## 4 Discussion  
Detection of changes due to field operations using InSAR coherence is challenging because a decreased coherence can also be the result of temporal decorrelation caused by natural events, including rain, snow, wind, seasonal changes in vegetation development, and also geometrical decorrelation (from tall and dense vegetation such as forests) [44; 45; 46]. In this study, we determine crop seeding and harvest through the detection of a sharp change in InSAR coherence, assuming that surface  
Figure 9: Maps of crop seeding (**a**) and harvest dates (**b**) derived using time-series InSAR coherence and VH backscattering coefficient. Proportion of pixels identified as seeded or harvested for each time. structure will remain stable for a period of time after these two types of field operations, inducing a low coherence for the pair of SAR images spanning the period within which the event happened. However, this cannot always be successful under at least two cases: 1) no-till seeding, for which high coherence could be anticipated because soil surface is not subject to significant interference by the operation, and 2) harvest with substantial amount of residue left standing in the field; in which case, there is low coherence even after harvest. The situation could be further complicated if different management practices are involved after crop harvest: 1) soil tillage at different time with different machine types that can induce different levels of surface roughness; 2) different treatments of crop residues, e.g., residue left standing, chopped and spread, or removed for other purposes at different rates; 3) incorporation of cover crops in the cropping system for soil protection. It was noted in the study area that cover crops developed to a substantial level in some of the summer crop fields after harvest. This reality, although providing challenges in detecting seeding and harvest dates through CCD, also presents an opportunity for the detection of other field operations and crop management practices, such as ploughing. High coherence can be observed during the mid-season for crops, which may induce ambiguity for the determination of seeding and harvest dates. For instance, the soybean field shown in Figure 6c and has a relatively high coherence (~0.5) in mid-August although the high VH indicated large volume scattering from plant canopy. This may be linked to certain canopy structure characteristics and deserves further investigation. This also illustrates the added value of SAR backscattering to the interpretation of crop field activities. In fact, combination of VV and VH has also been found to be useful for crop type discrimination and biophysical parameter estimation [47; 48; 49]. This issue could be alleviated with a shorter revisiting cycle of InSAR acquisition because field disturbances can be better resolved in time. Physical environment conditions, such as snow and rain, can influence not only SAR backscattering signatures but also InSAR coherence [50; 51; 52]. Low coherence at the beginning and end of season was observed when snow cover was present on the ground and the thaw or freezing process started. According to meteorological records of the nearby station (Figure 2b), snow cover ended at the end of April and started in early November in 2019, thus soil surface (or plant) conditions during these periods will not be captured by the time series SAR data, and SAR data contributed less to interpretation of crop field operations. Although acquisition of radar data is less impacted by weather condition than that of optical sensors, meteorological events can still introduce changes to a certain degree, either in structure or in dielectric property, that are not due to field operations or crop growth dynamics. This can be observed from perturbation of VV and VH intensities during the days reported with precipitation in the later growing season.