# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 1 Introduction
However, the spatial resolution of GOSAT and GOME-2 is low (~40 \(\times\) 80 km for GOME-2; ~10 km in diameter for GOSAT), and the phenological state reflected by pixels with coarse spatial resolution may not adequately represent the seasonal variations in different vegetation communities because of variations in phenological behaviors [28; 29; 30; 31]. Orbital Carbon Observatory-2 (OCO-2) delivers a SIF product with a higher spatial resolution (~1.3 \(\times\) 2.2 km), tracking phenology events that closely match GPP-based phenology in forests located in mid- and high-latitude regions [32]. However, the limited spatial coverage of OCO-2 restricts its ability to provide comprehensive phenology tracking across the entire globe. Establishing the relationship between SIF and other remote sensing metrics through machine-learning methods can produce higher-resolution continuous SIF datasets. For example, GOSIF, developed by [PERSON] and [PERSON], integrates OCO-2 SIF with meteorological data, which offers a spatial resolution of 5 km and performs better in phenological tracking [33; 34]. Existing studies confirm that remote sensing observations of surface phenology can be significantly affected by differences in data resolution [35; 36; 30]. For instance, [PERSON] et al. demonstrated that phenological signal mixing within coarse-resolution pixels can lead to biases in trend quantification [37]. Similarly, [PERSON] et al. highlighted that in regions with strong heterogeneity, factors such as topography, land use, urban heat island effects, or coastal influences can drive significant small-scale phenological variations. These fine-grained details are often lost or obscured in coarse-resolution data due to pixel-averaging effects, further underscoring the irreplaceable value of high spatial resolution data in precisely capturing local phenological signals and bridging the gap between ground observations and regional-scale low-to-medium resolution phenological products [38]. In addition, the phenological conditions tracked by SIF data at different scales are commonly used to detect the temporal trends of phenological metrics, such as SOS and EOS [39]. [PERSON] et al. [40] explored the phenological trends in the Northern Hemisphere region during 2007-2018 using GOME-2 SIF, GOSIF, and CSIF (with a 5 km spatial resolution), and, for the SOS, the SOS data extracted by the three different scales of SIF showed a consistent trend of significant advancement; for EOS, the EOS data extracted by GOME-2 SIF indicated a non-significant delayed trend, which was inconsistent with the non-significant advancement trend of EOS extracted at other scales. There may be differences in the ecological processes and trends revealed at different spatial scales, and this phenomenon deserves further exploration. However, previous studies have mainly focused on the comparison of different SIF data sources or extraction algorithms, leaving a gap in understanding the differences in SIF for phenology identification and trend detection at spatial scales. Phenological events result from periodic changes in vegetation induced by the combined influence of various environmental factors, including temperature, precipitation, sunlight, and soil conditions [8; 41]. Previous studies have explored the response of phenology extracted from different SIF products to multiple climatic factors. For example, [PERSON] et al. [42] found a strong correlation between spring phenology (SOS) and temperature using GOME-2 SIF data. [PERSON] et al. [43] considered soil temperature and soil moisture in their study using GOSIF data and concluded that soil temperature, rather than air temperature, dominated spring phenology and influenced soil moisture to indirectly affect phenology. [PERSON] et al.