Publications

Huan, Y; Sun, DY; Wang, SQ; Zhang, HL; Qiu, ZF; Bilal, M; He, YJ (2021). Remote sensing estimation of phytoplankton absorption associated with size classes in coastal waters. ECOLOGICAL INDICATORS, 121, 107198.

Abstract
As a second-order expression of the phytoplankton absorption coefficient (a(ph)(lambda)), the specific absorption coefficient of phytoplankton (a(ph)*(lambda)) is an essential optical parameter that refines the a(ph)(lambda) normalized by phytoplankton pigment concentration. Based on the in-situ samples collected in the Bohai Sea, Yellow Sea, and the East China Sea, this study presents a combined approach to determine the specific absorption coefficients of micro- (a(m)(lambda)), nano- (a(n)*(lambda)), and picophytoplankton (a(p)*(lambda)). Together with a(m)*(lambda) estimated by the two-component assumption model, our method effectively extracted a(n)*(lambda) and a(p)*(lambda) through the least square method. Independent in-situ validation datasets tested the performances of the proposed a(m)*(lambda), a(n)*(lambda), and a(p)*(lambda) by modeling a(ph)(lambda), and generated encouraging and acceptable predictive errors. The derived mean absolute percentage errors ranged from approximately 35%-55% for several typical wavebands (namely, 412, 443, 490, 555, 660, and 680 nm). Validation by using satellite-ground synchronization samples also produced comparative predictive errors. The spatial distribution of a(ph)(lambda) and the absorption of micro-, nano-, and picophytoplankton were mapped through applying the proposed specific absorption coefficients to Geostationary Ocean Color Imager (GOCI) images. This showed a spatial rule that pico- and nanophytoplankton dominate the total absorption, rather than microphytoplankton. The annual Moderate Resolution Imaging Spectroradiometer (MODIS) a(ph)(443) product from 2002 to 2019 was used to assess the transferability of those specific absorption coefficients and showed a good performance. The accurate acquirement of a(ph)(lambda) can provide basical datasets for further environment research, such as estimating primary ocean productivity.

DOI:
10.1016/j.ecolind.2020.107198

ISSN:
1470-160X