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.

The sensitivity of remote sensing instruments for measuring inundation extent can vary widely. 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 excess surface water extends into the forests surrounding lakes and streams. In addition, cloud cover can impede timely acquisition of imagery by optical sensors. Here, we examine sensitivity of L-band Global Navigation Satellite Systems Reflectometry (GNSS-R) to flooded conditions relative to the well-known signatures of inundation by L-band SAR, and confirm that there is noticeable sensitivity of GNSS reflected signal to inundated areas, including wetlands covered by vegetation, captured by the strong response of the specular reflection by the underlying water surface.

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.

Global Navigation Satellite System - Reflectometry (GNSSR) techniques have proven successful to retrieve several geophysical parameters such as, ocean wind speed, soil moisture, altimetry, wetland dynamics, snow depth estimations. In this paper, the L2C GPS signals measured from the Soil Moisture Active Passive (SMAP) radar after its malfunction is used to investigate the effect of the vegetation water content on the electromagnetic signal. The SMAP-Reflectometry (SMAP-R) measurements are obtained at V and H polarizations allowing for not only signal-to-noise ratio (SNR) analysis but also for polarimetric ratio (PR) studies.

The CYGNSS constellation was successfully launched on 15 Dec 2016 and has been operating continuously in science data-taking mode since March 2017. Updates will be presented on the mission status, calibration and validation activities for its science data products, and recent scientific applications of the measurements. Those applications include the use of ocean wind measurements to estimate air-sea latent heat flux, the assimilation of wind measurements made near tropical cyclones into hurricane numerical prediction models, the retrieval of soil moisture from the scattering measurements made over land, and the imaging of inland flooding using overland measurements.

The objective of this paper is to model and study the sensitivity of bistatic microwave scattering versus changes in wetland characteristics as observed by a GNSS-R satellite system such as CYGNSS. We develop a simplified scattering model starting from the Water Cloud Model traditionally used in monostatic radar problems. Vegetation is idealized as a cloud of randomly oriented scattering elements over a rough surface representing either soil or water. The bistatic scattering coefficient is modeled as the incoherent sum of soil, water and vegetation scattering weighted by the fraction of each contribution within the CYGNSS footprint. The model is tested against CYGNSS observations across the Everglades National Park for which high-resolution land-cover and water depth maps are available. We show that our simplified model is able to capture to first order the variability of bistatic scattering versus changes in water depth and water fraction. This effort is a step forward towards the development of an effective algorithm to map the dynamic state of tropical wetlands and other regions subject to flooding using CYGNSS measurements.

The CYGNSS constellation of eight smallsats was successfully launched in low Earth orbit on December 15, 2016. Each satellite carries a four channel bistatic radar receiver which measures GPS signals scattered from the Earth surface, from which ocean surface wind speed is determined. The use of a constellation, and the way their orbits are configured relative to one another, enable critical sampling properties. In particular, short time scale physical processes like the rapid intensification phase of a tropical cyclone can be resolved. Results from the 2017 Atlantic hurricane season will be used to demonstrate this.

The NASA Earth Venture Cyclone Global Navigation Satellite System (CYGNSS) is a constellation of eight observatories in a 35° inclination, ~530 km altitude Earth orbit. Each observatory carries a 4-channel bistatic wind scatterometer receiver. Measurements of the ocean surface scattering cross section are converted to 10 meter-referenced wind speed. The mission improves the temporal sampling of winds in tropical cyclones (TCs) with a revisit time of 2.8 hours (median) and 7.2 hours (mean) at all locations between 38 deg North and 38 deg South latitude. Operation at the 1575 MHz GPS L1 frequency permits wind measurements in the TC inner core that are often obscured from other spaceborne remote sensing instruments by intense precipitation in the eye wall and inner rain bands. The potential for improved storm surge forecast skill is examined using simulated CYGNSS science data products for Hurricane Irene. We present and compare ADCIRC 2DDI storm surge hindcasting results of Hurricane Irene using four meteorological forcing scenarios: 1) “True” meteorological data obtained from HWRF reanalysis runs; 2) “Worst-case forecast” using low-resolution NOGAPS forecast wind and pressures; 3) “Best-case forecast” using high-resolution HWRF forecast winds and pressures; and 4) a simulated “CYGNSS forecast” with wind field given by a parameterized model trained using CYGNSS-derived values for the maximum wind speed and radius of maximum winds. The results suggest that the improved temporal resolution of the CYGNSS-derived winds has a positive impact on storm surge modeling predictions.

Calibration algorithms are being developed for the CYclone Global Navigation Satellite System (CYGNSS) mission in anticipation of a late-2016 launch date. Antenna temperature (TA) of oceanic scenes will be used to confirm the relationship between receiver noise temperature and physical temperature-which will drift over time. In this work, we develop an open ocean TA model for CYGNSS to support the L1A calibration process. This model needs to be as simple as possible, while still meeting accuracy requirements. We show that, for purposes of the CYGNSS L1A calibration, it is possible to use a single value of TA = 99.4 K, within the 2 K accuracy requirement.

Oceanic remote sensing of rain rate and surface wind speed is possible using low frequency passive microwave sensors on both space-based and aircraft-based platforms. The particular observing geometries of these sensors allows for simplifying assumptions about the rain in the field of view - assumptions that are not possible for the Hurricane Imaging Radiometer (HIRad). HIRad's unique observing capabilities are such that a single atmospheric column can affect the observations at multiple cross-track positions. This motivated the development of the Coupled Pixel Model (CPM) atmospheric retrieval algorithm. This newly developed retrieval algorithm performance is limited by beam averaging. With increasing earth incidence angle (EIA), it becomes more difficult to deconvolve distinct neighboring rain bands.

The Hurricane Imaging Radiometer (HIRAD) is an airborne passive microwave radiometer designed to provide high resolution, wide swath imagery of surface wind speed in tropical cyclones from a low profile planar antenna with no mechanical scanning. Wind speed and rain rate images from HIRAD's first field campaign (GRIP, 2010) are presented here followed, by a discussion on the performance of the newly installed thermal control system during the 2012 HS3 campaign. The paper ends with a discussion on the next generation dual polarization HIRAD antenna (already designed) for a future system capable of measuring wind direction as well as wind speed.