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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #137316
Title: EVALUATING DIFFERENT NDVI COMPOSITE TECHNIQUES USING NOAA-14 AVHRR DATA

Author
item CHEN, PEI-YU - TEXAS A&M UNIV
item SRINIVASAN, RAGHAVAN - TEXAS A&M UNIV
item FEDOSEJEVS, GUNAR - CANADA CTR REMOTE SENSING
item Kiniry, James

Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/9/2002
Publication Date: 9/30/2003
Citation: Chen, P.-Y., Srinivasan, R., Fedosejevs, G., Kiniry, J.R. 2003. Evaluating Different NDVI Composite Techniques Using NOAA-14 AVHRR Data. International Journal of Remote Sensing. 24(17):3403-3412.

Interpretive Summary: When analyzing satellite data, clouds can contaminate the results, causing errors in determining the amount of leaf area predicted from a satellite image. One index for plant leaf area, which is derived from satellite data, is often contaminated by such clouds. This paper describes a method of minimizing cloud contamination. We used two groups of data for crop seasons in summer: one with all available satellite data and the other from solely cloud-free satellite data. The objective of this study was to compare the two types of data for Texas. The seasonal profiles produced from satellite data agreed with the field measured leaf area index data, reaching maximum values at similar time. However, irregular patterns occurred due to cloud contamination. Cloud detection for individual satellite pictures is strongly recommended. Appropriate data pre-processing is important for predicting crop condition and biomass studies from satellite data.

Technical Abstract: The 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 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. 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.