## 1 Introduction  
Soil texture refers to the distribution of mineral particles in the soil, which come in different shapes and sizes, and is closely related to various soil functions and qualities, including ion exchange dynamics, soil aeration, fertiliser retention, water retention, and chemical characteristics. It controls the soil's water holding capacity, permeability, solute movement, and aeration, and dominates the soil's physical, chemical, biological, and hydrological processes, which are closely related to plant growth and soil erosion [1,2]. Using soil mineralogy and grain size, it is crucial to swiftly and affordably detect the properties of soil texture and their spatial distribution for smart agricultural management, ecosystem services, environmental sustainability, and food security [3]. For a long time, laboratory analysis methods have mostly been used for soil texture analysis. The test results are reliable, but they are expensive, methodologically time consuming, labor intensive, and inefficient. Additionally, they may cause harmful reagents to have an impact on the environment. For instance, since soil texture and electromagnetic radiation physical characteristics are strongly related, the stretching and bending of N-H, O-H, and C-H are the primary causes of the soil's spectral reflectance absorption characteristics [4; 5; 6]. The characteristics of different particle compositions within soil texture are thus captured by soil reflectance data, including particle size distribution, scattering, and absorption properties. Notably, sand has a noticeable effect on light scattering, which results in increased reflectivity in the visible and near-infrared spectral ranges. The reflectivity within these spectral regions is lower in silt and clay, which exhibit a relatively lesser scattering effect but absorb a significant quantity of light energy [7]. Therefore, via the analysis of soil reflectance data, the content of particles with different particle sizes in the soil and their interactions with light can be inferred. By using VNIR-SWIR spectroscopy (0.4-2.5 m), the geographic distribution of soil texture can be predicted without causing harmful damage [8]. In recent years, some scholars have conducted extensive research on soil properties, revealing the relationship between soil texture and VNIR-SWIR reflectance spectra. For example, the absorption peak of sand is usually around 900 nm, the absorption peak of clay is usually between 1400 nm and 1700 nm, and the absorption peak of silt is around 1900 nm-2200 nm [9; 10]. When the position and intensity of these absorption peaks are detected, different types of soil can be extracted. After extensive tests, VNIR-SWIR reflectance spectroscopy is considered an alternative method for quickly predicting soil texture, promoting the transition from laboratory spectra to hyperspectral remote sensing images. Compared to laboratory spectral data, hyperspectral satellite images such as Hyperion and PRISMA have a wide range of repeated observations and coverage, which makes the accurate prediction of the spatial distribution of soil texture on a large scale possible [11; 12]. At the same time, GF5 AHSI data have high spatiotemporal resolutions (30 m), wide widths, and richer spectral channels (330 bands). These data have been widely reported in soil attribute recognition and can be used to quantitatively estimate soil texture composition [13]. However, regardless of laboratory spectra or hyperspectral satellite data, a large number of studies have shown that the reflection spectra of sand, clay, and silt exhibit a similar trend within the VNIR-SWIR range, with higher reflectivity in the visible region, gradually stabilizing in the near-infrared region and gradually decreasing in the infrared region [14]. Although there are some differences in particle size, mineral composition, and water content, they may still have similar spectral characteristics.