# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 4 Conclusions  
In this paper, 12-day repeat pass Sentinel-1 interferometric coherence calculated for three acquisition geometries was analysed in relation to moving events recorded on agricultural grasslands in Central Estonia over the vegetative season of 2015. Results showed that after a moving event, median \(\gamma_{vv}\) and \(\gamma_{vh}\) values were statistically significantly higher when compared to pre-event values. The group containing coherence measurements from 0 to 12 days after a moving event gave the best separation between moved and not moved grasslands. The coherence was higher even 24 to 36 days after a moving event when compared to values from not moved measurements. The increase of coherence for the VH polarisation was higher than for the VV polarisation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**RON** & **T\(-\)1** & **T\(+\)1** & **T\(+\)2** & **T\(+\)3** & **(T\(+\)1)\(-\)(T\(-\)1)** & **(T\(+\)2)\(-\)(T\(-\)1)** & **(T\(+\)3)\(-\)(T\(-\)1)** \\ \hline R160 & 0.16 (0.16) & 0.26 (0.29) & 0.22 (0.25) & 0.20 (0.20) & 0.097 (0.13) & 0.060 (0.085) & 0.040 (0.039) \\ R58 & 0.15 (0.15) & 0.22 (0.22) & 0.19 (0.19) & 0.18 (0.18) & 0.063 (0.065) & 0.041 (0.040) & 0.024 (0.025) \\ R80 & 0.15 (0.15) & 0.19 (0.20) & 0.18 (0.20) & 0.17 (0.17) & 0.038 (0.053) & 0.031 (0.049) & 0.021 (0.020) \\ \hline \end{tabular}
\end{table} 
Table 5: Median coherence values for time separation groups and VV polarisation. Values without parentheses indicate unfiltered data, while precipitation filtered data is given in parenthesis. Several factors impeded the increase of coherence after a moving event. The factors were farming activities, such as sowing or ploughing, height of the cut, rapid growth of grass, and precipitation before image acquisitions. In the future, 6-day pairs consisting of Sentinel-1A and 1B acquisitions should alleviate some of these factors. However, use of C-band SAR repeat pass interferometric coherence for moving detection is feasible. The aforementioned factors that introduce ambiguities in event detection could be addressed by including ancillary data in the analysis. Further research should reveal how various farming methods, soil types, soil moisture, relative humidity, strong winds, topography, and grassland species influence the relationship between coherence and moving. The accuracy of moving detection using various estimation methods should be analysed. For instance, NPAs could possibly use these findings to optimise the work of validating the moving requirement set by the European Union CAP. Additionally, the results may be beneficial for a general service for classification of management practises in agricultural areas. This study was supported by grant no. IUT2-16 of the Ministry of Education and Science of Estonia. Authors appreciate the help from [PERSON] and [PERSON] with weather radar precipitation estimates, Estonian Weather Services for providing weather radar and meteorological station data, [PERSON] for providing GPS logs from moving tractors, and [PERSON] and [PERSON] for allowing measurement campaign to be carried out on their grasslands. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] designed and conducted the field survey; [PERSON] performed the experiments; [PERSON] and [PERSON] analysed the data; [PERSON] and [PERSON] wrote the great majority of the paper. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: