Toward a Reliable Correction of NOAA AVHRR Orbital Drift - Julien, Yves; Sobrino, José A. (Frontiers in Remote Sensing, 2022)
NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High-Resolution Radiometer) orbital drift prevents the use of its derived Land Surface Temperature (LST) data for global studies of temperature trends, especially for the 80s and 90s over land. In a previous work, we showed how orbital drift correction methods could be validated by simulating reference and drifted time series from alternative MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data, thanks to their high (15 minutes) temporal resolution. In this study, we show how these alternative data allow identifying orbital drift effects on different land covers, and how these effects could be mitigated with novel approaches. We also identify two key statistical parameters to assess orbital drift correction performance: the bias between corrected and drifted time series, as well as the trend of their difference. We present two methods and compare their results with an alternative orbital drift correction, validated against in situ data by their authors. Considering an ideal case, where the whole influence of the orbital drift is known, our novel approach allows for an almost complete removal of the orbital drift effect (null bias and 0.05 K/yr difference trend). However, in real cases, when we have only access to the drifted time series, our approach performance decreases slightly, mainly through a larger spread of the retrieved statistics. As for the alternative correction method, its performance is poorer, even if it actually succeeds in removing part of the observed orbital drift. These results, as well as the new insights we provide on orbital drift effect on LST, pave the way towards a reliable correction of NOAA AVHRR orbital drift. We therefore recommend the use of simulated LST time series such as the ones use in this work for the validation of orbital drift correction methods.