## 4 Discussion
### Limitations of the RSVI  
The RSVI we developed shows great potential in terms of correlation and consistency, but there are also certain limitations in some aspects (Figures 14 and 15). In regions with Mediterranean, subhumid, humid, and very humid climates, the RSVI exhibited better correlations with the TVDI. Conversely, in arid and semiarid climate regions, the correlation was relatively weak. We speculate that the sparse vegetation in arid and semiarid regions, primarily consisting of desert grasslands, is heavily influenced by soil background effects [121]. Moreover, among the different vegetation types, grasslands and croplands demonstrated stronger correlations, which could be attributed to reasons similar to those observed in arid regions, where forest samples were limited and mostly comprised sparsely growing shrubs highly impacted by the soil background. To address these limitations, incorporating indices such as the soil-adjusted vegetation index (SAVI) or the transformed soil-adjusted vegetation index (TSAVI), which can mitigate some of the soil background effects, may yield improved results [122]. These considerations are important for enhancing the applicability and accuracy of the RSVI in diverse environmental contexts. While the RSVI has shown promise, it is important to recognize that its application may have limitations not only in arid and semiarid regions but also in other climatic regions and across various vegetation types. For instance, in tropical regions with dense forests, the index might be less effective due to the complex structure of the vegetation, which can obscure the soil-vegetation interactions that RSVI relies upon. Additionally, in temperate regions where seasonal variations significantly influence vegetation dynamics, the RSVI might need to be adjusted or complemented with other indices that account for seasonal phenology. For example, the integration of temporal data could help in differentiating between natural vegetation changes and stress-induced variations. Furthermore, the index might face challenges in wetlands or areas with significant water bodies, where the spectral signals could be confounded by water reflectance or sub-surface vegetation [123]. Addressing these limitations in future research will require a multifaceted approach. One potential solution is to tailor the RSVI by incorporating additional indices that are specifically sensitive to the unique environmental conditions of different regions. For example, in tropical regions, including indices that account for leaf area index (LAI) or canopy density could improve the performance of RSVI [124]. In temperate regions, incorporating phenological indices could help adjust for seasonal variations [125]. Additionally, the use of hyperspectral imagery, which offers finer spectral resolution, could allow for the detection of subtle vegetation and soil interactions that are not captured by traditional multispectral indices [126]. By refining the RSVI to account for these regional and vegetation-specific factors, its applicability and accuracy can be significantly enhanced. Furthermore, an emerging area of interest in improving vegetation monitoring, particularly under drought conditions, is the use of Solar-Induced Fluorescence (SIF) remote sensing. SIF provides a direct measurement of photosynthetic activity, offering a valuable tool for assessing vegetation health under varying environmental conditions, including droughts [127]. In the study by [PERSON] et al., which utilized SIF to track canopy photosynthesis under drought conditions in a semiarid grassland, the potential of SIF to enhance drought monitoring was highlighted [128]. The research demonstrated that SIF can effectively capture the physiological status of vegetation, particularly under stress conditions such as drought, where traditional indices like RSVI may not fully reflect the extent of vegetation stress.