## 1 Introduction  
Bathymetric mapping is an important research direction in island research, involving various aspects such as navigation safety, fisheries, military applications, and nearshore engineering. Traditionally, light detection and ranging (LiDAR) systems were always used to map bathymetry in coastal areas [1, 2]. However, in recent years, the rapid development of satellite-based optical remote sensing technology has provided some new solutions for bathymetric mapping in optical shallow water areas. This approach not only overcomes certain limitations of traditional depth measurement techniques that require ground-borne platforms but also offers advantages such as wide-area coverage and convenient data acquisition [3, 4, 5, 6, 7, 8, 9]. Considering the spatial heterogeneity of bathymetry, high-resolution bathymetric mapping images have always been of high research value in the field of bathymetric mapping [10]. Currently, available mainstream optical remote sensing data typically come from the Landsat series and the Sentinel-2 series, with spatial resolutions reaching 30 m and 10 m, respectively. Although the quality of these data is already quite high, the pursuit of higher resolution is still the goal of researchers. In order to obtain ultra-high-resolution images at the meter or even sub-meter level, it is usually necessary to rely on commercial satellite data such as Gaofen-2, GeoEye-1, Worldview-2/3, etc., but the high cost of these data limits their widespread application on a large scale. The research of [PERSON] [11] shows that although the value of each band in the fused red-green-blue (RGB) image no longer represents the true radiance or reflectance, reliable bathymetric information can still be extracted from the RGB image derived from Google Earth Pro through the statistical-based classical log-ratio model (CLRM). Based on this, this study proposes a scheme for obtaining high-resolution images through the fusion of Gaofen-1 satellite data, aiming to replace commercial satellite data for the inversion of high-resolution bathymetric images, with the goal of obtaining high-quality bathymetric data while controlling costs. This method provides an economical and efficient solution for high-resolution underwater topographic mapping. The GF-1B/C/D satellite constellation (hereinafter referred to as GF-1 satellites) was launched by China on 31 March 2018, and contains three high-resolution optical satellites (GF-1B, GF-1C, and GF-1D) with the same performance and status, aiming at real-time monitoring of natural resources with full elements, full coverage, and all-weather conditions. The GF-1 satellites can provide panchromatic images with a spatial resolution of 2 m and multispectral images with a spatial resolution better than 8 m. Currently, optical satellite bathymetric methods are usually applied to water depths ranging from 0 to 20 m [12] and can be divided into two main categories: traditional multispectral empirical models and semi-analytical models. Traditional empirical models such as the basic linear models and the CLRM are relatively mature, but these methods heavily rely on in situ measurement data [7; 13; 14; 15]. In contrast, semi-analytical models are based on radiative transfer theory and do not require in situ data; many researchers have developed various methods based on physical algorithms [16; 17; 18; 19], though these methods have high requirements for imaging conditions and imaging quality. While GF-1 satellite data have the advantage of high spatial resolution, the water depth inversion from single-scene images is limited by factors such as cloud cover and a lack of in situ water depth data, making it challenging to obtain high-quality water depth inversion results. When using Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) satellite bathymetric data as control points for water depth inversion, the low temporal resolution of GF-1 satellites and the fixed orbit of ICESat-2 result in an uncontrollable number of matching control points between different islands.