This study carried out an X- and C-band spaceborne synthetic aperture radar (SAR) observation synchronized with the field experiment in the Bohai Sea of China in January 2024. Based on the co-located X- and C-band SAR multipolarized data from TerraSAR-X/TanDEM-X, Gaofen-3, and Sentinel-1, we elucidated the signatures of thin ice and quantified their separability. Underlying the identified thin ice information, we further demonstrated the feasibility of separating thin ice and seawater from the ice-water mixture. Combining with the LiDAR-measured surface roughness, we investigated its correlation with different X- and C-band SAR signatures, of which the optimal parameter was used to develop the regression models for estimating thin ice surface roughness. The comprehensive application of multiband, multipolarization SAR contributes to resolve the ambiguity of observed thin ice signatures. Our case study highlights the potential role of multipolarized (or even fully polarized) X-band SAR for thin ice observations.

Nighttime light plays an important role in social economy and human activities by observing artificial light at night. Accurate radiometric calibration is the basis of quantitative applications and influences the accuracy of surface information revision. A nighttime light payload, named Glimmer Imager (GLI) is onboard the Sustainable Development Goals Satellite (SDGSAT-1). The payload can acquire nighttime light observations with a spatial resolution of 10 m in the panchromatic bands, and 40 m in Red/Green/Blue bands. In this paper, we proposed a vicarious radiometric calibration method for the GLI of SDGSAT-1 based on point light source, and conducted experiment in the Dunhuang radiometric calibration site. Atmospheric profile, aerosol optical depth (AOD), and total column water vapor content were measured during the in-situ measurements, and were used to simulate the at-sensor radiance at the entrance of satellite payload via MODerate resolution atmospheric TRANsmission (MOTRAN). Our result shows that there is a strong linear correlation between the simulated atsensor radiance and the DN value, with R2larger than 0.91. Moreover, uncertainties of the vicarious radiometric calibration induced by relative spectral response shift, AOD content measurements, atmospheric profile measurements, AOD type assumption, and specialized point light itself are also discussed. Results show that the overall uncertainty of vicarious radiometric calibration for nighttime light payload onboard SDGSAT-1 based on point light sources is between 4.00% and 5.50%. The comparison between GLI/SDGSAT-1 and DNB/VIIRS at Athens, Denver, and Milan shows a strong correlation with R2larger than 0.80. Our analysis reveals the feasibility of vicarious radiometric calibration method based on point light source for nighttime light payload.

Intertidal flats are vulnerable areas that are difficult to access and that are home to many species. We are using synthetic aperture radar (SAR) data for the monitoring of exposed intertidal flats to assess their economic use through intensive aquacultures, and to identify areas of strong morphodynamics. Polarimetric decomposition of Gaofen-3 SAR data of various spots on the Chinese coast revealed that aquacultural rafts show different backscattering mechanisms than the surrounding mud flats. Moreover, Sentinel-1 SAR-C data are used to generate topographic maps of extended intertidal flats on the German North Sea coast. Those maps, derived for different time periods, will be used to assess morphodynamic changes.

In recent years, mariculture has been extensively developed in China. However, this rapid and uncontrolled expansion poses risks to the coastal environment, economic development and biodiversity conservation. Synthetic aperture radar (SAR) remote sensing can overcome the limitation of weather and has the advantage of all-day observing, which can effectively image the floating aquaculture area (FAR). In this study, we applied various deep learning methods to extract FAR by high-resolution Gaofen-3 (GF-3) SAR data. The trained and predicted datasets were constructed and trained by using U-Net, FCN, and SegNet networks. The results show that the U-Net and SegNet networks are more suitable than FCN, SegNet, and DeeplapV3+ networks for mariculture raft extraction in different scenarios. The best overall accuracy, F1 measure and kappa coefficient best overall accuracy are 95%, 0.84 and 0.80, respectively.

Nonlinear internal waves (NLIWs) are widely observed in the coastal area around Hainan Island in the South China Sea (SCS), which play a critical role in local mixing, thermohaline circulation and nearshore ecosystem. However, the generation of NLIWs around Hainan Island are rarely studied. We are investigating on the source sites and generation mechanisms of NLIWs around Hainan Island based on the synergistic analysis of synthetic aperture radar (SAR) observations, field measurements and numerical analyses. It is found that the NLIWs in different regions around Hainan Island have different generation regimes. NLIWs northeast of Hainan Island are generated from the Luzon Strait, NLIWs southeast of Hainan Island arise from Xisha Islands, while the NLIWs in the northern and southern shelf area south of Hainan Island are mainly from Xisha Islands and local continental shelf, respectively.

