# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 1 Introduction  
Almost all life on Earth depends inevitably on primary production. Grasslands are efficient producers of organic matter, having a high net primary production (NPP) to gross primary production (GPP) ratio, NPP/GPP. [PERSON] et al. [1] reported that for grasslands, the three-year mean NPP/GPP ratio estimated with the improved MODIS 1 km \(\times\) 1 km grid MOD17 product is 0.65, while for deciduous broadleaf forests, this parameter is 0.35. Additionally, grasslands are important from several other points of view, such as biodiversity, agricultural production, and tourism, among others [2; 3; 4]. A significant portion of Europe's agricultural area is grasslands [5]. The European Union has established the Common Agricultural Policy (CAP) that enables the payment of subsidies to farmers [6]. \"Greening\" is a measure in the CAP that makes the system of direct payments more environmentally-friendly, requiring the grasslands to be maintained in good agricultural and environmental conditions. One of the obligations of farmers is to maintain their grasslands by moving or grazing on a yearly basis. National Paying Agencies (NPAs) are required to validate the movingrequirement to verify subsidy claims. The validation is usually performed by selecting a limited test area, where the checks are then carried out using visual interpretation of very high resolution satellite images and on-site field inspections. The validation process, however, could be made more effective via a more comprehensive use of remote sensing data, especially after the successful launch of the Copernicus program [7]. Use of Earth Observation (EO) data would allow the NPAs to perform the checks over a much larger area, leading to a more effective use of resources and helping to reduce the number of false payments. Monitoring of agricultural areas has been an important part of remote sensing since the very first EO instruments. Optical remote sensing offers mature methods for the monitoring of agricultural yield [8; 9; 10] and management practices [11; 12]. However, [PERSON] et al. [13] showed that many important agricultural areas are so persistently and pervasively covered by clouds that less than half of their 8 day composites would be even 70% clear of cloud cover. Authors also concluded that in these areas and time periods, optical polar-orbiting imaging is not likely to be a viable option for operational monitoring. Similarly, laser scanning, while known to be capable of producing reliable vegetation height estimates [14; 15], is predominantly applied in the case of forests or bushes, and is too expensive for continuous wide area monitoring. Use of spaceborne synthetic aperture radar (SAR) data would therefore be beneficial, as the SAR signal can penetrate clouds in all but extreme weather conditions, providing continuous data covering a large area. As in the case of optical remote sensing, investigations of SAR signal attributes in relation to vegetation parameters have been widespread since the first data became available, and research covers different agricultural applications and technical approaches. The launch of Sentinel-1A and -1B SAR satellites now offers an opportunity to investigate agricultural monitoring methods based on regular dense time series of free and open data. Regarding the monitoring of management practices, studies have shown the potential of approaches based on the analysis of backscatter intensities, polarimetric SAR (PolSAR), and interferometric SAR (InSAR). The possibility of detecting harvesting events of sugarcane using backscatter intensities of TerraSAR-X, ASAR/ENVISAT, and PALSAR/ALOS sensors was analysed by [PERSON] et al. [16], who concluded that the events can be detected if the acquisition date is close to the harvesting date and the sugarcane is not dried out before harvesting.