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The location of water on the Earth's surface is critical for a wide range of science and application uses. Global navigation satellite system reflectometry (GNSS-R) is a remote sensing technique with demonstrated potential for identifying areas of the Earth's surface that are inundated, given that GNSS-R signals are sensitive to smooth surfaces, allowing for the detection of surface water. However, the signal is known to saturate even at relatively low quantities of surface water within an observational footprint, complicating the interpretation of GNSS-R observations. To date, the minimal detectable size of a water body within the GNSS-R signal footprint has remained elusive. This manuscript examines this sensitivity of one GNSS-R mission, the cyclone global navigation satellite system (CYGNSS), by using a high resolution surface water extent product as truth (dynamic surface water extent). This analysis reveals that for the Tonle Sap Lake region in Cambodia, the lower limit of water area detectable by CYGNSS was 0.62 km(2 )of water surface area within a 0.05(degrees) grid cell, or 2% surface water cover. This finding indicates that at least for this region, a probabilistic interpretation of CYGNSS observations may be more appropriate than a binary classification. Such an interpretation establishes a foundation for synergistic use of CYGNSS GNSS-R with other surface water remote sensing technologies.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Multistatic SAR systems enable significant benefits such as tomographic imaging and 3D surface deformation estimation at the expenses of system complexity. Multistatic SAR requires a more complicated timing analysis than monostatic SAR since multiple platforms are transmitting and/or receiving, usually in the same cycle. The nadir-return and ambiguity analyses also become more challenging due to existence of multiple pulses-in-air from different platforms, direct path leakages, and non-zero bistatic angles. Therefore, it is critical to develop a unique timing and ambiguity analysis tool in the early design phase of multistatic SAR systems. Here we introduce such a tool and present example results.

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The Distributed Ape,ture Radar Tomographic Sensors (DARTS) project at the NASA Jet Propulsion Laboratory aims to mature and demonstrate multi-static SAR measurements for fine-scale 3D imaging of surface topography, vegetation, and surface deformation and change. This project explores the use of drones as SAR platforms and integrates software-defined radar on RF system-on-chip for compact and flexible radar instruments. This paper highlights the progress in DARTS hardware development, experiments, and data processing. Recent experiments have successfully demonstrated monostatic interferometry as well as acquisition and processing of bi-static SAR imagery. By leveraging the advantages of multistatic SAR and drone-based and airborne platforms, the project aims to build a testbed for future mission design and enhanced SAR imaging capabilities for scientific applications.

The ongoing Distributed Aperture Radar Tomographic Sensors (DARTS) project at NASA Jet Propulsion Laboratory aims to mature and demonstrate multi-static SAR measurements for fine-scale 3D imaging of surface topography, vegetation, and surface deformation and change. The project explores the use of drones as SAR platforms and integrates software-defined radar on RF system-on-chip for compact and flexible radar instruments. This paper highlights the progress in DARTS hardware development, experiments, and data processing. The recent experiments have successfully demonstrated monostatic interferometry as well as acquisition and processing of bi-static SAR imagery. By leveraging the advantages of multi-static SAR and drone-based platforms, the project aims to build a testbed for future missions design and enhanced SAR imaging capabilities for scientific applications.

Recent results have highlighted the potential ability of bistatic and multistatic synthetic aperture radar (SAR) tomographers to measure vegetation structure and surface topography. However, the quality of SAR tomographic measurements with multiple platforms is impacted by the phase instability in each platform’s oscillator. The phase noise, if uncompensated, may lead to degradation in the SAR data products such as increased sidelobe levels, reduced peak amplitude of the impulse response, and low-frequency phase modulation, among others. In this work, we model and examine the effects of oscillator phase noise on tomographic SAR signals for spaceborne missions flying in formation. A synchronization process is also adopted to help mitigate oscillator phase errors by measuring and predicting relative phase offsets at prescribed temporal intervals. A simulation tool was developed to examine the point target response (PTR) as seen by realistic satellite constellations in low Earth orbit using different quality oscillators, radar configurations, and synchronization configurations. A first analysis of a multiplatform tomographic SAR mission suggests that a system without a dedicated physical link with minimal effects on the PTR may be achievable using current oscillators. Our analysis also shows that phase noise has differing effects on multistatic radar modes. Tomograms formed with a system operating in single-input–multiple-output (SIMO) mode are the most affected by an oscillator phase noise error, followed by multiple-input–multiple-output (MIMO), with negligible effects on the single-input single-output (SAR-SISO) mode. These trade studies and the simulation tool can be used to help inform the design of future multistatic radar missions.