Sea ice drift (SID) is crucial for understanding sea ice dynamics and for navigation safety. This study focuses on a comprehensive analysis of SID retrieval in the Arctic based on spaceborne synthetic aperture radar (SAR) data. A state-of-the-art method combining feature tracking and pattern matching techniques was applied to sequential Sentinel-1 (S1) SAR data in 2020 to derive SID from the central Arctic to the Fram Strait. The SAR retrievals were validated with drifting buoys. For temporal intervals of S1 data of approximately 24 hours, 15,254 collocations were collected from January to June and from October to December, yielding a 0.00 cm/s bias for the drift velocity magnitude and 0.27 degrees for direction with corresponding root mean square error (RMSE) of 0.47 cm/s and 4.73 degrees. Using temporal intervals of S1 data of less than 24 hours, we retrieved SID from July to September. A total of 644 collocations yields a comparison with a bias of 0.52 cm/s and 4.62 degrees for the drift magnitude and direction, respectively. The corresponding RMSE values are 1.85 cm/s and 20.73 degrees. The comparisons demonstrate better performance than the operational SAR-based SID product using the MCC method and are consistent with seasonal trends in drift velocity with the coarse-resolution product. We also analyzed the variations in SAR retrievals and further estimated appropriate temporal intervals, making it feasible to conduct long-term SID retrievals based on spaceborne SAR data at high spatial resolution in the Arctic.

The Back Propagation Neural Network (BPNN) method and the MPI method are used to retrieve the sea state parameters (significant wave height, SWH) and ocean wave spectra from the Sentinel-1 Extra Wide Swath mode (EW) and Interferometric Wide Swath (IW) data acquired in the Arctic ocean. The Radar Altimeter (RA) and SWIM measurement are used to validate the retrieved SWH and the wave spectra, respectively. Both comparisons suggest good agreement between SAR retrievals and the reference data. The retrieved SWH by BPNN has a bias and an RMSE of 0.17 m and 0.71 m, respectively compared with RA data. The retrieved ocean wave spectra based on MPI method have a similar structure and energy distribution with the SWIM spectra, with a bias and an RMSE of 0.03 m and 0.7 m in terms of SWH. The further validation of the sea surface wind speed with the ASCAT and RA SSW show the stable performance.

The absolute radiometric accuracy of thermal infrared channels of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the FengYun-4A (FY4A) satellite was assessed with field experiment data in a vicarious calibration on 18, 2019, and August 20, 2019, in Lake Qinghai. Continuous water temperature records were collected by an unmanned surface vehicle equipped with a radiometer during the experimental period. We proposed a spectral matching method based on atmospheric transfer code to create consistent equivalent spectral radiance, and the results predicted by atmospheric transfer simulation were convolved with instrument response functions to obtain predicted brightness temperature for comparison with satellite-derived temperature. Our results indicated that AGRI had an average temperature bias of 0.12 K with an RMSE of 0.17 and 0.61 K with an RMSE of 0.22 K in band 12 and band 13 on August 18, while these biases decreased to -0.01 K with an RMSE of 0.13 and -0.48 K with an RMSE of 0.17 K on August 20, respectively. This radiometric accuracy indicated that AGRI TIR bands are well functional compared with their preflight requirement of less than 1 K. The uncertainty analysis also suggested that atmospheric conditions would alter the calibration accuracy by influencing the transmittance and path thermal radiance. Meanwhile, cloud cover also requires more attention to select the appropriate concurrent satellite pixels to reduce the possible cloud edge effects. It is important and necessary for FY4A AGRI to conduct more intense automatic observations and obtain more data to improve the accuracy of radiometric calibration and monitor the operational status of satellite instruments.

In this article, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization [vertical-horizontal (VH) or horizontal-vertical (HV)] data in extra-wide (EW) swath mode based on the support vector machine (SVM) method. The classification basis includes the S1 radar backscatter and texture features, which are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e., entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparisons based on a few cases show good agreements between the synthetic aperture radar (SAR)-derived sea ice cover using the proposed method and visual inspections, of which the accuracy reaches approximately 90%-95%. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of the extracted sea ice cover by using S1 data is more than 80%.

