Sea surface temperature (SST) prediction is not only theoretically important but also has a number of practical applications across a variety of ocean-related fields. Although a large amount of SST data obtained via remote sensor are available, previous work rarely attempted to predict future SST values from history data in spatiotemporal perspective. This letter regards SST prediction as a sequence prediction problem and builds an end-to-end trainable long short term memory (LSTM) neural network model. LSTM naturally has the ability to learn the temporal relationship of time series data. Besides temporal information, spatial information is also included in our LSTM model. The local correlation and global coherence of each pixel can be expressed and retained by patches with fixed dimensions. The proposed model essentially combines the temporal and spatial information to predict future SST values. Its structure includes one fully connected LSTM layer and one convolution layer. Experimental results on two data sets, i.e., one Advanced Very High Resolution Radiometer SST data set covering China Coastal waters and one National Oceanic and Atmospheric Administration High-Resolution SST data set covering the Bohai Sea, confirmed the effectiveness of the proposed model.

Cloud occlusion is a common problem in the satellite remote sensing (RS) field and poses great challenges for image processing and object detection. Most existing methods for cloud occlusion recovery extract the surrounding information from the single corrupted image rather than the historical RS image records. Moreover, the existing algorithms can only handle small and regular-shaped obnubilation regions. This letter introduces a deep convolutional generative adversarial network to recover the RS sea surface temperature images with cloud occlusion from the big historical image records. We propose a new loss function for the inpainting network, which adds a supervision term to solve our specific problem. Given a trained generative model, we search for the closest encoding of the corrupted image in the low-dimensional space using our inpainting loss function. This encoding is then passed through the generative model to infer the missing content. We conduct experiments on the RS image data set from the national oceanic and atmospheric administration. Compared with traditional and machine learning methods, both qualitative and quantitative results show that our method has advantages over existing methods.

In this letter, we investigated how to apply a deep learning method, in particular convolutional neural networks (CNNs), to an ocean front recognition task. Exploring deep CNNs knowledge to ocean front recognition is a challenging task, because the training data is very scarce. This letter overcomes this challenge using a sequence of transfer learning steps via fine-tuning. The core idea is to extract deep knowledge of the CNN model from a large data set and then transfer the knowledge to our ocean front recognition task on limited remote sensing (RS) images. We conducted experiments on two different RS image data sets, with different visual properties, i.e., colorful and gray-level data, which were both downloaded from the National Oceanic and Atmospheric Administration (NOAA). The proposed method was compared with the conventional handcraft descriptor with bag-of-visual-words, original CNN model, and last-layer fine-tuned CNN model. Our method showed a significantly higher accuracy than other methods in both datasets.

Ocean eddies, as a ubiquitous phenomenon of the global ocean, are extremely important for ocean energy and material exchanges. Therefore, efficient eddy detection and tracking are crucial for advancing our understanding of ocean dynamics. This work presents a framework for automatic ocean eddy detection and tracking by leveraging state-of-the-art machine learning algorithms. First, we propose a new convolutional neural network model for multieddies detection. This model is capable of extracting accurate boundary information of eddies and fitting the gap between semantic context and sea surface height (SSH). Second, a tracking algorithm is designed to track eddies lasting a number of days and provide visualization of the dynamical processes governing eddies' movements. Finally, we have made our data set publicly available, which is named SCSE-Eddy and can be used as a benchmark to evaluate the performances of artificial intelligence (AI)-based eddy detection methods. The data set covers daily remotely sensed SSH data located in the South China Sea and its eastern sea areas over a period of 15 years. The experimental results show that our methods achieve promising performances compared to existing approaches, especially for the eddies with indistinct geographical border. We believe that this work opens a new avenue for oceanographers to better discover and understand the physical properties of ocean eddies.

Ocean front is one typical geophysical phenomenon acting as oases in the ocean for fishes and marine mammals. Accurate ocean-front prediction is critical for fishery and navigation safety. However, the formation and evolution of ocean fronts are inherently non-linear and are influenced by various factors such as ocean currents, wind fields, and temperature changes, making ocean front prediction a considerable challenge. This study proposes a temporal sensitive network named Attention-ConvNet to address this challenge. Ocean fronts exhibit significant multiscale characteristics, requiring analysis and prediction across various temporal and spatial scales. The proposed network designs a Hierarchical Attention Mechanism (HAM) that efficiently prioritizes relevant spatial and temporal information to meet the specific requirement. What is more, the proposed network utilizes a complex Hierarchical Branching Convolutional Network (HBCNet) architecture, which allows our network to leverage the complementary strengths of spatial and temporal information, effectively capturing the dynamic and complex variations of ocean fronts. In general, the network prioritizes and focuses on the most relevant information of front dynamics, which ensures its ability to effectively predict the ocean front. External experiments demonstrate that our network significantly outperforms conventional methods, confirming its capability for precise ocean-front prediction. The codes will be publicly available at https://github.com/yuhudeyue/Ocean-Front-Prediction-Model.

Identifying fronts manually from satellite images is a tedious and subjective task. Accordingly, edge detection algorithms are introduced for automatic detection of fronts. However, traditional algorithms cannot be applied to cloud-contaminated images, because missing data caused by occasional cloud coverage interferes with front detection. To diminish this risk, this letter proposes a new algorithm for a quick and an accurate detection of fronts from an instant cloud-contaminated sea surface temperature (SST) image, instead of depending on the daily or weekly averaged SST images. This algorithm adopts a data-driven analog interpolation method, which estimates missing values from the historical data of the same region. After reducing the contour between the interpolated data and the original data, an instant front detection algorithm is proposed based on microcanonical multiscale formalism (MMF). The algorithm utilizes MMF to detect singularity exponents (SEs), and then enhances the features detected in a cloud-contaminated region. Finally, a threshold is set to extract fronts from SE. Experimental results on an AVHRR satellite SST image of 12:00 o'clock covering China Coastal waters confirmed the effectiveness of the proposed algorithm.