# Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
## 4 Discussion
Yet the calibration procedure usually involved the acquisition of the PlanetScope imagery of a relatively large spatial extent to be compared with corresponding reference imagery typically of coarser spatial resolutions (e.g., acquisition of PlanetScope and MODIS imagery of the same spatial extent), which could be financially prohibitive across the study sites. The concordance among PlanetScope, PhenoCam, and visual phenology indicated the role of near-surface and high-resolution remote sensing in bridging ground-based phenological observations with conventional satellite-based phenological measures, which were usually derived from the coarse spatial resolution images (e.g., MODIS), and more recently moderate resolution ones (e.g., Harmonized Landsat and Sentinel-2). The scale difference in the conventional satellite-based phenological studies made it challenging to understand how the seasonal trend captured by remote sensing could be reconciled with crop phenological development trajectory. In recent years, the increasing studies of near-surface remote sensing showed the capability of the PhenoCams in tracking the phenological dynamics across the organism-to-ecosystem scales. Besides the PhenoCams, our study also demonstrated the potential of the PlanetScope imagery in facilitating the ground-satellite phenological bridging, which could further benefit the understanding of the effects of scale and representation in phenological retrievals. Yet compared to the PhenoCams, the PlanetScope phenological estimates showed a slightly positive bias for both crop emergence (bias = 4.63 days) and maturity (bias = 6.56 days) in this study. The relatively delayed estimates from the PlanetScope were partially attributed to the difference between the PhenoCam field of view and the associated satellite pixel, and the difference in crop phenological trajectories captured by the different vegetation indices. During the stage of crop emergence, the weak signal of vegetation and associated strong signal of soil background made the capturing of the subtle change of the emergence more difficult in the satellite imagery, compared to near-surface remote sensing. The bias for the crop maturity stage could partially be explained by the heterogeneous status of crop coloration under the camera and satellite field of view. Additionally, the near-surface remote sensing-derived GCC index typically registered the greenness of crop canopies, while the satellite-derived NDVI captured both photosynthetic and structural status. The lag of structural changes in crop growth relative to the changes in its color and pigments might also cause detection bias [70]. In this study, both near-surface PhenoCams and high-resolution PlanetScope imagery showed strong couplings with the observed crop emergence and maturity stages. At fine spatial scales, the coupling relationships also had great potential to be extended to other crop-specific phenological stages (e.g., the silking stage of corn), with the Beck phenological model and appropriately retrieved phenological metrics. Yet as a retroactive approach, the Beck phenological model may have limited ability to conduct near real-time crop phenology monitoring. Compared to natural and managed non-agricultural systems, the crop growth and phenological development in the agricultural systems usually exhibited more diverse and heterogeneous patterns across farm fields. The characterization of these diverse field-level phenological patterns, driven by a combination of climate and farming practices (e.g., sowing date and fertilizer application), could largely be enabled using near-surface and high-resolution satellite imagery. The capability of both imagery in characterizing fine-scale variability in crop phenology had important implications for the efforts toward more sustainable agricultural management and farm-level decision-making, particularly in small-holder agricultural systems.