Signals of opportunity (SoOp) for transmissometry is a practical method for measuring the effective impact of vegetation canopies using ubiquitously available radio frequencies with potential benefit to snow and boreal forest remote sensing as well as precision agriculture. Physical modeling of the microwave scattering within forest scenes is necessary for correctly interpreting changes in measured transmissivity. The SoOp Coherent Bistatic (SCoBi) model and simulator is updated to include explicit tree architecture information. A preliminary comparison between uniformly distributed, randomly oriented trees and fixed tree architectures is performed over a sample forest. Simulations indicate that explicit architecture information can have a strong influence on the received signal. The updated SCoBi model will be used to simulate various forest structures to understand the impact of the canopy architecture in tranmissivity estimates.

NISAR will provide data that addresses many topics within the ecosystems, solid Earth, and cryosphere science disciplines because of the mission’s near-global land coverage; PolSAR and InSAR suitability; fixed, continual, and regular acquisition strategy; and free and open data policy. The science community is well prepared to use the data, but that is not the case for all applications communities. Nevertheless, the information that can be derived from SAR is needed by many agencies to meet their mission mandates through improved or additional knowledge of ground conditions, providing data for models, and supporting disaster response and recovery. To facilitate extensive usage of NISAR’s data beyond the scientific community, the NISAR project and science team members have actively engaged the applications communities since the mission was in formulation. Here the activities and some of the outcomes are described.

Use of Synthetic Aperture Radar (SAR) data has been steadily increasing over the past decade as data from sensors become freely available. In this article, our focus is on the NASA-ISRO SAR (NISAR), the Observational Products for End-Users from Remote Sensing Analysis (OPERA), and Surface Deformation and Change (SDC), and how their user engagement activities combine to build a cohesive user engagement effort for SAR remote sensing. All of NASA remote sensing data is openly and freely available through its Distributed Active Archive Centers (DAAC), and SAR data is no exception. Data going back to SEASAT in 1978 can be accessed through the Alaska Satellite Facility, NASA’s SAR DAAC. The NISAR mission is expected to significantly increase the volume of SAR data available to the end users after its launch in early 2024. OPERA project is already underway preparing high-level data products suited for end-users and will be leveraging NISAR data when it becomes available. Finally, NASA is already conducting the SDC mission study to define a mission architecture for the next decade.

This paper explores the retrieval of snow water equivalent (SWE) through the use of machine learning techniques and active radar data collected over the 2020 SnowEx campaign. The retrieval makes use of active radar measurements provided by NASA's SWESARR instrument for direct sensing of snowpack sensitivity to SWE. The example results show that an RMSE of 1.93 cm can be obtained through a combined use of SAR data with sufficient ancillary data. Such results may indicate successful SWE estimation by means of pairing spaceborne SAR measurements with sufficient auxiliary information.

The NISAR mission will provide data covering nearly all the Earth's land areas and sea ice designed specifically to meet requirements of the NISAR science disciplines. However, for each science use case, the same data could potentially be used by one or more applications, often with little or no change in the core observables. Here we describe NISAR's capabilities for supporting a wide range of applications and the activities undertaken to inform, engage, and train the community that will increase the mission's societal impact.

This paper describes the concepts and recent activities of the HydroSAR project, a collaborative effort between the University of Alaska Fairbanks, the NASA Goddard and Marshall Space Flight Centers, and the Jet Propulsion Laboratory, focused on the automatic production of SAR-derived information for weather-related hazards. This paper will introduce the main processing algorithms as well as the broad product portfolio of the HydroSAR project, which includes information on surface water extent and surface water depth, in addition to capturing agriculture activity and other event-related changes. We will also highlight the cloud-based implementation of HydroSAR, which enables the system to generate data quickly and over large spatial scales. To demonstrate the performance of the service, we present results from a recent event response, where HydroSAR was used to support mapping activities related to the 2020 South Asia monsoon floods, inundating at least 25% of Bangladesh and large areas of the Bihar Province, India. In-region partner ICIMOD supported the performance assessment of the delivered products and algorithms.

Despite a lot of efforts to respond flood hazards with remote sensing data, it is still difficult to generate an accurate flood map using solely optical or radar imagery. While optical data is relatively high-resolution and does not suffer from speckle noise compared to radar data, it is very likely to be impacted by cloud and shadow. On the other hand, radar imagery can be used in all weather conditions due to its capability of penetrating clouds. Although a significant improvement of flood monitoring capability is achieved by using radar data, it is still challenging to map urban floods because of strong backscattering by man-made structures. Therefore, complementary use of optical and radar imagery in flood response is required, particularly in urban areas. In this study, we have adopted the Bayesian Joint Probability function to combine two different flood products generated from SAR and optical imagery. Flood detection with SAR data relies on the difference of backscatter signals between standing water and rougher land surface, while a Normalized Difference Water Index (NDWI) approach is used for optical data. Specifically, Planet Dove data with its 3m spatial resolution is used with higher weight values to detect flood extent in urban areas.

