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The paper mainly studies trade complementarity between China and three Baltic States, namely Lithuania, Latvia and Estonia. The paper first introduces China and the three Baltic States' current trade situation. It then makes an empirical analysis on the trade complementarity between China and the three Baltic States by using models of revealed comparative advantage (RCA) and trade complementarity index (TCI) respectively, which reveals that complementarities of China to the Baltic States are mainly in the laborintensive products, while complementarities of the three Baltic States to China are in the resource-intensive products. However, the current structure of imported goods from the Baltic States to China is different from the results of complementarity analysis. In this study an expanded trade gravity model is used to analyze trade potential, which helps to develop feasible trade strategies and it shows that trade between China and the Baltic States needs to be fully exploited.

Ionospheric phase delay can seriously affect the low-frequency spaceborne Synthetic Aperture Radar (SAR) systems. Ionospheric correction is, therefore, vital to improving the measurement accuracy of Interferometric SAR (InSAR). InSAR ionospheric correction relies mainly on two kinds of approaches, i.e., one uses SAR azimuth offsets measures, and the other achieves through a Range Split-Spectrum (RSS) technique. However, the two approaches have different spatial sensitivities to the ionosphere, but they can complement each other. In this study, we present an integration InSAR ionospheric correction method. This method uses the Helmert Variance Component Estimation (H-VCE) to reasonably allocate the weights for the ionospheric measurements obtained from the azimuth offset-based and RSS techniques to improve the ionospheric correction performance. The azimuth offset can be derived from a pixel offset-tracking procedure or multiple-aperture InSAR (MAI). We demonstrated the proposed method on 28 MHz FBS mode ALOS-1 PALSAR images acquired before and after the Wenchuan earthquake in Sichuan Province, China, 2008. This study also applied the proposed method to 14 MHz FBD mode ALOS-1 PALSAR images with no significant surface deformation and high coherence in Chile. After the ionospheric correction with the proposed method, we found that the ionospheric errors in the Wenchuan and Chile cases have varied from -31.0 to 21.1 cm and -4.7 to 93.9 cm, respectively. The results show that the proposed method can effectively remove the long-wavelength ionospheric delay. Our study reveals that this method can also alleviate the influence of local ionospheric disturbance simultaneously.

Summary The Greenland Ice Sheet (GrIS) is the largest ice body in the northern hemisphere. Quantifying the spatiotemporal characteristics of its mass change is crucial for understanding global climate change. Many studies have been focused on the long-term ice mass change and acceleration, but a more detailed analysis of multitemporal signals, including annual, interannual, and transient variations, is still imperative to study the periodic ice mass change. Here, we comprehensively characterize the multitemporal mass changes of the entire GrIS and subregions using a variational mode decomposition (VMD) method, applied to Gravity Recovery and Climate Experiment (GRACE) satellite data, surface mass balance model output, climate parameters, and GPS observations. We found both the interannual and transient mass variations of the southern subregions have larger amplitudes associated with pronounced precipitations, indicating that the southern mass change patterns are more vulnerable to short-term climate variability. We also found that the reported rapid mass loss in 2010, 2012, 2016, and 2019 should be attributed to the interannual signals, which result from low precipitations, significant runoff, and evapotranspiration. The largest interannual mass variation was discovered in 2019 (−235 Gt), but due to the combined effect of interannual loss (−147 Gt) and transient attenuation (−380 Gt), the greatest mass loss (−527 Gt) was observed at the end of 2012. Our study emphasizes the importance of multitemporal signals in characterizing the spatiotemporal GrIS mass change and exploring the coupling effect between ice mass change and climate conditions.