摘要：Various space missions have measured the total solar irradiance (TSI) since 1978. Among them the experiments
Precision Monitoring of Solar Variability (PREMOS) on the PICARD satellite (2010–2014) and the
Variability of Irradiance and Gravity Oscillations (VIRGO) on the mission
Solar and Heliospheric Observatory, which started in 1996 and is still operational. Like most TSI experiments, they employ a dual-channel approach with different exposure rates to track and correct the inevitable degradation of their radiometers. Until now, the process of degradation correction has been mostly a
manual process based on assumed knowledge of the sensor hardware. Here we present a new
data-driven process to assess and correct instrument degradation using a machine-learning and data fusion algorithm, that does not require deep knowledge of the sensor hardware. We apply the algorithm to the TSI records of PREMOS and VIRGO and compare the results to the previously published results. The data fusion part of the algorithm can also be used to combine data from different instruments and missions into a composite time series. Based on the fusion of the degradation-corrected VIRGO/PMO6 and VIRGO/DIARAD time series, we find no significant change (i.e
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