Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis

Given the importance of soil moisture for hydrological applications, such as weather and flood forecasting, passive microwave remote sensing is a promising approach for retrieving soil moisture due to its high sensitivity to near-surface soil moisture, applicability to all weather conditions, direct relationship with the soil dielectric constant, and reduced effects from vegetation and roughness. However, passive microwave (radiometer) observations suffer from being relatively low spatial resolution, on the order of 36 km. It is proposed that this scale issue may be overcome by using active microwave (radar) observations, which have much higher resolution when using Synthetic Aperture Radar (SAR) techniques (<3km), and this is the approach being taken by NASA's Soil Moisture Active Passive (SMAP) mission, with a scheduled launch in late 2014. The rationale behind SMAP is to use the synergy between active and passive observations in a downscaling approach to overcome the individual limitations of each observation type, and ultimately provide a merged soil moisture data set at intermediate resolution (~9 km). The objective of this study is to test the proposed baseline downscaling approach for the SMAP mission using airborne data, thus assessing its viability for future application to SMAP data. The approach is based on the hypothesis of a near-linear relationship between radiometer brightness temperature (Tb) and SAR backscatter (σ), and has thus far received very limited testing. The experimental dataset used in this study was collected during the Soil Moisture Active Passive Experiment (SMAPEx) field campaigns over a study site in south-eastern Australia approximately 38km × 36km in size, equivalent to a SMAP radiometer pixel. This research focuses on the brightness temperature downscaling algorithm; according to the SMAP Algorithm Theoretical Basis Documents these downscaled brightness temperatures will subsequently be converted to soil moisture at fine resolution through the traditional passive microwave retrieval algorithm. The baseline downscaling algorithm was applied to high resolution data from SMAPEx, which include 1km resolution brightness temperature collected by the Polarimetric L-band Multibeam Radiometer (PLMR) and ~ 10m resolution backscatters collected by the Polarimetric L-band Imaging Synthetic aperture radar (PLIS). To minimize noise in the radar data and to approximate the SMAP radiometer/radar pixel ratios (36km Tb to 9km resolution using 3km σ) the PLIS data were aggregated to 250m resolution, so as to downscale 1km Tb to 250m resolution, thus keeping the same ratio of radiometer/SAR spatial resolution as the SMAP mission. Results showed that the Root-Mean-Square Error (RMSE) in Tb downscaled at 100m resolution was around 10K at h-polarization and 8K at v-polarization over a cropping area. This RMSE was reduced to 9K and 7K respectively when downscaling to 250m resolution, due to a decreased spatial heterogeneity during averaging. It was also noted that results at v-polarization were slightly better than those at h-polarization, since the backscatter is more linearly related to Tb at v-polarization than Tb at h-polarization. The accuracy of the downscaling over grassland sites was improved by approximately 3K with respect to the cropping area. This was attributed to the more heterogeneous conditions in cropping areas, compared to the relatively uniform conditions in the grassland area. However, one limitation of this study was the availability of only three days of data for estimating the linearity between radar and radiometer observations.

Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission augmented with a physical surface scattering model were analyzed to develop a semiempirical model, which consists of three main modeling components for soil moisture, vegetation, and surface roughness. CYGNSS data collected from March 2017 to March 2020 were collocated with the soil moisture data from the Soil Moisture Active Passive (SMAP) mission and climatology vegetation water content (VWC) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. The matchup data were binned as a function of soil moisture, VWC, and incidence angle. The CYGNSS data were calibrated using a coherent reflection equation to obtain an effective reflectivity. The response of CYGNSS effective reflectivity to soil moisture changes is consistent with the change of the Fresnel reflectivity based on Mironov’s soil dielectric constant model used by the SMAP and Soil Moisture Ocean Salinity (SMOS) missions for soil moisture retrieval. The CYGNSS effective reflectivity decreases approximately linearly (in dB) with respect to the NDVI-VWC. The estimated values of vegetation attenuation parameter ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> ) agree with values published in the literature and are corroborated with the estimated values of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> using the SMAP dual-polarized channel algorithm based on land cover types. A CYGNSS surface scattering map has been derived and reveals a mixed contribution of coherent and incoherent scattering effects and the effects of topography. The semiempirical model, leveraging two of the key modeling functions used by microwave radiometry, will pave the way for a synergistic use of reflectometry and radiometry data for multiparameter retrieval and development of consistent soil moisture products.

