## 1 Introduction  
Satellite remote sensing is an important earth observation technology. However, it cannot provide data with both high temporal and spatial resolution simultaneously [1]. For example, Landsat images have a spatial resolution of 30 m, which can meticulously describe ecological conditions and land cover changes, but its revisit cycle is 16 days, which limits its performance in dynamic monitoring [2]. On the contrary, Moderate-Resolution Imaging Spectroadiometer (MODIS) images have sufficient temporal resolution for daily observation but fail to recognize detailed spatial features. Remote sensing images from two different sensors can be fused to obtain images with both high spatial and temporal resolution. Image super-resolution and spatiotemporal fusion are the two major techniques used to study this problem from computer vision and remote sensing points of view, respectively [3; 4; 5]. The key to image super-resolution is to predict the point spread function (PSF), which can accurately describe the generation process of low-spatial resolution pixels [6]. The reverse application of PSF to low-spatial-resolution images can generate high-spatial-resolution images. The PSF can combine physical properties and other information in the interpolation process to achieve super-resolution [7; 8; 9]. When there are enough data, the PSF can be generated by image learning techniques like convolutional neural networks (CNNs) [10]. With the development of computer vision science, super-resolution has been widely applied in the biological and medical fields [11; 12]. Remote sensing, however, involves a wide range of areas, which makes it hard to obtain a universal PSF. This fact limits the application of super-resolution to remote sensing fields [13]. The spatiotemporal fusion algorithm is the other solution used to obtain high-spatiotemporal-resolution images. This kind of algorithm makes full use of the spatiotemporal relationships among multi-source remote sensing data to generate the target image from existing images [14; 15]. Unlike image super-resolution, it cannot directly generate details from low-resolution images. It utilizes the spatiotemporal consistency of remote sensing images and combines two images with different spatial resolutions to obtain a high-resolution image [16]. Representations of this method are the spatial and temporal adaptive reflectance fusion model (STARFM) [17; 18; 19; 20], the enhanced STARFM (ESTARFM) [21], and the unmixing-based Spatial-Temporal Reflectance Fusion Model (U-STFM) [22]. The ESTARFM proposed by [PERSON] et al. (2010) is based on the STARFM model [21]. The ESTARFM improves the accuracy of the results of heterogeneous pixels by using conversion coefficients, improving the selection of similar pixels, and calculating the weight of similar pixels [21]. The ESTARFM has been applied for various purposes, such as the estimation of surface evaporation and the prediction of the concentration of suspended particulate matter in an estuary [23; 24]. This study focused on utilizing the spatiotemporal fusion algorithm to determine the concentration of chlorophyll-a (Chl-a) in ocean color products. Chl-a is an important indicator of eutrophication [25]. The coastal water environment is complex, and the distribution of Chl-a in coastal waters is of great significance for the development of marine primary productivity and coastal ecological monitoring [26; 27; 28; 29]. The integration of multi-source Chl-a remote sensing data can make full use of the advantages of remote sensing technology. It can help improve coastal ecological monitoring and support the development of fisheries by providing more complete data on the eutrophication condition of water bodies [30; 31]. However, the long revisit cycles of terrestrial remote sensing and the presence of missing data in ocean color remote sensing pose challenges in obtaining suitable pairs of images. This fact restricts the performance of spatiotemporal fusion.