1. Next step in the grazing detection project with the ESA
In short, we are developing a grazing detection methodology based on Sentinel-1 and Sentinel-2 imagery time series. This work is important for the reform of the Common Agricultural Policy, which will replace on-the-spot grazing checks with satellite monitoring. As about 20% of grasslands in Europe are being grazed, our detection methodology will help to complete the grassland maintenance checks alongside the mowing detection.
So far, we have gathered a significant amount of ground reference data – a total of 7,445 animals (61% cattle, 32% sheep, 2% horses) were labelled all over Estonia.
Cattle (left) and sheep (right). Source: Estonian orthophoto, Land Board.
Next, we are planning to carry out a field survey in Estonia, which will focus on counting the number of animals on the test parcels throughout the active grazing period (from May to August). The main objective of the in situ data collection is to record the grazing intensity (LU/ha) and grassland conditions on a weekly basis.
Jelizaveta Vabištševitš, EO analyst, project manager
2. Our new cloud mask
On 23rd of April KappaZeta presented the Cloud Mask project results on Very High-resolution Radar & Optical Data Assessment (VH-RODA) 2021 workshop. VH-RODA 2021 (20-23 April) held presentations and discussions of current status and future developments related to the calibration and validation of spaceborne very high-resolution SAR and optical sensors and data products with the focus on synergies between optical and SAR communities, presentation of standards and best practices for data quality.
We named our cloud classification mask KappaMask. Thus, we presented that our KappaMask outperformed Sen2Cor with 92% vs 57% dice coefficient on validation set. The pixel-wise metric is considering the performance for cloud, semi-transparent cloud, cloud shadow and clear area. Check out our cloud mask comparison slider-pages with rule-based masks (Sen2Cor, Fmask) from here and machine learning-based Sinergise S2cloudless mask from here. The labelling process is still in progress (so far, we have ~3500 labelled subscenes 512×512 pixels at 10 m resolution covering summer season in Northern Europe). Therefore, we are expecting even more outstanding results with further labelling and fine-tuning!
Marharyta Domnich, machine learning engineer