A dual-copolarization algorithm to classify inland open water bodies free of flooded vegetation using an L-band radar is presented and evaluated, with a view to applying the method to the Soil Moisture Active Passive (SMAP) mission for hydrological science and soil moisture retrieval applications. Past radar-based water body detection algorithms have applied a threshold to a single-polarization measurement, with water body detection declared if the observed cross section is less than the specified threshold. However, such methods are subject to ambiguities associated with scene variability and terrain slopes, making a universal threshold value difficult to derive and complicating the global application of such methods. Because SMAP will provide measurements in both HH and VV polarizations, the copolarization ratio is also available for water body detection. A threshold of −3 dB applied to the HH/VV polarization ratio is found effective in detecting water bodies at 40° incidence angle based on analysis of theoretical model predictions and measurements from airborne synthetic aperture radar and the spaceborne Aquarius scatterometer. When the water surface is calm and its radar response is very small (i.e., at the radar thermal noise level), the HH/VV ratio method fails. However, a combination of an HH/VV threshold (at −3 dB) and an HH threshold (at −25 dB) is shown to allow water body classification even in this situation. This proposed “combined” algorithm is assessed in four different geophysical scenarios. The resulting water body detection error is shown to be less than 10% for these cases, which satisfies SMAP requirements to allow accurate soil moisture retrieval, and the corresponding false alarm rate is smaller than 2%. The robustness of the proposed approach to subpixel heterogeneity has been also investigated. The performance of the algorithm remains sensitive to the noise level of the radar observations: for SMAP, a radar noise-equivalent sigma0 of −28.5 dB or less is required in order to facilitate acceptable performance.

On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.

The Soil Moisture Active Passive (SMAP) mission is one of the first-tier projects recommended by the U.S. National Research Council Committee on Earth Science and Applications from Space. The SMAP mission is in formulation phase and it is scheduled for launch in 2014. The SMAP mission is designed to produce high-resolution and accurate global mapping of soil moisture and its freeze/thaw state using an instrument architecture that incorporates an L-band (1.26 GHz) radar and an L-band (1.41 GHz) radiometer. The simultaneous radar and radiometer measurements will be combined to derive global soil moisture mapping at 9 [km] resolution with a 2 to 3 days revisit and 0.04 [cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> ] (1 sigma) soil water content accuracy. The radar measurements also allow the binary detection of surface freeze/thaw state. The project science goals address in water, energy and carbon cycle science as well as provide improved capabilities in natural hazards applications.

Pan-tropical soil moisture mapping based on a three-layer model from CYGNSS GNSS-R data

Retrieving landscape freeze/thaw state from Soil Moisture Active Passive (SMAP) radar and radiometer measurements

<strong class="journal-contentHeaderColor">Abstract.</strong> Soil moisture (SM) plays a significant role in many natural and anthropogenic systems which are essential to supporting life on Earth. Thus, accurate measurement and assessment of changes in soil moisture globally is of great value, including long-term historical assessment. Since the on-board cycle and detailed parameters of disparate sensors are different, the European Space Agency established the Climate Change Initiative (CCI) program to harmonize the available multisource SM data, producing long time-series surface SM datasets starting from 1978 to the present. However, the Soil Moisture Active Passive (SMAP) mission, launched in 2015, has shown more satisfactory performance in both spatial accuracy and in capturing pattern of temporal changes. In this paper, a random forest (RF) model was proposed to extend the superior SMAP dataset historically (named RF_SMAP), using the corresponding CCI data time-series. We assumed that the temporal changes in the SMAP dataset are similar generally to those in the available CCI dataset. Accordingly, the RF model was constructed using the temporal characteristics extracted from the CCI SM v05.2 data (coupled with three terrain characteristics and two location characteristics), which was migrated to the prediction of the RF_SMAP dataset. The available <em>in-situ</em> SM data and the real SMAP data from April 2015 to April 2016 were used as references to validate the predicted RF_SMAP data. It was shown that compared with the CCI dataset, the predicted RF_SMAP dataset is closer to the <em>in-situ</em> SM data and the real SMAP data. Moreover, the historical RF_SMAP dataset is more accurate than the widely used Global Land Evaporation Amsterdam Model (GLEAM) dataset in terms of average root mean square error (RMSE), bias (Bias), and Kling-Gutpa efficiency (KGE). Thus, the RF_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset, with an unbiased root mean square error (ubRMSE) of 0.035. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring.