Recent results from CYGNSS measurements over land show the importance of coherent scattering. It is envisioned that future GNSS-R instruments will have the ability to separate and detect coherent scattering and downlink complex-valued coherent DDM measurements. This additional information will allow carrier phase altimetry, the separation of coherent and non-coherent scattered power, and the evaluation of geo-physical phenomena at along-track resolutions 10x greater than current spaceborne instruments, such as TDS-1 or CYGNSS. In this paper, we will present a prospective design for the on-board detection of coherent reflections. We also investigate how to achieve enhanced along-track resolution.

GNSS-R measurements from inland waters and wetlands show strong coherent reflections. These measurements can be utilized to detect the presence of surface water and to measure its extent; however, there are numerous geophysical phenomena may affect the received signal properties, such as wind induced waves and vegetation attenuation of both the electromagnetic signal and the surface water waves. Under-standing the impact of each of these geophysical effects in the inland water scene is useful for developing and assessing the capabilities of GNSS- R retrieval algorithms for detection and monitoring of dynamically changing inland water scenes. This paper proposes a combination of models to characterize these effects on the coherent GNSS reflection to a first order.

Many sensors are suitable for accurate delineation of open water extent, but in vegetated environments, the vegetation canopy can obscure the presence of standing water from detection. Detecting inundation extent in these vegetated environments is especially critical for identifying flooding extent where surface water may exceed flood boundaries and extend into forests surrounding nearby lakes and streams. Regular and timely observations of water surfaces by optical sensors can be impeded by both cloud cover and by vegetation. Here, two microwave techniques for identifying inundation extent will be investigated and compared: L -band global navigation satellite systems reflectometry (GNSS-R) and L - and C -band synthetic aperture radar (SAR); and will confirm that there are correspondences between metrics derived from GNSS reflected signals and L -band SAR to inundated area, including wetlands covered by vegetation.

GNSS-R is a technique that has demonstrated sensitivity to inland water bodies. Observations from CYGNSS can be used to map inland water bodies and extracting information from CYGNSS observations is the subject of many ongoing investigations. While the information in CYGNSS observations is useful, we are exploring methods to leverage the strengths of CYGNSS together with the strengths of other observations. This work is driven by the development of a Bayesian approach for combining synergistic observations together with those from CYGNSS. To support this approach, we developed methods for representing information from CYGNSS observations probabilistically. In this paper, we develop a logistic regression model to estimate surface water probability from CYGNSS observations. Understanding how to use CYGNSS to estimate surface water is the necessary first step in the development of a data fusion approach to surface water mapping. Although this work focuses on utilizing the GNSS-R data from CYGNSS, the data fusion approach we develop will serve as the preparatory framework for utilization of all GNSS-R constellations in hydrological data fusion in the future.

Recent results from CYGNSS have highlighted the importance of coherent GNSS reflections for measuring and mapping surface water. However, the GNSS-R instrument aboard CYGNSS was intended for measuring diffuse scattering from the ocean, and the way in which it processes measurements is not optimal for coherent reflections. The goal of this work is to review recent investigations into how on-board algorithms in future instruments can take full advantage of coherent GNSS-R measurements, especially with how they apply to observation of inland water. It is understood that the proper utilization of coherent reflections will open the door to a number of new and interesting science applications for GNSS-R. Several algorithms are proposed.

Distributed Aperture Radar Tomographic Sensors (DARTS) is a mission concept being studied at the NASA Jet Propulsion Laboratory in collaboration with the California Institute of Technology to enable global and repeated imaging of surface topography and three-dimensional vegetation structure using single-pass tomographic SAR technique. The observing system consists of a distributed formation of multiple small synthetic aperture radar platforms deployed in space with variable distances to achieve look angle diversity and sensitivity to the vertical distribution of vegetation components. Our goal is to identify the optimal system configuration starting from documented community needs and mature the critical technologies that lead to a viable implementation of DARTS. Here, we provide an overview of DARTS and describe our approach for designing and demonstrating single-pass SAR tomographic systems as part of an on-going funded NASA Instrument Incubator Program effort.

