The LJ1-01 satellite is the first dedicated nighttime light remote sensing satellite in the world and offers a higher spatial resolution than the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) satellites of the United States. This study compared the LJ1-01 nighttime light data with NPP/VIIRS data in the context of modeling socio-economic parameters. In the eastern and central regions of China, 10 parameters from the four aspects of gross regional product (annual average population, electricity consumption, and area of land in use) were selected to build linear regression models. The results showed that the LJ1-01 nighttime light data offered better potential for modeling socio-economic parameters than the equivalent NPP/VIIRS data; the former can be an effective tool for establishing models for socio-economic parameters. There were significant positive correlations between the two types of nighttime light data and the 10 socio-economic parameters; that for the gross regional product was the highest.

The Luojia1-01 satellite provides high-resolution, high-sensitivity nighttime light data at a resolution of 130 m. To effectively use the Luojia1-01 nighttime light data for global applications, the problems of relative and absolute positioning accuracy should be solved. This paper proposes a high accuracy regional geometric processing method of nighttime light imagery. We utilized a nighttime light image matching algorithm to obtain tie points, which are used in the planar block adjustment with ground control points. Then, orthorectification of all images is implemented. Finally, we obtain the nighttime light map of China by mosaicking all the nighttime light orthoimages. According to the experimental results for 275 Luojia1-01 images, the root mean square error of the tie points is 0.983 pixels and the root mean square error of independent checkpoints is 195.491 m (less than 1.5 pixels) after the planar block adjustment. The experimental results prove the validity and feasibility of the proposed method.

To ensure the accuracy of large-scale optical stereo image bundle block adjustment, it is necessary to provide well-distributed ground control points (GCPs) with high accuracy. However, it is difficult to acquire control points through field measurements outside the country. Considering the high planimetric accuracy of spaceborne synthetic aperture radar (SAR) images and the high elevation accuracy of satellite-based laser altimetry data, this paper proposes an adjustment method that combines both as control sources, which can be independent from GCPs. Firstly, the SAR digital orthophoto map (DOM)-based planar control points (PCPs) acquisition is realized by multimodal matching, then the laser altimetry data are filtered to obtain laser altimetry points (LAPs), and finally the optical stereo images’ combined adjustment is conducted. The experimental results of Ziyuan-3 (ZY-3) images prove that this method can achieve an accuracy of 7 m in plane and 3 m in elevation after adjustment without relying on GCPs, which lays the technical foundation for a global-scale satellite image process.

The geolocation accuracy of Synthetic Aperture Radar (SAR) images is crucial for their application in various industries. Five high-resolution SAR satellites, namely ALOS, TerraSAR-X, Cosmo-SkyMed, RadarSat-2, and Chinese YG-3, provide a vast amount of image data for research purposes, although their geometric accuracies differ despite similar resolutions. To evaluate and compare the geometric accuracy of these satellites under the same ground control reference, a validation field was established in Xianning, China. The rational function model (RFM) was used to analyze the geometric performance of the five satellites based on the Xianning validation field. The study showed that each image could achieve sub-pixel positioning accuracy in range and azimuth direction when four ground control points (GCPs) were placed in the corners, resulting in a root mean square error (RMSE) of 1.5 pixels. The study also highlighted the effectiveness of an automated GCP-matching approach to mitigate manual identification of points in SAR images, and results demonstrate that the five SAR satellite images can all achieve sub-pixel positioning accuracy in range and azimuth direction when four GCPs are used. Overall, the verification results provide a reference for SAR satellite systems’ designs, calibrations, and various remote sensing activities.