A tri-frequency microwave synthetic aperture radar (SAR), designed for the estimation of snow water equivalent (SWE), was recently developed as part of the SWESARR (Snow Water Equivalent Synthetic Aperture Radar and Radiometer) instrument. The SAR operates at 9.65 GHz, 13.6 GHz, and 17.25 GHz, and at two polarizations (VV, VH), with a nominal bandwidth of 140 MHz. The SAR was first flight tested in December 2018 and later, along with SWESARR's radiometer, participated in the SnowEx science flight campaign in the fall 2019. During these flights, the SAR collected comprehensive data sets of a variety of terrains, including calibration sites where several trihedral corner reflectors had been deployed. Analysis of these data sets indicated the radar performed according to the design specifications.

Radio Frequency Interference (RFI) is a growing problem in Synthetic Aperture Radar (SAR). The choice of operational frequency of remote sensing instruments is dictated by the physics of the parameters that are to be observed. On the other hand, the frequency spectrum becomes more crowded with the increasing demand for wireless services. This requires cohabitation of multiple systems at the same or neighbouring frequency bands. As a result, RFI can introduce artefacts and degrade the derived SAR products. A mitigation of RFI is critical. New removal techniques can be implemented with Digital Beamforming (DBF) radars. In this paper, we present a Pulse and Range-Dependent Time Minimum Variance Distortionless Response (PRDTMVDR) Beamformer. The antenna pattern (AP) is adaptively changed for each pixel based on the inherent imaging geometry of SAR and thus the RFI suppression is improved compared to an AP that is fixed for the pulse duration.

Rapid generation of synthetic aperture radar (SAR) based flood extent and flood depth maps provide valuable data in disaster response efforts. We present a simple but powerful method using dual-polarimetric SAR imagery. A RGB false-color map is generated using pre- and post-flooding imagery, allowing operators to distinguish between existing standing water in pre-flooding data and recently flooded areas. This method works very well in areas of standing water, while large omission errors can be seen in urban areas due to the double-bounce effect. A flood depth map is also estimated by using an external DEM. Compared with FEMA flood product, flood water depth from the proposed method showed low bias with small dispersion. This automatic flood mapping system will contribute to the rapid assessment for disaster relief efforts.

A new airborne synthetic aperture radar (SAR) system was recently developed for the estimation of snow water equivalent (SWE). The radar is part of the SWESARR (Snow Water Equivalent Synthetic Aperture Radar and Radiometer) instrument, an active passive microwave system specifically designed for the accurate estimation of SWE. The dual polarization (VV, VH) radar operates at three frequency bands (9.65 GHz, 13.6 GHz, and 17.25 GHz), with bandwidths of up to 200 MHz. The radar flew its first flight campaign in November 2019, along with SWESARR's -already operational - radiometer. The radar collected comprehensive data sets over various terrains that show a successful system performance. The instrument is slated to participate in future SnowEx campaigns.

The United States National Aeronautics and Space Administration (NASA) has initiated a five-year study of a Surface Deformation and Change Observing System Architecture. The goal of the study is to define an observing system that would meet the recommendations of the 2017 US National Academy of Sciences, Engineering, and Medicine Decadal Survey for developing an affordable synthetic aperture radar-based system or systems that can address fundamental scientific questions about the nature of hazards, disasters, landscape change, and serve applications communities as well. The study is to consider the existing international constellation of SAR systems, both civil and commercial, as well as new technologies that could be employed to address scientific and applications objectives and to improve affordability. The study will be conducted in four phases: 1) expanding the architectural trade space; 2) assessing the science and applications value of each identified architecture; 3) down selecting to three final candidates and performing detailed assessments; 4) final reporting and preparation for mission formulation. The first phase is approximately 2 years in duration, and will concentrate on the scope of research and application areas to be addressed, defining the possible architectures to consider, and beginning the assessment of their performance. The study team comprises five NASA centers. Broad participation from the research, applications, technology, and commercial sectors is planned.

