Research on key factors of satellite laser pointing calibration based on terrain matching: a case of GF-7 satellite

Abstract

The satellite laser geometry calibration method based on terrain matching (terrain matching calibration) has been extensively employed in satellite laser geometry calibration for its simplicity and lack of need for ground probes. In this study, the key factors for the accuracy of the above-mentioned calibration method, i.e. namely the terrain slope and the number of laser points, are examined with the GaoFen-7 (GF-7) satellite as an example. Terrain is classified into six levels in according with the slope classification standards, i.e. Flat (<2°), Micro-slope (2°–5°), Gentle-slope (5°–15°), Moderate-slope (15°–25°), Slope (25°–35°) and Steep-slope (35°–55°). Moreover, a different number of laser points are randomly selected from each the respective terrain slope for calibration. The accuracy of calibration is verified using the true laser pointing obtained based on the ground detector calibration method. As indicated by the experimental results, the terrain matching calibration achieves the optimal experimental conditions when there are over 50 laser points with a terrain slope greater than 15°, or there exist over 20 laser points with a terrain slope greater than 25°. In both cases, the laser pointing accuracy after calibration can exceed 3 arc seconds. This study can provide technical guidance for high-precision terrain matching calibration.

Keywords:

• Terrain matching
• Laser pointing
• Calibration
• GF-7

Disclosure statement

All authors declare that they have no known competing financial interests or personal relationships in this paper.

Data availability statement

The AW3D30 DSM data are available via the JAXA Earth Observation Research Center website (https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm, accessed on 10 March 2023); the GF-7 satellite laser data are available on the Natural Resources Satellite Remote Sensing Cloud Service Platform (http://sasclouds.com/chinese/normal/, accessed on 15 October 2022).

Additional information

Funding

This work was partly supported by the National Natural Science Foundation of China (NO.42371391) and High level scientific and technological innovation talent project of the Ministry of natural resources (MNR) (12110600000018003930).