1. Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks
For the last year we have been developing Sentinel-2 cloud mask labelled dataset, available for community https://zenodo.org/record/5095024#.YP8HpY4zaUk. The dataset consists of 4403 labelled subscenes at 10 m resolution from 155 Sentinel-2 (S2) Level-1C (L1C) products distributed over the Northern European terrestrial area. Temporally there are around 30 S2 products per month from April to August and 3 S2 products per month for September and October. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations in Northern Europe. The classes in mask are the following:
- MISSING: missing or invalid pixels;
- CLEAR: pixels without clouds or cloud shadows;
- CLOUD SHADOW: pixels with cloud shadows;
- SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15 km);
- CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2 km to 2 km);
- UNDEFINED: pixels that the labeler is not sure which class they belong to.
The dataset was labelled with help of CVAT and Segments.ai labelling tools (with the possibility of integrating active learning process in Segments.ai, the labelling was performed semi-automatically and verified by human labeler). The files are in NetCDF format that have preprocessed and oversampled at 10 m resolution B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B10, B11, B12 and some of the products have additionally Fmask, Sen2Cor, S2cloudless and MAJA masks for comparison. We believe that sharing such dataset is a big step for improving cloud mask performance for the whole community! Feel free to use and share!
Marharyta Domnich, machine learning engineer
2. Building the Harvesting Time Recommendation Service: weekly surveys
It is summer and with complex weather conditions it can be a difficult task for farmers to choose the right time for harvesting crops. To find this out and reach the challenging goal we are performing fieldworks in scope of Harvesting Time Recommendation for maximum crop Yield (HaTRY) project, to collect samples of three typical crops grown in Northern Europe and perform the analysis with help of Estonian Crop Research Institute (ETKI) lab.
During dedicated fieldwork we collect grains from three winter wheat, three winter rapeseed and three spring barley fields (total of nine fields) and then bring them to the ETKI lab for the analysis. The main goal is to collect continuous data before the fields will be harvested, which will represent the key parameters describing crop ripening (protein content, grain moisture, chlorophyl content, oil content, etc.).
When all the monitored crop fields will have been harvested and the samples collected and analyzed, we are going to match and compare the analysis results from the laboratory with Sentinel-1, Sentinel-2 feature set signatures and validate the model which will help us to predict the best harvesting times in the future service.
Olga Wold, geospatial data quality specialist
