# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 2 Data and Methods
### Data  
#### 2.2.1 SIF Products  
In this study, the 2008-2017 SIF dataset from the GOME-2 satellite, mounted on the MetOp--A platform, is used. The satellite observes daily at specific time points in a sun-synchronous orbit, minimizing the effects of variations in solar zenith angles on SIF measurements. The GOME-2 SIF data mainly stem from the filling of the solar Fraunhofer lines around the 740 nm band [44], which were derived through dimensionality reduction techniques and the inversion of a simplified radiative transfer model, specifically designed to isolate the spectral components of atmospheric absorption, surface reflection, and fluorescence emission. To ensure data quality, cloud-contaminated pixels (cloud fraction \(\geq\)20%) were masked, and observations with solar zenith angles \(>\)70\({}^{\circ}\) were excluded. For this study, we utilized 3-level, grid-averaged data at the global scale (0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)), with the temporal resolution adjusted to every eight days for consistency with other SIF datasets. This study uses the 2008-2017 GOSIF (Global OCO-2 SIF) dataset, which features an extensive time span, an appropriate seasonal cycle, and high resolution. GOSIF was developed using a machine-learning algorithm that was trained on data from the Orbiting Carbon Observatory-2 (OCO-2). It utilizes vegetation data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and meteorological reanalysis data as its input variables and has a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of eight days, spanning from 2001 to the present. GOSIF builds a SIF prediction model through the training of  
Figure 1: Distribution of deciduous forests in the Northern Hemisphere at middle and high latitudes. machine-learning algorithms and efficiently predicts SIF data on a global scale [45]. The prediction results are verified with the original SIF observations, demonstrating high accuracy and reliability, and they are widely used in large-scale phenological extraction, assessment of terrestrial ecosystem productivity, and inter-annual changes in vegetation phenology. In addition to the GOME-2 SIF and GOSIF datasets, the global 1-km resolution SIF 8-day product from 2008-2017 was used in this study. This product was constructed using a similar framework to the GOME-2 SIF, incorporating machine-learning technology and a spatial downscaling integration framework based on geographic stochastic processes tailored to the characteristics of GOME-2 SIF data. It maintains a high degree of consistency with the original results from multiple satellites in terms of spatial distribution and values, providing accurate data for the study of global vegetation photosynthesis. This study also employs the 2008-2017 GRSIF001 dataset, which combines high spatiotemporal resolution, seamless global coverage, and robust temporal scalability. GRSIF001 was generated using the geographically random light gradient boosting machine (GR-LGBM), a novel machine-learning model designed to address spatial heterogeneity in geographic variables [46]. The model integrates multi-source inputs, including MODIS visible-near-infrared reflectance, ERA5 meteorological variables (e.g., vapor pressure deficit, air temperature), and vegetation type data, to downscale raw GOME-2 SIF retrievals. The dataset features an unprecedented spatial resolution of 0.01\({}^{\circ}\) and an eight-day temporal resolution, spanning from 2008 to 2018. GR-LGBM incorporates spatial random constraints and convolutional upsampling strategies to enhance local feature representation while maintaining global consistency. Validation against independent satellite observations (e.g., OCO-2, TROPOMI) and ground-based tower measurements demonstrated strong agreement, with a fivefold cross-validated R\({}^{2}\) of 0.885 and low RMSE (0.069 mW/m\({}^{2}\)/sr/nm).