Scaling-up and Model Inversion methods with narrow-band Optical Indices for Chlorophyll Content Estimation in closed Forest Canopies with Hyperspectral Data
Radiative transfer theory and modeling assumptions were applied at laboratory and field scales in order to study the link between leaf reflectance and transmittance and canopy hyperspectral data for chlorophyll content estimation. This study was focused on 12 sites of Acer saccharum M. (sugar maple) in the Algoma Region, Canada, where field measurements, laboratory-simulation experiments, and hyperspectral compact airborne spectrographic imager (CASI) imagery of 72 channels in the visible and near-infrared region and up to 1-m spatial resolution data were acquired in the 1997, 1998, and 1999 campaigns. A different set of 14 sites of the same species were used in 2000 for validation of methodologies. Infinite reflectance and canopy reflectance models were used to link leaf to canopy levels through radiative transfer simulation. The closed and dense ( 4) forest canopies of Acer saccharum M. used for this study, and the high spatial resolution reflectance data targeting crowns, allowed the use of optically thick simulation formulae and turbid-medium SAILH andMCRM canopy reflectance models for chlorophyll content estimation by scaling-up and by numerical model inversion approaches through coupling to the PROSPECT leaf radiative transfer model. Study of the merit function in the numerical inversion showed that red edge optical indices used in the minimizing function such as 750 710 perform better than when all single spectral reflectance channels from hyperspectral airborne CASI data are used, and in addition, the effect of shadows and LAI variation are minimized. Estimates of leaf pigment by hyperspectral remote sensing of closed forest canopies were shown to be feasible with root mean square errors (RMSE’s) ranging from 3 to 5 5 g cm2. Pigment estimation by model inversion as described in this paper using these red edge indices can in principle be readily transferred to the MERIS sensor using the 750 705 optical index.
| Main Authors: | , , , , |
|---|---|
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers
2001
|
| Subjects: | Chlorophyll, Hyperspectral, Leaf reflectance, Optical indices, Radiative transfer, Forestry, Geophysical techniques, Vegetation mapping, |
| Online Access: | http://hdl.handle.net/10261/10427 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Radiative transfer theory and modeling assumptions
were applied at laboratory and field scales in order to study the
link between leaf reflectance and transmittance and canopy hyperspectral
data for chlorophyll content estimation. This study was
focused on 12 sites of Acer saccharum M. (sugar maple) in the Algoma
Region, Canada, where field measurements, laboratory-simulation
experiments, and hyperspectral compact airborne spectrographic
imager (CASI) imagery of 72 channels in the visible and
near-infrared region and up to 1-m spatial resolution data were
acquired in the 1997, 1998, and 1999 campaigns. A different set
of 14 sites of the same species were used in 2000 for validation of
methodologies. Infinite reflectance and canopy reflectance models
were used to link leaf to canopy levels through radiative transfer
simulation. The closed and dense ( 4) forest canopies of
Acer saccharum M. used for this study, and the high spatial resolution
reflectance data targeting crowns, allowed the use of optically
thick simulation formulae and turbid-medium SAILH andMCRM
canopy reflectance models for chlorophyll content estimation by
scaling-up and by numerical model inversion approaches through
coupling to the PROSPECT leaf radiative transfer model. Study of
the merit function in the numerical inversion showed that red edge
optical indices used in the minimizing function such as 750 710
perform better than when all single spectral reflectance channels
from hyperspectral airborne CASI data are used, and in addition,
the effect of shadows and LAI variation are minimized. Estimates
of leaf pigment by hyperspectral remote sensing of closed forest
canopies were shown to be feasible with root mean square errors
(RMSE’s) ranging from 3 to 5 5 g cm2. Pigment estimation by
model inversion as described in this paper using these red edge indices can in principle be readily transferred to the MERIS sensor
using the 750 705 optical index. |
|---|