## 4 Discussion
The site-specific parameterization of the LandTrendr model also influences the fitting algorithm and recovery estimates [116; 117]. As consistent model parameterization was implemented in this study, some environments may have more accurate recovery estimates than other locations. The phenological metric analysis indicated no significant change in the trends of vegetative emergence or senescence at the broad scale used in this study. The variability in the seasonal change across the study zones suggests a high degree of influence from the specific environmental contexts of each trail. A lack of phenological trend has been identified in other large and mountainous study sites, such as the Tibetan Plateau [118]. While general trends of spring advancement and autumnal delay have been identified, the large latitudinal span of each study zone complicates analysis of the entire zone, as previous studies have identified spatial variability in the phenological shifts across latitudes. For instance, along the Appalachian Trail (AT), the phenological shifts analyzed using citizen science datasets revealed variation in the phenological change based on the latitude, with the greatest advancement of trees and understory plants (\(\sim\)10 days/\({}^{\circ}\)C) occurring in the mid-Atlantic region, while other regions remained phenologically stable [4]. Elevated effects often play an important role in phenological shifts; one study in the Great Smoky Mountains National Park showed that elevation had the greatest effect in spring months [119]. In addition, the variability in the response between vegetation species is an important factor, as different species within an environment advance their phenologies at different rates as a response to climatic changes. For example, studies have found spring-flowering herbs to indicate an earlier emergence than trees [120]. These species-specific responses underscore the importance of considering the vegetation composition when interpreting phenological trends. Identification of shifts in the senescence delay and emergence advancement may also vary based on the index being used; for example, a study in the western U.S. found that the NDVI typically shows a longer spring season and an earlier peak day than the normalized microwave reflection index (NMRI), an index sensitive to the moisture content in vegetation [121]. While this analysis could not determine significant trends for the senescence delay, autumnal phenological metrics are less strongly linked to temperature increases than spring phenological metrics [122]. Precipitation and soil moisture are significant drivers of senescence timing, but with such large variability in these drivers across each study zone, their impact is less clear. The MODIS NDVI boxplot analysis revealed distinct seasonal signatures in each trail, showcasing their unique environmental contexts. The AT produced the highest NDVI values, indicating the dense and productive vegetation in the temperate and moisture-rich climate [123]. The PCT is characterized by a drier and more varied climate, passing through many more coregions, ranging from arid desert to alpine environments. The CDT, however, exhibits the lowest median NDVI values, consistent with the trail's expansive desert and steppe regions, where sparse vegetation and limited moisture constrains vegetative productivity. The annual analysis of each trail's first \"no snow\" dates showed a significant trend toward earlier snowmelt on the PCT and AT. The shortened snowmelt periods and the associated earlier spring onset along the PCT may be linked to a trend of lengthened vernal windows, as described by [PERSON] et al. [124]. Analysis of the CDT showed a less variable snowmelt trend, with no signs of advancement, possibly owing to its higher elevation zones providing a buffering effect against year-to-year variability. A limitation of this study is the sampling bias and irregularity of the satellite observations across large study areas and diverse climates.