天基星空背景观测覆盖范围计算方法研究

针对日趋复杂的空间环境，天基观测由于其分布灵活和全天候观测的特性受到越来越多的关注，该类问题为典型的星空背景观测（above-the-horizon，ATH）问题。基于分段积分思想，提出了一种ATH观测三维空间覆盖范围的计算方法，涵盖了该问题所有可能的10种积分形式，适用于因观测约束参数不同取值而形成的任意几何构型场景。不仅在二维平面覆盖问题上与现有方法进行了比较并验证，还首次解决了ATH在三维空间中覆盖体积的定量解析计算难题。仿真结果表明，平面和空间覆盖最大化所对应的最优观测轨道高度存在明显差异，空间覆盖计算模型能更为准确有效地表示卫星覆盖性能，同时能为星座优化设计提供参考。     

An atmosphere-breathing propulsion system using inductively coupled plasma source

CubeSats have attracted more research interest recently due to their lower cost and shorter production time. A promising technology for CubeSat application is atmosphere-breathing electric propulsion, which can capture the atmospheric particles as propulsion propellant to maintain long-term mission at very low Earth orbit. This paper designs an atmosphere-breathing electric propulsion system for a 3 U CubeSat, which consists of an intake device and an electric thruster based on the inductively coupled plasma. The capture performance of intake device is optimized considering both particles capture efficiency and compression ratio. The plasma source is also analyzed by experiment and simulation. Then, the thrust performance is also estimated when taking into account the intake performance. The results show that it is feasible to use atmosphere-breathing electric propulsion technology for CubeSats to compensate for aerodynamic drag at lower Earth orbit.     

Frequency steering of spaceborne clocks based on XPNAV-1 observations

Due to high stable rotations, timing of pulsars provides a natural tool to correct the frequency deviation of spaceborne atomic clocks. Based on processing the observational data about a year of Crab pulsar given by XPNAV-1 satellite, we study the possibility of correcting the frequency deviation of spaceborne atomic clocks using pulsar timing. According to the observational data in X-ray band and the timing model parameters from radio observations, the pre-fit timing residuals with a level of 6...     

Robust extension of the simple sea-surface irradiance model to handle cloudy conditions for the global ocean using satellite remote sensing data

Sea-surface solar radiation (abbreviated as photosynthetically available radiation, PAR) in the visible wavelength (400–700 nm) is an essential parameter to estimate marine primary productivity and understanding phytoplankton dynamics, upper ocean physics and biogeochemical processes. Although many remote-sensing models were developed to estimate daily PAR (DPAR) from ocean colour data, these models often produce biases in the DPAR products under cloudy-sky and complex atmospheric conditions due to the lack of parameterization to deal with the cloud cover conditions and insufficient in-situ DPAR data. This study presents an Extended Sea-surface Solar Irradiance Model (ESSIM) for estimating DPAR over the global ocean. The ESSIM uses the direct and diffuse components from the Simple sea-surface Solar Irradiance Model (SSIM) along with a new parameter to handle cloudy conditions. The ESSIM produced DPAR products with greater accuracy under both clear and cloudy conditions. Its performance was tested on the time-series MODIS-Aqua images and compared with the concurrent in-situ data and the results from two global models. Results showed that the DPAR values produced by ESSIM agree with in-situ data better than the global models for all-sky conditions (with a mean relative error of 11.267 %; a root mean square error of 5.563 Em^?2day^?1; and a mean net bias of 2.917 Em^?2day^?1). The ESSIM performed slightly better than the SSIM for clear conditions and the Frouin's Operational Algorithm (FOA) for all-sky conditions. As the new parameterization accounts for cloudy conditions, the ESSIM produced more accurate results for cloud cover conditions across latitudes (up to 60°). The time-series Level-3 binned MODIS-Aqua data (global gridded) also demonstrated that the ESSIM improved the accuracy of DPAR products and produced spatially and temporally consistent DPAR products over the global ocean regardless of the seasons and sky conditions.     

Land use and cover change (LUCC) impacts on Earth’s eco-environments: Research progress and prospects

Land use and cover change (LUCC) is one of the key variables dominating land–atmosphere interactions and strongly affects the Earth’s eco-environments by altering surface properties. Numerous studies have been carried out to assess the impact of LUCC. However, the Earth is a large, open and complex system characterized by complex interactions between its eco-environments and drivers. This study aimed to summarize previous studies of the impact of LUCC on the Earth’s eco-environments and discuss the progress and limitations in suggesting future directions. Previous studies have confirmed that LUCC has a wide range of impacts on the Earth’s eco-environments, which are represented by the alternation of climate (temperature, precipitation, wind, and humidity), hydrology (soil moisture, runoff, and evapotranspiration), ecology and environmental (air, water, and soil) pollution. Physically, the impacts were mainly attributed to the disturbance of the surface radiation budget and matter conservation caused by LUCC. Although great achievements have been made, several challenges remain because of the unavoidable uncertainties in data sources and methodologies and the complexity of eco-environmental evolution. Therefore, data assimilation, physical-based investigations, contribution isolation, and full-process analysis are required to overcome these challenges in future research. The results of this study helped to capture the impact of LUCC and its physical mechanisms, which provide useful clues for future research and support the relative land use management for sustainable development.     

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).