Share Email Print

Proceedings Paper

Two-stage reference channel calibration for collocated UV and VIS Multi-Filter Rotating Shadowband Radiometers
Author(s): Maosi Chen; John Davis; Zhibin Sun; Wei Gao
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Multi-Filter Rotating Shadowband Radiometer (MFRSR) and its UV version (UV-MFRSR) are ground-based instruments for measuring solar UV and VIS radiation, deployed together in field at most USDA UV-B Monitoring and Research Program (UVMRP) sites. The performance of the traditional calibration method, Langley Analysis (LA), varies with MFRSR channels and sites, resulting in less confidence in some irradiance products. A two-stage calibration method is developed. We attributed the variation in Langley Analysis performance to the monotonically changing total optical depth (TOD) in the cloud screened points. Constant TOD is an assumption in LA. Since (1) aerosol is the main source of TOD variation at the 368nm channel and (2) UV-MFRSR measures direct normal and diffuse horizontal simultaneously, we used the radiative transfer model (i.e. MODTRAN) to create the look-up table of the ratio of direct normal and diffuse (DDR) with respect to aerosol optical depth (AOD) and solar zenith angle to evaluate the quality of the Langley Offset (VLO) by giving lower weights to VLO generated from points with monotonic AOD variation. With one or two calibrated channels as Reference Channels (RC), the most stable points in RC were selected and LA was applied on those time points to generate VLO at the adjacent un-calibrated channel. The test of this method on the UV-B program site at Homestead, Florida showed that (1) The long-term trend of the original LA VLO is impacted by the monotonic changing in AOD at 368nm channel; and (2) more clustered and abundant VLO at all channels are generated compared with the original Langley method.

Paper Details

Date Published: 4 September 2015
PDF: 14 pages
Proc. SPIE 9610, Remote Sensing and Modeling of Ecosystems for Sustainability XII, 96100L (4 September 2015); doi: 10.1117/12.2185500
Show Author Affiliations
Maosi Chen, Colorado State Univ. (United States)
John Davis, Colorado State Univ. (United States)
Zhibin Sun, Colorado State Univ. (United States)
Wei Gao, Colorado State Univ. (United States)

Published in SPIE Proceedings Vol. 9610:
Remote Sensing and Modeling of Ecosystems for Sustainability XII
Wei Gao; Ni-Bin Chang; Jinnian Wang, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?