Ocean-moored buoys play an important role in global ocean environment monitoring. Motivated by building a sustainable ocean buoy observational network, a spatial optimization approach is proposed to site buoy stations to maximize spatial monitoring efficiency (SME). To achieve this goal, a non-linear, continuous maximum coverage location model named CMCP-Ocean was established, associated with a measurement method of the SME. Meanwhile, a heuristic framework based on the particle swarm optimization (PSO) algorithm was built to solve the CMCP-Ocean model, and optimization strategies including the multi-core parallel computing strategy, the particle velocity updating strategy based on spatial matching, and two potential station selection strategies related to the centroid-based random radiation method (CRRM) and random grid division method (RGDM) were established to improve computing performance. The effectiveness and efficiency of the PSO-based algorithm and the CMCP-Ocean model were verified by a series of experiments; the proposed computing schema named PSO-for-CMCP-Ocean has also proven to be practical and efficient. Finally, the PSO-for-CMCP-Ocean was applied to the buoy station selection of water mass monitoring in the Laizhou Bay of China, and a multi-scale sustainable site planning solution is reported.

Accurately predicting the long-term trajectory of a surface drifting buoy (SDB) is challenging. This paper proposes a promising solution to the SDB trajectory prediction based on artificial intelligence (AI) technologies. Initially, a scalable mathematical model for trajectory prediction is developed, transforming the challenge of predicting trajectory points into predicting velocities in eastward and northward directions. Subsequently, a four-layer trajectory prediction calculation framework (FLTPCF) is established, outlining a complete workflow for the real-time online training of marine environment data and SDBs’ trajectory prediction. Thirdly, for facilitating accurate long-term trajectory prediction, a hybrid artificial neural network trajectory prediction model, named CNN–BiGRU–Attention, integrates a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention mechanism (AM), tuned for spatiotemporal feature extraction and extended time-series reasoning. Extensive experiments, including ablation studies, comparative analyses with state-of-the-art models like BiLSTM and Transformer, evaluations against numerical methods, and adaptability tests, were conducted for justifying the CNN–BiGRU–Attention model. The results highlight the CNN–BiGRU–Attention model’s excellent convergence, accuracy, and generalization capabilities in predicting 24, 48, and 72 h trajectories for SDBs with varying drogue statuses and under different sea conditions. This work has great potential to promote the intelligent degree of marine environmental monitoring.

Ocean current is one of the most important parameters in ocean observation, and ocean current measurement based on electromagnetic induction is becoming more and more important because of its advantages such as simple structure and high measurement accuracy. However, it is difficult to detect weak current signals in a complex marine environment. In this paper, an electromagnetic induction current measurement scheme based on lock-in amplification technology is proposed. Key technologies such as the evaluation of induced current intensity, overall design, circuit design, and orientation design of the current meter were studied. The prototype of the electromagnetic current meter was developed and tested in the laboratory and at sea. The repeatability of current velocity and current direction was higher than 1.5 cm/s and 1.5°, respectively. A comparison test between the electromagnetic current meter prototype and Nortek ADCP (Acoustic Doppler Current Profiler) installed on a buoy at sea was carried out, and the correlation coefficients of the current velocity and current direction datum were 0.90 and 0.96, respectively. Through continuous on-site and fault-free operations at sea, the experimental data show that the electromagnetic current meter has good adaptability at sea, which provides feasible technical and equipment support for ocean current observation.

Fiber Bragg grating (FBG) has been widely used in many fields for the measurement of temperature, strain, and other parameters, but single-mode FBG has a cross-sensitivity problem between temperature and strain. In this paper, a new scheme is proposed to solve the problem by using parallel core-cladding FBGs, whose grating areas are located at the interface region of the core and cladding of the fiber. The sensing principle of parallel core-cladding FBGs for the simultaneous measurement of temperature and strain is theoretically analyzed. The femtosecond laser point-by-point method of the parallel core-cladding FBGs is experimentally investigated. Two parallel FBGs, namely core FBG and cladding FBG, are formed efficiently and conveniently by a single time inscription. Two transmission peaks have been observed, which are excited by the core mode and the cladding mode, respectively. The experiment results demonstrated that the proposed compact sensor can detect the temperature with sensitivity of 11.043 pm/°C and strain with sensitivity of 1.2101 pm/με up to 1.114×10 ³ με, and the relative errors of temperature and strain are less than 5.8% and 8.3%. The proposed parallel core-cladding FBGs are much more compact as one FBG element compared with the conventional structure with two cascaded FBGs or parallel FBGs. The parallel core-cladding FBGs can overcome the cross-sensitivity problem of single-mode FBG, which has great application value and potential in fiber sensing fields for dual-parameter.

At present, the methods and instruments for measuring the precipitation on land may not effectively work in the offshore corrosion environment which is characterized by salt and humid. The research look at investigating the reliable and precision approach of measuring rainfall and a capacitive rain gauge for the marine environment is developed. Firstly, according to the working principle and performance requirements of the capacitive rain gauge, the modular mechanical structure and capacitance voltage conversion circuit of the rain gauge are designed, and the calibration and stability experiments of the prototype are tested to evaluate the measurement error and exam the accuracy of the rain gauge measurement results changing over time. Then, environmental adaptability experiment is carried out on the capacitive rain gauge to explore its performance in the environment of temperature and humidity changes as well as salt spray. Finally, the feasibility of the rain gauge used in the marine environment is verified by the sea test of the prototype. The measurement error of the developed capacitive rain gauge is less than 1 mm, which provides a realization method for the measurement of precipitation in the marine environment.