A multi-channel calibration method for multi-filter rotating shadow-band radiometer

Maosi Chen; John Davis; Hongzhao Tang; Zhiqiang Gao; Wei Gao

doi:10.1117/12.929454

24 October 2012 A multi-channel calibration method for multi-filter rotating shadow-band radiometer

Maosi Chen, John Davis, Hongzhao Tang, Zhiqiang Gao, Wei Gao

Proceedings Volume 8513, Remote Sensing and Modeling of Ecosystems for Sustainability IX; 851305 (2012) https://doi.org/10.1117/12.929454
Event: SPIE Optical Engineering + Applications, 2012, San Diego, California, United States

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%).

Citation Download Citation

Maosi Chen, John Davis, Hongzhao Tang, Zhiqiang Gao, and Wei Gao "A multi-channel calibration method for multi-filter rotating shadow-band radiometer", Proc. SPIE 8513, Remote Sensing and Modeling of Ecosystems for Sustainability IX, 851305 (24 October 2012); https://doi.org/10.1117/12.929454

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