Search Results (218)

A storm surge, classified as an extreme natural disaster, refers to unusual sea level fluctuations induced by severe atmospheric disturbances such as typhoons. Existing reanalysis data, such as ERA5, significantly underestimates the location and maximum wind speed of typhoons. Therefore, this study initially assesses the accuracy of tropical cyclone positions and peak wind speeds in the ERA5 reanalysis dataset. These results are compared against tropical cyclone parameters from the IBTrACS (International Best Track Archive for Climate Stewardship). The position deviation of tropical cyclones in ERA5 is mainly within the range of 10 to 60 km. While the correlation of maximum wind speed is significant, there is still considerable underestimation. A wind field reconstruction model, incorporating tropical cyclone characteristics and a distance correction factor, was employed. This model considers the effects of the surrounding environment during the movement of the tropical cyclone by introducing a decay coefficient. The reconstructed wind field significantly improved the representation of the typhoon eyewall and high-wind-speed regions, showing a closer match with wind speeds observed by the HY-2B scatterometer. Through simulations using the FVCOM (Finite Volume Community Ocean Model) storm surge model, the reconstructed wind field demonstrated higher accuracy in reproducing water level changes at Tanxu, Gaoqiao, and Zhangjiabang stations. During the typhoon’s landfall in Shanghai, the area with the greatest water level increase was primarily located in the coastal waters of Pudong New Area, Shanghai, where the highest total water level reached 5.2 m and the storm surge reached 4 m. The methods and results of this study provide robust technical support and a valuable reference for further storm surge forecasting, marine disaster risk assessment, and coastal disaster prevention and mitigation efforts. Full article

High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers, scatterometers, and synthetic aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10° longitude × 10° latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5 and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15 m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center (5%). Full article

Sea ice is a crucial component of the global climate system. The China–French Ocean Satellite Scatterometer (CFOSAT/SCAT, CSCAT) employs an innovative rotating fan beam system. This study applied principal component analysis (PCA) to extract classification features and developed an ensemble machine learning approach for sea ice detection. PCA identified key features from CSCAT’s backscatter information, representing outer and sweet swath observations. The ensemble model’s performances (OA and Kappa) for the Northern and Southern Hemispheres were 0.930, 0.899, and 0.844, 0.747, respectively. CSCAT achieved an accuracy of over 0.9 for close ice and open water but less than 0.3 for open ice, with misclassification of open ice as closed ice. The sea ice extent discrepancy between CSCAT and the National Snow and Ice Data Center (NSIDC) was −0.06 ± 0.36 million km² in the Northern Hemisphere and −0.03 ± 0.48 million km² in the Southern Hemisphere. CSCAT’s sea ice closely matched synthetic aperture radar (SAR) imagery, indicating effective sea ice and open water differentiation. CSCAT accurately distinguished sea ice from open water but struggled with open ice classification, with misclassifications in the Arctic’s Greenland Sea and Hudson Bay, and the Antarctic’s sea ice–water boundary. Full article

In this study, wind speed sensitivity and calibration bias were first determined for Surface Water and Ocean Topography (SWOT) satellite Ka-band Radar Interferometer (KaRIn) Normalized Radar Backscatter Cross Section (NRCS) data at VV and HH polarizations. Here, the calibration bias was estimated by comparing the KaRIn NRCS with collocated simulations from a model developed using Global Precipitation Measurement (GPM) satellite Dual-frequency Precipitation Radar (DPR) data. To recalibrate the bias, the correlation coefficient between the KaRIn data and the simulations was estimated, and the data with the corresponding top 10% correlation coefficients were used to estimate the recalibration coefficients. After recalibration, a Ka-band NRCS model was developed from the KaRIn data to retrieve ocean surface wind speeds. Finally, wind speed retrievals were evaluated using the collocated European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis winds, Haiyang-2C scatterometer (HY2C-SCAT) winds and National Data Buoy Center (NDBC) and Tropical Atmosphere Ocean (TAO) buoy winds. Evaluation results show that the Root Mean Square Error (RMSE) at both polarizations is less than 1.52 m/s, 1.34 m/s and 1.57 m/s, respectively, when compared to ECMWF, HY2C-SCAT and buoy collocated winds. Moreover, both the bias and RMSE were constant with the incidence angles and polarizations. This indicates that the winds from the SWOT KaRIn data are capable of correcting the sea state bias for sea surface height products. Full article

