# Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
## 4 Discussion  
As two important growth stages, the crop emerged, and mature stages determine the length of the crop growing season and have noted implications for agricultural biogeochemical cycles and crop primary production. We assessed the near-surface PhenoCam and high-resolution PlanetScope imagery in characterizing these two stages of corn and soybean, in comparison with corresponding visual observations. The diverse set of fine-scale sensor-derived phenological metrics had varying agreements with visually observed ones, with RMSE ranging from about 5 days to more than 30 days, indicating the importance of selecting appropriate transition date analysis methods. For both PhenoCam and PlanetScope derived metrics, the CUR-based Greenup and Gu-based UD estimates showed good congruence with visual crop emergence observations (e.g., about one-week RMSE value, small bias, and R square around 0.77 for PhenoCam-Visual emerged comparisons; about 8-11 days RMSE value, 5-9 days bias, and R square around 0.6 for PlanetScope-Visual emerged comparisons). The TRS- and DER-based SOS metrics, on the other hand, deviated from visual crop emergence observations, with RMSE about three weeks for PhenoCam-Visual comparisons and four weeks for PlanetScope-Visual comparisons. The TRS- and DER-based EOS estimates reconciled well with visually observed crop maturity dates, with about 5 days RMSE value, almost no bias, and R square over 0.75 for PhenoCam-Visual mature comparisons, and about 9 days RMSE and bias values with R square around 0.6 for PlanetScope-Visual mature comparisons. Yet the CUR-based Dormancy and Gu-based RD estimates showed a large difference from crop maturity observations. The RMSE and bias of these two estimates were about 3 weeks for PhenoCam-Visual comparisons and more than four weeks for PlanetScope-Visual comparisons. The agreements resonated with our previously developed crop phenological monitoring framework, yet from a novel fine-scale perspective, to shed light on how sensor-derived phenological characteristics could be directly interpreted in the context of visually observed crop phenological stages of physiological significance. Among the transition date analysis methods, the DER-, CUR-, and Gu-based methods extracted critical crop phenophases based on characteristic points of time series curves (e.g., local maxima/minima in the curvature change rate). By contrast, the TRS-based method identified phenophases via flexibly defined thresholds. In this study, the TRS-based EOS metrics with the 50% threshold aligned well with the visual crop maturity measures. Yet the TRS-based SOS metrics, estimated with the 50% threshold, were much delayed compared to visual crop emergence observations. We further tested a range of threshold values  
Figure 11: The comparisons among PlanetScope, PhenoCam, and visual phenology with all the shared site-year observations for crop emergence and maturity stages. The CUR-based Greenup metric was estimated for crop emergence and the DER-based EOS metric was estimated for crop maturity. from 5% to 60% with an increment of 5% and found that TRS-based SOS metrics with lower threshold values (e.g., 10%) tended to achieve lower RMSE and Bias values, yet lower R square values for both PhenoCam-Visual emerged comparisons (Figure S1) and PlanetScope-Visual emerged comparisons (Figure S2). With the crop emergence being defined as the timing when the crops were first visible in the PhenoCam photos, the earlier detection dates by lower thresholds of the TRS-based method might be closer to visual emergence dates. Those detection results, however, were also sensitive to noises and fluctuations of time-series curves, leading to lower R square values. For different vegetation indices, crop species, and geographical regions, different thresholds might be needed for the optimized performance of TRS-based methods [17; 67; 68], which could pose challenges for crop phenology detection from a practical viewpoint.