HR: 0830h
AN: B51E-1016    [PDF]
TI: Remote Quantification of Vegetation Biophysical Characteristics: Algorithms, Calibration and Validation
AU: * Gitelson, A A
EM: gitelson@calmit.unl.edu
AF: Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska-Lincoln, USA, 113 Nebraska Hall, Lincoln, NE 68588-0517 United States
AU: Vina, A
EM: avina@calmit.unl.edu
AF: Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska-Lincoln, USA, 113 Nebraska Hall, Lincoln, NE 68588-0517 United States
AU: Rundquist, D C
EM: drundquist1@unl.edu
AF: Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska-Lincoln, USA, 113 Nebraska Hall, Lincoln, NE 68588-0517 United States
AB: There is considerable interest in assessing biophysical characteristics of vegetation, such as vegetation fraction (VF), leaf area index (LAI), and the magnitude of carbon sources and sinks for agricultural lands, grasslands, and forests. In this paper, we discuss novel techniques to assess remotely VF, LAI and CO2 fluxes in crops. We suggest Visible Atmospherically Resistant Index (VARI) for estimating VF; VARI uses only visible (either green or red edge, blue, and red) spectral channels. The index showed linear relationship with VF in wheat, maize and soybean. To estimate LAI and green leaf biomass we used reflectances in two spectral channels either in the green around 550 nm, or in the red edge near 700 nm, and in the near infra-red (NIR) beyond 750 nm. The technique was tested in agricultural fields under maize and soybean canopies, and proved suitable for accurate estimation of LAI ranging from 0 to more than 6. To estimate CO2 fluxes we used a model developed for assessing leaf chlorophyll content. At canopy level the model relates total chlorophyll concentration in the canopy (per volume) with CO2 fluxes. Differences of reciprocal reflectances [(rGreen)-1-(rNIR)-1] and [(rRedEdge)-1-( rNIR)-1] accounted for more than 80 percent of the variability in mid-day canopy photosynthesis of maize and soybean. The technique was validated by an independent data set; root mean square error in predicting mid-day canopy photosynthesis was 0.17 mg/m2/s by [(rRedEdge)-1-(rNIR)-1] and 0.2 mg/m2/s by [(rGreen)-1-(rNIR)-1].
DE: 0400 Biogeosciences
DE: 1640 Remote sensing
DE: 1694 Instruments and techniques
SC: Biogeosciences [B]
MN: 2003 Fall Meeting