# Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
## 1 Introduction  
Vegetation phenology regulates the biosphere's seasonal carbon, nutrient, and water dynamics, and is a first-order control on ecosystem processes and productivity [1, 2, 3, 4]. It is also a sensitive indicator of the biological impacts of climate change and mediates vegetation feedbacks to the climate system by influencing the biogeochemical and physical processes (e.g., carbon sequestration, surface albedo, and energy balance) [5, 6, 7, 8]. In agricultural systems, crop phenology maintains a crucial role in estimating crop net primary production and yields, modeling surface energy-water-carbon fluxes, and managing farming practices (e.g., irrigation scheduling, fertilizer management, and harvesting operations) [9, 10, 11]. These crop management activities largely depend on crop phenological development stages, as biochemical and physiological characteristics (e.g., light use efficiency) of crops differ seasonally along the growth trajectory [12]. The crops at different phenological stages exhibit divergent responses to environmental changes and weather anomalies [12, 13, 14]. For instance, the anthesis stage of corn is particularly susceptible to heat stress, and elevated temperature at this stage has damaging effects on the corn growth, causing subsequent yield loss [15, 16]. As climate change is inclined to increase the frequency of heat stress andother extreme weather events, detecting the crop phenological progress throughout the season is critical in supporting resilient and sustainable agricultural development. Time series of satellite imagery facilitates the characterization of crop phenological patterns over space and time and expands the scope of crop phenological monitoring beyond traditional field observations [17; 18; 19]. The remotely sensed spectral reflectances and derived vegetation indices across the electromagnetic regions have been widely studied to track the seasonal dynamics of crop growth, and to derive critical phenological transition dates (e.g., onset of greenness) in association with crop biological growth stages (e.g., crop emergence) [20; 21]. Specifically, a range of curve-fitting-based phenological models has been developed to smooth the satellite time series as well as to capture the seasonal variations in crop canopy development along the phenological trajectory [22]. Those curve-fitting-based models include the double logistic function and its variants, the Quadrat function, the Savitzky-Golay function, the Fourier transform (or harmonic) function, the wavelet function, the asymmetric Gaussian function, and the spline-relevant functions [23; 24; 25; 26; 27]. With the modeled seasonal dynamics through satellite time series, crop phenological transition dates characteristic of biological events of canopy phenology can further be extracted using phenophase analysis methods. The phenophase analysis methods include the threshold-based method (e.g., defined thresholds based on the curve amplitude), the curve derivative method (e.g., first and second derivatives of the curve), the curvature change rate method (e.g., extremes in curvature change rate of the curve), the moving average method (e.g., a departure from the moving average-based trend), and the pheno-network method (e.g., bridging coefficient in the phenology-tailored complex network) [28; 29; 30; 31; 32]. Those methods are designed to characterize distinct changes in canopy biophysical or biochemical properties that tend to be connected with crop phenophase transitions. In particular, [PERSON] [19] developed a remote sensing phenological monitoring framework that systematically integrates a combination of curve-fitting and phenophase analysis methods for crop phenological characterization, and found that a range of corn and soybean phenological stages can be estimated under the devised framework with the phenological reference of district-level Crop Progress Reports (CPRs), the United States Department of Agriculture (USDA).