knowledge of the nominal topography (measured by the Shuttle Radar Topography Mission; SRTM), and sufficiently large interferometric height ambiguity, it is possible to solve for the topography, as seen at 35 GHz, and to monitor differences in the observed topography which may be due to differences in volume scattering, surface motion, and surface dielectric change.

The SAR/InSAR component of the NASA DesdynI mission for measuring vertical vegetation structure from space consists of four possible approaches. These include the use of radar backscatter to estimate biomass, to employ PolInSAR relative phase for measuring the vertical extent, the use of interferometric phase and a ground reference, or the use of interferometric correlation magnitude alone. Temporal decorrelation is a significant contributor to decorrelation of interferometric echoes and is not always separable from volumetric decorrelation hence contributing to uncertainties in vegetation parameter estimates obtained using just correlation magnitude. In this text we analyze data that is close to the best case scenario for isolating temporal decorrelation. With almost zero baseline and a repeat pass of one day, SIR-C data over the eastern US serves as our case study of temporal decorrelation.

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.

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.

In this paper, two data sets consisting of co-located full-waveform lidar and InSAR observations are discussed, one over the Duke Forest, near Durham, North Carolina, and the other, the Harvard Forest, located in Western Massachusetts. Data for the Duke forest consists of AIRSAR and GeoSAR (both airborne sensors) interferometric SAR observations spanning in frequency from X-band down to P-band, and data from the GSFC's SLICER instrument. For the Harvard Forest, spaceborne data from JAXA's ALOS/PALSAR mission is used in conjunction with GSFC's LVIS instrument. Early work with SLICER and GeoSAR data has used a lookup table approach for generating a table that correlates the InSAR observables of differential height between X-and P-band observations, and X-band correlation magnitude to lidar derived height. This table was then used for estimating heights over the remaining swath, where lidar data was not available. A similar technique can be used for spaceborne data, in this case, over the Harvard Forest. In this paper, the comparison between lidar observations and the InSAR Duke observations are shown, and then followed by a preliminary treatment highlighting relationships in the ALOS/PALSAR Harvard data that can be exploited for similar purposes.

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.

We propose the use of multiantenna radars for precipitation measurement from moving platforms. The primary motivation is measurement of vertical motion from spaceborne radars. Preliminary analysis of the concept and application to a specific example indicate that such a system would be feasible

An analysis technique is presented for quantifying statistically and under a unified framework non-stationarity in polarimetric SAR imagery. The unified framework is provided by a multi-resolution analysis (MRA) based on a particular wavelet frame that works like a differential operator. The wavelet MRA provides local estimates of a statistics called structure function, that in turn can characterize two types of non-stationary behavior: smoothed singularities (e.g. edges, point targets); self-similar processes with stationary increments (e.g. fractional Brownian motion). In the polarimetric case we are interested in the combined dependencies on scale and polarization state. To the purpose an extension of the wavelet MRA is introduced for deriving wavelet representations of an intensity image synthesized at any polarization state (pol-MRA). A novel formalism called the polarimetric structure signature condenses in graphical form the properties of points of discontinuity with respect to scale and polarization transformations. A test case illustrates the application of the pol-MRA technique to the analysis of weak but polarimetrically diverse linear features embedded in clutter.

A design constraint traceable to the early days of spaceborne synthetic aperture radar (SAR) is known as the minimum antenna area constraint for SAR. In this paper, it is confirmed that this constraint strictly applies only to the case in which both the best possible resolution and the widest possible swath are the design goals. SAR antennas with area smaller than the constraint allows are shown to be possible, have been used on spaceborne SAR missions in the past, and should permit further, lower-cost SAR missions in the future.

A methodology, example and accuracy assessment are given for a continental-scale mosaic of the Amazon River basin at 100 m resolution using the JERS-1 satellite. This unprecedented resource of L-band SAR data collected by JERS-1 during the low-flood season of the river amounts to a collection of 57 orbits of the satellite and a total of some 1500 1 k/spl times/1 kB images. Interscene overlap both in the along-track and cross-track directions allows common reference points to be used to correct individual scene geolocation inaccuracies that have been derived from the satellite ephemeris. The set of common reference points is assembled into a matrix formulation that is used to solve for individual scene geometric offsets. By correcting for these offsets, each scene is placed within a global coordinate system, which can then be used as the basis for creating a final, visually seamless mosaic. The methodology employed in this approach allows for a mathematical foundation to be applied to the mosaicking process as well as providing a unique, traceable solution for correctly geolocating satellite imagery.

Radar backscatter experiments were conducted at 35 and 95 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 over a wide range of conditions and the generated data were 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.

A three-step process is presented for estimation of forest biophysical properties from orbital polarimetric SAR data. Simple direct retrieval of total aboveground biomass is shown to be ill-posed unless the effects of forest structure are explicitly taken into account. The process first involves classification by (1) using SAR data to classify terrain on the basis of structural categories or (2) a priori classification of vegetation type on some other basis. Next, polarimetric SAR data at L- and C-bands are used to estimate basal area, height and dry crown biomass for forested areas. The estimation algorithms are empirically determined and are specific to each structural class. The last step uses a simple biophysical model to combine the estimates of basal area and height with ancillary information on trunk taper factor and wood density to estimate trunk biomass. Total biomass is estimated as the sum of crown and trunk biomass. The methodology is tested using SIR-C data obtained from the Raco Supersite in Northern Michigan on Apr. 15, 1994. This site is located at the ecotone between the boreal forest and northern temperate forests, and includes forest communities common to both. The results show that for the forest communities examined, biophysical attributes can be estimated with relatively small rms errors: (1) height (0-23 m) with rms error of 2.4 m, (2) basal area (0-72 m/sup 2//ha) with rms error of 3.5 m/sup 2//ha, (3) dry trunk biomass (0-19 kg/m/sup 2/) with rms error of 1.1 kg/m/sup 2/, (4) dry crown biomass (0-6 kg/m/sup 2/) with rms error of 0.5 kg/m/sup 2/, and (5) total aboveground biomass (0-25 kg/m/sup 2/) with rms error of 1.4 kg/m/sup 2/. The addition of X-SAR data to SIR-C was found to yield substantial further improvement in estimates of crown biomass in particular. However, due to a small sample size resulting from antenna misalignment between SIR-C and X-SAR, the statistical significance of this improvement cannot be reliably established until further data are analyzed. Finally, the results reported are for a small subset of the data acquired by SIR-C/X-SAR.>