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Ocean internal waves (IWs) are widespread submesoscale dynamical phenomena in oceans, and they have important impacts on energy transfer, nutrient transport, and human activities. In this study, Sentinel-1 synthetic aperture radar (SAR) images from 2014 to 2023 were collected to construct a global IW dataset (S1-IW-2023) through a series of optimized data processing. S1-IW-2023 was constructed to address the issue of insufficient data and lack of variation in the deep learning IW datasets; it can be used in IW studies using deep learning methods to enhance model generalizability and robustness. Moreover, considering the limitations of existing convolutional neural network (CNN)-based IW detection models in handling complex interferences in SAR images, resulting in frequent false positives, false negatives, or inaccurate bounding box positioning, we employed transfer learning to train a Transformer-based hierarchical IW detector, IWD-Net, that extracts features via Swin Transformer and fuses the visual, semantic, and contextual features of IWs via a multiscale feature fusion network. Experimental results demonstrated the effectiveness of applying Transformer concepts to IW detection for addressing complex interferences. This study provides an efficient and stable new method for extracting IW features from massive SAR data and lays the foundation for applying Transformer concepts to detect IWs in SAR images.

The Geostationary Orbiting Satellite (GOS) offers extensive opportunities for the study of oceanic internal waves (IWs) through high-frequency observations. In this study, the spatial and temporal distributions of sunglint from 3 GOSs (Himawari-8, FY-4A, and GK-2A) were calculated, and the observation times of IWs in various seas were also recorded. The GOS can continuously observe IWs at a frequency of 10 min for 2 to 3 h. As demonstrated by the application to IWs in the Andaman Sea, the GOS effectively captures the surface features of IWs, including soliton number, the length and wavelength of the leading wave, and the speed and direction of propagation. Furthermore, the GOS can be used to track the dynamic processes of IWs within a short duration and provide more accurate “instantaneous” phase speeds. In the case of the Indonesian Seas, the average error of the GOS-derived phase speeds is 0.13 m/s compared to the Korteweg–de Vries phase speeds. Additionally, a 7-day observation from FY-4A suggests the possibility of diurnal IWs in the Sulu Sea. The advent of high-temporal-resolution GOS provides an enriched dataset for oceanic IW studies, which will contribute greatly to a more comprehensive understanding of IW mechanisms.

The launch of Seasat, TIROS-N and Nimbus-7 satellites equipped with ocean observation sensors in 1978 opened the way for remote sensing applications in ocean observation [...]

In this study, typhoon eyes have been delineated using wavelet analysis from the synthetic aperture radar (SAR) images of ocean surface roughness and from the warm area at the cloud top in the infrared (IR) images, respectively. Envisat SAR imagery, and multi-functional transport satellite (MTSAT) and Feng Yun (FY)-2 Chinese meteorological satellite IR imagery were used to examine the typhoons in the western North Pacific from 2005 to 2011. Three cases of various typhoons in different years, locations, and conditions have been used to compare the typhoon eyes derived from SAR (on the ocean surface) with IR (at the cloud-top level) images. Furthermore, the best track data from the Joint Typhoon Warning Center (JTWC), Chinese Meteorological Administration (CMA), and the Japan Meteorological Agency (JMA) are checked for the calibration. Because of the vertical wind shear, which acts as an upright tilt, the location of the typhoon eye on the ocean surface differs from that at the top of the clouds. Consequently, the large horizontal distance between typhoon eyes on the ocean surface and on the cloud top implies that the associated vertical wind shear profile is considerably more complex than generally expected. This result demonstrates that SAR can be a useful tool for typhoon monitoring study over the ocean surface.

Tracking and monitoring ocean features which have short coherent time periods from sequential satellite images requires that the images have both very high spatial resolutions and short temporal sampling intervals (i.e., repeated cycles). Satellite images from a single sensor in a polar-orbiting satellite usually cannot meet these requirements since high spatial resolution of the sensor may result in relatively long temporal sampling interval and vice versa, such as the case of Synthetic Aperture Radar (SAR). This paper presents a new multi-sensor approach to overcome the long temporal sampling interval associated with a single SAR sensor while taking advantage of high spatial resolution of SAR images for the application of ocean feature tracking.Currently, there are two SAR sensors on different satellites, the European Remote Sensing Satellite-2 (ERS-2) and the ENVIronment SATellite (ENVISAT), having acquisition time offset around 28 minutes with almost exactly the same path.That is, ERS-2 is following ENVISAT with a 28-minutes delay, which is a good time-scale for ocean mesoscale feature tracking.A pair of SAR images from ERS-2 and ENVISAT collected on April 27, 2005 has been chosen to track ocean surface features by using wavelet analysis. As demonstrated in the case studies, this technique is robust and capable to derive ocean surface drift near an oil slick and around a big eddy in the South China Sea (SCS).