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Governments pay agencies to control the activities of farmers who receive governmental support. Field visits are costly and highly time-consuming; hence remote sensing is widely used for monitoring farmers' activities. Nowadays, a vast amount of available data from the Sentinel mission significantly boosted research in agriculture. Estonia is among the first countries to take advantage of this data source to automate mowing and ploughing events detection across the country. Although techniques that rely on optical data for monitoring agriculture events are favourable, the availability of such data during the growing season is limited. Thus, alternative data sources have to be evaluated. In this paper, we developed a deep learning model with an integrated reject option for detecting grassland mowing events using time series of Sentinel-1 and Sentinel-2 optical images acquired from 2000 fields in Estonia in 2018 during the vegetative season. The rejection mechanism is based on a threshold over the prediction confidence of the proposed model. The proposed model significantly outperforms the state-of-the-art technique and achieves event accuracy of 73.3% and end of season accuracy of 94.8%.

Short temporal baseline regular Synthetic Aperture Radar (SAR) interferometry is a tool well suited for wide area monitoring of agricultural activities, urgently needed in European Union Common Agricultural Policy (CAP) enforcement. In this study, we demonstrate and describe in detail, how mowing and ploughing events can be identified from Sentinel-1 6-day interferometric coherence time series. The study is based on a large dataset of 386 dual polarimetric Sentinel-1 VV/VH SAR and 351 Sentinel-2 optical images, and nearly 2000 documented mowing and ploughing events on more than 1000 parcels (average 10.6 ha, smallest 0.6 ha, largest 108.5 ha). Statistical analysis revealed that mowing and ploughing cause coherence to increase when compared to values before an event. In the case of mowing, the coherence increased from 0.18 to 0.35, while Sentinel-2 NDVI (indicating the amount of green chlorophyll containing biomass) at the same time decreased from 0.75 to 0.5. For mowing, there was virtually no difference between the polarisations. After ploughing, VV-coherence grew up to 0.65 and VH-coherence to 0.45, while NDVI was around 0.2 at the same time. Before ploughing, both coherence and NDVI values were very variable, determined by the agricultural management practices of the parcel. Results presented here can be used for planning further studies and developing mowing and ploughing detection algorithms based on Sentinel-1 data. Besides CAP enforcement, the results are also useful for food security and land use change detection applications. ; Peer reviewed

Short temporal baseline regular Synthetic Aperture Radar (SAR) interferometry is a tool well suited for wide area monitoring of agricultural activities, urgently needed in European Union Common Agricultural Policy (CAP) enforcement. In this study, we demonstrate and describe in detail, how mowing and ploughing events can be identified from Sentinel-1 6-day interferometric coherence time series. The study is based on a large dataset of 386 dual polarimetric Sentinel-1 VV/VH SAR and 351 Sentinel-2 optical images, and nearly 2000 documented mowing and ploughing events on more than 1000 parcels (average 10.6 ha, smallest 0.6 ha, largest 108.5 ha). Statistical analysis revealed that mowing and ploughing cause coherence to increase when compared to values before an event. In the case of mowing, the coherence increased from 0.18 to 0.35, while Sentinel-2 NDVI (indicating the amount of green chlorophyll containing biomass) at the same time decreased from 0.75 to 0.5. For mowing, there was virtually no difference between the polarisations. After ploughing, VV-coherence grew up to 0.65 and VH-coherence to 0.45, while NDVI was around 0.2 at the same time. Before ploughing, both coherence and NDVI values were very variable, determined by the agricultural management practices of the parcel. Results presented here can be used for planning further studies and developing mowing and ploughing detection algorithms based on Sentinel-1 data. Besides CAP enforcement, the results are also useful for food security and land use change detection applications.

The Electric Solar Wind Sail (E-sail) is a novelpropulsion concept that enables faster space travel tomany solar system targets. E-sail uses charged solarwind particles as the source of its propulsion. This isachieved by deploying long, conducting and chargedtethers, which get pushed by the solar wind byCoulomb drag [1].E-sail technology is being developed to technicalreadiness level (TRL) 4-5 by the European Union'sSeventh Framework Programme for Research andTechnological Development, EU FP7, in a projectnamed ESAIL (http://www.electric-sailing.fi/fp7).Prototypes of the key parts are to be produced. Thedesign will be scalable so that a real solar winddemonstration mission could be scaled up from them.We review here the latest results of the constantlyevolving E-sail project.