Correcting AVHRR Long Term Data Record V3 estimated LST from orbital drift effects

NOAA (National Oceanic and Atmospheric Administration) satellite series is known to suffer from what is known as the orbital drift effect. The Long Term Data Record (LTDR [Pedelty et al., 2007]), which provides AVHRR (Advanced Very High Resolution Radiometer) data from these satellites for the 80s and the 90s, is also affected by this orbital drift. To correct this effect on Land Surface Temperature (LST) time series, a novel method is presented here, which consists in adjusting retrieved LST time series on the basis of statistical information extracted from the time series themselves. This method is as simple and straightforward as possible, in order to be implemented easily for such a large dataset as the LTDR. The correction is applied on a pixel by pixel basis, and relies on a 2nd order polynomial fit of per satellite solar zenithal angle (SZA) anomalies against time. If the pixel time series is identified as contaminated by the orbital drift for any of the different satellite active periods, LST anomalies are fitted linearly against both time and the 2nd order polynomial fit of SZA anomalies. This double fit allows for the removal of orbital drift influence without removing eventual trends in the signal, which is of utmost importance for vegetation change detection. When applied to simulated LST time series, this method shows errors comparable to the errors associated to LST estimation for most cases. When applied to LTDR LST time series, the approach normalizes the distribution of LST values at the beginning and end of each satellite activity period, and visual inspection of the time series does not show any residual orbital drift in the corrected EST time series. The approach also improves the significance of retrieved trends through the whole time span of the LTDR dataset. The application of this method to the whole LTDR dataset could lead to the compilation of the first coherent global dataset of land surface temperature. (C) 2012 Elsevier Inc. All rights reserved.