Abstract
The data quality of the satellite-retrieved water-leaving reflectance (Rrs) depends on the accuracy of radiometric calibration and the performance of atmospheric correction. A radiometric calibration scheme (RCS) has been developed to ensure the accuracy of Rrs through the gain adjustment factors (GAFs) to adjust the satellite calibrated data. The GAF is obtained from the ratio of the simulated reflectance at the top of atmosphere to the calibrated values. The simulated reflectance is computed by a satellite image simulation model (SISM) based on a dataset of climatological global Rrs images according to the same geometric angles of the image pixels. The dataset, taken as a kind of the pseudo-invariant calibration sites for in situ measurements, is generated from the average of standard satellite-retrieved Rrs during more than two decades (1997-2019). The SISM inputs the aerosol properties retrieved from the satellite level 1B data (L1B) and uses the same algorithms of the data-processing system. The results show that the accuracy of the calibration of the website downloaded Chinese Ocean Color and Temperature Scanner on the Haiyang-1C satellite (COCTS/HY-1C) is beyond the requirement of the operational data-processing system (higher than 10%). The daily GAFs can be used to recalibrate the L1B data and monitor the daily sensor degradations. The influences of GAFs are assessed on different meteorological conditions, indicating that the values decrease with the increase of the aerosol optical depths (AODs) but the average of the GAF image is little affected by the meteorological conditions. The uncertainty of GAFs was tested by the different inputs of Rrs values and the results show that they are actually little affected by errors of the Rrs inputs. Therefore, the RCS, taking the advantage of vicarious calibration, offers a tool to recalibrate the COCTS/HY-1C L1B data for the data reprocessing system.
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