Sea surface salinity (SSS) can be measured by L-band (1.4 GHz) radiometry. However, the L-band brightness temperature is sensitive to ocean surface roughness, so that the precise knowledge of sea state can help to improve the accuracy of SSS retrievals. Cyclone Global Navigation Satellite System (CyGNSS) mission measures tropical ocean wind speeds, which can provide further knowledge about sea state. This letter, by investigating the sensitivity of brightness temperature derived from two L-band radiometry satellite missions [i.e., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP)] to CyGNSS data, first explores the potential of using spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) to improve the accuracy of SSS measurements. The statistical results from two-year CyGNSS data show that the systematic uncertainties of the brightness temperature up to 0.5 K in the SMOS/SMAP can be minimized at low wind speed, which proves the concept that CyGNSS wind speed data can be used to improve the accuracy of SSS retrievals across the oceans. The findings strongly suggest that the spaceborne GNSS-R instruments could be utilized as cost-effective hosted payloads in designing future ocean remote sensing missions.

Earth’s changing environment impacts every aspect of life on our planet and climate change has profound implications on society. Studying Earth as a single complex system is essential to understanding the causes and consequences of climate change and other global environmental concerns. NASA’s Earth Science Division (ESD) shapes an interdisciplinary view of Earth, exploring interactions among the atmosphere, oceans, ice sheets, land surface interior, and life itself. This enables scientists to measure global and climate changes and to inform decisions by government, other organizations, and people in the United States and around the world. The data collected and results generated are accessible to other agencies and organizations to improve the products and services they provide, including air quality indices, disaster prediction and response, agricultural yield projections, and aviation safety. ESD’s Flight Program provides the space based observing systems and infrastructure for mission operations and scientific data processing and distribution that support NASA’s Earth science research and modeling activities. The Flight Program currently has 21 operating Earth observing space missions, including the recently launched Global Precipitation Measurement (GPM) mission, the Orbiting Carbon Observatory-2 (OCO-2), the Soil Moisture Active Passive (SMAP) mission, and the International Space Station (ISS) RapidSCAT and Cloud-Aerosol Transport System (CATS) instruments. The ESD has 22 more missions and instruments planned for launch over the next decade. These include first and second tier missions from the 2007 Earth Science Decadal Survey, Climate Continuity missions and selected instruments to assure availability of key climate data sets, operational missions to ensure sustained land imaging provided by the Landsat system, and small-sized competitively selected orbital missions and instrument missions of opportunity belonging to the Earth Venture (EV) Program. Some examples are the NASA-ISRO Synthetic Aperture Radar (NISAR), Surface Water and Ocean Topography (SWOT), ICESat-2, SAGE III on ISS, Gravity Recovery and Climate Experiment Follow On (GRACE FO), Tropospheric Emissions: Monitoring of Pollution (TEMPO), Cyclone Global Navigation Satellite System (CYGNSS), ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), and Global Ecosystem Dynamics Investigation (GEDI) Lidar missions. An overview of plans and current status will be presented.

Eastern Mediterranean salinification observed in satellite salinity from SMAP mission

The L-band passive microwave data from the Soil Moisture Active Passive (SMAP) observatory are investigated for remote sensing of ocean surface winds during severe storms. The surface winds of Joaquin derived from the real-time analysis of the Center for Advanced Data Assimilation and Predictability Techniques at Penn State support the linear extrapolation of the Aquarius and SMAP geophysical model functions (GMFs) to hurricane force winds. We apply the SMAP and Aquarius GMFs to the retrieval of ocean surface wind vectors from the SMAP radiometer data to take advantage of SMAP's two-look geometry. The SMAP radiometer winds are compared with the winds from other satellites and numerical weather models for validation. The root-mean-square difference (RMSD) with WindSat or Special Sensor Microwave Imager/Sounder is 1.7 m/s below 20-m/s wind speeds. The RMSD with the European Center for Medium-Range Weather Forecasts direction is 18° for wind speeds between 12 and 30 m/s. We find that the correlation is sufficiently high between the maximum wind speeds retrieved by SMAP with a 60-km resolution and the best track peak winds estimated by the National Hurricane Center and the Joint Typhoon Warning Center to allow them to be estimated by SMAP with a correlation coefficient of 0.8 and an underestimation by 8%–18% on average, which is likely due to the effects of spatial averaging. There is also a good agreement with the airborne Stepped-Frequency Radiometer wind speeds with an RMSD of 4.6 m/s for wind speeds in the range of 20–40 m/s.

