In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.

The China France Oceanography Satellite (CFOSAT) and Haiyang‐2B (HY‐2B) satellites were successively launched in China on the 29^th and 25^th of October 2018, respectively. HY‐2B carries the second Chinese radar altimeter along with three other microwave sensors, whereas CFOSAT contains the first Surface Waves Investigation and Monitoring (SWIM) instrument and a fan beam rotating scatterometer. As missions for measuring the dynamic marine environment, both satellites can measure the nadir significant wave height (SWH). For data obtained from a newly launched satellite to be utilized with confidence, the quality of SWH from the two satellites requires validation. In this study, the HY‐2B altimeter and CFOSAT nadir SWHs have been validated against the National Data Buoy Center (NDBC) buoys and the Jason‐3 altimeter SWH data, respectively, which resulted in CFOSAT nadir SWH having the best accuracy and HY‐2B having the best precision. The SWHs of the two missions are also calibrated by Jason‐3 and NDBC buoys. Following calibration, the root mean square error (RMSE) of CFOSAT and HY‐2B are 0.21 and 0.27 cm, respectively, when compared to Jason‐3, and 0.23 and 0.30 cm, respectively, compared to the buoys. Our results show that the two missions can provide good‐quality SWH and can be relied upon as a new data resource of global SWH.

The accuracy of a wave model can be improved by assimilating an adequate number of remotely sensed wave heights. The Surface Waves Investigation and Monitoring (SWIM) and Scatterometer (SCAT) instruments onboard China‐France Oceanic SATellite provide simultaneous observations of waves and wide swath wind fields. Based on these synchronous observations, a method for retrieving the SWH over an extended swath is developed using the deep neural network approach. With the combination of observations from both SWIM and SCAT, the SWH estimates achieve significantly increased spatial coverage and promising accuracy. As evidenced by the assessments of assimilation experiments, the assimilation of this “wide swath SWH” achieves an equivalent or better accuracy than the assimilation of the traditional nadir SWH alone and enhances the positive impact when assimilated with the nadir SWH. Therefore, insights into the better utilization of wave remote sensing in assimilation are presented.

Despite many investigations/studies on the surface wave‐induced stress, the global feature of the wave‐induced stress has not been obtained previously as that requires a simultaneous observation of wave spectra and wind on a global scale. The China France Oceanography Satellite (CFOSAT) provided an opportunity for the first time to evaluate the global wave‐induced stress and its contribution to the total wind stress. In this study, the global spatial distributions of wave‐induced stress and its correlated index for August to November in 2019 are presented using the simultaneous ocean surface winds and wave spectra from the CFOSAT. The main results show that the wave‐induced stress is fundamentally dependent on the wind and wave fields on a global scale and shows significant temporal and spatial variations. Further analyses indicate that there is an upward momentum flux under strong swells and low wind speeds (below ∼5 m/s), and an anticorrelation between the dimensionless wave‐induced stress and the proportion of swell energy to the total. Finally, the variations of the surface wave induced wind stress are clear asymmetric between northern and southern hemispheres in late summer but symmetric in late fall, which are closely associated with the seasonal changes in large‐scale atmospheric circulation.

Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles.

In this study, we analyzed the ocean surface wind speed dependence of off‐nadir data from the Surface Waves Investigation and Monitoring (SWIM) aboard the Chinese‐French Oceanography Satellite (CFOSAT). When the data were analyzed, the SWIM normalized radar cross section (NRCS) at most incidence angles decreased along the NCEP Global Forecast System wind speed. This trend is consistent with that in a Ku‐band geophysical model function at low incidence angles (KuLMOD2S), although there was a systematic bias between the two. The consistency indicates that the SWIM data quality was normal in terms of wind speed dependence. The bias was recalibrated using a numerical weather prediction‐based ocean calibration method and the wind speeds were retrieved from the recalibrated NRCS data using the KuLMOD2S model. Finally, the retrievals at off‐nadir as well as the wind products at nadir were validated using collocated CFOSAT scatterometer (CSCAT) wind measurements. When the results were validated, the retrievals at 0.5° were consistent with the averaged wind products at nadir and both had a root mean square difference (RMSD) around 1.4 m/s compared to the CSCAT winds. Moreover, the winds retrieved from the SWIM data at most incidence angles were in good agreement with the CSCAT winds,and the RMSD values were within 2.0 m/s for incidence angles less than 7.5°, although the retrieval accuracy was poor at larger incidence angles. These results demonstrate that the SWIM data at incidence angles below 7.5° have an ability to retrieve the wind speed.