# Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
## 1 Introduction
To date, remotely sensed time series analysis of crop phenology has been mostly conducted at coarse spatial resolutions, as a tradeoff of the satellites (e.g., Moderate Resolution Imaging Spectroradiometer [MODIS]) to secure high temporal revisit rates for repeated monitoring. However, the pixels at coarse spatial resolutions may contain a heterogeneous matrix of agricultural fields with varying planted and harvested schedules, which confounds the crop phenological signal and makes it challenging to be connected to field-based crop phenological observations, particularly for smallholder and precision agricultural systems. The decoupling between satellite- and field-based phenological characterizations caused by the scale disparity has remained a significant hurdle in the phenological studies [33]. Over the recent years, near-surface remote sensing (e.g., digital repeat photography and Drone imagery) has emerged as valuable tools for bridging the gap between ground-based phenological observations at local scales and satellite-based phenological measures at ecosystem scales [34; 35; 36]. In particular, the North American PhenoCam network, initiated in 2008, is established to provide systematic and consistent canopy phenological monitoring with unifying observation protocols using digital cameras, and now includes more than 500 camera sites across different biomes [37; 38; 39]. Similar phenological camera networks have also been established in Europe (e.g., European Phenology Camera Network), Japan (e.g., Japanese Phenological Eyes Network), Australia (e.g., Australian PhenoCam Network), etc., [40; 41; 42]. Those phenological cameras can take repeated photos of vegetation in time-lapse mode (e.g., every 30 min from sunrise to sunset of the day) from a fixed position in the field to track vegetation dynamics throughout the season [43; 44]. On one hand, the high-frequency digital repeat photography embodies rich color and structural information of vegetation to enable systematic visual phenological interpretations over the course of a year. On the other hand, the digital numbers recorded by the photography and derived indices (e.g., Green Chromatic Coordinate [GCC]) can be analyzed in a time series fashion, analogous to satellite time series phenological analysis, to track the canopy seasonal phenological development trajectory at the organism-to-ecosystem level. The phenophase transition dates (e.g., onset of greenness) can subsequently be extracted from such seasonal trajectory for sensor-based phenological characterizations [35; 45]. With the dual potential roles, the phenological cameras have been explored to evaluate and validate the satellite-based (e.g., MODIS, Advanced Very High Resolution Radiometer (AVHRR), Visible Infrared Imaging Radiometer Suite (VIIRS), and Landsat) phenological measures, to refine the phenological and earth system models, and to study the vegetation responses to climate change [34; 35; 46; 47; 48; 50; 51]. Yet most of those near-surface remote sensing studies have been focused on forest and grassland ecosystems [35; 46; 47; 48; 50; 51]. Little attention has been directed to assessing the linkage between sensor-derived phenological characteristics and visually observed crop growth stages in agricultural systems. Despite the fine-scale enhanced phenological monitoring, near-surface remote sensing may be limited by its spatial coverage, time span, and sensor geometry and configurations. Apart from near-surface remote sensing, the accelerated development of satellite remote sensing opens up new opportunities to reconcile ground-level crop scouting and satellite-based agricultural phenological monitoring. During the past several years, the emergence of innovative satellite platforms, namely satellite constellations, has largely boosted our capability to monitor land surface dynamics.