# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 1 Introduction  
Deciduous forests constitute about 32% of all forests globally [1] and are an important component of the terrestrial carbon sink [2, 3, 4]. Deciduous forests in the Northern Hemisphere are primarily found in temperate regions, including the eastern United States and southern Canada, Europe, and East Asia. These regions harbor a rich diversity of tree species, such as oaks, maples, birches, and elms, which together form highly diverse ecosystems. Such ecosystems are essential for maintaining biodiversity, regulating climate, and providing ecosystem services. The phenological dynamics of deciduous forests directly influence the net carbon dioxide (CO\({}_{2}\)) exchange in their ecosystems. For example, if the length of the growing season increases by one day, the net exchange of CO\({}_{2}\) in the ecosystemtheoretically increases by 5.98 gCm\({}^{-2}\)[5]. This shift can have significant implications for annual growth, reproductive success, competitive dynamics, and the geographic distribution of these forests [6; 7]. Therefore, an accurate assessment of deciduous forest phenology is essential for understanding the seasonal dynamics of carbon, water, and energy within ecosystems. It also provides insights into how deciduous forests respond to climate change. Remote sensing technology has demonstrated significant advantages in monitoring vegetation phenology by providing information on vegetation dynamics over a wide coverage area or even globally, which is crucial for accurately understanding and predicting the impacts of climate change on vegetation growth [8]. However, disparities in spatial resolution among vegetation indices can critically impact the accuracy of phenological parameter extraction. For instance, while the Normalized Difference Vegetation Index (NDVI) effectively tracks vegetation coverage dynamics [9; 10; 11; 12], its saturation in high biomass areas and susceptibility to soil background interference in sparse vegetation regions [13] may lead to mixed-pixel effects, thereby introducing scale-dependent biases. The Enhanced Vegetation Index (EVI) and its two-band variant, EVI2, address saturation limitations through algorithmic improvements [14; 15; 16; 17] and exhibit reduced sensitivity to soil variations [18; 19], making them integral to global phenology products like MODIS (MCD12Q2) and VIIRS (VNP12Q2) [20; 21]. Notably, the sensitivity of these indices to vegetation growth remains constrained by spatial resolution: coarse-resolution pixels may fail to detect subtle phenological shifts in low-biomass regions [13]. These limitations fundamentally underscore the inherent uncertainties of traditional vegetation indices in cross-scale phenological monitoring. Unlike traditional vegetation indices, solar-induced chlorophyll fluorescence (SIF) serves as a remote sensing indicator derived from the physiological properties of plants. It represents the energy re-emitted as fluorescence by chlorophyll molecules after absorbing light energy during photosynthesis. This indicator not only directly reflects plant photosynthetic activity but also exhibits resistance to some degree of cloud and soil background interference. In addition, SIF is closely related to the utilization of light energy by plants as well as total primary productivity (GPP) [22; 23; 24]. Given the strong correlation between SIF and vegetation's physiological status, SIF demonstrates higher sensitivity and reliability than traditional vegetation indices in monitoring phenology [25; 26], making it a reliable tool for assessing the impacts of climate change on vegetation growth and monitoring vegetation phenology. Increasingly, various SIF products have been used for phenological metrics extraction, as seen in previous studies exploring vegetation phenology that used SIF products provided by the Greenhouse Gas Observing Satellite (GOSAT) and the Global Ozone Monitoring Experiment-2 (GOME-2) [22; 27].