The sea surface temperature (SST) is an important parameter of the energy balance on the Earth’s surface. SST prediction is crucial to marine production, marine protection, and climate prediction. However, the current SST prediction model still has low precision and poor stability. In this study, a medium and long-term SST prediction model is designed on the basis of the gated recurrent unit (GRU) neural network algorithm. This model captures the SST time regularity by using the GRU layer and outputs the predicted results through the fully connected layer. The Bohai Sea, which is characterized by a large annual temperature difference, is selected as the study area, and the SSTs on different time scales (monthly and quarterly) are used to verify the practicability and stability of the model. The results show that the designed SST prediction model can efficiently fit the results of the real sea surface temperature, and the correlation coefficient is above 0.98. Regardless of whether monthly or quarterly data are used, the proposed network model performs better than long short-term memory in terms of stability and accuracy when the length of the prediction increases. The root mean square error and mean absolute error of the predicted SST are mostly within 0–2.5 °C.

Monitoring of offshore aquaculture zones is important to marine ecological environment protection and maritime safety and security. Remote sensing technology has the advantages of large-area simultaneous observation and strong timeliness, which provide normalized monitoring of marine aquaculture zones. Aiming at the problems of weak generalization ability and low recognition rate in weak signal environments of traditional target recognition algorithm, this paper proposes a method for automatic extraction of offshore fish cage and floating raft aquaculture zones based on semantic segmentation. This method uses Generative Adversarial Networks to expand the data to compensate for the lack of training samples, and uses ratio of green band to red band (G/R) instead of red band to enhance the characteristics of aquaculture spectral information, combined with atrous convolution and atrous space pyramid pooling to enhance the context semantic information, to extract and identify two types of offshore fish cage zones and floating raft aquaculture zones. The experiment is carried out in the eastern coastal waters of Shandong Province, China, and the overall identification accuracy of the two types of aquaculture zones can reach 94.8%. The results show that the method proposed in this paper can realize high-precision extraction both of offshore fish cage and floating raft aquaculture zones.

Timely monitoring of marine aquaculture has considerable significance for marine ecological protection and maritime safety and security. Considering that supervised learning needs to rely on a large number of training samples and the characteristics of intensive and regular distribution of the laver aquaculture zone, in this paper, an inaccurate supervised classification model based on fully convolutional neural network and conditional random filed (FCN-CRF) is designed for the study of a laver aquaculture zone in Lianyungang, Jiangsu Province. The proposed model can extract the aquaculture zone and calculate the area and quantity of laver aquaculture net simultaneously. The FCN is used to extract the laver aquaculture zone by roughly making the training label. Then, the CRF is used to extract the isolated laver aquaculture net with high precision. The results show that the $kappa$ coefficient of the proposed model is 0.984, the $F_1$ is 0.99, and the recognition effect is outstanding. For label production, the fault tolerance rate is high and does not affect the final classification accuracy, thereby saving more label production time. The findings provide a data basis for future aquaculture yield estimation and offshore resource planning as well as technical support for marine ecological supervision and marine traffic management.

Recent breakthroughs in artificial intelligence (AI), particularly in deep learning, have transformed the processing and analysis of large-scale remote sensing (RS) data. These advancements deliver remarkable performance in tasks such as imagery interpretation and quantitative inversion, significantly boosting the efficiency and reach of RS interpretation. AI extends the utility of RS data across diverse fields, including disaster monitoring, environmental protection, and urban planning, cementing its role as a cornerstone technology in RS. This review systematically explores the paradigm transfer in RS driven by AI, proposing a dual-dimensional “data-task” evolution framework that traces the transition from single-modal, single-task models to multi-modal, multi-task architectures in imagery interpretation and quantitative RS research. Specifically, architectural innovations overcome the limitations of single-sensor systems by fusing optical, SAR, and textual data, enabling feature reuse and enhancing cross-task synergy through collaborative mechanisms. The emergence of RS agents introduces a closed-loop intelligence paradigm—integrating multi-modal perception, automated task planning, and interactive decision feedback—transforming RS interpretation from passive perception to active cognition. Despite these advances, significant challenges persist, such as data heterogeneity, edge computing limitations, and model security vulnerabilities, all of which are thoroughly assessed in this review. Looking ahead, we advocate next-generation deep learning architectures that integrate expert knowledge and human feedback to address the evolving demands of Earth observation. This review charts a clear path toward more reliable, adaptable, and intelligent RS systems, heralding a new era of AI-powered Earth observation.

Mariculture is crucial in environmental monitoring and safety assurance of marine environments. Certain mariculture areas are often partially or completely submerged in water, which causes the target signal to be extremely weak and difficult to detect. A method of target recognition and classification based on the convolutional neural network called semantic segmentation can fully consider the space spectrum and context semantic information. Therefore, this study proposes a target extraction method on the basis of multisource feature fusion, such as nNDWI and G/R ratio. In this work, the proposed recognition algorithm is verified under the conditions of uniform distribution of strong, weak, and extremely weak signals. Results show that the G/R feature is superior under the condition of uniform distribution of strong and weak signals. Its mean pixels accuracy is 2.32% higher than RGB (combination of red band, green band, and blue band), and its overall classification accuracy is 98.84%. Under the condition of extremely weak signal, the MPA of the multisource feature method based on the combination of G/R and nNDWI is 10.76% higher than RGB, and the overall classification accuracy is 82.02%. Under this condition, the G/R features highlight the target, and nNDWI suppresses noise. The proposed method can effectively extract the information of weak signal in the marine culture area and provide technical support for marine environmental monitoring and marine safety assurance.

Hyperspectral imagery (HSI) delivers detailed spectral information critical for remote sensing applications such as high-accuracy land cover classification, quantitative parameter retrieval, and environmental monitoring. However, satellite-borne HSI often suffers from limited spatial resolution owing to inherent sensor constraints, whereas airborne HSI is constrained by restricted spatial coverage. Reconstructing high-resolution HSI from multispectral imagery emerges as a promising strategy to address these challenges. In this study, we propose the Frequency-Enhanced Spatial-Spectral Network (FESSN), a computationally efficient architecture that innovatively integrates multi-domain fusion across spatial, spectral, and frequency domains to achieve superior reconstruction performance. A key innovation is the neural network-driven frequency enhanced modulation (FEM), which adaptively refines spectral amplitudes and phases via fast Fourier transform, providing interpretable, parameter-efficient enhancements to bridge spatial-spectral modeling gaps. Meanwhile, a Mamba-based Multi-Scale Spatial Fusion module (MMSAF) that seamlessly integrates local features with long-range dependencies, and a U-shaped Spectral Module (USEM) that integrates Mamba and attention mechanisms to model inter-group and intra-group relationships, while adhering to spectral sparsity priors. The experimental results demonstrate FESSN outperforming six state-of-the-art methods in metrics like RMSE (up to 5.31% improvement), PSNR, SAM, ERGAS, and SSIM. Downstream tasks in land cover classification further validate its utility, positioning FESSN as a breakthrough in efficient, high-fidelity HSI reconstruction. To facilitate reproducibility and further research, the code will be publicly available at https://github.com/KustAIRS/TGRS-FESSN.