Sentinel-1 (S1) extra-wide (EW) swath data in cross-polarization (horizontal-vertical, HV or vertical-horizontal, VH) are strongly affected by the scalloping effect and thermal noise, particularly over areas with weak backscattered signals, such as sea surfaces. Although noise vectors in both the azimuth and range directions are provided in the standard S1 EW data for subtraction, the residual thermal noise still significantly affects sea ice detection by the EW data. In this article, we improve the denoising method developed in previous studies to remove the additive noise for the S1 EW data in cross-polarization. Furthermore, we propose a new method for eliminating the residual noise (i.e., multiplicative noise) at the subswath boundaries of the EW data, which cannot be well processed by simply subtracting the reconstructed 2-D noise field. The proposed method of removing both the additive and multiplicative noise was applied to EW HV-polarized images processed using different Instrument Processing Facility (IPF) versions. The results suggest that the proposed algorithm significantly improves the quality of EW HV-polarized images under various sea ice conditions and sea states in the marginal ice zone (MIZ) of the Arctic. This is of great support for the utilization of cross-polarization synthetic aperture radar (SAR) images in wide swaths for intensive sea ice monitoring in polar regions.

In this paper, we presented a preliminary study of retrieving sea surface wind speed (SSWS) from the Senitnel-1 SAR data acquired in Horizontal-Horizontal (HH) polarization based on a machine learning algorithm, to acquire wind field over Arctic ocean with high spatial resolution and accuracy. The interesting is that we found the simple backward propagation (BP) neutral network performs well under guidance of the widely applied Geophysical Model Functions (GMF) CMODs. The network is trained using collocations of S1 data and ASCAT measurements. Compared to the buoy measurements, the bias, root mean square error (RMSE) and scatter index (SI) of wind speed retrieved by the BP neural network model are 0.1 m/s, 1.38 m/s and 19.85%, respectively.

In this paper, we describe a method for radiometric calibration of Fengyun-3D (FY-3D) MERSI-II TIR, bands 24 and 25 that combines vicarious and cross-calibration of the corresponding bands of Aqua MODIS, bands 31 and 32. A field campaign was conducted on lake Qinghai, China, on August 18, 2019. The surface measurements were performed before and after the passing time of Terra MODIS and FY-3D MERSI-II. Path radiance and transmittance were then calculated via the radiative transfer code MODTRAN 4.0, and the difference caused by the relative spectral response (RSR) of the sensors was eliminated by the spectral matching. Our experimental results indicate that the obtained radiometric calibration accuracy of FY-3D MERSI-II is stable during on-orbit operation.

The detection of wind turbines in a strong clutter background is analyzed at variance of polarimetric synthetic-aperture radar (SAR) configurations. The area of interest is the intertidal zone near Jiangsu, China and two detectors are used, the polarimetric notch filter (PNF) and a change detector that optimizes the ratio between covariance matrices. The detection performance is quantitatively analyzed using the receiver operating characteristic (ROC) curve, while the scattering mechanisms that characterize wind turbines are analyzed using the Yamaguchi decomposition. Experimental analysis shows that: 1) wind turbines result in a nontrivial scattering mechanism and 2) full-polarimetric measurements achieve the best detection performance independently of the two detectors.

In this paper, we demonstrate an application of spaceborne synthetic aperture radar (SAR) constellation data to derive sea surface tidal current at high spatial resolution. The maximum cross correlation (MCC) method, which has been widely applied to optical remote sensing data to derive sea surface velocities, is applied to X-band SAR data from TerraSAR-X (TSX) and TanDEM-X (TDX), which were operating in pursuit monostatic mode. Because of the short temporal interval of TSX and TDX's pursuit acquisitions, temporal de-correlation is minimized to derive tidal current fields that exhibit significant temporal and spatial variations. In addition, we combined data from TDX and another X-band SAR, COSMO-SkyMed, to obtain a virtual SAR constellation data pair, which was also analyzed using the MCC method to derive the tidal current field. Case studies of Hangzhou Bay in the East China Sea and Amrum Island in the North Sea are presented. The SAR-derived tidal current fields were compared to the results of numerical model simulations, high-frequency (HF) radar measurements and in situ measurements. MCC coefficients that are greater than 0.8 are an appropriate threshold for the further derivation of tidal currents. Comparisons to finite volume community ocean model, HF radar and general estuarine transport model results yield root-mean-square errors of 0.13, 0.06, and 0.05 m/s, respectively. Measurements from three field stations were also compared to the MCC SAR retrievals, yielding differences of 0.3, 0.07, and -0.09 m/s.

In this study, we focus on the investigations of oil slicks observed by TerraSAR-X and TanDEM-X operating over the drilling platforms in Forties oil field, the largest one in the North Sea. Previous researches indicate that oil spills observed by consecutive SAR images represent the same leaks, but in this paper, we presented four cases which suggest completely different situations of oil spill variations in the sea surface. Our new findings suggest that groups of oil slicks with similar features observed by consecutive SAR acquisitions might have leaked at different times during the interval between the two consecutive observations.

