2024-03-28T11:39:04Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/106352016-10-10T10:29:25Zcom_10261_74com_10261_6col_10261_453
Zarco-Tejada, Pablo J.
Haboudane, D.
Miller, John R.
Tremblay, Nicolas
Dextraze, L.
2009-02-12T13:03:46Z
2009-02-12T13:03:46Z
2002
http://hdl.handle.net/10261/10635
Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote
sensing reflectance in the assessment of vegetation biophysical variables both in forestry (Zarco-
Tejada et al., 2001) and agriculture (Haboudane et al., 2002). Those indices are, however, the
combined response to variations of several vegetation and environmental properties, such as leaf
area index (LAI), leaf angle distribution function (LADF), leaf chlorophyll a+b content (Cab),
canopy shadows, background reflectance, and illumination-observational conditions. Of
particular significance to precision agriculture is Cab, which is related to the nitrogen
concentration and serves as a measure of crop response to nitrogen application. We present a
modeling approach to predict Cab from hyperspectral remote sensing while minimizing soil
reflectance (ρs), shadow effects, and considering LAI variations. This method was developed
using simulated data, followed by assessment using hyperspectral airborne imagery. Simulations
consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative
transfer (RT) models and developing optical indices that minimize bi-directional and soil
background effects.
eng
openAccess
Leaf Chlorophyll a+b and canopy LAI estimation in crops using R-T models and Hyperspectral Reflectance Imagery
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