An increasing challenge for P-band Synthetic Aperture Radar (SAR) is Radio Frequency Interference (RFI). RFI results in image distortions and degrades the derived science products. This makes it critical to apply RFI removal techniques to restore the image quality. New advanced techniques can be achieved with Digital Beamforming (DBF) radars such as EcoSAR. In this paper, we present a Range-Dependent Time Minimum Variance Distortionless Response (RDTMVDR) Beamformer and apply it to EcoSAR flight data during post-processing. The antenna pattern (AP) is adaptively changed for each range line which increases the RFI suppression compared to a fixed AP for each pulse. The interferometric image quality is assessed before and after RFI suppression.

Monitoring of mountain glaciers using SAR interferometry is challenging, yet it is necessary to improve our understanding of glacier dynamics in a changing climate, by estimating their precise movements. Although there are significant difficulties of spatial and temporal decorrelation for observing mountain glaciers, multiple aperture interferometry (MAI) can be a promising solution compared to other existing methods. In this study, we will present temporal variations of Himalayan glaciers' behavior focusing on the velocity estimation and glacier outline mapping. These results will be provided for estimating the rate of glacier melt, and in turn play an important role in better understanding the hydrology and climate change in the Himalayan region.

Wideband radar systems operating at L- or P-band encounter an increasing amount of Radio Frequency Interference (RFI). If this RFI is not removed from the obtained radar signal, the quality of extracted science data can be decreased significantly. The removal of RFI can be improved with Digital Beamforming (DBF), a technique that is becoming more important in many radar applications. DBF enables to digitally steer the antenna beam into different directions and to place nulls into the antenna pattern. This technology is implemented in EcoSAR, a synthetic aperture radar (SAR) operating at P-band. Further, in data acquired by EcoSAR in Costa Rica in 2014, the impact of multiple RFI sources can be observed. This gives the opportunity to implement and test new RFI mitigation techniques based on DBF. In this paper, we estimate RFI with DBF and subtract it from EcoSAR data. The method is applied to several acquisitions and results are being presented.

Forest vertical structure parameters are one of critical components for understanding of the global forest carbon storage and cycle, as well as climate changes. Polarimetric SAR Interferometry (Pol-InSAR) techniques and waveform lidar have been widely and successfully used for extracting 3D forest structure profiles by means of both SAR and lidar airborne systems, but individually. Therefore, fusing both data sets and developing new algorithms and models to understand forest structure are critical. We have used waveform lidar data acquired by NASA's LVIS (Land, Vegetation, and ICE sensor and SAR data acquired by various airborne and spaceborne SAR systems over mangrove forests in Pongara national park, Gabon.

High Mountain Asia (HMA), often referred to as the “third pole” of the world because its high elevation glaciers, contains the largest amount of fresh water outside the polar ice sheets. The region's hydrology is strongly controlled by variations in the timing and distribution of runoff from snow and glacier melt. Recent improvements in remote sensing technologies and atmospheric / land surface models provides new approaches for assessing the HMA cryosphere. A recently-funded NASA program aims to apply these tools to advance understanding of HMA cryospheric processes. Here we present an overview of planned team activities during the three-year project.

EcoSAR is a single-pass (dual antenna) digital beamforming, P-band radar system that is designed for remote sensing of dense forest structure. Forest structure retrievals require the measurement related to the vertical dimension, for which several techniques have been developed over the years. These techniques use polarimetric and interferometric aspects of the SAR data, which can be collected using EcoSAR. In this paper we describe EcoSAR system in light of its interferometric capabilities and investigate forest structure retrieval techniques.

The ground-level digital elevation model (DEM) or digital terrain model (DTM) information are invaluable for environmental modeling, such as water dynamics in forests, canopy height, forest biomass, carbon estimation, etc. We propose to extract the DTM over forested areas from the combination of interferometric complex coherence from single-pass TanDEM-X (TDX) data at HH polarization and Digital Surface Model (DSM) derived from high-resolution WorldView (WV) image pair by means of random volume over ground (RVoG) model. The RVoG model is a widely and successfully used model for polarimetric SAR interferometry (Pol-InSAR) technique for vertical forest structure parameter retrieval [1][2][3][4]. The ground-level DEM have been obtained by complex volume decorrelation in the RVoG model with the DSM using stere-ophotogrammetric technique. Finally, the airborne lidar data were used to validate the ground-level DEM and forest canopy height results.

Next Generation Digital Beamforming (DBF) Synthetic Aperture Radar (SAR) is a technological area being pursued at the NASA Goddard Space Flight Center (GSFC). Two such systems - DBSAR-2 and EcoSAR-have been recently developed and tested. The new instruments employ advanced architectures characterized by multi-mode operation, software defined waveform generation, digital beamforming, and configurable radar parameters. The instruments have been developed to support several disciplines in Earth and Planetary sciences. This paper will describe EcoSAR and DBSAR-2 advanced features and report on the latest SAR processing and calibration efforts.

