Synchronization monitoring atmospheric corrector (SMAC) sensor is equipped with polarization and intensity data, providing the possibility of retrieving more accurate aerosol parameters for main sensor’s atmospheric correction. In this study, a novel aerosol retrieval algorithm based on intensity and polarization data was proposed. First, we analyze and construct the ratio relationship of different intensity and polarization channels of SMAC on different surface types and analyze the correlation between these ratios and normalized difference vegetation index (NDVI) and scattering angle (SCA). Then, for the first time, high-precision surface prior knowledge for SMAC aerosol retrieval is constructed, and then, intensity and polarization data were used to retrieve high-precision aerosol products simultaneously. These results are compared and validated with Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based observations aerosol products, and aerosol optical depth (AOD) product comparison between the SMAC and MODIS showed that they had similar spatial distribution, scattered dots of them with a Pearson correlation coefficient (R) of 0.84 and a root-mean-square error (RMSE) of 0.11. Meanwhile, their differences were also analyzed. The validation between the ground-based sites and the SAMC retrieval results also showed a good performance with R of 0.88 and RMSE of 0.10. These results revealed that the new algorithm is an effective method for retrieving reliable AOD products for the main sensor’s atmosphere correction.

Analysis ready data (ARD) has recently become a popular standard for medium-resolution satellite images. The most representative datasets, namely Sentinel-2 Level-2A products and Landsat Collection 2 Level-2 Science Products, have met ARD standards, with public access. The Chinese Gaofen (GF) series Level-1A wide field of view (WFV) product, which has a similar ground sampling distance (GSD) to Sentinel-2 Level-2A products, is open to the public for its scenes and rational function model (RFM). However, the lower geometric processing accuracy of WFV products of approximately 3-pixel geolocation deviation restricts their wide application and further development towards ARD. The causes of low accuracy are twofold, namely the impact of calibration errors and the inaccuracy of the exterior orientation parameters. In terms of improving the accuracy of RFM, bundle adjustment and the bias compensation model (BCM) are currently the most widely accepted methods. However, existing approaches do not account for the imaging characteristics of WFV scenes, which are only for narrow field-of-view scenes and do not help with nonlinear internal distortions. To address the shortcomings of these methods, a joint orientation method for WFV cameras is proposed in this study. Owing to the high level of geometric accuracy of Sentinel-2 Level-1C reference scenes, this study analyzed the expression of WFV camera attitude errors on the image side. A polynomial exterior orientation model suitable for WFV cameras is also provided. We performed a refined calibration of the nonlinear internal distortions using multi-temporal GF-6 Level-1A WFV images, because of the additional error introduced by the cross-track inconsistency of the Sentinel-2 reference data. The method developed in this study has been experimentally verified to achieve satisfactory geometric positioning accuracy with GF-6 WFV products from 3-pixel to 0.3-pixel (5 m). This study allows optimization of the geometric performance of GF-6 Level-1A WFV products to the level of Sentinel-2 Level-1C products, which is of considerable importance in developing GF WFV images to ARD.

Gaofen7 (GF-7) is the world’s first Earth observation satellite to integrate optical stereo cameras and laser altimeters, mainly serving global 1:10,000 scale stereo mapping. GF-7 can synchronously acquire laser altimetry points (LAPs) and stereo images with sub-meter resolution, aiming to effectively improve the vertical positioning accuracy of stereo images through joint geopositioning of LAPs and stereo images. This study proposed a quantitative evaluation model for theoretical vertical accuracy of GF-7 stereo images, LAPs, and the joint geopositioning of LAPs and stereo images. Without using ground control points (GCPs), the estimated theoretical vertical root mean square errors (RMSEs) of GF-7 stereo images, LAPs, and joint geopositioning of LAPs and stereo images were 2.02-8.49, 0.09, and 0.59-0.75 m, respectively. Subsequently, accuracy verification experiments were conducted using GF-7 data covering Hubei Province, China. The actual vertical RMSE of stereo images and joint geopositioning of LAPs and stereo images were 3.19 and 0.68 m, respectively, and the actual vertical RMSE of LAPs was consistent with the theoretical value. The experimental results confirm that the method proposed in this study is effective. The results of theoretical estimation and experimental verification both indicate that with the assistance of LAPs, the vertical accuracy of GF-7 stereo images without GCPs can meet the requirements of 1:10,000 scale mapping. The results of this study have important reference value for improving GF-7 data processing methods and guiding the design of future satellites that integrate optical stereo cameras and laser altimeters.

