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..

Globally, wetlands are a critical habitat of numerous plants and animal species and play a major role in maintaining Earth’s biodiversity. Wetlands ecosystems are particularly challenging to characterize and monitor because many tend to be in remote regions that are difficult to access. As optical satellite remote sensing instruments observe only the top of the canopy, they are limited in they cannot directly observe surface inundation beneath vegetation canopies. The NISAR mission is tasked with mapping surface inundation in wetlands. NISAR’s unique capability enables consistent and reliable observations of wetlands inundation extent and dynamics. This capability supports characterization of changes in wetlands systems driven by a changing climate and will support a better understanding of the impacts to carbon fluxes, ecosystems services and biodiversity in these critical systems.Focusing on our efforts in Pacaya Samiria Nation Reserve, Peru, we provide an overview of NISAR’s capability to study wetlands ecosystems. Pacaya Samiria has been selected to be the NISAR calibration-validation site for tropical wetlands. Here, we have established a network of in situ sensors for validation of inundation state and extent in a complex tropical wetlands environment. Our sensor network is supported by ancillary measurements of vegetation structure drawn from drone and ground LiDAR and photogrammetry. We also describe the calibration validation approach that will be implemented as part of supporting the NISAR objectives, and we present preliminary results using ALOS PALSAR-2 data and possibly first light images from NISAR.

Flooding is one of the deadliest and costliest natural disasters. Climate change-induced flooding events are increasing worldwide, disproportionately impacting low-income and developing communities. While early warning systems save lives, satellite-based observation systems are vital for the disaster relief and recovery phases. Current satellite-based operational flood products are largely based on either optical remote-sensing methods, which exhibit a limited ability to detect water through clouds and vegetation, or microwave remote sensing, which provides relatively low spatial and temporal resolution. New small satellite constellations using radar or GNSS reflectometry (GNSS-R) have been shown to enhance our ability to overcome these deficiencies. In this work, we quantify the performance of using GNSS-R measurements from the NASA Cyclone Global Navigation Satellite Systems (CYGNSS) satellite constellation to map surface water in South Sudan and the Sudd wetland in comparison with a set of representative operational products. We make quantitative comparisons of our results with operational flood products based on Visible Infrared Imaging Radiometer Suite (VIIRS) and MODIS and with C-band Sentinel-1 synthetic aperture radar. We find that our method detects 35.4% more surface water than Sentinel-1, while the VIIRS- and MODIS-based products underestimate by 4.8% and 83.7%, respectively. We use several metrics commonly used to evaluate classification performance: precision, true positive rate (TPR), true negative rate (TNR), F2-score, and the Matthews correlation coefficient (MCC) and assess the comparisons in this statistical framework. We discuss the consequences of our findings, including ways CYGNSS data may enhance current flood products and assist decision-makers and emergency managers.

When GNSS signals reflect off of the surfaces of lakes, rivers, wetlands, and other inland water bodies, the surfaces are often sufficiently smooth to produce coherent reflections. The observable produced from coherent reflections made by GNSS Reflectometry (GNSS-R) instruments exhibits particular features with respect to diffusely scattered signals by rough land and wind-driven oceans allowing detection of such smooth bodies. Several different GNSS-R coherence detection approaches have been reported in the literature and developed among the GNSS-R community over the last several years; however, the merits of each approach are difficult to compare because they are often applied to different scenarios and quantified in different ways, independently of each other. This paper provides a unified comparison of a wide variety of different GNSS-R coherence detection approaches, which is the most extensive published to date. The approaches are applied to a common data set from the NASA CYGNSS satellites that includes both the standard Level-1 DDM science product as well as raw baseband signal recordings. Additionally, simulated observables are generated with varying coherent and non-coherent reflection components to exercise algorithms over a wide range of SNRs and relative powers. Objective measures of accuracy are used to quantify the performance of each approach in the context of relative implementation complexity. Conclusions are presented on the pros/cons of the various methods as they relate to various applications such as real-time in-orbit coherence detection or post-processing on the ground.

