HR: 16:30h
AN: B24B-03    [Abstracts]
TI: Quantitative Remote Estimation of Land Surface Biophysical Parameters
AU: Rundquist, D C
EM: drundquist@calmit.unl.edu
AF: University of Nebraska, 307 Hardin Hall, Lincoln, NE 68583, United States
AU: * Gitelson, A A
EM: gitelson@calmit.unl.edu
AF: University of Nebraska, 307 Hardin Hall, Lincoln, NE 68583, United States
AU: Keydan, G P
EM: keydan@calmit.unl.edu
AF: University of Nebraska, 307 Hardin Hall, Lincoln, NE 68583, United States
AB: Characterization of cropland and grassland properties over large areas requires not only the use of imaging remote sensors but also algorithms suitable for robust retrievals of important biophysical properties of vegetation. In this work we offer four techniques for retrieving biophysical characteristics of cropland and grassland using remote sensing. We suggest using the Visible Atmospherically Resistant Vegetation Index (VARI), based on bands only in the visible portion of the spectrum (red and either green or the red edge), for retrieving the fractional cover of green vegetation. The Wide Dynamic Range Vegetation Index (WDRVI), a non- linear transformation of NDVI, allows retrieval of LAI in a wide range of cropland- and grassland-biomass situations. The Green NDVI and Red Edge NDVI were designed to assess the photosynthetically active component of total absorbed photosynthetically active radiation (fAPAR). The Green and Red Edge Chlorophyll Indices are suggested for measuring the total chlorophyll content in a vegetation canopy, which equates for cropland and grassland to gross primary production (GPP). Our results document accurate estimation of fAPAR, fractional cover of green vegetation, LAI, chlorophyll content and GPP using close-range sensing, an airborne imaging spectrometer (AISA-Eagle) and satellite observations. The techniques were calibrated and validated for maize and soybean in agricultural fields under both irrigated and rainfed conditions.
DE: 0402 Agricultural systems
DE: 0428 Carbon cycling (4806)
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting