# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 1 Introduction
From these examples, it is noticeable that urbanization effects on vegetation spring phenology using images with coarser resolutions is larger than finer resolutions, which implies large uncertainties when using satellite images to study these effects. Therefore, it is necessary to investigate the potential reason(s) causing the inconsistent results in previous studies. Knowledge of these reason(s) can improve the accuracy and reliability of the future studies on this topic. Most previous studies applied satellite images with relatively coarser spatial resolutions (e.g., 500 m and 1 km) to explore urbanization effects on vegetation spring phenology because these satellite images have very high frequency to compose a smooth time series. Unfortunately, several recent studies on the spatial scale effect on remote sensing-detected phenology show that the large divergence among previous studies may be linked to the spatial resolutions of satellite images used [33; 34; 35]. For instance, in light of a recent study [35], the scale effect of coarse images on vegetation spring phenology extraction was verified by comparing phenological shifts between finer resolution (30 m) and coarser resolution (500 m). This study found that spring vegetation phenology detections at coarser resolution are influenced by the vegetation species with earlier spring phenological dates. Similarly, a study using simulated images with spatial resolutions from 250 m to 35\(\times\)250 m for detecting spring phenology found that the scale effect is significantly associated with heterogeneity of vegetation properties, land cover types, and seasonal greenness variation [34]. Another simulation study [33] confirmed the mixed pixel effect in vegetation spring phenology detections through simulating mixed pixels with diverse combinations of two vegetation endmembers (cropland and deciduous forest). In a word, three above-mentioned studies demonstrated that vegetation spring phenology monitoring might be affected by spatial resolutions and vegetation heterogeneity in pixels. Because of the non-homogeneity of land features in urban areas, almost all pixels at coarse resolutions can be deemed as mixed pixels. As a result, relative differences of vegetation spring phenology extraction between rural and urban areas may be biased (i.e., overestimation or underestimation of urbanization effects) when using coarse-resolution satellite images. However, to the best of our knowledge, there is no study that investigates and compares the consistency of the urbanization effects on vegetation spring phenology at different spatial resolutions. Therefore, this study will use NDVI time series with a wide range of spatial resolutions (from 10 m to 8 km) to export vegetation spring phenology in Beijing city and the surrounding rural area by a widely used phenology detection toolbox, TIMESAT. The 10 m NDVI time series was generated through fusing 10 m Sentinel-2 and 30 m Landsat images. Other coarser (from 30 m to 8 km) NDVI time series were produced by aggregating the 10 m product rather than using real coarse satellite images because real satellite images cannot cover a wide range of spatial resolutions, and aggregation products eliminate the effects of different atmospheric conditions, viewing geometry, and sensor configuration on the results so that the only focus is the scale effect. There are three objectives of this study: (1) to explore the consistency or discrepancy of urbanization effects on vegetation spring phenology at different spatial resolutions, (2) to identify possible reasons for any discrepancies, if needed, and (3) to give suggestions to future studies on how to select suitable satellite images to investigate urban vegetation spring phenology.