Making space a valuable asset for everyone

Participate in KappaOne webinar on 30th of June! KappaOne: Sentinel-1 data layers for the subsidy checks under CAP
We begin with KappaOne webinars. Do not miss your chance to participate in the very first on 30th of June 3pm EEST. Register here: In the first webinar our EO analyst Jelizaveta Vabištševitš is going to demonstrate how KappaOne facilitates subsidy checks under the CAP (common agricultural policy).  
Why do we need Sentinel-1 data service?
The Sentinel-1 data is different than most other satellite data because it is not dependent on weather or daylight, and thus is able to provide data on days where other satellites cannot because of clouds. We have a new blog post by Olga Wold, our geospatial data quality specialist. 

Advanced speckle filtering
Our raster processing got a major upgrade that significantly improves the quality of our backscatter and coherence rasters. We analyzed, modified and combined multiple published methods when designing a new filter for KappaOne.

KappaZeta is making Sentinel-1 data very easy to use
Copernicus Sentinel satellites produce several terabytes of data every day. For many users the data is complex to understand and use. KappaZeta is addressing this problem for Sentinel-1 with pre-processing and providing the derived layers in an analysis-ready format. The goal is to have a service, where obtaining the right satellite image takes either a single mouse click or an API command. The vision will be implemented under the InCubed programme of European Space Agency.

Our crop monitoring service prototype is live
In the beginning of May we launched our crop monitoring prototype service. It is the first phase of the Harvesting Time Recommendation service prototype and allows the farmers to see the ongoing season timeseries of Sentinel-1 and Sentinel-2 field-based features and NDVI/RGB/NRG images which are being updated as soon as new images are available.
Synthetic NDVI for proxy biomass calculations
Plant biomass, an indicator of the ecological state of an area, can be modeled from the Normalized Difference Vegetation Index (NDVI). The ESA’s Sentinel-2 satellite provides NDVI data openly, but this is often obstructed by clouds. Hence, we developed an MVP to explore the modeling of NDVI from Sentinel-1 data using Generative Models.

News archive >>
EU Subsidy Checks  
Replacing on-the-spot-checks on grasslands with an automated solution
Radar Time Series
Ready to use in various machine learning algorithms
Research and Consulting 
Developing new models and classifiers for different use cases