First Copernicus satellite was launched in 2014 and by 2018 the Sentinel-1 and -2 systems have reached their operational status with nominal data production volumes. Despite the numerous outreach and promotional events, the data is still underused in the public sector and there is remarkable potential to be employed. Main factors limiting the use are: i) lack of awareness about the capabilities and limitations among the end users; ii) lack of knowledge how to process the data and retrieve reliable information out of it; iii) insufficient applied research / piloting projects to test the academic research results in large scale (e.g. country level) real life conditions to discover and fix the possible problems not evident in the small scale research projects. Agricultural statistics is one of the areas where Copernicus data can help to raise the current methods to the next level. Instead of field surveys sampling based indirect estimates it is now possible to directly measure whole geographical coverage and get virtually total representation. Despite the high resolution satellite data has been available for decades its usage has been limited due to high price, sparse temporal and spatial coverage. Copernicus addresses both of the shortcomings with free and open data policy and unprecedented spatial and temporal coverages. Largest improvements are expected for the applications, which need dense time series, fast updates and temporal process monitoring, where static once a year imaging has not been sufficient. Agricultural statistics seems to be a model example here. Thanks to the long traditions, large and established user community of optical remote sensing the uptake of Sentinel-2 data has been relatively rapid. The reason that optical satellite imagery is easy and intuitive to interpret should not also be underestimated. The usefulness of Sentinel-1 data has been so far undeservedly underestimated, which is best illustrated by the fact that large satellite imagery processing cloud environments like Google Earth Engine and Amazon Web Services don’t even provide Sentinel-1 SLC format data, despite that they are the most information rich data products of Sentinel-1. The reasons behind the very limited usage Sentinel-1 SLC data are likely higher technical complexity, not so intuitive data interpretation and smaller community of radar remote sensing experts among the universities and companies. Still Sentinel-1 is a very valuable complement to Sentinel-2 as it is virtually weather independent (no gaps in the time series due to cloud cover) and it is directly sensitive to the water content of the soil and vegetation cover, which is very important for describing the state of the agricultural landscapes. The abstract introduces Sentinel-1 repeat pass coherence as an important parameter for describing agricultural landscapes. Methods section describes the coherence computation and its meaning for interpreting the resulting coherence images. Results section describes coherence for grasslands mowing detection and discusses its usage and limitations beyond, for other agricultural applications and statistics computation. Conclusion underlines the main benefits with potential applications and gives recommendations for applied research projects to pave the way for operational use.