Melting and evolution of Arctic Ocean sea ice in summer are one of the main factors affecting sea ice albedo and climate. At present, remote sensing images of sea ice are mainly acquired with the help of payload platforms, such as aircraft or satellites. The above methods have a large measurement scale and a short period. Sea ice research also requires suitable equipment for fine-scale, visualized long-term tracking observations of target sea ice regions. This article investigates the feasibility of measuring interannual variability in sea ice morphology in the Arctic sea ice environment using a low-power gimbal camera automated by satellite communications. Year-round observational data from the arctic field were used. The sea ice images were defogged and edge-enhanced by a newly developed algorithm with the help of human visual enhancement principles. During the 11th Chinese National Arctic Science Expedition, the system was placed on perennial ice in the northern Beaufort Basin to monitor sea ice appearance changes and the external state of the ice mass balance buoy. The system fuses visually enhanced sea ice images with ice balance buoy data from the same ice floe in a multi-scale environmental sequence to obtain sea ice observations with image sequences. The data complement the need for long-duration, fixed-ice, and short-visual-range observations in sea-ice studies. The equipment design methods and image processing algorithms involved in the experimental process have accumulated engineering experience for this application area. The research methodology provides a new framework for long-term, and visualized sea ice observation methods.

In recent years, with the large-scale reduction of Arctic sea ice, the supplement of chlorophyll sensor data in seawater has become an essential part of environmental assessment. Accurately predicting the chlorophyll sensor data in seawater is of great significance to protect the Arctic marine ecological environment. A machine learning prediction method combined with wavelet transform is proposed. This process uses data from upper ocean observation buoys placed in the Arctic Ocean (A.O.) to predict the sensor analogue of chlorophyll-a (C.A.) in the upper ocean of the Arctic Ocean. Choose the best wavelet transform method and prevent the LSTM gradient from disappearing. A model combining SAE (stacked autoencoder) Bi (bidirectional) LSTM (long short-term memory) and wavelet transform is proposed. Experiments were conducted to compare the predictive performance of buoy data input as univariate at two different times and locations in the Arctic Ocean. The results show that compared with other models (such as LSTM), in the SAE Bi LSTM model, the data of the two sites have the highest prediction accuracy. The best wavelet transform methods are fourth-order four-layer and first-order four-layer. The observational data of the Chukchi Sea from 2018 to 2019 obtained the best prediction results. The root mean square error (RMSE) value is 0.02003 volts; the average absolute error (MAE) is 0.0841 volts. This research provides a new method for predicting the chlorophyll sensor parameters in the upper ocean through the sea ice buoy observed at a given point, which helps to improve the accuracy of the ocean sensor parameter prediction on the Arctic ice buoy.

Accurate short-term small-area meteorological forecasts are essential to ensure the safety of operations and equipment operations in the Antarctic interior. This study proposes a deep learning-based multi-input neural network model to address this problem. The newly proposed model is predicted by combining a stacked autoencoder and a long- and short-term memory network. The self-stacking autoencoder maximises the features and removes redundancy from the target weather station’s sensor data and extracts temporal features from the sensor data using a long- and short-term memory network. The proposed new model evaluates the prediction performance and generalisation capability at four observation sites at different East Antarctic latitudes (including the Antarctic maximum and the coastal region). The performance of five deep learning networks is compared through five evaluation metrics, and the optimal form of input combination is discussed. The results show that the prediction capability of the model outperforms the other models. It provides a new method for short-term meteorological prediction in a small inland Antarctic region.

In the inland areas of Antarctica, the establishment of an unmanned automatic observation support system is an urgent problem and challenge. This article introduces the development and application of an unmanned control system suitable for inland Antarctica. The system is called RIOD (Remote Control, Image Acquisition, Operation Maintenance, and Document Management System) for short. At the beginning of this research project, a mathematical model of heat conduction in the surface observation chamber was established, and the control strategy was determined through mathematical relationships and field experiments. Based on the analysis of local meteorological data, various neural network models are compared, and the training model with the smallest error is used to predict the future ambient temperature. Moreover, the future temperature is substituted into the mathematical model of thermal conductivity to obtain the input value of the next input power, to formulate the operation strategy for the system. This method maintains the regular operation of the sensor while reducing energy consumption. The RIOD system has been deployed in the Tai-Shan camp in China’s Antarctic inland inspection route. The application results 4.5 months after deployment show that the RIOD system can maintain stable operation at lower temperatures. This technology solves the demand for unmanned high-altitude physical observation or astronomical observation stations in inland areas.

To address coverage gaps in high-latitude space weather monitoring caused by constraints in energy, bandwidth, and labeled samples, this study presents a systematic solution deployed in Hailar, China. We constructed a Cloud–Edge–Terminal system featuring wind–solar hybrid energy and RK3588-based edge computing, achieving six months of stable ionospheric–geomagnetic observation under −40 °C. Furthermore, we propose a Dual-Adversarial Recurrent Autoencoder (DA-RAE) for anomaly detection. Utilizing a single-source domain strategy, the model learns physical manifolds from quiet-day data, enabling zero-shot anomaly perception in the unsupervised target domain. Field tests in March 2025 demonstrated superior generalized anomaly detection capabilities, successfully identifying both transient space weather events and environmental equipment faults (baseline drifts). This work validates the value of edge intelligence for autonomous operations in extreme environments, providing a reproducible paradigm for global ground-based networks.