HR: 15:30h
AN: B33D-07    [Abstracts]
TI: Multi- Sensor Translation, Continuity, and Scaling of Vegetation Indices Using Hyperspectral Data
AU: * Huete, A R
EM: ahuete@ag.arizona.edu
AF: Department of Soil, Water and Environmental Science, University of Arizona, 429 Shantz Building #38, Tucson, AZ 85721-0038 United States
AU: Kim, Y
EM: ywkim@ag.arizona.edu
AF: Department of Soil, Water and Environmental Science, University of Arizona, 429 Shantz Building #38, Tucson, AZ 85721-0038 United States
AU: Didan, K
EM: kamel@ag.arizona.edu
AF: Department of Soil, Water and Environmental Science, University of Arizona, 429 Shantz Building #38, Tucson, AZ 85721-0038 United States
AU: Miura, T
EM: tomoakim@hawaii.edu
AF: Department of Natural Resources and Environmental Management, University of Hawaii at Manoa, 1910 East-West Road, Sherman Lab 101, Honolulu, HI 96822 United States
AU: Privette, J
EM: privette@ltpmail.gsfc.nasa.gov
AF: NASA Goddard Space Flight Center, Code 920 Building 33, Greenbelt, MD 20771 United States
AB: Long term data records require the effective integration of new sensor technologies and improved algorithms to better characterize global and climate change impacts on ecosystems, while preserving the fundamental attributes of the existing data record. In this study, we use fine resolution, hyperspectral data sets from the AVIRIS and Hyperion sensors, to investigate inter-sensor translation and continuity issues related to the long term measurement of inherent surface properties and their spatial and temporal variations. Hyperspectral data sets were convolved to AVHRR, MODIS, and VIIRS sensor bandpasses and inter-sensor translation functions of reflectances and vegetation indices were derived and analyzed for a variety of surface and sun-target-sensor geometries and across a range of ecosystems and scales. The "continuity" relationships developed were then tested with interannual extracts of real MODIS and AVHRR data and included the normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI), and the enhanced vegetation index (EVI). We found translation and scaling issues to be important in extension of long term, multiple-sensor datasets involving different spectral and spatial resolutions. We found that with calibration, a consistent atmosphere correction scheme, and generalized compositing procedure, translation of multiple-sensor datasets can be achieved with some limitations.
DE: 1620 Climate dynamics (3309)
DE: 1640 Remote sensing
DE: 0400 Biogeosciences
SC: Biogeosciences [B]
MN: 2004 AGU Fall Meeting