Instrument Degradation Monitoring with Big Data Analysis of Global Pseudo-Invariant Pixels

The traditional vicarious calibration methods for long-term radiometric trending of sensors are primarily based on invariant or pseudo-invariant sites on Earth, which typically rely on large spatially homogeneous areas and require delicate selection of samples. This study employs big data analysis of global pseudo-invariant pixels (PIPs) to monitor the long-term degradation of the Medium Resolution Spectral Imager (MERSI) onboard the FengYun(FY)-3 series satellites. Specifically, the PIPs are determined from image pairs acquired at different times by the iteratively re-weighted multivariate alteration detection (IR-MAD) technique. The global PIPs from several regions located in the middle to low latitude lands are obtained through the big data analysis of long-term time series data. This method requires no prior knowledge of the targets and extend the calibration sites to the pixel-level targets. Results of instrument degradation trending derived from the big data analysis of PIPs from different regions are consistent, which also align well with those obtained through traditional vicarious calibration methods. This method is not sensor-specific and can significantly enhance the frequency of vicarious calibration.