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

Abstract

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.

Estimasi Radius Deformasi Upwelling Dari Data Satelit Dan Argo Float

“…Koefisien korelasi data satelit dan data in situ (r=0,94193) menunjukkan hubungan yang kuat. Penelitian Feng et al (2023) juga menunjukkan nilai koefisien yang tinggi dengan nilai r=0,9975 (Modis-T-iQuam2) dan r=0,9944 (Himawari-8-iQuam2). Bias antara SPL estimasi dari satelit dan Argo adalah 0,01682 (Tebel 1).…”

“…Bias antara SPL estimasi dari satelit dan Argo adalah 0,01682 (Tebel 1). Penelitian sebelumnya menunjukkan nilai bias 0,0879 (Modis-T-iQuam2) dan 0,0429 (Modis-A-iQuam2) (Feng et al 2023). Nilai standar sebesar deviasi 0,28736.…”