Sea ice charts are provided operationally by the Canadian Ice Service (CIS) for the convenience of people in high-latitude regions. Hence, approaches to ice-water discrimination are in demand. An approach is proposed in this paper, in which no manual interpretation is involved in the selection of training data. It is done based on the discrepancy of incidence angle dependence between sea ice and open water. This approach has been tested on five synthetic aperture radar (SAR) images from Envisat Advance SAR (ASAR). The validation shows that the approach works fairly well in identifying large areas of open water with an average accuracy of 92.97%.

The TerraSAR-X and TanDEM-X (TSX/TDX) satellites adopt the HELIX operation manner of formatted flight to form a mono-rail dual antenna system for earth observation. In the monostatic mode, TSX/TDX operates independently, and the time interval for the observation over the same region is relatively short. Therefore, using TSX and TDX monostatic mode data, we can apply the maximum cross-correlation (MCC) method to extract the sea surface current fields (SSC), which can minimize the temporal de-correlation and improve the retrieval accuracy. In this paper, we presented a case study to demonstrate the derivation of sea surface current fields in high spatial resolution using this unique TSX/TDX configuration.

The motivation of this work is to investigate the characteristics of Normalized Radar Cross Section (NRCS) of X-band SAR under high winds using a TerraSAR-X (TS-X) image taken in Typhoon Megi in the South China Sea. NRCS derived from TS-X is compared with the simulated NRCS using the C-band GMF CMOD5 and the Ku-band GMF NSCAT4. The wind velocity is ranged from 25 to 36 m s-1in the analysis. Results show the Root-Mean-Square Error (RMSE) between the CMOD5 simulations and the observations of TS-X is 1.7 dB, while it decreases to 0.86 dB in the comparisons between NSCAT4 simulations and TS-X observations. We also investigate the effect of NRCS by rain on X-band SAR, related to high winds. Analysis shows that NRCS from NSCAT4 is closer to TS-X than CMOD5, even at high rain rates. Besides, rainfall enlarges the difference of NRCS on Ku-band, X-band and C-band at a low rain rate (-1), while rainfall lessens the difference when the rain rate is greater than 5 mm h-1. However, NRCS on Ku-band, X-band and C-band SAR tend to be approached when wind velocity increases at various rain rates.

A geophysical model function (GMF), which is denoted by XMOD2, is developed to retrieve sea surface wind field from X-band TerraSAR-X/TanDEM-X (TS-X/TD-X) data. In contrast to the previously developed XMOD1, XMOD2 consists of a nonlinear GMF, and thus, it depicts the difference between upwind and downwind of the sea surface backscatter in X-band synthetic aperture radar (SAR) imagery. By exploiting 371 collocations within situbuoy measurements that are used as the tuning data set together with analysis wind model results, the retrieved TS-X/TD-X sea surface wind speed using XMOD2 shows a close agreement with buoy measurements with a bias of$-$0.32 m/s, a root-mean-square error (RMSE) of 1.44 m/s, and a scatter index (SI) of 16.0%. Further validation using an independent data set of 52 cases shows a bias of$-$0.17 m/s, an RMSE of 1.48 m/s, and an SI of 17.0% comparing with buoy measurements. To apply XMOD2 to TS-X/TD-X data acquired at HH polarization, we validate three X-band SAR polarization ratio models that were tuned using TS-X dual-polarization data by comparing the retrieved sea surface wind speed with buoy measurements.

Simultaneous measurements of significant wave height (SWH) obtained from two independent active microwave sensors of Radar Altimeter 2 (RA-2) and Advanced Synthetic Aperture Radar (ASAR) onboard the ENVIronmental SATellite (ENVISAT) are used for a global verification of ocean wave models (WAMs). In the present study, SWH is retrieved from ASAR wave mode data using the empirical algorithm called C-band WAVE algorithm for ENVISAT, which is capable of representing total SWH irrespective of the cutoff of SAR. Comparisons of two radar measurements with the reanalyses ERA-Interim model (with assimilation of RA-2 measurements) and the German operational WAM (Deutscher Wetterdienst Global Sea wave Model, without assimilation before 2008) show that both WAMs agree well with ASAR and RA-2 measurements. However, the discrepancies of agreement indicate to which extent that the assimilation of RA-2 measurements can improve the performance of WAMs. Moreover, differences in the comparisons of ASAR and RA-2 measurements with the same WAM of ERA-Interim reveal that, although assimilation of RA-2 significantly improves the accuracy of model on grids near the RA-2 tracks, the improvement decreases along with the increase of distance between model grids and RA-2 tracks.

