## 1 Introduction  
Vegetation is a crucial part of natural ecosystems and an indispensable natural resource [1]. The root system of vegetation is closely related to the soil, and vegetation branches, stems, leaves, etc., are closely related to soil erosion [2]. Photosynthesis, the conversion from inorganic into organic matter, is a crucial process in maintaining the Earth's carbon-oxygen balance [3]. Transpiration amplifies the circulation of water, which is a vital element of life in ecosystems [4]. Therefore, changes in the vegetation type and quality can notably affect the cycle and profile of the whole ecosystem. The fractional vegetation cover (FVC), a critical parameter characterizing vegetation features, is important for the study of ecology, hydrology, and meteorology and in other aspects. It is often used as fundamental data for research on regional or globalization issues [5, 6]. Consequently, FVC investigations are important. The FVC is usually defined as the vertical projection of a vegetation canopy area to the ground and can be expressed as a fraction or percentage of the reference area [7]. Within the context of remote sensing, the FVC can be defined as the percentage of the green vegetation area to the total observed area [8]. The FVC is an important phenotypic factor in agriculture, forestry, and ecology and an important indicator for the study of changes in each circle of the entire ecosphere and their interactions. Moreover, the FVC is a key biophysical parameter for capturing horizontal exchange at the surface-atmosphere boundary using soil, vegetation, and atmospheric transfer models [9; 10; 11]. Considering that the FVC is a vital indicator, it is important to measure it at different scales for different applications [12]. For example, the remote sensing technique has provided an important data source for large-scale FVC studies for decades due to loaded positions and greater ease of efficient, large-scale monitoring [13; 14]. The first commonly used method is the vegetation index-based method. It aims to extract the FVC with the help of various vegetation indices, such as the normalized difference vegetation index (NDVI), perpendicular vegetation index (PVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), and transformed soil adjusted vegetation index (TSAVI) [15; 16]. Through the different vegetation and non-vegetation characteristics determined by these vegetation indices, thresholds obtained from various algorithms can be used to distinguish the vegetation cover from the background. Two aspects must be investigated in this method: one is the determination of a suitable vegetation index for different application scenarios, and the other is the establishment of a suitable algorithm to obtain a fixed or self-adaptive threshold for vegetation extraction. Using the vegetation index-based method, [PERSON] et al. produced a map of the extent of many moves in the Philippines using Google Earth MVI [17]. Another commonly used technique is the regression method. This method involves first establishing a mathematical regression model based on several data sources and the FVC over a relatively small range of sample points and then generalizing the results to the entire study area [18]. Currently, with the rapid development of computer technology, machine learning [19; 20; 21; 22] and deep learning [23; 24; 25] methods are emerging. Large-scale remote sensing FVC measurements require more accurate ground data at smaller scales as validation data, which can be obtained from traditional small-scale FVC measurements [26]. The validation dataset strives to meet the following objectives: (1) the instrument should be economical, practical, and easy to operate; (2) the instrument should capture valid, accurate, and objective ground observations; (3) the measurement duration should be short; and (4) the influence of human factors should be minimized [27; 28].