Multispectral vegetative canopy parameter retrieval

Christoph C. Borel; David J. Bunker

doi:10.1117/12.898491

7 October 2011 Multispectral vegetative canopy parameter retrieval

Christoph C. Borel, David J. Bunker

Proceedings Volume 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII; 81740T (2011) https://doi.org/10.1117/12.898491
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

Abstract

Precision agriculture, forestry and environmental remote sensing are applications uniquely suited to the 8 bands that DigitalGlobe's WorldView-2 provides. At the fine spatial resolution of 0.5 m (panchromatic) and 2 m (multispectral) individual trees can be readily resolved. Recent research [1] has shown that it is possible for hyper-spectral data to invert plant reflectance spectra and estimate nitrogen content, leaf water content, leaf structure, canopy leaf area index and, for sparse canopies, also soil reflectance. The retrieval is based on inverting the SAIL (Scattering by Arbitrary Inclined Leaves) vegetation radiative transfer model for the canopy structure and the reflectance model PROSPECT4/5 for the leaf reflectance. Working on the paper [1] confirmed that a limited number of adjacent bands covering just the visible and near infrared can retrieve the parameters as well, opening up the possibility that this method can be used to analyze multi-spectral WV-2 data. Thus it seems possible to create WV-2 specific inversions using 8 bands and apply them to imagery of various vegetation covered surfaces of agricultural and environmental interest. The capability of retrieving leaf water content and nitrogen content has important applications in determining the health of vegetation, e.g. plant growth status, disease mapping, quantitative drought assessment, nitrogen deficiency, plant vigor, yield, etc.

Citation Download Citation

Christoph C. Borel and David J. Bunker "Multispectral vegetative canopy parameter retrieval", Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 81740T (7 October 2011); https://doi.org/10.1117/12.898491

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KEYWORDS

Vegetation

Reflectivity

Sensors

Data modeling

Absorption

Atmospheric modeling

Near infrared

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