Snow depth and sea ice thickness in the Polar Regions are significant indicators of climate change and have been measured over several decades by ice-tethered buoys. However, sea ice temperature profiles measured by ice-tethered buoys are rarely used to infer snow depth and sea ice thickness owing to the lack of automatic discrimination algorithms, restricting the use of the data for sea ice thermodynamics studies. In this study, snow depth and sea ice thickness were retrieved through the measurements of sea ice temperature profiles using discrimination algorithms of the change point and the maximum likelihood detection methods. The data measured by 50 ice-tethered buoys were used to evaluate the accuracy of the results determined by the algorithm. Influences on the seasonal sea ice thermodynamic state, vertical interval of temperature sensors on the buoys, and initial ice thickness on the estimation errors were also evaluated. The performance of the discrimination algorithm for the data from the Arctic and Antarctic regions was also compared. There were no identifiable differences between the estimation errors from the Arctic and Antarctica. Increases in both the interval of the temperature sensors and the initial ice thickness enlarged the error for the estimation of ice thickness. A procedure developed in this study strengthens the potential application of measurements from the ice-tethered buoys only with the measurements of the vertical temperature profile of the layer of snow-covered ice, but not the measurements of ice basal and surface positions using acoustic sounding.

Temperature profiles of sea ice have been recorded more than a few decades. However, few high-precision temperature sensors can complete the observation of temperature profile of sea ice, especially in extreme environments. At present, the most widely used sea ice observation instruments can reach an accuracy of sea ice temperature measurement of 0.1 °C. In this study, a multilayer sea ice temperature sensor is developed with temperature measurement accuracy from −0.0047 °C to 0.0059 °C. The sensor system composition, structure of the thermistor string, and work mode are analyzed. The performance of the sensor system is evaluated from −50 °C to 30 °C. The temperature dependence of the constant current source, the amplification circuit, and the analog-to-digital converter (ADC) circuit are comprehensive tested and quantified. A temperature correction algorithm is designed to correct any deviation in the sensor system. A sea-ice thickness discrimination algorithm is proposed in charge of determining the thickness of sea ice automatically. The sensor system was field tested in Wuliangsuhai, Yellow River on 31 January 2018 and the second reservoir of Fen River, Yellow River on 30 January 2018. The integral practicality of this sensor system is identified and examined. The multilayer sea ice temperature sensor will provide good temperature results of sea ice and maintain stable performance in the low ambient temperature.

Continuous power supply for unmanned and automatic observation systems without suitable energy-storage capabilities in the polar regions is an urgent problem and challenge. However, few power-supply systems can stably operate over the long term in extreme environments, despite excellent performance under normal environments. In this study, a standalone hybrid wind–solar system is proposed, based on operation analysis of the observing system in the Arctic Ocean, the polar environments, and renewable-energy distribution in the polar regions. Energy-storage technology suitable for cold regions is introduced to support the standalone hybrid wind–solar system. Mathematical models of the power system at low temperature are also proposed. The low-temperature performance and characteristics of lead–acid battery are comprehensively elucidated, and a dedicated charging strategy is developed. A hybrid wind–solar charging circuit is developed using a solar charging circuit, a wind turbine charging circuit, a driver circuit, a detection circuit, an analog-to-digital converter (ADC) circuit, and an auxiliary circuit. The low temperature stability of charging circuit is test from −50 °C to 30 °C. Temperature correction algorithm is designed to improve the efficiency of the power supply system. The power generation energy of the power system was simulated based on the monthly average renewable energy data of Zhongshan Station. A case study was applied to examine the technical feasibility of the power system in Antarctica. The five-month application results indicate that the power system based on renewable energy can maintain stable performance and provide sufficient power for the observing system in low ambient temperatures. Therefore, this power system is an ideal solution to achieve an environmentally friendly and reliable energy supply in the polar regions.

The density–depth relationship of the Antarctic ice sheet is important for establishing a high-precision surface mass balance model and predicting future ice-sheet contributions to global sea levels. A new algorithm is used to reconstruct firn density and densification rate by inverting monostatic radio wave echoes from ground-operated frequency-modulated continuous wave radar data collected near four ice cores along the transect from Zhongshan Station to Dome A. The inverted density profile is consistent with the core data within 5.54% root mean square error. Due to snow redistribution, the densification rate within 88 km of ice core DT401 is correlated with the accumulation rate and varies greatly over horizontal distances of <5 km. That is, the depth at which a critical density of 830 kg m⁻³is reached decreases and densification rate increases in high-accumulation regions but decreases in low-accumulation regions. This inversion technique can be used to analyse more Antarctic radar data and obtain the density distribution trend, which can improve the accuracy of mass-balance estimations.

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.