In addition to technical issues related to the instruments used, differences between soil moisture (SM) measured using ground-based methods and microwave remote sensing (RS) can be related to the main features of the study areas, which are intricately connected to hydraulic–hydrological conditions and soil properties. When long-term analysis is performed, these discrepancies are mitigated by the contribution of SM seasonality and are only evident when high-frequency variations (i.e., signal anomalies) are investigated. This study sought to examine the responsiveness of SM to seasonal variations in terrestrial ecoregions located in areas covered by the in situ Romanian Soil Moisture Network (RSMN). To achieve this aim, several remote sensing-derived retrievals were considered: (i) NASA’s Soil Moisture Active and Passive (SMAP) L4 V5 model assimilated product data; (ii) the European Space Agency’s Soil Moisture and Ocean Salinity INRA–CESBIO (SMOS-IC) V2.0 data; (iii) time-series data extracted from the H115 and H116 SM products, which are derived from the analysis of Advanced Scatterometer (ASCAT) data acquired via MetOp satellites; (iv) Copernicus Global Land Service SSM 1 km data; and (v) the “combined” European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) product v06.1. An initial assessment of the performance of these products was conducted by checking the anomaly of long-term fluctuations, quantified using the Absolute Variation of Local Change of Environment (ALICE) index, within a time frame spanning 2015 to 2020. These correlations were then compared with those based on raw data and anomalies computed using a moving window of 35 days. Prominent correlations were observed with the SMAP L4 dataset and across all ecoregions, and the Balkan mixed forests (646) exhibited strong concordance regardless of the satellite source (with a correlation coefficient R_ALICE > 0.5). In contrast, neither the Central European mixed forests (No. 654) nor the Pontic steppe (No. 735) were adequately characterized by any satellite dataset (R_ALICE < 0.5). Subsequently, the phenological seasonality and dynamic behavior of SM were computed to investigate the effects of the wetting and drying processes. Notably, the Central European mixed forests (654) underwent an extended dry phase (with an extremely low p-value of 2.20 × 10⁻¹⁶) during both the growth and dormancy phases. This finding explains why the RSMN showcases divergent behavior and underscores why no satellite dataset can effectively capture the complexities of the ecoregions covered by this in situ SM network. Full article

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records. Full article

Scatterometers are highlighted in polar applications, such as sea ice extent retrieval. There are currently three types of spaceborne scatterometer in operation, among which the rotating pencil beam scatterometer and the rotating fan beam scatterometer have similar rotating observation geometry, but different beam sampling. However, it is difficult to objectively evaluate the performance of the two rotating beam scatterometers using the obtained data. This is because there are significant differences in their system parameters, which in turn affects the objectivity of the evaluation. Considering the high flexibility of the rotating fan beam scatterometer, this study proposes a dual-mode sea ice extent retrieval method for the rotating fan beam scatterometer. The dual modes refer to the rotating fan beam mode (or full incidence mode) and the equivalent rotating pencil beam mode (or single incidence mode). The two modes share the same system and spatiotemporal synchronous backscatter measurements provide the possibility of objectively comparing the rotating pencil beam and rotating fan beam scatterometers. The comparison, validation, and evaluation of the dual-mode sea ice extent of China France Oceanography Satellite Scatterometer (CSCAT) were performed. The results indicate that the sea ice extent retrieval of the equivalent rotating pencil beam mode of the rotating fan beam scatterometer is realizable, and compared to the existing rotating pencil beam scatterometers (such as the OceanSat Scatterometer on ScatSat-1, OSCAT, on ScatSat-1, and the Hai Yang 2B Scatterometer, HSCAT-B), the derived sea ice extent is closer to that of Advanced Microwave Scanning Radiometer 2 (AMSR2). For the two modes of CSCAT, when compared to AMSR2, the sea ice extent of the CSCAT full incidence mode has smaller values of root mean squared error (RMSE), error-of-ice (EI), and ice edge location distance (LD) than those of the CSCAT single incidence mode. These suggest that the rotating fan beam scatterometer shows better observation abilities for sea ice extent than the rotating pencil beam scatterometers. Full article

