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Proceedings Paper

A multi-channel calibration method for multi-filter rotating shadow-band radiometer
Author(s): Maosi Chen; John Davis; Hongzhao Tang; Zhiqiang Gao; Wei Gao
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Paper Abstract

In order to improve the accuracy of solar radiation related parameters’ for crop modeling, a new calibration method (Multi-Channel Calibration) for Multi-Filter Rotating Shadow-band Radiometer (MFRSR) is proposed. It uses the Angstrom Law that links aerosol optical depth (AOD) at multiple wavelengths as the primary constraint. It also uses the bi-channel Langley Regression to provide an additional constraint. Starting with any initial guess of calibration coefficient (V0) at 870 nm, two consecutive steps, both involves calling trust region based non-linear optimization module (CONDOR), are implemented to solve (1) the intermediate parameter Angstrom coefficient and the set of biased V0s at other channels corresponding to the initial one at 870 nm channel; and (2) the final V0s of all permissible channels. The result shows that Unlike Langley method, the Multi-Channel Calibration method return V0 at all permissible channels. Besides, the new method can converge to the same (less than 0.5%) final V0s with the starting guess in a wide range. Most important, the comparison between AODs derived from those final V0s and those of AERONET sunphotometers suggests the upper limit of the error of those final V0s is less than 1.03%, which is a great improvement over the Langley V0s (7.45%).

Paper Details

Date Published: 24 October 2012
PDF: 14 pages
Proc. SPIE 8513, Remote Sensing and Modeling of Ecosystems for Sustainability IX, 851305 (24 October 2012); doi: 10.1117/12.929454
Show Author Affiliations
Maosi Chen, Colorado State Univ. (United States)
John Davis, Colorado State Univ. (United States)
Hongzhao Tang, Colorado State Univ. (United States)
USDA UV-B Monitoring and Research Program, Natural Resource Ecology Laboratory, Colorado State Univ. (United States)
Zhiqiang Gao, Colorado State Univ. (United States)
Wei Gao, Colorado State Univ. (United States)

Published in SPIE Proceedings Vol. 8513:
Remote Sensing and Modeling of Ecosystems for Sustainability IX
Wei Gao; Thomas J. Jackson, Editor(s)

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