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TitleAn Automated Cloud Detection Method for Daily NOAA-14 AVHRR Data for Texas, USA
AuthorChen, P Y; Srinivasan, R; Fedosejevs, G; Narasimhan, B
SourceInternational Journal of Remote Sensing 23, 15, 2002 p. 2939-2950,
Alt SeriesEarth Sciences Sector, Contribution Series 20043122
PublisherInforma UK Limited
Mediapaper; on-line; digital
File formatpdf
Released2010 11 25
AbstractThe normalized difference vegetation index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) data are influenced by cloud contamination, which is common in individual AVHRR scenes. Maximum value compositing (MVC) of NDVI data has been employed to minimize cloud contamination. Two types of weekly NDVI composites were built for crop seasons in summer: one from all available AVHRR data (named the traditional NDVI composite) and the other from solely cloud-free AVHRR data (named the conditional NDVI composite). The MVC method was applied to both composites. The main objective of this study was to compare the two types of NDVI composites using Texas data. The NDVI seasonal profiles produced from the conditional NDVI composites agreed with the field measured leaf area index (LAI) data, reaching maximum values at similar time. However, the traditional NDVI composites showed irregular patterns due to cloud contamination primarily. Our study results suggest that cloud detection for individual AVHRR scenes is strongly recommended before producing weekly NDVI composites. Appropriate AVHRR data pre-processing is important for composite products to be used for short-term vegetation condition and biomass studies, where the traditional NDVI composite data do not eliminate cloud-contaminated pixels. In addition, this study showed that atmosphere composition affected near-infrared reflectance more than visible reflectance. The near-infrared reflectance was increasingly adjusted through atmospheric correction.

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