Mangroves are among the most-carbon rich forest in subtropics and tropics, containing on average 1,023 Mg carbon per hectare [1]. In order to better estimate mangrove biomass, carbon dynamics and land coverage changes, mangrove canopy height is a key parameter. However, there is a surprisingly absence of information needed for global-scale mangrove height mapping because of the lack of high spatial resolution data, available spaceborne data sets, and modeling techniques. In recent studies, the first single-pass TanDEM-X data showed a great possibility of mangrove canopy height estimate with accuracies comparable to airborne lidar canopy height model with single- and dual-Pol-InSAR techniques. Based on the method mentioned in [2], we here generated large-scale mangrove canopy height map with a 12-m spatial resolution over Sundarbans, the world largest mangrove forest, from existing global TDX acquisitions. The inversion result for mangrove canopy height was validated against field measurement data; a correlation coefficient of 0.852 and a RMSE of 0.774 m.

Radio frequency interference (RFI) has strong influence on radar systems, especially for wideband airborne radars operating in the P-band (UHF). EcoSAR is a 435 MHz Synthetic Aperture Radar (SAR) system that employs a wideband digital beamforming architecture for the measurement of science parameters. RFI in EcoSAR measurements, degrades the quality of the SAR data and has to be removed from raw echoes. In this paper, we describe the current methodology used to mitigate RFI with EcoSAR, and provide an example on its performance. We also discuss the advantages and disadvantages of the proposed methods and discuss potential improvements.

Digital Beamforming permits the implementation of non-conventional measurement techniques, which can overcome fundamental limitations of conventional radar systems. In SAR systems, this technique can enable three-dimensional measurements using a single radar platform. A split phase center technique is implemented with NASA's digital beamforming SAR. The technique has the potential to provide volume structure estimates in tall forests and glaciers using a single radar platform.

The second generation Digital Beamforming SAR (DBSAR-2) is a state-of-the-art airborne L-band radar being developed at the NASA Goddard Space Flight Center (GSFC). The instrument employs a 16-channel radar architecture characterized by multi-mode operation, software defined waveform generation, digital beamforming, and configurable radar parameters. The instrument has been design to support several disciplines in Earth and Planetary sciences. This technology seeks to establish the Next Generation SAR as a science instrument while setting a path future airborne and spaceborne SAR missions.

TanDEM-X (TDX) enables to generate an interferometric coherence without temporal decorrelation effect that is the most critical factor for a successful Pol-InSAR inversion, as have recently been used for forest parameter retrieval. This paper presents mangrove forest height estimation only using single-pass/single-baseline/dual-polarization TDX data by means of new dual-Pol-InSAR inversion technique. To overcome a lack of one polarization in a conventional Pol-InSAR inversion (i.e. an underdetermined problem), the ground phase in the Pol-InSAR model is directly estimated from TDX interferograms assuming flat underlying topography in mangrove forest. The inversion result is validated against lidar measurement data (NASA's G-LiHT data).

Determining the Earth's surface topography and deformation with interferometric synthetic aperture radar involves measurement of phase, which, for a typical coherent radar signal, can only be done modulo 2π. The cycle of ambiguity inherent in the phase measurement has to be unwrapped over all observation dimensions (e.g., azimuth, range, and time) to remove the 2π ambiguity of the phase measurements. For a time series of SAR images, useful for reducing noise in topographic applications or measuring time-varying surface deformation, the necessary steps to connect ambiguous radar phase measurements are more challenging, and the operation may be termed 3-D phase unwrapping. We describe a 3-D unwrapping approach using an extended Kalman filter. Our approach readily exploits existing information, and is robust in the presence of noise. For all tested data sets, it provides improved accuracy compared to existing approaches.

We present a subsidence study based on the analysis of displacement measurements from ENVISAT-ASAR Persistent Scatterer Interferometry and on the extraction of horizontal gradient maps. Land subsidence is analyzed for three cities in central Mexico: Chalco, Aguascalientes and Morelia. Even though stratigraphic and tectonic backgrounds of these areas impose different subsidence patterns as imaged through the interferometric analysis, the three case studies all reveal the necessity of a combined analysis of subsidence magnitudes and horizontal gradients for better definition of the areas with higher vulnerability to surface faulting. Such an analysis can be used as the basis for subsequent hazard and risk mapping.