On the use of long‐term global data of land reflectances and vegetation indices derived from the advanced very high resolution radiometer

Abstract

Abstract. The availability of advanced very high resolution radiometer (AVHRR) time series of global shortwave data for the past two decades motivated many scientists to investigate interannual variability and trends in land surface conditions. For these studies the observed change in radiances due to two varying factors, namely, sensor responsivity and illumination conditions, must be known a priori because of the degradation of AVHRR shortwave channels and the orbit drift of afternoon spacecraft. The current work analyzes the behavior of global land AVHRR shortwave time series data for the last 12 years, processed using postlaunch calibration, and investigates their usefulness for the monitoring of global land surface processes. Its focus is on verifying the postlaunch calibrations for the AVHRR sensors on board NOAA 11 and 14. It is assumed that the NOAA 9 AVHRR calibration is correct so that the changing illumination effects can be parameterized based on its data. After accounting for the illumination effects, the residual trends in data, averaged over global deserts and rain forests, are attributed to calibration discrepancies. In particular, NOAA 11 calibration was found to yield only small residuals, whereas NOAA 14 calibration produced significant unrealistic global increase in both reflectances and vegetation indices. The artificial trends caused by the combination of calibration residuals and satellite-orbit drift should be removed to alleviate their misidentification as real trends in Earth's climate system and to make statistical studies of anomalies more reliable. This study draws attention to the above aspects of time series analysis with the available global AVHRR data and suggests ways to improve these data for interannual variability studies.

Geographic Information Systems (Gis) and Remote Sensing in the Management of Shallow Tropical Lakes

“…On the other hand it is likely that a long-term or integrative study will combine data from different sources. This requires a calibration between technology sensing technologies Discrepancies in post-launch calibrations of certain remote sensing devices may cause artefacts such as surface area change [30], and so may the shift from remote sensing one source to another [61,62]. However, it is possible to integrate cartographic and multi-source remote sensing data into a homogeneous time series.…”

Joint characterization of vegetation by satellite observations from visible to microwave wavelengths: A sensitivity analysis

“…He reviews instrument performance and satellite orbit characteristics. For NOAA 11, which covers the period of this study, anomalies in channel 1 and :2 reflectances should be no more than 4-1% and 4-2%, respectively [Gutman, 1999] For SSM/I, vegetation information is similar at all frequencies. Separating vegetation via individual SSM/I channels (vertical or horizontal polarization) cannot be done.…”

Untitled

“…This requires a calibration between remote sensing technologies. Discrepancies in post-launch calibrations of certain remote sensing devices may cause artefacts such as surface area change (Gutman, 1999), and so may the shift from one remote sensing source to another (Petit and Lambin, 2001;2002). However, it is possible to integrate cartographic and multi-source remote sensing data into a homogeneous time series (loc.…”