The presence of melt ponds on the surface of Arctic Sea ice affects its albedo, thermal properties, and overall melting rate; thus, the detection of melt pond onset is of significant importance for understanding the Arctic’s changing climate. This study investigates the utility of a novel method for detecting the onset of melt ponds on sea ice using a satellite-based, dual-sensor C-band approach, whereby Sentinel-1 provides horizontally polarized (HH) data and Advanced SCATterometer (ASCAT) provides vertically polarized (VV) data. The co-polarized ratio (VV/HH) is used to detect the presence of melt ponds on landfast sea ice in the Canadian Arctic Archipelago in 2017 and 2018. ERA-5 air temperature and wind speed re-analysis datasets are used to establish the VV/HH threshold for pond onset detection, which have been further validated by Landsat-8 reflectance. The co-polarized ratio threshold of three standard deviations from the late winter season (April) mean co-pol ratio values are used for assessing pond onset detection associated with the air temperature and wind speed data, along with visual observations from Sentinel-1 and cloud-free Sentinel-2 imagery. In 2017, the pond onset detection rates were 70.59% for FYI and 92.3% for MYI. Results suggest that this method, because of its dual-platform application, has potential for providing large-area coverage estimation of the timing of sea ice melt pond onset using different earth observation satellites. Full article

A new ASCAT coastal wind product based on a 12.5 km grid size is presented. The new product contains winds up to the coast line and is identical to the current operational coastal product over the open ocean. It is based on the assumption that within a wind vector cell land and sea have constant radar cross section. With an accurate land fraction calculated from ASCAT’s spatial response function and a detailed land mask, the land correction can be obtained with a simple linear regression. The coastal winds stretch all the way to the coast, filling the coastal gap in the operational coastal ASCAT product, resulting in three times more winds within a distance of 20 km from the coast. The Quality Control (QC), based on the regression error and the regression bias error, reduces this abundance somewhat. A comparison of wind speed pdfs with those from NWP forecasts shows that the influence of land in the land-corrected scatterometer product appears more reasonable and starts not as far offshore as that in the NWP forecasts. The VRMS difference with moored buoys increases slightly from about 2.4 m/s at 20 km or more from the coast to 4.2 m/s at less than 5 km, where coastal wind effects clearly contribute to the latter difference. While the QC based on the regression bias error flags many WVCs that compare well with buoys, the land-corrected coastal product with more abundant coastal winds appears useful for nowcasting and other coastal wind applications. Full article

There is a heightened risk of an oil spill occurring in the Arctic, as climate change driven sea ice loss permits an increase in Arctic marine transportation. The ability to detect an oil spill and monitor its progression is key to enacting an effective response. Microwave scatterometer systems may be used detect changes in sea ice thermodynamic and physical properties, so we examined the potential of C-band polarimetric radar for detecting diesel fuel beneath a thin sea ice layer. Sea ice physical properties, including thickness, temperature, and salinity, were measured before and after diesel addition beneath the ice. Time-series polarimetric C-band scatterometer measurements monitored the sea ice evolution and diesel migration to the sea ice surface. We characterized the temporal evolution of the diesel-contaminated seawater and sea ice by monitoring the normalized radar cross section (NRCS) and polarimetric parameters (conformity coefficient (μ), copolarization correlation coefficient (ρco)) at 20° and 25° incidence angles. We delineated three stages, with distinct NRCS and polarimetric results, which could be connected to the thermophysical state and the presence of diesel on the surface. Stage 1 described the initial formation of sea ice, while in Stage 2, we injected 20L of diesel beneath the sea ice. No immediate response was noted in the radar measurements. With the emergence of diesel on the sea ice surface, denoted by Stage 3, the NRCS dropped substantially. The largest response was for VV and HH polarizations at 20° incidence angle. Physical sampling indicated that diesel emerged to the surface of the sea ice and trended towards the tub edge and the polarimetric scatterometer was sensitive to these physical changes. This study contributes to a greater understanding of how C-band frequencies can be used to monitor oil products in the Arctic and act as a baseline for the interpretation of satellite data. Additionally, these findings will assist in the development of standards for oil and diesel fuel detection in the Canadian Arctic in association with the Canadian Standards Association Group. Full article

This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state. Full article

A polarized bidirectional reflectance distribution function (pBRDF) matrix was developed based on the two-scale roughness theory to provide consistent simulations of fully polarized microwave emission and scattering, required for the ocean–atmosphere-coupled radiative transfer model. In this study, the potential of the two-scale pBRDF matrix was explored for simulating ocean full-polarization backscattering and bistatic-scattering normalized radar cross sections (NRCSs). Comprehensive numerical simulations of the two-scale pBRDF matrix across the L-, C-, X-, and Ku-bands were carried out, and the simulations were compared with experimental data, classical electromagnetic, and GMFs. The results show that the two-scale pBRDF matrix demonstrates reasonable dependencies on ocean surface wind speeds, relative wind direction (RWD), geometries, and frequencies and has a reliable accuracy in general. In addition, the two-scale pBRDF matrix simulations were compared with the observations from the advanced scatterometer (ASCAT) onboard MetOP-C satellites, with a correlation coefficient of 0.9634 and a root mean square error (RMSE) of 2.5083 dB. In the bistatic case, the two-scale pBRDF matrix simulations were compared with Cyclone Global Navigation Satellite System (CYGNSS) observations, demonstrating a good correlation coefficient of 0.8480 and an RMSE of 1.2859 dB. In both cases, the two-scale pBRDF matrix produced fairly good simulations at medium-to-high wind speeds. The relatively large differences at low wind speeds (<5 m/s) were due probably to the swell effects. This study proves that the two-scale pBRDF matrix is suitable for the applications of multiple types of active instruments and can consistently simulate the ocean surface passive and active signals. Full article

