Retrieval and Evaluation of Chlorophyll-A Spatiotemporal Variability Using GF-1 Imagery: Case Study of Qinzhou Bay, China

Ze-Lin Na, Huan-Mei Yao, Hua-Quan Chen, Yi-Ming Wei, Ke Wen, Yi Huang and Peng-Ren Liao
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Ze-Lin Na: School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China
Huan-Mei Yao: School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China
Hua-Quan Chen: School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China
Ke Wen: School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China
Yi Huang: School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China
Peng-Ren Liao: School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China

Sustainability, 2021, vol. 13, issue 9, 1-13

Abstract: Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify ‘red tide’ events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the Phaeocystis ‘red tide’ bloom, Qinzhou Bay, was selected as the study area. Based on the Gaofen-1 remote sensing satellite image and water quality monitoring data, the sensitive bands and band combinations of the nearshore Chl-a concentration of Qinzhou Bay were screened, and a Qinzhou Bay Chl-a retrieval model was constructed through stepwise regression analysis. The main conclusions of this work are as follows: (1) The Chl-a concentration retrieval regression model based on 1/B4 (near-infrared band (NIR)) has the best accuracy (R 2 = 0.67, root-mean-square-error = 0.70 ?g/L, and mean absolute percentage error = 0.23) for the remote sensing of Chl-a concentration in Qinzhou Bay. (2) The spatiotemporal distribution of Chl-a in Qinzhou Bay is varied, with lower concentrations (0.50 ?g/L) observed near the shore and higher concentrations (6.70 ?g/L) observed offshore, with a gradual decreasing trend over time (?0.8).

Keywords: remote sensing monitoring; leave-one-out cross-validation; stepwise regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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