1. Sentinel-2 KappaMask
For the last year we were developing our own free and open AI based cloud mask, which can detect pixels corrupted by cloud shadows and various types of clouds. This complex task was successfully finished and KappaMask is the most accurate cloud mask for Sentinel-2 for Northern European terrestrial summer conditions!
Figure 1.1. Visual comparison of KappaMask with Sen2Cor
Now we are going to the global scale. A lot of challenging tasks are facing KappaZeta team, as introduction of the new areas and conditions is going to be done. Currently snowy areas are misclassified as clouds and ocean surface is currently often mistaken by our model as cloud shadows and semi-transparent clouds. Moreover, we are introducing the area outside of Europe, which will contain deserts, tropical areas, etc.
To achieve this challenging goal, we are going to further develop the model and extend our existing open-source labelled dataset “Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks”, which you are welcome to check and use in Zenodo platform!
Olga Wold, Geospatial data quality specialist
2. HaTRY project about to end
HaTRY (Harvesting Time Recommendation for maximum crop Yield) project has reached to a stage, where we are analysing data collected during last harvesting season and making conclusions.
Our field surveys on 9 test sites were successful, and together with Estonian Crop Research Institute (ETKI) we have gathered valuable information about crop development stages and gained some deeper insight into ripening stage, which was most important for our project.
It might be naive to think, that ripening process and optimal harvesting time is easily detected only from Sentinel parameters, but our hypothesis was, that to some extent it’s possible. Field survey results show that there are definitely some patterns and correlation between Sentinel parameters and different growth stages, and a common signature for ripening process can be described. Beside NDVI and VHVV ratio decrease during ripening, we could also detect the increase in coherence VV values before harvesting dates for all three crops (Figure 1). Of course, there will always remain question whether the farmer harvested the crop in optimal time? In many cases they didn’t, and there were different reasons for that (queue in the dryer, lack of machinery). We will write a dedicated blog post about our findings, but until then enjoy some of our graphs, where our geospatial data quality specialist Olga Wold compared winter rapeseed growth stages, Sentinel parameters and lab analysis for collected seed samples.
Mihkel Järveoja, Project manager / GIS developer
