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Title: On the use of NOAA/AVHRR data for BRDF studies : effects and implications of the data processing chain
Author: Evans-Jones, K. L.
Awarding Body: University of Wales Swansea
Current Institution: Swansea University
Date of Award: 2000
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Considerable attention has been given to the use of data from satellite sensors to estimate land-surface biophysical properties. In particular, data acquired at different sensor view angles and solar illumination angles with respect to the Earth's surface have been combined with mathematical models of radiation scattering (also known as Bidirectional Reflectance Distribution Function (BRDF) models) to estimate albedo, LAI and fAPAR. One of the most widely used sources of multi-angle image data has been the NOAA/AVHRR series of satellites. Most BRDF research has focused on the development of various classes of reflectance models. Ultimately, however, all such models rely on the provision of well calibrated, cloud-masked, geometrically registered and atmospherically corrected image data. For the specific case of the NOAA/AVHRR, this study examines the effects and implications of each of these data pre-processing stages for BRDF studies. A number of important conclusions are reached. Firstly, there is considerable ambiguity in the literature regarding the radiometric calibration of AVHRR data. To this end, a consistent methodology is developed and presented. Second, a range of algorithms exist which may be used to identify clouds and cloud shadow in AVHRR data. The APOLLO algorithm (Saunders and Kriebel, 1988) is adopted because it is the most rigorous cloud-mask and studies suggest that BRDF models are sensitive to residual cloud contamination. Third, geometric correction is achieved through a combination of satellite navigation and ground control points. This stage is believed to introduce the greatest uncertainty into the provision of reliable directional reflectance data. Finally, atmospheric correction is performed using a fast radiative transfer code (SMAC). A series of modifications to SMAC are developed to provide an enhanced performance, similar to that of a more computationally-intensive code (6S).
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available