This study introduces some innovations in the data processing algorithm for Chinese FY meteorological satellites. Issues about satellite image navigation, radiation calibration, and data assimilation are discussed. A time series of the earth＇s disk center-line count provides information on the orientation of the satellite spin axis. With this information, the altitude parameters of the satellite and then the earth disk location in the south-north direction may be solved. In each spin cycle, the satellite views the sun and the earth. Given the satellite position and altitude, the angle （β） subtended at the satellite by the sun and the earth can be calculated and predicted. Thus, the earth＇s disk location in the east-west direction is fixed. Based on this principle, we derived an automatic image navigation algorithm for FY2 geosynchronous meteorological satellites with an accuracy approaching pixel level. The FY2 meteorological satellite traveling in a geostationary orbit suffers a large amount of radiation from the sun. The radiation varies on both diurnal and annual scales, which causes radiation responses in the thermal infrared （IR） bands wherein the wavelengths greater than 3.5 μm vibrate periodically on scales of hours to years. These vibrations must be precisely calibrated. First, based on the accurate estimation of the radiant contribution from the front-optics, the variation characteristics of the calibration parameters are obtained on a temporal scale of hours from the space-borne inner-blackbody （IBB） measurement results. Second, the in-orbit measured radiation of the lunar surface is referenced and utilized to correct the sys- tematic bias of the IBB calibration from daily to annual scales. By using such algorithms, we achieved a calibration accuracy of the FY2 satellite＇s IR imagery of less than 1 K. The on-orbit satellite instrument parameters play an important role in data quality; however, they may be mis-measured due to limitations in the measurement conditions or may be change
Radiometric calibration(RC)is an essential solution to guarantee measurements from infrared photonic sensors with certain accuracy,the main task of which is to determine the radiometric responsivity of sensor and usually be solved by comparing with some radiation source(i.e.,blackbody),called source-based RC(SBRC).In addition to the complexity in manufacture,the nonideal characteristics of an available source will inevitably introduce unexpected uncertainties to reduce the final calibration accuracy by around 0.2–0.5 K in SBRC.Therefore,we propose an original source-independent RC(SIRC)principle based on modeling instead of comparing for SBRC,where the incident background radiation to detector,as a dominated factor influencing the responsivity characteristics of a photonic sensor,is modeled to implement RC for both two fundamental types(photoconductive and photovoltaic)of HgCdTe photonic detectors.The SIRC merely requires the temperature information of main components of a sensor other than some complex source and its assembly,and provides a traceable way at lower uncertainty costs relative to the traditional SBRC.The SIRC is being implemented in Fengyun-2 satellites since 2019,which ensures a long-term stable service of Chinese geostationary meteorological satellites for the global observation system under the framework of World Meteorological Organization.Moreover,a 20-year-period traceable Fengyun-2 dataset to be recalibrated with SIRC will benefit the further climate applications.
