Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China Sea

The coast of the East China Sea (ECS) is one of the regions most frequently affected by harmful algal blooms in China. Remote sensing monitoring could assist in understanding the mechanism of blooms and their associated environmental changes. Based on imagery from the Second-Generation Global Imager (SGLI) conducted by Global Change Observation Mission-Climate (GCOM-C) (Japan), the accuracy of satellite measurements was initially validated using matched pairs of satellite and ground data relating to the ECS. Additionally, using SGLI data from the coast of the ECS, we compared the applicability of three bloom extraction methods: spectral shape, red tide index, and algal bloom ratio. With an RMSE of less than 25%, satellite data at 490 nm, 565 nm, and 670 nm showed good consistency with locally measured remote sensing reflectance data. However, there was unexpected overestimation at 443 nm of SGLI data. By using a linear correction method, the RMSE at 443 nm was decreased from 27% to 17%. Based on the linear corrected SGLI data, the spectral shape at 490 nm was found to provide the most satisfactory results in separating bloom and non-bloom waters among the three bloom detection methods. In addition, the capability in harmful algae distinguished using SGLI data was discussed. Both of the Bloom Index method and the green-red Spectral Slope method were found to be applicable for phytoplankton classification using SGLI data. Overall, the SGLI data provided by GCOM-C are consistent with local data and can be used to identify bloom water bodies in the ECS, thereby providing new satellite data to support monitoring of bloom changes in the ECS.

[1]  H. Murakami,et al.  Use of AERONET-OC for validation of SGLI/GCOM-C products in Ariake Sea, Japan , 2022, Journal of Oceanography.

[2]  Q. Song,et al.  Assessment of VIIRS on the Identification of Harmful Algal Bloom Types in the Coasts of the East China Sea , 2022, Remote. Sens..

[3]  J. Ishizaka,et al.  A simple method for algal species discrimination in East China Sea, using multiple satellite imagery , 2022, Geoscience Letters.

[4]  Fang Shen,et al.  Simple methods for satellite identification of algal blooms and species using 10-year time series data from the East China Sea , 2019 .

[5]  Chuanmin Hu,et al.  In Search of Red Noctiluca scintillans Blooms in the East China Sea , 2019, Geophysical Research Letters.

[6]  Zengling Ma,et al.  Changes in community structure and photosynthetic activities of total phytoplankton species during the growth, maintenance, and dissipation phases of a Prorocentrum donghaiense bloom. , 2019, Harmful algae.

[7]  M. Abbaspour,et al.  Harmful algal blooms (red tide): a review of causes, impacts and approaches to monitoring and prediction , 2019, International Journal of Environmental Science and Technology.

[8]  H. Murakami,et al.  GCOM-C Data Validation Plan for Land, Atmosphere, Ocean, and Cryosphere , 2018 .

[9]  Yan Bai,et al.  A semianalytical MERIS green‐red band algorithm for identifying phytoplankton bloom types in the East China Sea , 2017 .

[10]  Toru M. Nakamura,et al.  Evaluation and Improvement of MODIS and SeaWIFS-derived Chlorophyll a Concentration in Ise-Mikawa Bay , 2015 .

[11]  Yan Bai,et al.  A novel method for discriminating Prorocentrum donghaiense from diatom blooms in the East China Sea using MODIS measurements , 2015 .

[12]  Gong Lin,et al.  A new approach to discriminate dinoflagellate from diatom blooms from space in the East China Sea , 2014 .

[13]  S. Phinn,et al.  A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans , 2014 .

[14]  Xiulin Lou,et al.  Diurnal changes of a harmful algal bloom in the East China Sea: Observations from GOCI , 2014 .

[15]  Q. Cheng,et al.  Satellite views of the seasonal and interannual variability of phytoplankton blooms in the eastern China seas over the past 14 yr (1998–2011) , 2013 .

[16]  Binghui Zheng,et al.  Temporal and spatial distribution of red tide outbreaks in the Yangtze River Estuary and adjacent waters, China. , 2013, Marine pollution bulletin.

[17]  E. Siswanto,et al.  Detection of harmful algal blooms of Karenia mikimotoi using MODIS measurements: a case study of Seto-Inland Sea, Japan. , 2013 .

[18]  Yan Zhou,et al.  HAB detection based on absorption and backscattering properties of phytoplankton , 2011, Remote Sensing.

[19]  Ronghua Ma,et al.  Moderate Resolution Imaging Spectroradiometer (MODIS) observations of cyanobacteria blooms in Taihu Lake, China , 2010 .

[20]  Minwei Zhang,et al.  Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery , 2010 .

[21]  J. Gower,et al.  Global monitoring of plankton blooms using MERIS MCI , 2008 .

[22]  R. P. Stumpf,et al.  Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes , 2008 .

[23]  Jennifer P. Cannizzaro,et al.  A novel technique for detection of the toxic dinoflagellate, Karenia brevis, in the Gulf of Mexico from remotely sensed ocean color data , 2008 .

[24]  W. Wurtsbaugh,et al.  Salinity controls phytoplankton response to nutrient enrichment in the Great Salt Lake, Utah, USA , 2006 .

[25]  G. Gong Absorption coefficients of colored dissolved organic matter in the surface waters of the East China Sea , 2004 .

[26]  Richard P. Stumpf,et al.  MONITORING KARENIA BREVIS BLOOMS IN THE GULF OF MEXICO USING SATELLITE OCEAN COLOR IMAGERY AND OTHER DATA , 2003 .

[27]  S. Maritorena,et al.  Bio-optical properties of oceanic waters: A reappraisal , 2001 .

[28]  Y. Kiyomoto,et al.  Ocean Color Satellite Imagery and Shipboard Measurements of Chlorophyll a and Suspended Particulate Matter Distribution in the East China Sea , 2001 .

[29]  Menghua Wang,et al.  Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. , 1994, Applied optics.

[30]  Frank E. Hoge,et al.  Inherent optical properties of the ocean: Retrieval of the absorption coefficient of chromophoric dissolved organic matter from fluorescence measurements , 1993 .

[31]  R. A. Neville,et al.  Passive remote sensing of phytoplankton via chlorophyll α fluorescence , 1977 .