With the rapid growth of Sentinel-1 synthetic aperture radar (SAR) data, how to exploit Sentinel-1 imagery and achieve effective and robust marine surveillance are crucial problems. In this paper, we present the OpenSARShip, a dataset dedicated to Sentinel-1 ship interpretation. The OpenSARShip, providing 11 346 SAR ship chips integrated with automatic identification system messages, owes five essential properties: specificality, large scale, diversity, reliability, and public availability. These properties make sure that the OpenSARShip achieves its objectives. The first is to provide researchers a benchmark dataset to develop applicable and adaptive ship interpretation algorithms and push the performance ceilings of data analysis. The other is to provide a dataset for performing application-oriented quality assessment for Sentinel-1 imagery, which can boost their applications in a targeting way. The construction and the organization of the OpenSARShip are discussed, which show the inside of the dataset and ensure the essential properties. The elaborate geometric and scattering analyses, the benchmark for classification, and the imagery applicability assessment by using the OpenSARShip all demonstrate the applicability and potential of the dataset.

We developed a deep learning model to forecast the sea surface temperature evolution associated with tropical instability waves.

This study develops an effective and robust method to mine bitemporal and dual‐polarization synthetic aperture radar (SAR) imagery information for coastal inundation mapping, based on deep convolutional neural networks. The specially tailored deep convolutional neural network‐based SAR coastal flooding mapping network (SARCFMNet) leverages two modifications to improve the accuracy and robustness: the physics‐aware input information design and the regularization. The proposed SARCFMNet is applied to the mapping and impact analysis of the coastal inundation caused by 2017 Hurricane Harvey near Houston, Texas, USA. Six pairs of Sentinel‐1 SAR images are analyzed along with corresponding ground truth data from Copernicus Emergency Management Service Rapid Mapping products and land‐cover types from Google Earth and OpenStreetMap. Flooded areas of 4,000 km² are extracted and analyzed. In an analyzed scene, 76% of the flooded area was agriculture area like pasture and cultivated crops field. A flooding map series shows the inundation shrinking rate is about 1% of the analyzed scene per day after the passage of Harvey. However, there was a delayed inundation in Glen Flora, Texas, after the heavy raining period. The average mapping accuracy and F1 score, that is, the harmonic mean of recall and precision, are 0.98 and 0.88, respectively. The impact of wind and cost‐sensitive loss functions on the development of SARCFMNet is also discussed. This study demonstrates the proposed method can accurately map hurricane‐induced inundation. The method can also be readily extended to other multitemporal SAR imagery classification applications.

In this study, a set of deep convolutional neural networks (CNNs) was designed for estimating the intensity of tropical cyclones (TCs) over the Northwest Pacific Ocean from the brightness temperature data observed by the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. We used 97 TC cases from 2015 to 2018 to train the CNN models. Several models with different inputs and parameters are designed. A comparative study showed that the selection of different infrared (IR) channels has a significant impact on the performance of the TC intensity estimate from the CNN models. Compared with the ground truth Best Track data of the maximum sustained wind speed, with a combination of four channels of data as input, the best multicategory CNN classification model has generated a fairly good accuracy (84.8%) and low root mean square error (RMSE, 5.24 m/s) and mean bias (−2.15 m/s) in TC intensity estimation. Adding attention layers after the input layer in the CNN helps to improve the model accuracy. The model is quite stable even with the influence of image noise. To reduce the side-effect of the very unbalanced distribution of TC category samples, we introduced a focal_loss function into the CNN model. After we transformed the multiclassification problem into a binary classification problem, the accuracy increased to 88.9%, and the RMSE and the mean bias are significantly reduced to 4.62 and −0.76 m/s, respectively. The results show that our CNN models are robust in estimating TC intensity from geostationary satellite images.

The Sentinel-1 synthetic aperture radar (SAR) allows sufficient resources for cross-pol wind speed retrievals over the ocean. In this paper, we present technical evaluation on wind retrieval from both Sentinel-1A and Sentinel-1B IW cross-pol images. Algorithms are based on the existing theoretical and empirical ones derived from the RADARSAT-2 cross-pol data. First, to better understand the Sentinel-1 observed normalized radar cross section (NRCS) values under various environmental conditions, we constructed a dataset that integrates SAR images with wind field information from scatterometer measurements. There are 11,883 matchup data in the experimental dataset. We then calculated the systemic noise floor of Sentinel-1 IW mode, and presented its unique noise characteristics among different sub-bands. Based on the calculated NESZ measurements, the noise is removed for all matchup data. Empirical relationships among the noise free NRCS ${\sigma }_{\mathrm{VH}}^0$ , wind speed, wind direction, and radar incidence angle are analyzed for each sub-band, and a piecewise model is proposed. We showed that a larger correlation coefficient, r, is achieved by including both wind direction and incidence terms in the model. Validation against scatterometer measurements showed the suitability of the proposed model.

Satellite-based synthetic aperture radar (SAR) can provide low-cost, frequent environment monitoring for dynamic intertidal zones. The critical problem is to realize pixel-level classification of SAR images of the intertidal zones with excellent and robust performance. Recently, deep learning, in particular deep convolutional neural networks, has provided us with promising solutions to this problem. Based on a sophisticated deep learning-based pixel-level classification model $\text{U}^{2}$ -Net, we propose an MB- $\text{U}^{2}$ -ACNet model suitable for intertidal zone land cover classification using dual-polarimetric SAR data integrated with environmental information, such as wind speed and tide level information. The MB- $\text{U}^{2}$ -ACNet model has a multibranch nested U-shaped encoding–decoding structure. We extract and fuse features from multiple data sources, including satellite remote sensing and environmental information, by establishing the multibranch structure. Furthermore, we propose an asymmetric convolution residual U-block for each encoding–decoding stage to improve the model’s feature extraction ability. Moreover, the model with attention mechanisms better distinguishes the importance of features from the channel’s perspectives and spatial dimensions. We construct a dataset with 106 Sentinel-1 SAR images from 2016 to 2020 for environmental monitoring in the intertidal zone of Shanghai Nanhui. On the dataset, the proposed model reaches the overall classification accuracy of 96.40% and the mean intersection over union score of 0.8307. The experiments show the advantages of the proposed model compared with the benchmarking models due to better feature extraction and multisource information fusion. In addition, the contributions of every added substructure are analyzed systematically.