Spaceborne GNSS reflectometry allows for the production of maps of rivers, wetlands and inundations using a significant change in the reflected signal while the ground track transects those inland water basins. We performed modeling of both coherent and non-coherent DDMs from Okeechobee Lake in Florida for overpasses by CYGNSS observatories under various wind conditions. To obtain the same level of quantitative matchup one needs to scale the modeled SNR curve by several dBs. Modeled results showed a good qualitative matchup with CYGNSS data over lake's open water. Potentially, vegetation of wetlands in the western part of Okeechobee Lake can attenuate the DDM coherent component. Water covered by vegetation and open water might produce comparable reflected powers, however, due to different mechanisms. The level of the reflected signal from rough open water is governed by both the wind speed and wind direction, representing confounding variables for determining vegetation height and boundaries in wetlands. In this paper we demonstrate implicating effects of the wind generated roughness of open water, while demonstrating modeling work to examine the effect of wetlands vegetation on the GNSS reflected signal is in progress.

An algorithm for detecting coherence in Cyclone Global Navigation Satellite System (CYGNSS) mission delayDoppler maps (DDMs) is presented. Because CYGNSS DDMs report only the observed power without phase information, the algorithm uses estimates of power "spread" within the DDM to flag coherency. Since the estimate used is a ratio of the powers in differing portions of the DDM, it is less sensitive to absolute power calibration and to the GPS C/A code type observed, and is applied to CYGNSS Level-1 uncalibrated DDMs. The basic detector formulation is described along with modifications to improve performance in lower signal-to-noise ratio (SNR) situations. The required detection thresholds are determined using matchups with CYGNSS "Raw I/F" mode measurements for which the DDM phase can be computed and used to identify coherence more precisely. Application of the final detector over a large CYGNSS data set suggests that approximately 8.9% of all inland returns are coherent. Inland regions persistently identified as coherent were found largely to be associated with the presence of water bodies. A smaller set of desert locations apparently having very low surface roughness were also found to be associated with persistent coherence. The detector was also applied to a set of ocean measurements, with the results showing that persistent coherence is limited to areas with sheltered waters. Ocean tests avoiding such regions indicate that the detector's false-alarm rate is approximately 0.0012% for the detection threshold used.

GNSS Reflectometry (GNSS-R) measurements are very sensitive to the presence of inland waters such as wetlands, floods, rivers and lakes. This paper reviews the basic characteristics of a GNSS-R ‘water detection’ research product, including resolution and temporal sampling of wetlands, and discusses the main known sources of errors. Additionally, a summary of GNSS-R applicability to the study of lakes is provided.

Current GNSS-R instruments, such as those used aboard the CYGNSS and TDS-1 satellites, form delay-Doppler maps (DDMs) using fixed integration schemes. The reflected GNSS signal is coherently integrated with a local replica signal and then non-coherently integrated to form a DDM measurement. For scenes dominated by diffuse scattering (i.e. most land and ocean surfaces at typical incidence angles), this approach works well given the short reflected signal coherence time that does not vary significantly over the typical range of incidence angles. However, the coherence properties of the reflected signal change significantly in some circumstances, such as over inland water bodies, coastal areas, wetlands, and at grazing incidence angles over the ocean and land. In this study, we investigate the possible benefits of a receiver adapting its coherent integration time to the instantaneous properties of the reflected signal. The algorithm proposed uses the complex DDM samples to estimate the coherence time efficiently on-the-fly. These estimations are then used to adapt the coherent integration time in the receiver to form DDM measurements. Example results using raw signal data collected from CYGNSS will be presented.

The accuracy of spaceborne ocean surface altimetry depends on precise knowledge of the altimeter satellite orbit. Previous studies of the potential utility of CYGNSS GNSS Reflectometry (GNSS-R) measurements for ocean altimetry have identified its orbit error as a limiting factor. To address this, a recent firmware upgrade to the CYGNSS satellites has enabled the downlink of additional GPS raw measurements from the navigation receiver and increased numerical precision of open-loop GNSS-R tracking information. In this paper, we present improvements to the accuracy of both the orbit determination and the ocean surface height retrievals as a result of these recent upgrades. JPL's GipsyX software is used to process 170 days of GPS navigation measurements for one of the 8 CYGNSS satellites. Incorporating high fidelity dynamic models and antenna group delay corrections, daily orbit overlaps throughout the period show mean RMS differences of 2.5 cm in height, 5.9 cm in cross-track, and 10 cm in along-track. Applying these new orbits reduced the standard deviation of retrieved sea surface height anomalies from 2.2 m to 1.9 m using 4 second smoothed measurements. Further improvements are expected using new ionospheric corrections and re-tracking methods currently in development.