## 5 Conclusions  
In this study, an improved DINEOF algorithm, termed Concentration-Stratified DINEOF, was developed to specifically address the challenges of high resolution, large data volume, and uneven spatiotemporal distribution of chl-a concentration in the Bohai Sea region. This method employed a coordinate-value correlative data division strategy, which stratified the Bohai Sea region into 10 subregions based on the annual average concen  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subregion** & **EOFs** & **RMSE (mg/m3)** & **VCR (\%)** & **CT (s)** \\ \hline
1 & 4 & 0.2315 & 92.28 & 18.8 \\
2 & 6 & 0.1888 & 90.06 & 17.5 \\
3 & 7 & 0.2171 & 87.42 & 206.6 \\
4 & 8 & 0.2475 & 86.33 & 91.1 \\
5 & 10 & 0.2183 & 87.02 & 57.5 \\
6 & 9 & 0.2249 & 82.60 & 24.1 \\
7 & 13 &tration of chl-a, in order to cluster points with stable spatiotemporal patterns and similar distribution characteristics into separate sub-datasets. Outlier detection and missing rate checks were performed in each sub-dataset for data quality control. In the reconstruction process, rather than relying on a fixed optimal EOF mode for the whole dataset, each sub-dataset was individually reconstructed using its respective optimal EOF mode, generating separate reconstruction results which were then merged to form a complete and final reconstructed image. The proposed CS-DINEOF method was successfully applied to reconstruct the 2019 GOCI Level-2A chl-a products of the Bohai Sea region. Compared to the ordinary DINEOF method, CS-DINEOF achieved a higher reconstruction accuracy, with notable improvements in all four validation parameters (RMSE, MAE, r, and SNR). Additionally, by reducing the volume of data in each sub-dataset via stratification, CS-DINEOF significantly enhanced the reconstruction efficiency, improving computational efficiency by approximately 228.9% under the same configuration. Although incorporating strict data quality control steps into CS-DINEOF would lead to a substantial reduction in data volume, particularly in the number of valid images, and the algorithm's sensitivity to extreme values somewhat limited RMSE improvement, further comparative analysis and discussions demonstrated that CS-DINEOF outperformed the ordinary DINEOF method across different missing conditions and sub-datasets. This illustrates that although the stratification based on annual average concentration only represents a simple data distribution pattern, it can effectively utilize the inherent spatiotemporal correlations within the original data to enhance reconstruction performance. The experiments conducted in this paper suggest that CS-DINEOF-based remote sensing image reconstruction can not only provide smoother transitions and higher spatial correlation but also reveal more local details, which are significantly valuable for supporting research on water quality and HABs in the Bohai Sea region.  
Conceptualization, R.Q. and T.H.; methodology, R.Q. and T.H.; software, T.H. and Z.X.; validation, R.Q. and T.H.; data curation, T.H.; writing--original draft preparation, T.H.; writing--review and editing, R.Q. and T.H.; visualization, T.H.; supervision, R.Q.; project administration, R.Q.; funding acquisition, R.Q. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key Research and Development Program of China (No. 2019 YFC1407902), the Innovation Program of Shanghai Municipal Education Commission (2021-01-07-00-07-E00093), and the Interdisciplinary Project in Ocean Research of Tongji University (2022-2-ZD-04).  
Not applicable.  
The GOCI Level-2A chl-a products utilized in this work can be obtained from the Korea Ocean Satellite Center (KOSC) ([[http://kosc.kiost.ac.kr/](http://kosc.kiost.ac.kr/)]([http://kosc.kiost.ac.kr/](http://kosc.kiost.ac.kr/))).  
The authors would like to thank the Korea Ocean Satellite Center (KOSC) for providing the GOCI data.  
The authors declare no conflicts of interest.