NASA's Soil Moisture Active Passive (SMAP) mission originally included both passive and active L-band measurement capability. It was the first satellite instrument to provide global L-band radar observations of normalized radar cross section ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma ^{o}$</tex-math></inline-formula> ) at multiple resolutions. The SMAP radar collected high resolution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1–3 km) synthetic aperture radar (SAR) measurements over most of the Earth's land mass. It simultaneously collected low resolution 6×30 km “slice” and full-footprint 29×35 km measurements. The SMAP radar operated for 83 days from day of the year 103 to 186 in 2015, before the transmitter failed. The SMAP radar was designed to make vegetation roughness measurements in support of the SMAP primary mission to measure soil moisture, but the radar data is useful for a variety of applications, particularly in the polar regions. Unfortunately, limitations in the data download volume precluded downlink of high resolution data over Antarctica, sea ice in the polar regions, and various islands. Nonetheless, low resolution slice and footprint data were collected and downlinked over these areas. To better exploit this low resolution data, this paper employs image reconstruction techniques to create twice-daily enhanced resolution SMAP radar images from the slice and footprint measurements. To validate the resolution enhancement, the enhanced resolution data are compared to SAR results over Greenland and South America. The new data set is provided to the science community to support cryosphere and climate studies.

The impact of tropical cyclones on the operation of bridges and railways are mostly dependent on the forecast wind speed trend (upward, downward or staying steady) at present in Hong Kong. There are requests to forecast the exceedance of wind speed thresholds for such operations with a lead time of many hours ahead. This study considers the technical feasibility of forecasting the wind speed along a recently built bridge in Hong Kong with a coupled mesoscale numerical weather prediction (NWP)–computational fluid dynamics (CFD) model. This bridge features numerous anemometers where the coupled model can be verified. It is found that these two tropical cyclone cases are very challenging, especially in representing the wind structure of the cyclone because of its relatively small circulation. As such, the timing of the maximum wind could be 2 to 4 h earlier than the actual observation. The maximum wind speed from CFD modeling could be higher than that from the NWP model alone by 5 to 10 m/s, which shows that CFD modeling could add value in the forecasting of wind speed exceedance, although the maximum simulated value is still lower than the actual observation by as much as 10 m/s. As a result, while the general wind speed trend may be forecast, the exceedance of definite values of wind speed limits is still practically rather challenging, given the present two cases of tropical cyclones.

This paper describes a computation method for obtaining dielectric constant using Global Navigation Satellite System reflectometry (GNSS-R) products. Dielectric constant is a crucial component in the soil moisture retrieval process using reflected GNSS signals. The reflectivity for circular polarized signals is combined with the dielectric constant equation that is used for radiometer observations. Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission, an eight-nanosatellite constellation for GNSS-R, are used for computing dielectric constant. Data from the Soil Moisture Active Passive (SMAP) mission are used to measure the soil moisture through its radiometer, and they are considered as a reference to confirm the accuracy of the new dielectric constant calculation method. The analyzed locations have been chosen that correspond to sites used for the calibration and validation of the SMAP soil moisture product using in-situ measurement data. The retrieved results, especially in the case of a specular point around Yanco, Australia, show that the estimated results track closely to the soil moisture results, and the Root Mean Square Error (RMSE) in the estimated dielectric constant is approximately 5.73. Similar results can be obtained when the specular point is located near the Texas Soil Moisture Network (TxSON), USA. These results indicate that the analysis procedure is well-defined, and it lays the foundation for obtaining quantitative soil moisture content using the GNSS reflectometry results. Future work will include applying the computation product to determine the characteristics that will allow for the separation of coherent and incoherent signals in delay Doppler maps, as well as to develop local soil moisture models.