The GaoFen-7 (GF-7) satellite is the China’s first civil optical stereo-mapping satellite with submeter resolution. GF-7 is also equipped with a laser altimeter, which can obtain sparse laser altimetry points (LAPs) to improve the vertical accuracy of stereo images. Verifying the geometric accuracy of GF-7 imagery without ground-control points (GCPs) is of great significance for evaluating the mapping capability of GF-7. In this study, 147 stereo images and 804 LAPs of the GF-7 satellite covering Jiangsu Province, Hebei Province, and Sichuan Province in China were selected as experimental data. Subsequently, 455 high-precision independent check points were used to verify the geometric accuracy of the GF-7 images. The results show that, without GCPs, the root-mean-square errors (RMSEs) in the horizontal direction of the experimental images in flat, hilly, mountainous, and high-mountainous terrains, and over the entire area were 4.91, 3.81, 4.38, 6.39, and 4.91 m, respectively. The RMSEs in the vertical direction were 3.42, 4.68, 3.00, 4.41, and 3.52 m, respectively. After performing combined block adjustments of the LAPs and stereo images, the RMSEs of the experimental images in the vertical direction decreased to 0.43, 0.66, 0.76, 1.14, and 0.68 m, respectively, whereas those in the horizontal direction saw only minor changes. Therefore, in the absence of GCPs, the geometric accuracy of GF-7 imagery can fully meet the accuracy requirements of China’s 1:10 000 scale stereo mapping by using the GF-7 LAPs as control points.

The Ziyuan3-03 (ZY3-03) satellite is an optical stereo mapping satellite that is able to synchronously provide triple linear-array stereo images and discrete laser altimetry points (LAPs) for plotting 1:50 000-scale maps. Considering that the relative horizontal error between the ZY3-03 nadir image and synchronously acquired LAPs is very small, this study designed a technical method to obtain accurate image coordinates of LAPs on stereo images, and this method successfully overcomes the shortcomings of previous methods. Subsequently, a combined block adjustment model of stereo images and LAPs was constructed. On this basis, this study proposed a combined block adjustment method using ZY3-03 stereo images and LAPs to improve the vertical accuracy of images. For our research, 19 ZY3-03 triple linear-array stereo images and 87 synchronously acquired LAPs of plain, hilly, and mountainous terrains located in China were selected to conduct the block adjustment experiment. Results indicated that the vertical root-mean-square error of the experimental images decreased from 5.27 to 2.58 m for plain and hilly terrains and decreased from 11.25 to 4.45 m for mountainous terrain. Moreover, the vertical accuracy of the experimental images met the requirement of 1:50 000-scale mapping and also exceeded the accuracy of combined adjustment with Ice, Cloud, and land Elevation Satellite (ICESat)-1 satellite LAPs and the same experimental images.

This letter expands the block adjustment method by, respectively, adopting high-precision global positioning system (GPS) points, geoscience laser altimeter system (GLAS) data, and Shuttle Radar Topography Mission (SRTM)-digital elevation model (DEM) as vertical control data. Block adjustment experiments were conducted with these control data, with 388 ZY-3 satellite stereo image pairs across over 18600 km2of land in Hubei province of China utilized as experimental images and 180 GPS points as checkpoints. The experimental results obtained show that, with the adoption of 23 GPS points as control points, the horizontal root-mean-square error (RMSE) and vertical RMSE of the ZY-3 image improved from 10.97 to 5.72 m and from 7.12 to 1.65 m, respectively. Furthermore, with the adoption of 326 GLAS data points as vertical control points, the vertical RMSE of the ZY-3 images of the flat terrain areas and mountain terrain areas improved to 1.69 and 3.62 m, respectively. Through block adjustment constrained by SRTM-DEM, the vertical RMSE of the ZY-3 images was 1.44 m for flat terrain areas and 3.05 m for mountain terrain areas, respectively. The vertical accuracy of the satellite images was significantly enhanced, especially on the flat terrain area, when GLAS and STRM-DEM were used for vertical control block adjustment, which is similar to the vertical accuracy of block adjustments adopting GPS points as control.

The Ziyuan-3 (ZY-3) satellite was designed to satisfy 1:50000 scale mapping requirements. This study uses 556 images obtained by ZY-3, from June 1 to October 31, 2013, covering an area of 3 500 000 km2in midwestern China. A total of 900 check points measured by a global positioning system were also used to conduct the planar accuracy verification. The experimental results show that the ZY-3 nadir sensor calibration images achieved a planar root mean square error (RMSE) of 10.8 m without the use of ground control points (GCPs). In addition, the verification of vertical accuracy employed 12 ZY-3 stereo image pairs distributed over an area of 14 000 km2around Taiyuan in the Shanxi Province of China, and a Digital Elevation Model with 0.5-m vertical accuracy was used for reference and validation. The vertical accuracy of forward interaction and stereo-extracted Digital Surface Model (DSM) from the stereo images were both validated without GCPs. The experimental results demonstrate that the overall vertical RMSE of the forward intersection was 6.58 m; it was 5.21 and 7.07 m for flat and mountainous terrain, respectively. Moreover, the overall vertical RMSE of DSM was 5.56 m; it was 4.37 and 5.69 m for flat and mountainous terrain, respectively. It can be seen from the experimental results of planar and vertical accuracy verification that ZY-3 imagery is able to satisfy the requirements of 1:50000 topographic mapping in China without using GCPs.