Deriving optical metrics of coastal phytoplankton biomass from ocean colour

详细信息	查看全文 | 推荐本文 |

• 作者：Susanne E. Craig^a ; ^{susanne.craig@dal.ca} ; Chris T. Jones^a ; ^{jonesc@mathstat.dal.ca} ; William K.W. Li^b ; ^{Bill.Li@dfo-mpo.gc.ca} ; Gordana Lazin^b ; ^{LazinG@mar.dfo-mpo.gc.ca} ; Edward Horne^b ; ^{Ed.Horne@dfo-mpo.gc.ca} ; Carla Caverhill^b ; ^{Carla.Caverhill@dfo-mpo.gc.ca} ; John J. Cullen^a ; ^{John.Cullen@dal.ca}
• 关键词：Remote sensing reflectance ; Coastal ocean ; Empirical orthogonal function analysis ; Chlorophyll a concentration ; Phytoplankton absorption
• 刊名：Remote Sensing of Environment
• 出版年：2012
• 期刊代码：79_00344257
• 类别：ear
• 出版时间：16 April, 2012
• 卷：119
• 期：Complete
• 页码：72-83
• 文件大小：1289 K

摘要

An approach to develop accurate local models for the estimation of chlorophyll a concentration (Chl a) and spectral phytoplankton absorption (a_ph(位)) from hyperspectral in situ measurements of remote sensing reflectance (R_rs(位)) in an optically complex water body is presented. The models are based on empirical orthogonal function (EOF) analysis of integral-normalised R_rs(位) spectra, and spectral normalisation was found to be key to the models鈥?success. Accurate model estimates of both Chl a and a_ph(位) were obtained, with R² values in log₁₀ space (N = 42) of 0.839 found for Chl a, and for a_ph(位), R² values ranging from 0.771 (547 nm) to 0.910 (655 nm). A statistical resampling exercise to create training and test data sets showed that stable models could be built with ~ 15 training spectra and corresponding measurements of Chl a and a_ph(位), providing important guidance for the implementation of this approach at other locations. The applicability of the models to a reduced-wavelength resolution (8 wavebands) dataset was tested, and showed that reduction in wavelength resolution had little impact on the models鈥?skill, with R² values obtained within ~ 1%of the hyperspectral (101 wavelengths) R² values for both Chl a and a_ph(位). That the reduced-wavelength resolution models performed as well as the hyperspectral models points to their potential utility for satellite sensors.