The development of soil moisture retrieval algorithms for the upcoming NASA/ISRO SAR mission motivates investigations into soil surface scattering effects and their influence on soil moisture retrievals. To better understand the conditions that impact retrieval performance, an investigation is performed into the second-order solution small slope approximation (SSA2) for rough surface backscattering. Scattering amplitudes are calculated for varying soil moisture and surface roughness conditions and compared to the first-order solution. The results show the ability of the second-order solution to model cross-pol scattering and help pinpoint physical conditions that may influence soil moisture retrievals. Overall, the results suggest that the first-order SSA solution should be applicable under the physical conditions where NISAR soil moisture retrievals are expected to be performed.

The use of a “time-series ratio” soil moisture retrieval approach is under consideration for the upcoming NISAR mission’s soil moisture product. As such, it is of interest to characterize the algorithm’s anticipated error budget as part of pre-launch activities. This paper develops an error model to estimate retrieval errors for the proposed algorithm. The model accounts for error contributions from speckle and thermal noise as well as uncertainties that arise as part of the retrieval process. Spatial and temporal behaviors of the retrieval errors are determined using a SMAP-based soil moisture climatology. The results show that soil moisture retrieval errors from the time-series ratio method meet the 0.06 m3/m3unbiased root mean square error (URMSE) performance metric established for the NISAR soil moisture product.

The National Aeronautics and Space Administration (NASA) - Indian Space Research Organization (ISRO) Synthetic Aperture Radar (NISAR) mission plan to launch a SAR operating at L- and S-band with a 12-day repeat frequency. A global soil moisture product at 200 m spatial resolution derived from 200 m NISAR radar measurements is currently under development. Although several retrieval algorithms are being investigated, this paper focuses on a “time series ratio” retrieval approach. In order to understand and assess the performance of this algorithm, an error model has been developed and is reported in this paper. The model is applied to examine errors as a function of the instrument characteristics and for a given location. Initial progress in including vegetation effects and in predicting errors as a function of spatial location is also described.

L- and S-band observations from NASA's Passive/Active L/S band (PALS) sensor from the SMEX02 campaign were used to estimate soil moisture. The retrieval process is based on the “alpha approximation” method. This method utilizes a time-series of normalized radar backscatter measurements as well as ancillary information to estimate soil moisture over the Walnut Creek watershed. The resulting retrieved soil moistures are compared to in-situ soil moisture measurements at multiple test sites within the watershed. The calculations show reasonable results for both L- and S-band and provide further insight into the use of L- and S-bands for the upcoming NASA/ISRO mission.

The NASA ISRO Synthetic Aperture Radar (NISAR) mission is currently under development and will provide global L-band radar observations that will be helpful for various soil moisture applications. The final NISAR soil moisture product will have 200m spatial resolution with 12-day exact revisit time. A time-series ratio algorithm was implemented using NISAR simulated UAVSAR data collected during the SMAPVEX12 field experiment. In this paper, the performance of the time series ratio algorithm was assessed using in situ observations. Performance of the soil moisture retrieval algorithm was also assessed for dual polarization and quad-polarization observations modes.

A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise$\sigma ^{o}$bias correction method. This method makes use of both ancillary data from numerical weather prediction models and a semiempirical geophysical model function. Care is taken, so the track-wise$\sigma ^{o}$bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term$\sigma ^{o}$downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected$\sigma ^{o}$measurements, a wind retrieval method is also presented and its product thoroughly assessed for a three-year period against European Centre for Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Advanced Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane weather research and forecasting (HWRF) model, and the stepped frequency microwave radiometer (SFMR) winds. The overall wind speed bias and standard deviation of the error (stde) against ECMWF are 0.16 and 1.19 m/s, while these are −0.11 and 1.12 m/s against ASCAT A/B, respectively. The same metrics against AMSR-2/GMI/WindSat (combined) are −0.19 and 1.11 m/s, respectively. The bias and stde against soil moisture active passive (SMAP) are −0.38 and 1.90 m/s, respectively. In the tropical cyclone environment, the bias and stde against HWRF are −0.54 and 2.90 m/s, and −4.71 and 5.88 m/s with SFMR. Finally, CyGNSS wind performance is gauged in the presence of rain. Below 10 m/s, the bias between CyGNSS and ECMWF increases as the rain rate increases. Between 10 and 15 m/s, biases are mostly absent. Above 15 m/s, results are inconclusive due to the low number of collocated rain samples. Overall, the presented CyGNSS wind speed product both exhibits consistency and reliability, showing promise of using GNSS-R derived winds for operational purposes.

