Near-nadir interferometric imaging SAR (Synthetic Aperture Radar) techniques are promising in measuring global water extent and surface height at fine spatial and temporal resolutions. The concept of near-nadir interferometric measurements was implemented in the experimental Interferometric Imaging Radar Altimeters (InIRA) mounted on Chinese Tian Gong 2 (TG-2) space laboratory. This study is focused on mapping the extent of high mountain lakes in the remote Qinghai–Tibet Plateau (QTP) areas using the InIRA observations. Theoretical simulations were first conducted to understand the scattering mechanisms under near-nadir observation geometry. It was found that water and surrounding land pixels are generally distinguishable depending on the degree of their difference in dielectric properties and surface roughness. The observed radar backscatter is also greatly influenced by incidence angles. A dynamic threshold method was then developed to detect water pixels based on the theoretical analysis and ancillary data. As assessed by the LandSat results, the overall classification accuracy is higher than 90%, though the classifications are affected by low backscatter possibly from very smooth water surface. The algorithms developed from this study can be extended to all InIRA land measurements and provide support for the similar space missions in the future.

With the continuous exploration of space science, a large number of domain-related materials and scientific literature are constantly generated, mostly in the form of text, which contains rich and unexplored domain knowledge. Natural language processing technology has made rapid development and pre-trained language models provide promising information extraction tools. However, due to the strong professionalism of space science, there are many domain concepts and technical terms. Moreover, Chinese texts have complex language structures and word combinations, which may yield suboptimal performance by general pre-trained models such as BERT. In this work, we investigate how to adapt BERT to Chinese space science and propose the space science-aware pre-trained language model, namely, SSuieBERT. We validate it through downstream tasks such as named entity recognition, relation extraction, and event extraction, which can perform better than general models. To the best of our knowledge, our proposed SSuieBERT is the first pre-trained language model in space science, which can promote information extraction and knowledge discovery from space science texts.