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Hourly remote sensing monitoring of harmful algal blooms (HABs) in Taihu Lake based on GOCI images

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Abstract

The increasingly serious harmful algal blooms (HABs) in Taihu Lake has brought huge losses to the local economy and people’s life in Taihu Lake. Satellite remote sensing technology has become one of the most important monitoring methods for HAB disasters due to its large-scale and long-term advantages. GOCI image has become the new data source of HAB monitoring because of its large size and high time resolution. Due to the low spatial resolution (500 m) and the existence of mixed pixels, the error of HAB area obtained by the NDVI method is large. In this paper, the linear mixing model (LMM) and the normalized difference vegetation index (NDVI) threshold method are combined to extract the HAB area from GOCI images with 500-m spatial resolution. Compared with the results of the HAB area extracted by Landsat8 OLI and MODIS data, three small areas in the study area were selected to verify the accuracy of the HAB area extracted from the GOCI image on October 2, 2015. The results show that when the NDVI threshold is 0.1, the area error of HABs is the smallest when the extracted HAB pixels mask the decomposition results of mixed pixels; besides, the area error of HABs extracted from the GOCI image is smaller than that from MODIS image; finally, GOCI image can extract the spatial dynamic distribution of HABs in Taihu Lake within 8 h a day, which has higher temporal resolution than the MODIS image. Compared with the NDVI threshold method and LMM method, the inversion accuracy is greatly improved, and the accuracy is stable in different regions. It can provide technical support for the decision-making and assessment of HAB ecological disasters.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

This research was supported by the Joint Funded Project of the Ministry of Education and the Ministry of Equipment Research and Development (Grant No. 6141A02022376), Open Fund of the Shaanxi Key Laboratory of Land Remediation (Grant No. 2018-ZY01), and Innovative Team Project of the Central University of Chang’an University for Basic Research and Business Expenses (Grant No. 300102350401). The authors would like to acknowledge the Leading Ocean Remote Sensing Research Activities in Korea for providing the GOCI images, the National Aeronautics and Space Administration for providing MODIS images, the US Geological Survey for providing Landsat8 OLI images, and the European Centre for Medium-Range Weather Forecasts for providing meteorological data. We’re also thankful for several anonymous reviewers for their help in improving this paper with their constructive suggestions.

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  1. College of Geological Engineering and Geomatics, Chang’an University, Xi’an, China

    Hongye Cao

  2. School of Land Engineering, Chang’an University, Xi’an, 710064, China

    Ling Han

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Hongye Cao is responsible for the conceptualization, methodology, software, validation, and visualization of this article, while Ling Han is responsible for funding acquisition and supervision.

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Cao, H., Han, L. Hourly remote sensing monitoring of harmful algal blooms (HABs) in Taihu Lake based on GOCI images. Environ Sci Pollut Res 28, 35958–35970 (2021). https://doi.org/10.1007/s11356-021-13318-6

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