## 1 Introduction
Therefore, specific improved reconstruction strategies are required in real-world applications. [PERSON] and [PERSON] [45] enhanced the DINEOF approach by introducing a depth-based dataset division strategy and an novel outlier detection process, yielding improved overall performance for gap filling. [PERSON] et al. [46] presented an improved algorithm I-DINEOF, partitioning the initial dataset into multiple smaller matrices and reconstructing each matrix with the most appropriate EOF, thereby mitigating the sensitivity of the ordinary DINEOF to missing data. Furthermore, another improved algorithm, VE-DINEOF, was proposed [47], which optimized the ordinary DINEOF workflow by determining the optimal EOF for each iteration independently, resulting in a higher reconstruction accuracy and efficiency. [PERSON] and [PERSON] [48; 29; 49] resolved the problems stemming from the substantial data volume and low reconstruction efficiency, as the global remote sensing dataset was divided into zonal sections at a certain latitude interval and DINEOF was applied to these sections simultaneously. Their follow-up research [48; 49] provided further evidence that adding more data from additional satellite sensors could significantly enhance both spatial coverage and reconstruction precision. These studies have emphasized the importance of adapting the improvement strategy of the DINEOF method to various application contexts. In particular, customized sub-dataset strategies tailored to specific applications have been proven to be an effective approach for optimizing reconstruction performance. However, the dataset division methods adopted in previous studies often rely on coordinate-based strategies, such as division into small spatial grids [46] or segmenting along dimensions [48; 29; 49]. These strategies split data simply based on spatial positions and commonly do not consider the spatiotemporal distribution characteristics inherent to the data values of the research object, which potentially lead to the loss of internal spatial correlations within the dataset. In this study, we embed a coordinate-value correlative data division strategy into the ordinary DINEOF algorithm and propose an improved DINEOF method called Concentration-Stratified DINEOF(CS-DINEOF). This method treats the annual average concentration of chl-a as a spatial distribution characteristic to stratify the gappy dataset, and applies the reconstruction algorithm to each sub-dataset independently, thus retaining the spatiotemporal correlation within each sub-dataset. The feasibility and advantage of the proposed algorithm are tested and evaluated in the experiments of chl-a concentration data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the proposed CS-DINEOF method demonstrates improved accuracy and efficiency in reconstruction results. Additionally, the gap-free chl-a images generated by the CS-DINEOF method offer more detailed information on the spatiotemporal variations in chl-a. These improvements prove that the proposed CS-DINEOF method can address the challenges posed by the high-resolution, large-volume, and uneven spatiotemporal distribution of chl-a data, thus holding significant promise for advancing the study of HABs in the region.