S-band observations from NASA's Passive/Active L/S Band (PALS) radar from the SMEX02 campaign were used to estimate soil moisture. The “alpha” method is applied for this process, in which a time series of ratios of normalized radar cross section values at successive measurements is used to infer the corresponding soil moisture time series given ancillary information on the minimum and maximum soil moisture values expected over the time series. Results are examined as a function of the polarization and crop type. The results show reasonable retrieval performance, indicating the potential of using S-band observations from the future NASA/ISRO SAR (NISAR) mission.

The NASA ISRO Synthetic Aperture Radar (NISAR) mission is currently under development and is scheduled for launch in 2022. The NISAR mission will provide global data sets of Earth land surface dynamics that are critical for multiple Earth Science disciplines including observations of ecosystem carbon and water cycles. Global L-band radar observations at high spatial resolution will be helpful for soil moisture applications. One of the goals of the NISAR mission is to provide a global soil moisture product at 200 m resolution with a global revisit frequency of 6 days. A time-series ratio algorithm was implemented using NISAR simulated SMAPVEX12 UAVSAR data, which is an L-band airborne radar backscatter measurement. For a NISAR-like configuration, backscatter at incidence angles from 30 to 50 degrees was considered in this study. The initial retrieval statistics following comparisons with in-situ ground truth show correlation coefficients (R) to be about 0.81, and the unbiased RMSE to be about 0.06 m3/m3. Results from dual co-polarization and/or cross-polarization modes were evaluated and considered for performance improvement.

NASA's CYclone Global Navigation Satellite System (CYGNSS) mission operates a constellation of 8 small satellites that provide ocean wind speed measurements with dense spatial coverage and a median revisit time of about 4 hours. This paper investigates the possibility of using the “rapid revisit” characteristics of CYGNSS measurements to create a detector of convective activity by examining the variability of CYGNSS wind speeds over small intervals in space and time.

In November 2019, the Ocean Surface Winds Team at NOAA-NESDIS-STAR released to the public their first version of sea surface winds for the Cyclone Global Navigation Satellite System (v1.0). The reported winds are provided on a 25km grid along each track. A boxcar averaging step is included in order to remove unwanted noise in the signal. Calibration issues are greatly reduced by the use of a track-wise bias correction applied to the normalized bi-static radar cross section. The wind speed is then retrieved on a track-by-track basis where both daily global and storm centered images are provided to the public at https://manati.star.nesdis.noaa.gov/datasets/CYGNSSData.php. For in-depth analyses, NetCDF files are also made available (access instruction provided on the aforementioned link). A comparison with collocated NOAA ASCAT A/B 25km wind product, between July and October 2018, resulted in a -0.13 m/s bias and a 1.23 m/s standard deviation.

In this paper we develop high wind portion of a Geophysical Model Function (GMF) for Scatsat-1 scatterometer measurements. Starting with NSCAT4 GMF the high wind portion has been modifying by utilizing measurements obtained by IWRAP instrument on board of NOAA P3 aircraft within tropical and extratropical cyclones. The Ao coefficient in NSCAT4 GMF was modeled so its slope is a linear function of a logarithm of wind speed and its first derivative of it equals to zero at saturation wind speed for particular incidence angle and measurements frequency as it was measured by IWRAP. The calibrated measured sigma0's from Scatsat-1 shows good agreement with the new GMF named NSCAT4.H at the high winds. The respective wind retrievals are higher and much better aligned with ASCAT high winds while the global winds statistic performance of ScatSat-1 winds remained unchanged. The new ScatSat-1 wind products produced by NSCAT4.H GMF has been implemented in NOAA's operational marine forecasting and warning applications and examples are presented here.

