Spatial-temporal distribution characteristics of chlorophyll-a in offshore waters of Yantai and Weihai based on GOCI data

Debin Song; Zhiqiang Gao; Jicai Ning; Weitao Shang; Xiaopeng Jiang; Maosi Chen

doi:10.1117/12.2320184

18 September 2018 Spatial-temporal distribution characteristics of chlorophyll-a in offshore waters of Yantai and Weihai based on GOCI data

Debin Song, Zhiqiang Gao, Jicai Ning, Weitao Shang, Xiaopeng Jiang, Maosi Chen

Author Affiliations +

Proceedings Volume 10767, Remote Sensing and Modeling of Ecosystems for Sustainability XV; 1076717 (2018) https://doi.org/10.1117/12.2320184
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

Based on GOCI data and the built-in CO2 algorithm, this paper investigated the spatial-temporal distribution characteristics of chlorophyll-a in offshore waters of Yantai and Weihai from 2014 to 2016. Results showed: The chlorophyll-a concentration in the study area has a significant spatial-temporal characteristics, showed a decreased tendency from estuary to offshore area in general. While the lowest value major in the north open seas, the highest value appeared in Sishili Bay and the coastal zone along Weihai, even extended to the western coastal area of Shandong Peninsula. The spatial difference of the concentration of chlorophyll-a in summer was significantly higher than that in winter, and the enrichment effect increased with the increase of temperature. From the perspective of temporal distribution, the chlorophyll-a level was highest in August and lowest in February, and there are small but obvious double peaks in the spring and autumn of May and October. Our work indicated that chlorophyll a concentration level in the study area showed a gradual upward trend in recent 3 years.

Citation Download Citation

Debin Song, Zhiqiang Gao, Jicai Ning, Weitao Shang, Xiaopeng Jiang, and Maosi Chen "Spatial-temporal distribution characteristics of chlorophyll-a in offshore waters of Yantai and Weihai based on GOCI data", Proc. SPIE 10767, Remote Sensing and Modeling of Ecosystems for Sustainability XV, 1076717 (18 September 2018); https://doi.org/10.1117/12.2320184

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