## 1 Introduction
The most traditional on-site FVC measurement method is the visual interpretation method, which can be divided into the direct visual estimation method [29] and the photo visual estimation method [30]. However, the visual estimation method is limited by subjectivity, an extreme dependence on observer experience, and a tendency to produce large errors. Therefore, various sampling methods, such as the point measurement method [31], sample strip method [1], shade method [32], and square viewpoint frame method [33], have been proposed for use in this area. The point measurement method entails the use of long, sharp needles stuck vertically in the sample plot, and the FVC is determined using the ratio of the number of needles passing through vegetation to the total number of needles. The shade method (ruler measurement) utilizes the relationship between a given scale and the dark shadows produced by vegetation to measure the FVC. However, the square viewpoint frame method considers the gaps in the small holes of the frame to obtain a sample within the study area. The quadrat method reduces the workload when increasing the observation accuracy due to the application of statistical principles. Relevant measurement instruments have also been developed to obtain FVC data via spatial quantitative meters and moving light meters. With the rapid development of large-scale integrated circuits, the photography technique is no longer an expensive, complex, or time-consuming option, as in film era, digital photography and digital image processing techniques have grown quickly [34]. Digital cameras provide instant shooting results, greatly improve intuition, and reduce the learning costs of workflows; methods for extracting FVC products from digital images have been proposed and modified owing to their efficiency and convenience [35]. [PERSON] et al. used a portable photographic instrument to measure the FVC in the field [36]. [PERSON] et al. also proposed an FVC extraction algorithm based on the LAB color space [20]. However, photography methods in actual shooting face certain challenges. Shadows often create extreme illumination contrasts, causing substantial luminance differences within a single image scene. A study revealed that the reflectance of shaded leaves is lower than that of sunlit leaves at all wavelengths [37]. The presence of shadows could disrupt the accurate recording of pixel information. When the field scene is not covered by a hood or similar structure, shadows are inevitable [38]. In many cases, shadows constitute a high-incidence area where commission and omission errors occur during FVC extraction. As a consequence, the FVC extraction capability in the shadow region must be addressed to improve the segmentation performance. To address the issue of low FVC accuracy based on photography methods under shadow conditions, several studies have proposed solutions from different perspectives. There are two primary approaches: first, specific techniques can be employed during data acquisition to compensate for the limitations due to shadows impeding the accurate recording of vegetation information [39; 40]; second, algorithms have been designed for the vegetation extraction process to minimize the impact of shadows. In numerous studies, the characteristics of high-dynamic range (HDR) imaging have been utilized to effectively expand the dynamic range of photos. This technique allows for better capture of vegetation information and is often combined with thresholding methods or deep learning techniques to calculate FVC [41; 42; 43; 44; 45]. [PERSON] et al. proposed acquiring polarization images in the shooting process to complement and enhance pixel information that cannot be effectively captured under shadows [46]. From a postprocessing perspective, [PERSON] et al. introduced the SHAR-LABFVC shadow-resistant algorithm, which alters the color space distribution of captured images to mitigate the adverse effect of shadows on the calculation of FVC [47].