Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors

Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on... The PROSPECT leaf optical properties and SAIL canopy reflectance models were coupled and inverted using a set of 96 AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) equivalent spectra gathered in afield experiment on sugar beet plots expressing a large range in leaf area index, chlorophyll concentration, and soil color. In a first attempt, the model accurately reproduced the spectral reflectance of vegetation, using six variables: chlorophyll a + b concentration (C ab ), water depth (C w ), leaf mesophyll structure parameter (N), leaf area index (LAI), mean leaf inclination angle (θ l ), and hot-spot size parameter (s). The four structural parameters (N, LAI, τ l , and s) were poorly estimated, indicating instability in the inversion process; however, the two biochemical parameters (C ab and C w ) were evaluated reasonably well, except over very bright soils. In a second attempt, three of the four structure variables were assigned a fixed value corresponding to the average observed in the experiment. Inversions performed to retrieve the remaining structure variable, leaf area index, and the two biochemical variables showed large improvements in the accuracy of LAI, but slightly poorer performance for C ab and C w . Here again, poor results were obtained with very bright soils. The compensations observed between the LAI and C ab or C w led us to evaluate the performance of two more-synthetic variables, canopy chlorophyll content or canopy water content, for these the inversions produced reasonable estimates. The application of this approach to Landsat TM (Thematic Mapper) data provided similar results, both for the spectrum reconstruction capability and for the retrieval of canopy biophysical characteristics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing of Environment Elsevier

Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors

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Publisher
Elsevier
Copyright
Copyright © 1995 Elsevier Ltd
ISSN
0034-4257
D.O.I.
10.1016/0034-4257(95)00018-V
Publisher site
See Article on Publisher Site

Abstract

The PROSPECT leaf optical properties and SAIL canopy reflectance models were coupled and inverted using a set of 96 AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) equivalent spectra gathered in afield experiment on sugar beet plots expressing a large range in leaf area index, chlorophyll concentration, and soil color. In a first attempt, the model accurately reproduced the spectral reflectance of vegetation, using six variables: chlorophyll a + b concentration (C ab ), water depth (C w ), leaf mesophyll structure parameter (N), leaf area index (LAI), mean leaf inclination angle (θ l ), and hot-spot size parameter (s). The four structural parameters (N, LAI, τ l , and s) were poorly estimated, indicating instability in the inversion process; however, the two biochemical parameters (C ab and C w ) were evaluated reasonably well, except over very bright soils. In a second attempt, three of the four structure variables were assigned a fixed value corresponding to the average observed in the experiment. Inversions performed to retrieve the remaining structure variable, leaf area index, and the two biochemical variables showed large improvements in the accuracy of LAI, but slightly poorer performance for C ab and C w . Here again, poor results were obtained with very bright soils. The compensations observed between the LAI and C ab or C w led us to evaluate the performance of two more-synthetic variables, canopy chlorophyll content or canopy water content, for these the inversions produced reasonable estimates. The application of this approach to Landsat TM (Thematic Mapper) data provided similar results, both for the spectrum reconstruction capability and for the retrieval of canopy biophysical characteristics.

Journal

Remote Sensing of EnvironmentElsevier

Published: Jun 1, 1995

References

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