Assessing the consistency of AVHRR and MODIS L1B reflectance for generating fundamental climate data records

Satellite detection of the global climate change signals as small as a few percent per decade in albedo critically depends on consistent and accurately calibrated Level 1B (L1B) data or Fundamental Climate Data Records (FCDRs). Detecting small changes in signal over decades is a major challenge not only to the retrieval of geophysical parameters from satellite observations, but more importantly to the current state-of-the-art calibration, since such small changes can easily be obscured by erroneous variations in the calibration, especially for instruments with no onboard calibration, such as the Advanced Very High Resolution Radiometer (AVHRR). Without dependable FCDRs, its derivative Thematic Climate Data Records (TCDRs) are bound to produce false trends with questionable scientific value. This has been increasingly recognized by more and more remote sensing scientists. In this study we analyzed the consistency of calibrated reflectance from the operational L1B data between AVHRR on NOAA-16 and -17 and between NOAA-16/AVHRR and Aqua/MODIS, based on Simultaneous Nadir Overpass (SNO) observation time series. Analyses suggest that the NOAA-16 and -17/AVHRR operationally calibrated reflectance became consistent two years after the launch of NOAA-17, although they still differ by 9% from the MODIS reflectance for the 0.63 mm band. This study also suggests that the SNO method has reached a high level of relative accuracy (similar to 1.5%) for estimating the consistency for both the 0.63 and 0.84 mm bands between AVHRRs, and a 0.9% relative accuracy between AVHRR and MODIS for the 0.63 mm band. It is believed that the methodology is applicable to all historical AVHRR data for improving the calibration consistency, and work is in progress generating FCDRs from the nearly 30 years of AVHRR data using the SNO and other complimentary methods. A more consistent historical AVHRR L1B data set will be produced for a variety of geophysical products including aerosol, vegetation, cloud, and surface albedo to support global climate change detection studies.