Accurate absolute radiometric calibration of spaceborne synthetic aperture radar (SAR) sensor is of great importance in quantitative oceanic monitoring. Traditionally, the calibration constant is determined by analyzing measurements of man-made calibrators deployed on land at a high expense. In this paper, a technique called numerical weather prediction (NWP)-based ocean calibration method for estimating the calibration constant on the basis of SAR observations over ocean is applied to the first Chinese C-band SAR satellite Gaofen-3 (GF-3). The ocean calibration is performed on GF-3 wave mode SAR images at VV and HH polarizations over a period of one year from September 2017 to August 2018. Verification against the independent scatterometer-derived winds shows that after NWP-based ocean recalibration, the accuracy of wind speed retrieval from GF-3 wave mode imagery could reach the wind speed estimation performance using the state-of-the-art of SAR data. These results indicate that GF-3 wind retrievals are promising for operational oceanic products and applications if the SAR data are appropriately calibrated by the proposed NWP-based ocean calibration approach.

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.

Wind shear coefficients (WSCs) are fundamental parameters for wind resource assessment, wind turbine design, and offshore wind farm operation. This work focuses on positive wind shear coefficients (WSCs); hereafter, WSC refers exclusively to positive-value wind shear coefficients derived from positive shear profiles. However, the characteristics of positive WSCs in the offshore waters of Zhuanghe have not yet been systematically investigated. In this study, two years of in situ observational data (2015–2017) were analyzed to characterize the temporal variability of positive WSCs under different atmospheric stability conditions. The results indicate that the atmospheric boundary layer in the study area exhibits pronounced seasonal and diurnal variations in stability, with stable and unstable conditions showing a distinct annual cycle. Increasing wind speed promotes neutral conditions as mechanically generated turbulence becomes dominant over buoyancy effects. The WSC ranges from 0.01 to 0.32 and exhibits pronounced seasonal and monthly variability. Under neutral and unstable atmospheric conditions, the diurnal variation in WSC is relatively consistent among different measurement heights, whereas stable conditions produce appreciable vertical differences in WSC throughout most of the year. These findings improve our understanding of offshore wind shear characteristics across various atmospheric stability regimes. Furthermore, they offer valuable guidance for offshore wind resource assessment, wind profile modeling, and the overall design and operation of wind farms in the Zhuanghe region.

Wind shear coefficients (WSCs) are fundamental parameters for wind resource assessment, wind turbine design, and offshore wind farm operation. However, the characteristics of WSCs in the offshore waters of Zhuanghe have not yet been systematically investigated. In this study, two years of in-situ observational data (2015-2017) were analyzed to characterize the temporal variability of WSCs under different atmospheric stability conditions. The results indicate that the atmospheric boundary layer in the study area is dominated by stable and unstable conditions, exhibiting a distinct U-shaped annual distribution. The diurnal evolution of atmospheric stability varies considerably among months and is strongly associated with both wind speed and wind direction. Under low wind speed conditions, stable and unstable atmospheres occur with comparable frequencies. As wind speed increases, the occurrence of neutral conditions increases steadily, and the atmosphere becomes predominantly neutral when wind speed exceeds 18.5 m s^-1. Northerly winds are generally associated with unstable atmospheric conditions, whereas southerly winds are primarily associated with stable conditions. The WSC ranges from 0.01 to 0.32 and exhibits pronounced seasonal and monthly variability. Under neutral and unstable atmospheric conditions, the diurnal variation of WSC is relatively consistent across different measurement heights. In contrast, under stable conditions, significant vertical differences in WSC are observed throughout most of the year, except in March and April. These findings improve the understanding of offshore wind shear characteristics under different atmospheric stability regimes and provide valuable guidance for offshore wind resource assessment, wind profile modeling, and the design, operation, and optimization of offshore wind farms in the Zhuanghe offshore region.

This study focuses on the Significant Wave Height (SWH) retrieval from the Ka-band radar interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite by combining backscattering and interference characteristics. To this end, the backscattering-related and interference-related parameters were jointly used as inputs to develop a machine learning model. Here, the backscattering-related data include normalized radar cross-section (NRCS), incidence angle, and the image spectra parameters extracted from KaRIn Level 1B (L1B) data, while the interference-related data correspond to the Level 2 (L2) volumetric correlation, which characterizes the influence of ocean wave scattering on interferometric coherence. The machine learning model is built upon a Multi-Layer Perceptron (MLP), which serves as a nonlinear fitting tool. SWH retrievals from the proposed method and the existing L2 SWH product as a reference were validated by the collocated European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, Haiyang2C (HY2C) and Haiyang2D (HY2D) altimeter data, and National Data Buoy Center (NDBC) buoy data. Validations show that both KaRIn SWH have a good agreement with collocations in terms of correlation coefficient (COR), BIAS and root mean square error (RMSE). Moreover, the retrieval accuracy from the proposed method (with an RMSE of about 0.29 m) is better than that of the L2 product (with an RMSE of about 0.46 m) when validated against the collocated ECMWF datasets. Ablation analysis further confirms that image spectra parameters and volumetric correlation are the dominant factors driving the retrieval accuracy improvement, with notable contribution differences among the sub-parameters of spectral features. This performance gain arises from the complementary physical mechanisms of backscattering and interferometric observables, which describe sea state information from independent dimensions. These accurate SWH retrievals can help correct sea state biases for collocated KaRIn sea surface height products and complement wave products from other satellite sensors.

Accurate and timely wildfire hotspot detection is essential for ecological sustainability and supporting climate resilience strategies. Although sensors such as MODIS and VIIRS have been widely used for wildfire detection, the potential of ocean color satellites for terrestrial wildfire monitoring remains largely unexplored. In this study, a Spectral–Spatial Attention U-Net (SSA-UNet) framework is proposed for wildfire hotspot detection using multispectral observations from the HY-1E Coastal Zone Color Scanner II (COCTS2) over the Three-North region of China. The proposed framework integrates spectral attention to enhance fire-sensitive bands and spatial attention to capture contextual wildfire patterns under complex environmental conditions. Experimental results show that SSA-UNet achieves a Precision of 0.8913, Recall of 0.7961, and F1-score of 0.8680, outperforming conventional threshold-based approaches and baseline deep learning models. Ablation experiments further demonstrate the effectiveness of the spectral–spatial attention mechanism, while band analysis highlights the important contributions of near-infrared, shortwave infrared, and thermal infrared observations for wildfire hotspot detection. The real wildfire case analysis further confirms the practical applicability of the proposed framework. The results demonstrate that HY-1E COCTS2 data have considerable potential for large-scale terrestrial wildfire monitoring when combined with deep learning techniques.