The Cyclone Global Navigation Satellite System (CYGNSS) mission provides ocean wind speed measurements using GNSS reflectometry observations from eight space-borne receivers. The various CYGNSS wind products including conventional CYGNSS L2 winds, NOAA L2 winds, and TES (Trailing Edge Slope) winds are currently available. These wind products were compared to the ECMWF model winds using 8 hours data on June 8, 2018. The initial results showed that all retrievals showed some correlations with the ECMWF model winds, but the bias and the standard deviation are quite different among the methods. Comparison of three products for larger data set will be evaluated and presented in detail.

In this paper, we present the SCATSAT-1 wind data processor developed by NOAA. The sigma0 from L1B produced by ISRO was used as an input to our processor. Ocean surface wind vector products are produced at the grid resolutions of 12.5 km and 25 km. We experimented with different objective functions and the number of solutions. The ambiguity removal method utilized in our processor is the Two-Dimensional Variational Ambiguity Removal (2DVAR). The rain flag algorithm was developed based on the Bayes' theorem by calculating the rain probability given the rain sensitive parameter threshold.Finally, we validated our Scatsat-1 wind retrievals by both statistical analyses and visual inspection of the wind field for meteorological consistency to determine which objective function produced the best results. The performance of the Scatsat-1 wind retrievals compared to the Global Data Assimilation System (GDAS) winds shows reasonable wind speed and wind direction biases and standard deviation differences.

A modeling study investigating the influence of wind direction on spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) near specular observations of the sea surface is reported. Because the delay doppler maps (DDMs) measured in GNSS-R include some non-specular contributions even for “specular” portions of the DDM, the study performs an examination of near specular DDM variations with wind direction using the widely used Geometrical Optics approximation of surface scattering for a surface described with the non-Gaussian Cox-Munk slope probability density function. Variations with wind direction of the normalized radar cross section (NRCS) mapped onto the surface are examined, and it is shown that these variations are negligible for surface portions contributing to the near specular portion of the DDM.

The Cyclone Global Navigation Satellite System (CYGNSS) mission launched Dec. 2016 and will provide ocean surface wind speed measurements from an eight satellite constellation using GPS reflectometry observations. The nature of CYGNSS coverage results in some locations on Earth experiencing multiple wind speed measurements within a short period of time (a “clump” of observations in time resulting in a “rapid revisit” series of measurements). Such observations could seemingly provide indications of regions experiencing rapid changes in wind speeds, and therefore be of scientific utility. Simulation studies of these measurements have been conducted including an analysis of the statistics of the duration of a “clump” and the maximum clump durations. The results show that the duration of a “clump” can extend as long as a few hours at higher latitudes, with gaps between clumps ranging from 6 to as high as 12 hours depending on latitude. The conference presentation will report initial results from “rapid revisit” analyses using on-orbit CYGNSS measurements.

A study of the polarimetric properties of near specular scattered fields from rough surfaces is reported using a circular polarization basis. Such studies are relevant for Global Navigation Satellite System- Reflectometry (GNSS-R) remote sensing of the sea surface. Particular emphasis in the study is placed on the use of field correlations for the remote sensing of wind direction over the sea surface.

The use of GNSS-R (Global Navigation Satellite System Reflectometry) for Earth remote sensing is becoming an increasingly attractive approach thanks to its inexpensive and passive method. While GNSS-R ocean altimetry has been studied extensively, previous studies have focused on the use of the delay waveform (DW) only in the retrieval of sea surface height. This paper presents sea surface height retrievals using a “full Delay-Doppler Map (DDM)” method, and applies the approach to measurements of TechDemoSat-1 (TDS-1), a recent space-borne mission. The End-to-End Simulator (E2ES) of GNSS-R waveforms developed for the CYGNSS mission is adapted for use with TDS-1 and applied as the forward model used in the retrieval process. Comparisons between measured and modeled DDMs have been conducted as a first step to validate this process. Retrievals of sea surface height using both the DW and full-DDM methods will be reported in the presentation. Potential methods for improving estimation error for future GNSS-R missions will also be described in the presentation.