In traditional GNSS applications, signals arriving at a receiver's antenna from nearby reflecting surfaces (multipath) interfere with the signals received directly from the satellites which can often result in a reduction of positioning accuracy. About two decades ago researchers produced an idea to use reflected GNSS signals for remote-sensing applications. In this new concept a GNSS transmitter together with a receiver capable of processing GNSS scattered signals of opportunity becomes bistatic radar. By properly processing the scattered signal, this system can be configured either as an altimeter, or a scatterometer allowing us to estimate such characteristics of land or ocean surface as height, roughness, or dielectric properties of the underlying media. From there, using various methods the geophysical parameters can be estimated such as mesoscale ocean topography, ocean surface winds, soil moisture, vegetation, snowpack, and sea ice. Depending on the platform of the GNSS receiver (stationary, airborne, or spaceborne), the capabilities of this technique and specific methods for processing of the reflected signals may vary. In this tutorial, we describe this new remotesensing technique, discuss some of the interesting results that have been already obtained, and give an overview of current and planned spacecraft missions.

The Cyclone Global Navigation Satellite System (CYGNSS) is a spaceborne mission concept focused on tropical cyclone (TC) inner core process studies. CYGNSS attempts to resolve the principle deficiencies with current TC intensity forecasts, which lies in inadequate observations and modeling of the inner core. CYGNSS consists of 8 GPS bistatic radar receivers deployed on separate nanosatellites. The primary science driver is rapid sampling of ocean surface winds in the inner core of tropical cyclones.

Measurements of near surface wind speed made by the Cyclone Global Navigation Satellite System (CYGNSS) constellation of GNSS-R satellites are evaluated and their uncertainty is assessed in two ways. A bottom-up assessment begins with a model for the error in engineering measurements and propagates that error through the wind speed retrieval algorithm analytically. A top-down assessment performs a statistical comparison between CYGNSS measurements and coincident “ground truth” measurements of wind speed. Results of the two approaches are compared. Overall performance, as determined by the top-down method, is decomposed using the bottom-up approach into its contributing sources of error. Overall root mean square (RMS) uncertainty in the CYGNSS retrievals is 1.4 m/s at wind speeds below 20 m/s. At higher wind speeds, an increase in the retrieval error is primarily caused by a decrease in the sensitivity of the ocean scattering cross section to changes in wind speed. In tropical cyclones, retrieval errors are compounded by unaccounted departures from a fully developed sea state. Overall RMS uncertainty in the CYGNSS retrievals is 17% at wind speeds above 20 m/s.

The calibration algorithm used by the Cyclone Global Navigation Satellite System (CYGNSS) mission to produce version 2.1 of its Level 1 (L1) science data products is described. Changes and improvements have been made to the algorithm, relative to earlier versions, based on the first year of on-orbit result. The L1 calibration consists of two parts: first, the Level 1a (L1a) calibration converts the raw Level 0 delay Doppler maps (DDMs) of processed counts into received power in units of watts. Second, the L1a DDMs are then converted to Level 1b DDMs of bistatic radar cross section values by unwrapping the forward scattering model, which are then normalized by the surface scattering area to arrive an observation of σ ₀ . An update to the bottom up term-by-term error analysis is also presented, using on-orbit results to better quantify the accuracy of the rolled-up L1 calibration. The error analysis considers uncertainties in all known input calibration parameters. Finally, a method for calibrating the time delay of CYGNSS measurements is presented.

The Cyclone Global Navigation Satellite System (CYGNSS) uses a bistatic radar configuration with the Global Positioning System (GPS) constellation as the active sources and the CYGNSS satellites as the passive receivers. The GPS effective isotropic radiated power (EIRP), defined as the product of transmit power and antenna gain pattern, is of great importance to the accurate Level 1B calibration of the CYGNSS mission. To address the uncertainties in EIRP, a ground-based GPS constellation power monitor (GCPM) system has been built to accurately and precisely measure the direct GPS signals. A PID thermal controller successfully stabilizes the system temperature over the long term. Radiometric calibration is performed to determine the system dynamic range and to calibrate GCPM gain. Single PRN calibration using a GPS signal simulator is used to compute the scale factor to convert the received counts into power in watts. The GCPM received power is highly repeatable and has been verified with DLR/GSOC's independent measurements. The transmit power (L1 C/A) of the full GPS constellation is estimated using an optimal search algorithm. Updated values for transmit power have been successfully applied to CYGNSS L1B calibration and found to significantly reduce the PRN dependence of CYGNSS L1 and L2 data products.

This paper presents the processing algorithms for geolocating and calibration of the Cyclone Global Navigation Satellite System (CYGNSS) level 1 land data products, as well as analysis of the spatial resolution of Global Navigation Satellite System Reflectometry (GNSS-R) coherent reflections. Accurate and robust geolocation and calibration of GNSS-R land observations are necessary first steps that enable subsequent geophysical parameter retrievals. The geolocation algorithm starts with an initial specular point location on the Earth’s surface, predicted by modeling the Earth as a smooth ellipsoid (the WGS84 representation) and using the known transmitting and receiving satellite locations. Information on terrain topography is then compiled from the Shuttle Radar Topography Mission (SRTM) generated Digital Elevation Map (DEM) to generate a grid of local surface points surrounding the initial specular point location. The delay and Doppler values for each point in the local grid are computed with respect to the empirically observed location of the Delay Doppler Map (DDM) signal peak. This is combined with local incident and reflection angles across the surface using SRTM estimated terrain heights. The final geolocation confidence is estimated by assessing the agreement of the three geolocation criteria at the estimated surface specular point on the local grid, including: the delay and Doppler values are in agreement with the CYGNSS observed signal peak and the incident and reflection angles are suitable for specular reflection. The resulting geolocation algorithm is first demonstrated using an example GNSS-R reflection track that passes over a variety of terrain conditions. It is then analyzed using a larger set of CYGNSS data to obtain an assessment of geolocation confidence over a wide range of land surface conditions. Following, an algorithm for calibrating land reflected signals is presented that considers the possibility of both coherent and incoherent scattering from land surfaces. Methods for computing both the bistatic radar cross section (BRCS, for incoherent returns) and the surface reflectivity (for coherent returns) are presented. a flag for classifying returns as coherent or incoherent developed in a related paper is recommended for use in selecting whether the BRCS or reflectivity should be used in further analyses for a specific DDM. Finally, a study of the achievable surface feature detection resolution when coherent reflections occur is performed by examining a series of CYGNSS coherent reflections across an example river. Ancillary information on river widths is compared to the observed CYGNSS coherent observations to evaluate the achievable surface feature detection resolution as a function of the DDM non-coherent integration interval.