In the present study, development of the new Geophysical Model Function (GMF) XMOD2 to derive the sea surface wind speed from the TerraSAR-X data is described. The XMOD2 has the similar model functions as the CMOD5 for the bias term B0and upwind/cross-wind term B2, while the upwind/downwind term B1is adjusted based on the sea surface backscatter properties of the TerraSAR-X. Coefficients of the XMOD2 are tuned using the collocations of TerraSAR-X measurements with in situ buoy measurements and results of the atmospheric model. By comparing the retrieved sea surface wind speed using XMOD2 to in situ buoy measurements, good agreement is achieved with a bias of 0.07 m/s and an RMSE of 1.57 m/s. We presented a case study of mapping the sea surface wind field over the offshore wind farming Alpha Ventus over the North Sea to demonstrate the application of TerraSAR-X data to investigate the significant spatial variations of the sea surface wind field in the offshore wind farm sites.

Safety of shipping is a growing concern. The causes of shipping casualties are various, while over 30% of the casualties are due to bad weather. Heavy sea state and severe weather conditions have caused the loss of more than 200 large cargo vessels within 20 years between 1981 and 2000.

An empirical algorithm to retrieve integral ocean wave parameters such as significant wave height (SWH), mean wave period, and wave height of waves with period larger than 12 s$({H}_{12})$from synthetic aperture radar (SAR) images over sea surface is presented. The algorithm is an extension to the Envisat Advanced SAR (ASAR) wave mode data based on the CWAVE approach developed for ERS-2 SAR wave mode data and is thus called CWAVE_ENV (CWAVE for Envisat). Calibrated ASAR images are used as the only source of input without needing prior information from an ocean wave model (WAM) as the standard algorithms used in weather centers. This algorithm makes SAR an independent instrument measuring integrated wave parameters like SWH and mean wave period to altimeter quality. A global data set of 25 000 pairs of ASAR wave mode images and collocated reanalysis WAM results from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to tune CWAVE_ENV model coefficients. Validation conducted by comparing the retrieved SWH to in situ buoy measurements shows a scatter index of 0.24 and 0.16 when compared to the ECMWF reanalysis WAM. Two case studies are presented to evaluate the performance of the CWAVE_ENV algorithm for high sea state. A North Atlantic storm during which SWH is above 18 m as observed by SAR and Radar Altimeter simultaneously is analyzed. For an extreme swell case that occurred in the Indian Ocean, the potential of using SWH measurements from ASAR wave mode data derived by the CWAVE_ENV algorithm is demonstrated.

As a scientific and technological continuation of the X-band Synthetic Aperture Radar (X-SAR) and Shuttle Radar Topography Mission (SRTM) missions, the new X-SAR, namely, TerraSAR-X (TSX), was launched on June 15, 2007. Since then, it has provided numerous high-quality data over land and ocean operationally. In this paper, surface wave refraction and diffraction are investigated using TSX imagery acquired over the coast of Terceira island situated in the North Atlantic. Peak wavelength and wave direction are determined by SAR 2-D image spectra. They are compared to measurements of X-band marine radar and results of the WAve prediction Model (WAM). Significant wave height in the near-shore shallow water region is estimated from TSX Spotlight mode data following the wave refraction laws and using the developed XWAVE empirical algorithm. Image spectra of the TSX subscenes in the full-coverage region are given to investigate significant changes of wave direction and length. By analyzing another TSX image acquired in StripMap mode, a shadow zone in the lee side of Terceira island is identified. It is influenced jointly by wave refraction and diffraction. Furthermore, a cross-sea pattern revealed in the image spectra is investigated. The cross sea is generated by the diffracted wave rays from the northern and southern coasts of the island. Less wave directional spreading for the cross-sea situation is observed as well when compared to the image spectra at the origin of diffraction.

The Synthetic Aperture Radar (SAR) onboard the European Satellite ERS-2 is operated in wave mode over the global oceans whenever no image mode data acquisition is requested. In the present paper new SAR algorithms to derive sea state at German Aerospace Center (DLR) are shortly introduced and used to determine SAR derived ocean parameters from wave mode data. Global Significant wave height statistics on a 3° by 3° grid derived by an empirical method (CWAVE) is presented in this paper for sea state analysis. A severe North Pacific storm that produced significant wave height above 10 m along the northern great circle shipping route is presented in the paper. The sea state generated is analyzed in more detail using the empirical method to derive significant wave height and compared to the ocean wave model WAM.