Dedicated to synchronously acquiring large-area, high-precision, and multi-scale ocean wind and wave information, a novel concept of a spaceborne ocean microwave dual-function integrated sensor is proposed in this paper. It integrates the functions of a scatterometer and SAR by sharing a single phased-array antenna. An overview of the scientific requirements and motivations for the sensor are outlined firstly. In order to fulfill the observation requirements of both the functions, the constraints on the system parameters such as frequency, antenna size, and incidence angle are analyzed. Then, the selection principles of these parameters are discussed within the limitations of antenna area, bandwidth, available time, and cost. Additionally, the constraints on the time sequence of transmitting and receiving pulses are derived to ensure that there is no conflict when the two functions operate simultaneously. Subsequently, a method for jointly designing the pulse repetition frequency (PRF) of both the functions is introduced, along with zebra maps to verify its effectiveness. At the end of the paper, the system and performance parameters of the sensor are given for further insight into it. Full article

The China–France oceanography satellite scatterometer (CSCAT) is a rotating fan-beam scanning observation scatterometer operating in the Ku-band, and its product quality is affected by rain contamination. The multiple azimuthal NRCS measurements provided by CSCAT L2A, the retrieved wind speed and wind direction provided by CSCAT L2B, as well as the rain data provided by GPM, are used to construct a new rain identification and rain intensity classification model for CSCAT. The EXtreme Gradient Boosting (XGBoost) model, optimized by the Dung Beetle Optimizer (DBO) algorithm, is developed and evaluated. The performance of the DBO-XGBoost exceeds that of the CSCAT rain flag in terms of rain identification ability. Also, compared with XGBoost without parameter optimization, K-nearest Neighbor with K = 5 (KNN5) and K-nearest Neighbor with K = 3 (KNN3), the performance of DBO-XGBoost is better. Its rain identification achieves an accuracy of about 90% and a precision of about 80%, which enhances the quality control of rain. DBO-XGBoost has also shown good results in the classification of rain intensity. This ability is not available in traditional rain flags. In the global regional and local regional tests, most of the accuracy and precision in rain intensity classification have reached more than 80%. This technology makes full use of the rich observed information of CSCAT, realizes rain identification, and can also classify the rain intensity so as to further evaluate the degree of rain contamination of CSCAT products. Full article

Polar sea ice extent exhibits a highly dynamic nature. This paper investigates the sea ice extent retrieval on a fine (6.25 km) grid based on the 12.5 km sampling data from the China France Ocean Satellite Scatterometer (CSCAT), which is generated by an adapted Bayesian sea ice detection algorithm. The CSCAT 12.5 km sampling data are analyzed, a corresponding sea ice GMF model is established, and the important calculation procedures and parameter settings of the adapted Bayesian algorithm for CSCAT 12.5 km sampling data are elaborated on. The evolution of the sea ice edge and extent based on CSCAT 12.5 km sampling data from 2020 to 2022 is introduced and quantitatively compared with sea ice extent products of Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Advanced Scatterometer onboard MetOp-C (ASCAT-C). The results suggest the sea ice extent of CSCAT 12.5 km sampling data has good consistency with AMSR2 at 15% sea ice concentration. The sea ice edge accuracy between them is about 7 km and 10 km for the Arctic and Antarctic regions, and their sea ice extent difference is 0.25 million km² in 2020 and 0.5 million km² in 2021 and 2022. Compared to ASCAT-C 12.5 km sampling data, the sea ice edge Euclidean distance (ED) of CSCAT 12.5 km data is 14 km (2020 and 2021) and 12.5 km (2022) for the Arctic region and 14 km for the Antarctic region. The sea ice extent difference between them is small except for January to May 2020 and 2021 for the Arctic region. There are significant deviations in the sea ice extents of CSCAT 12.5 km and 25 km sampling data, and their sea ice extent difference is 0.3–1.0 million km². Full article