A rapid absolute radiometric calibration method for ocean color satellite sensors on-orbit

By using 865nm and 443nm data to establish machine learning models and OSOAA radiation transmission models, the problem of changes in the radiation response characteristics of aqua-color satellite sensors is solved, and fast and accurate absolute radiation calibration is achieved, and data quality is improved.

CN120336975BActive Publication Date: 2025-08-26HAINAN FUTAN REMOTE SENSING TECH CO LTD
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Patent Information

Application Number
CN202510813161.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-06-18
Publication Date
2025-08-26
Estimated Expiration
2045-06-18

AI Technical Summary

Technical Problem

In the prior art, after the aqua-color satellite sensor is in orbit, the applicability of absolute radiation calibration data before launch is reduced due to vibration and environmental changes, and the radiation response characteristics cannot be accurately characterized. The existing calibration methods have shortcomings in global coverage and dynamic range.

Method used

Using 865nm aerosol optical thickness data and 443nm normalized ionized luminance data, a machine learning model was established, combined with the OSOAA radiation transmission model, and by reconstructing the top radiance of the on-site atmosphere, rapid absolute radiation calibration was achieved.

Benefits of technology

The overall operating status evaluation of the sensor is completed in a short time, which can reflect the sensor performance within a wide dynamic range, improve data quality, and has a high correlation coefficient with the comparison results of the existing calibration methods and small errors.

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Abstract

The present invention discloses a method for rapid on-orbit absolute radiometric calibration of an ocean color satellite sensor, belonging to the technical field of radiometric calibration. The method comprises the following specific steps: I. analyzing the optical properties of the atmosphere and water at the station site using aerosol optical depth data at 865 nm and normalized water-leaving radiance data at 443 nm in historical AEROENT-OC observation data; II. establishing a machine learning model with on-site multispectral data as input and hyperspectral data as output; and utilizing on-site station data at different locations to calculate the sensor's on-orbit absolute radiometric calibration coefficient in an alternative calibration manner, and verifying the sensor's on-board calibration results. This method can evaluate the overall operating status of the sensor in a relatively short period of time during the initial launch phase, and can fully reflect the sensor's performance over a wide dynamic range.
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