As climate change-induced global flooding increases in both frequency and magnitude, having accurate and timely flood maps becomes essential for humanitarian and future flood mitigation efforts. The Dartmouth Flood Observatory (DFO) aids humanitarian organizations and inundation observation research efforts through its archive of historical flood events extending back through 1985, as well as by providing current daily flood maps derived from a combination of observations, and also precipitation-based modeling products. Both could benefit from the addition of microwave observations that penetrate through clouds, rain, and vegetation, such as GNSS-R data now becoming available on a daily basis. In this work, we discuss a current collaboration to combine CYGNSS data with operational MODIS flood maps and evaluate the expected benefits for an example scenario over the recent anomalous flooding in South Sudan.

While CYGNSS exhibits a high revisit rate, the sparse, quasi-random tracks make it challenging to utilize data at short timescales for inundation mapping. In order to make use of the high revisit rate of CYGNSS, we implement a model-driven approach in which we compare simulated CYGNSS measurements over a binary water mask with actual CYGNSS measurements. We consider a simple water body like a reser-voir that has limited or no vegetation and exhibits dynamic changes in surface water extent within the CYGNSS mission timeframe. We find that our method has the potential to reliably detect changes in surface water extent down to 4 sq. km., corresponding to a 3% increase. Using simple, non-physics-based flood model outputs as inputs to the End-to-End Simulator (E2ES) can lead to the ability to estimate surface water extent over a given area using sparse CYGNSS measurements on sub-weekly timescales.

Quantitative observations of dynamic changes in water extent and depth of the world's wetlands are currently limited by traditional remote sensing methods, which have difficulty observing surface water beneath dense vegetation and clouds. A novel remote sensing technique known as GNSS Reflectometry (GNSS-R) has shown great potential in the detection of terrestrial surface water beneath vegetation. The Cyclone Global Navigation Satellite System (CYGNSS) is a GNSS-R small satellite constellation that exhibits sub-daily revisit rates over tropical wetlands. In this work, we present a retrieval algorithm to predict water depth and surface water extent using CYGNSS observations over the Everglades. We test our algorithm over three regional approaches and varying smoothing filters. Results indicate that CYGNSS signal-to-noise ratio (SNR) is highly correlated with both water depth and extent over shallow, vegetated water.

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.

The Cyclone Global Navigation Satellite System (CYGNSS) has recently shown exciting potential for GNSS reflectometry (GNSS-R) to resolve small-scale and dynamic hydrological features over land (such as rivers, lakes, wetlands, and urban flooding), even when obstructed by dense vegetation. Since CYGNSS is a small satellite constellation, sub-daily measurement frequencies provide a unique opportunity to observe short timescale changes. However, since CYGNSS observations occur in sparse, quasi-random tracks, it is more difficult to understand the true observability of events as compared to imaging instruments. Changes in SNR that would indicate a dynamic change in the scene are confounded by inherent variability due to other sources, including vegetation, geometry changes, instrument gain calibration, and surface roughness due to wind. While the literature does detail the use of CYGNSS measurements to map surface water changes, the lack of valid ground data makes it difficult to quantify the true accuracy. Here, we present results from a simulation study currently underway that utilizes a GNSS-R coherent scattering model to understand the observability and accuracy of CYGNSS measurements of dynamic changes in inland water bodies. The goal is to quantify the sensitivity and resolution of observations of dynamic spatial and temporal variations of inland water bodies.

Spaceborne GNSS reflectometry has shown ability to observe the global inland water distribution. It allows generating dynamic maps of rivers, wetlands and inundations using the large variation in the received power while the ground track crosses those objects. Here, we analyze the change of the reflected signal's power and coherence due to surface roughness and cover (ice) observed in the CYGNSS delay-Doppler maps for Qinghai Lake, China. It is shown that a significant attenuation of the reflected coherent signal and emergence of the diffuse component occurs due to wind-generated surface waves. The comparable attenuation effect of the coherent component is observed in the case of the frozen lake. It takes place due to the lower value of the average Fresnel reflection coefficient of ice. A similar effect may occur for reflections from wetlands, when open water and water covered by vegetation could produce comparable reflected powers, however, due to different mechanisms. Understanding the relative scattering characteristics of wetlands in contrast with rougher bodies such as lakes is important for the development of algorithms to detect their dynamic changes.