## 1 Introduction  
Harmful algal blooms (HABs) represent a deleterious ecological phenomenon stemming from the explosive proliferation of phytoplankton or bacteria in marine or brackish waters, which disrupt the delicate balance of ecosystems, negatively affect fisheries, and release toxic substances harmful to human health [1, 2, 3]. Over the past 30 years, HAB events have shown a significant increase in frequency, scale, and geographical distribution, evolving into a worldwide ecological problem [4]. Therefore, the monitoring of HABs has been a topic of concern for marine environmental scientists in the past several decades [5]. Chlorophyll-a (chl-a) serves as the main pigment of phytoplankton, and chl-a concentration is commonly adopted to characterize the diversity of phytoplankton and algae species [6]. [PERSON] et al. [7] compared seven indicators of phytoplankton biomass and concluded that chl-a concentration can provide the most appropriate estimate of primary productivity. Consequently, chl-a has been regarded as a key indicator in HABs research and the detection of water nutrient levels [8]. In the fields of marine environment and water quality studies, chl-a concentration is defined as a proxy for phytoplankton concentration in surface waters [9, 10]. Research on the chl-a concentration in seawater can be conducive to obtaining timely insights into algae progression and marine ecological status, and this provides early warnings for HABs events. Traditional approaches of chl-a estimation involve field observation through on-site sensors and laboratory analysis of collected water samples, which are limited by their high-cost, time-consuming, and labor-intensive nature; therefore, they are unsuitable for large spatiotemporal scales [11; 12]. Instead, satellite remote sensing technology, with its enhanced spatiotemporal coverage, has greatly advanced our comprehension of near-surface ocean phenomena and played an important role in supporting the monitoring of aquatic-related processes, especially HABs [13; 14]. Therefore, a large amount of work based on remote sensing products has been conducted to explore chl-a, the important water quality variable, and carry out research on concentration inversion algorithms [15; 16; 17; 18; 19], environmental factors influencing mechanisms [20; 21], trend prediction [4; 22; 23], and other aspects to assist HABs monitoring and prevention. Achieving the large-scale, long-duration monitoring of chl-a concentration requires regularly collected and continuous data series [24]. However, challenges arise in ocean remote sensing data and their inversion products in the form of poor data quality and missing data due to factors such as cloud cover [25; 26; 27], solar glare pollution [28], and sensor faults. These issues significantly compromise the spatial and temporal continuity as well as the utility of data products [29]. To address these challenges, research has been conducted on techniques for data reconstruction. Conventional methods mainly include Optimal Interpolation (OI) [30], Singular Spectrum Analysis (SSA) [31], Expectation Maximization (EM) [32], and certain distance-based approaches such as Inverse Distance Weighting (IDW) [33] and Kriging [34; 35]. In the field of remote sensing data applications, Data Interpolating Empirical Orthogonal Function (DINEOF) [36; 37] is acknowledged as a robust technique [38]. Based on Empirical Orthogonal Functions (EOFs), the DINEOF method utilizes the inherent spatiotemporal consistency of the data, rather than relying on additional prior information, to derive values at missing locations. With the increasing availability and utilization of marine remote sensing data, the DINEOF method has been successfully employed in the reconstruction of missing data from various marine variables, such as sea surface temperature [39; 40], salinity [41], and chl-a [42; 43; 44]. The performance of the DINEOF method can be heavily impacted by the characteristics of the research variable, region, and input dataset [44].