Interferometric imaging radar altimeter (InIRA) is the first spaceborne Ku-band interferometric synthetic aperture radar (InSAR) which is specially designed for ocean surface topography altimetry. It is on the Tiangong II space laboratory, which was launched on 15 September 2016. Different from any other spaceborne synthetic aperture radar (SAR), InIRA chooses a near-nadir incidence of 1°~8° in order to increase the altimetric precision and swath width. Limited by the size of the Tiangong II capsule, the baseline length of InIRA is only 2.3 m. However, benefitting from the low orbit, the signal-to-noise ratio of InIRA-acquired data is above 10 dB in most of the swath, which, to a certain extent, compensates for the short baseline deficiency. The altimetric precision is simulated based on the system parameters of InIRA. Results show that it is better than 7 cm on a 5-km grid and improves to 3 cm on a 10-km grid when the incidence is below 7.4°. The interferometric data of InIRA are processed to estimate the altimetric precision after a series of procedures (including image coregistration, flat-earth-phase removal, system parameters calibration and phase noise suppression). Results show that the estimated altimetric precision is close to but lower than the simulated precision among most of the swath. The intensity boundary phenomenon is first found between the near range and far range of the SAR images of InIRA. It can be explained by the modulation of ocean internal waves or oil slick, which smooths ocean surface roughness and causes the modulated area to appear either brighter or darker than its surroundings. This intensity boundary phenomenon indicates that the available swath of high altimetric precision will be narrower than expected.

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.

The interferometric imaging radar altimeter (InIRA), mounted on the Tiangong-2 space laboratory, utilizes a small incidence and a short interferometric baseline to achieve altimetry for wide swathes of ocean surface topography and inland water surface elevation. To obtain a high-precision digital elevation model (DEM), calibration of the interferometric system parameters is necessary. Because InIRA utilizes the small-incidence interference system design, serious coupling occurs between the interferometric parameters. Commonly used interferometric calibration methods tend to fall into the local optimal solution for InIRA. Because evolutionary algorithms have a stronger robustness and global search ability, they are better suited to handling the solution space structure under the coupling of complex interferometric parameters. This article establishes an interferometric calibration optimization model for InIRA by utilizing the relative flatness of the lake surface as an inequality constraint. Furthermore, an adaptive penalty coefficient constraint evolutionary algorithm is designed to solve the model. The proposed method was tested on actual InIRA data, and the results indicate that it efficiently adjusts interferometric parameters, enhancing the precision of measurements for Qinghai Lake elevation.

Two-dimensional (2D) Water Surface Elevations (WSEs) can be measured for the first time using a new generation of radar altimeter, which uses the interferometric synthetic aperture radar (InSAR) technique on near-nadir swath to measure elevations of both the inland surface waters and the ocean with unprecedented swath and spatial resolution. The Interferometric Imaging Radar Altimeter (InIRA) on the Tiangong-2 space laboratory of China is the first spaceborne interferometric radar altimeter, which was launched in 2016. Calibration is essential to a guarantee of the measurement accuracy. Not only the cross-track errors, but also the along-track errors have to be estimated and corrected. This paper proposes a correction method to remove the azimuth time shift of the radar system using geographic positions of the surrounding lakeshores and riverbanks. Real acquisitions of InIRA over the largest inland lake of China, i.e., the Qinghai Lake, have been selected to validate the method. Both azimuth and range system time errors, the media delays, and the cross-track phase errors have been estimated and corrected on the level-1 complex products. The derived 2D WSEs over an area of ~400 square kilometers have been compared with the high-resolution data of Sentinel-3A Synthetic Aperture Radar Altimeter (SRAL). The validation results have shown good agreements in both along-track and cross-track directions with a root mean square error of 28.1 cm and a mean absolute error of 22.7 cm.

Keywords: Key Laboratory of Space Utilization, Technology and Engineering Center for space Utilization, Chinese Academy of Sciences, Beijing, China Elevation measurements of the continental water surface have been poorly collected with in situ measurements or occasionally with conventional altimeters with low accuracy. Techniques using InSAR at near-nadir angles to measure the inland water elevation with large swath and with high accuracy have been proposed, for instance, the WSOA on Jason 2 and the KaRIn on SWOT. However, the WSOA was abandoned unfortunately and the SWOT is planned to be launched in 2021. In this paper, we show real acquisitions of the first spaceborne InSAR of such kind, the Interferometric Imaging Radar Altimeter (InIRA), which has been working on Tiangong II spacecraft since 2016. We used the 90-m SRTM DEM as a reference to estimate the phase offset, and then an empirical calibration model was used to correct the baseline errors.

Remote sensing change detection based on a map reference and an up-to-date image boosts timely observation of the Earth's surface when earlier images are lacking for comparison. However, the semantic gap between high-level map categories and low-level image details hinders the extraction of homogeneous features for robust temporal association in change detection. Unlike conventional approaches that either compare pixel-level visual similarity or propagate segmentation errors, we propose a novel framework, Language-VIsion Discriminator for dEtecting changes, LaVIDE, which bridges the semantic gap between high-level map categories and low-level image details using language as an intermediary. Specifically, we intro duce restricted prompt learning to generate context-aware textual prompts that align map semantics with image content, and an object-aware embedding enhancement strategy to integrate object level attributes (e.g., shape, boundary) into map representations. These components enable robust cross-modal alignment within a unified language-vision feature space. Extensive experiments on four benchmarks, DynamicEarthNet, HRSCD, BANDON, and SECOND, demonstrate that LaVIDE outperforms state-of-the art methods by significant margins, achieving 18.4% and 5.2% improvements in IoU on multi-class and single-class change detection tasks, respectively. Our framework not only advances the accuracy of map-image change detection but also provides a practical solution for rapid map updating with minimal human intervention, promising broad impacts in urban planning, disaster assessment, and ecological conservation. Code and datasets are available at: https://github.com/ShuGuoJ/LAVIDE.git.