Wind speed retrieval from the Gaofen-3 synthetic aperture radar for VV- and HH-polarization using a re-tuned algorithm

In this study, a re-tuned algorithm based on the geophysical model function (GMF) C-SARMOD2 is proposed to retrieve wind speed from Synthetic Aperture Radar (SAR) imagery collected by the Chinese C-band Gaofen-3 (GF-3) SAR. More than 10,000 Vertical-Vertical (VV) and Horizontal-Horizontal (HH) polarization GF-3 images acquired in quad-polarization stripmap (QPS) and wave (WV) modes have been collected during the last three years, in which wind patterns are observed over open seas with incidence angles ranging from 18° to 52°. These images, collocated with wind vectors from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis at 0.125° resolution, are used to re-tune the C-SARMOD2 algorithm to specialize it for the GF-3 SAR (CSARMOD-GF). In particular, the CSARMOD-GF performs differently from the C-SARMOD2 at low-to-moderate incidence angles smaller than about 34°. Comparisons with wind speed data from the Advanced Scatterometer (ASCAT), Chinese Haiyang-2B (HY-2B) and buoys from the National Data Buoy Center (NDBC) show that the root-mean-square error (RMSE) of the retrieved wind speed is approximately 1.8 m/s. Additionally, the CSARMOD-GF algorithm outperforms three state-of-the-art methods – C-SARMOD, C-SARMOD2, and CMOD7 – that, when applied to GF-3 SAR imagery, generating a RMSE of approximately 2.0–2.4 m/s

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Evaluation of Chinese Quad-polarization Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval

Our work describes the accuracy of Chinese quad-polarization Gaofen-3 (GF-3) synthetic aperture radar (SAR) wave mode data for wave retrieval and provides guidance for the operational applications of GF-3 SAR. In this study, we evaluated the accuracy of the SAR-derived significant wave height (SWH) from 10,514 GF-3 SAR images with visible wave streaks acquired in wave mode by using the existing wave retrieval algorithms, e.g., the theoretical-based algorithm parameterized first-guess spectrum method (PFSM), the empirical algorithm CSAR_WAVE2 for VV-polarization, and the algorithm for quad-polarization (Q-P). The retrieved SWHs were compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field with 0.125° grids. The root mean square error (RMSE) of the SWH is 0.57 m, found using CSAR_WAVE2, and this RMSE value was less than the RMSE values for the analysis results achieved with the PFSM and Q-P algorithms. The statistical analysis also indicated that wind speed had little impact on the bias with increasing wind speed. However, the retrieval tended to overestimate when the SWH was smaller than 2.5 m and underestimate with an increasing SWH. This behavior provides a perspective of the improvement needed for the SWH retrieval algorithm using the GF-3 SAR acquired in wave mode

Selection of the key earth observation sensors and platforms focusing on applications for Polar Regions in the scope of Copernicus system 2020-2030

An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030. Payload selection is constrained by the variables that can be measured, the power consumption, and weight of the instrument, and the required accuracy and spatial resolution (horizontal or vertical). It involved 20 measurements with observation gaps according to the user requirements that were detected in the top 10 use cases in the scope of Copernicus space infrastructure, 9 potential applied technologies, and 39 available commercial platforms. Additional Earth Observation (EO) infrastructures are proposed to reduce measurements gaps, based on a weighting system that assigned high relevance for measurements associated to Marine for Weather Forecast over Polar Regions. This study concludes with a rank and mapping of the potential technologies and the suitable commercial platforms to cover most of the requirements of the top ten use cases, analyzing the Marine for Weather Forecast, Sea Ice Monitoring, Fishing Pressure, and Agriculture and Forestry: Hydric stress as the priority use cases.Peer ReviewedPostprint (published version

Sea Surface Roughness Observed by High Resolution Radar

Changes in the sea surface roughness are usually associated with a change in the sea surface wind field. This interaction has been exploited to measure the sea surface wind speed by scatterometry. A number of features on the sea surface associated with changes in roughness can be observed by synthetic aperture radar (SAR) because of the change in Bragg backscatter of the radar signal by damping of the resonant ocean capillary waves. With various radar frequencies, resolutions, and modes of polarization, sea surface features have been analyzed in numerous campaigns, bringing various datasets together, thus allowing for new insights in small-scale processes at a larger areal coverage. This Special Issue aims at investigating sea surface features detected by high spatial resolution radars, such as SAR

Retrieval of Ocean Surface Currents and Winds Using Satellite SAR backscatter and Doppler frequency shift

Ocean surface winds and currents play an important role for weather, climate, marine life, ship navigation, oil spill drift and search and rescue. In-situ observations of the ocean are sparse and costly. Satellites provide a useful complement to these observations. Synthetic aperture radar (SAR) is particularly attractive due to its high spatial resolution and its capability to extract both sea surface winds and currents day and night and almost independent of weather.The work in this thesis involves processing of along-track interferometric SAR (ATI-SAR) data, analysis of the backscatter and Doppler frequency shift, and development of wind and current retrieval algorithms. Analysis of the Doppler frequency shift showed a systematic bias. A calibration method was proposed and implemented to correct for this bias. Doppler analysis also showed that the wave contribution to the SAR Doppler centroid often dominates over the current contribution. This wave contribution is estimated using existing theoretical and empirical Doppler models. For wind and current retrieval, two methods were developed and implemented.The first method, called the direct method, consists of retrieval of the wind speed from SAR backscatter using an empirical backscatter model. In order to retrieve the radial current, the retrieved wind speed is used to correct for the wave contribution. The current retrieval was assessed using two different (theoretical and empirical) Doppler models and wind inputs (model and SAR-derived). It was found that the results obtained by combining the Doppler empirical model with the SAR-derived wind speed were more consistent with ocean models.The second method, called Bayesian method, consists of blending the SAR observables (backscatter and Doppler shift) with an atmospheric and an oceanic model to retrieve the total wind and current vector fields. It was shown that this method yields more accurate estimates, i.e. reduces the models biases against in-situ measurements. Moreover, the method introduces small scale features, e.g. fronts and meandering, which are weakly resolved by the models.The correlation between the surface wind vectors and the SAR Doppler shift was demonstrated empirically using the Doppler shift estimated from over 300 TanDEM-X interferograms and ECMWF reanalysis wind vectors. Analysis of polarimetric data showed that theoretical models such as Bragg and composite surface models over-estimate the backscatter polarization ratio and Doppler shift polarization difference. A combination of a theoretical Doppler model and an empirical modulation transfer function was proposed. It was found that this model is more consistent with the analyzed data than the pure theoretical models.The results of this thesis will be useful for integrating SAR retrievals in ocean current products and assimilating SAR observables in the atmospheric, oceanic or coupled models. The results are also relevant for preparation studies of future satellite missions

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

High-resolution polar low winds obtained from unsupervised sar wind retrieval

High-resolution sea surface observations by spaceborne synthetic aperture radar (SAR) instruments are sorely neglected resources for meteorological applications in polar regions. Such radar observations provide information about wind speed and direction based on wind-induced roughness of the sea surface. The increasing coverage of SAR observations in polar regions calls for the development of SAR-specific applications that make use of the full information content of this valuable resource. Here we provide examples of the potential of SAR observations to provide details of the complex, mesoscale wind structure during polar low events, and examine the performance of two current wind retrieval methods. Furthermore, we suggest a new approach towards accurate wind vector retrieval of complex wind fields from SAR observations that does not require a priori wind direction input that the most common retrieval methods are dependent on. This approach has the potential to be particularly beneficial for numerical forecasting of weather systems with strong wind gradients, such as polar lows