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

Abstract 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 LST 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.

[1]  David P. Roy,et al.  Generating a long-term land data record from the AVHRR and MODIS Instruments , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[2]  M. Iqbal.  An introduction to solar radiation , 1983 .

[3]  José A. Sobrino,et al.  NOAA-AVHRR Orbital Drift Correction From Solar Zenithal Angle Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[4]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.

[5]  José A. Sobrino,et al.  Global trends in NDVI-derived parameters obtained from GIMMS data , 2011 .

[6]  P. Sen.  Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[7]  G. Gutman,et al.  On the monitoring of land surface temperatures with the NOAA/AVHRR: Removing the effect of satellite orbit drift , 1999 .

[8]  Jeffrey L. Privette,et al.  Directional effects in a daily AVHRR land surface temperature dataset over Africa , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Scott J. Goetz,et al.  Effects of orbital drift on land surface temperature measured by AVHRR thermal sensors , 2002 .

[10]  José A. Sobrino,et al.  Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco , 2000 .

[11]  Alexander Ignatov,et al.  Equator crossing times for NOAA, ERS and EOS sun-synchronous satellites , 2004 .

[12]  José A. Sobrino,et al.  Atmospheric water vapor content over land surfaces derived from the AVHRR data: application to the Iberian Peninsula , 1999, IEEE Trans. Geosci. Remote. Sens..

[13]  Jeffrey L. Privette,et al.  Near-real time retrievals of land surface temperature within the MODIS Rapid Response System , 2007 .

[14]  C. Tucker,et al.  Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999 , 2001, International journal of biometeorology.

[15]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[16]  J. A. Gatlin,et al.  Monitoring vegetation in the Nile Delta with NOAA-6 and NOAA-7 AVHRR imagery , 1982 .

[17]  C. Tucker,et al.  Higher Northern Latitude NDVI and Growing Season Trends from 1982 to 1999 , 2001 .

[18]  R. Treadon,et al.  Correcting the orbit drift effect on AVHRR land surface skin temperature measurements , 2003 .

[19]  Compton J. Tucker,et al.  Development of a daily long term record of NOAA-14 AVHRR land surface temperature over Africa , 2006 .

[20]  José A. Sobrino,et al.  The Yearly Land Cover Dynamics (YLCD) method: An analysis of global vegetation from NDVI and LST parameters , 2009 .

[21]  J. C. Price.  Using spatial context in satellite data to infer regional scale evapotranspiration , 1990 .

[22]  Lena Iredell,et al.  Characteristics of the TOVS Pathfinder Path A Dataset , 1997 .

[23]  José A. Sobrino,et al.  Comparison of cloud-reconstruction methods for time series of composite NDVI data , 2010 .

[24]  José A. Sobrino,et al.  Changes in land surface temperatures and NDVI values over Europe between 1982 and 1999 , 2006 .

[25]  M. J. Salinger.  Climate Variability and Change: Past, Present and Future – An Overview , 2005 .

[26]  Juan C. Jiménez-Muñoz,et al.  Temporal analysis of normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters to detect changes in the Iberian land cover between 1981 and 2001 , 2011 .

[27]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .