Research & development

KappaOne S1 ARD layers – weather independent vegetation index Cloud-independent agricultural monitoring solution Duration: 2024–2025 Funder: European Space Agency Planned outcomes: Develop consistent and accurate sNDVI data that is unaffected by cloud cover. Provide the sNDVI solution to agricultural experts to navigate changing conditions with greater confidence and agility, regardless of weather patterns. Read more about the project
Assessment of Timber Stock and Modeling of Carbon Stock Based on Satellite Remote Sensing to Accelerate the Green Transition and Increase Transparency of the Carbon Market Satellite-based forest growing stock volume and CO₂ stock estimation in Estonia Duration: 2024–2025 Funder: European Regional Development Fund; amount of the grant 98 102 €. Planned outcomes: Models for the quantitative assessment of the growing stock volume and of sequestered carbon in the territory of Estonia
AI-based tillage detection for improved agricultural and climate policies Transforming agricultural monitoring with AI and satellite data. Duration: 2023–2024 Funder: European Space Agency Planned outcomes: Feasibility study of conservation (i.e. non-inversion) tillage detection with satellite imagery. Tillage detection AI models for different tillage types based on Sentinel-1, Sentinel-2, Landsat 8 & 9 imagery time series. Read more about the project
Satellite monitoring services for crop insurance Enabling novel and more efficient solutions for customer pooling and loss adjustment processes in crop insurance. Duration: 2022 – 2023 Funder: European Regional Development Fund and Enterprise Estonia; amount of the grant 434,760 €. Planned outcomes: Satellite monitoring prototypes for customer pooling and loss adjustment processes in crop insurance and user feedback related to it.
AI-based Cloud Mask Processor for Sentinel-2 : Phase 2 Extending the "KappaMask" processor developed under Phase 1 of the project “AI-based Cloud Mask Processor for Sentinel-2” to global coverage. Duration: 2021–2022 Funder: European Space Agency Planned outcomes: KappaMask extended to global conditions, which is compatible with ESA Sentinel-2 L2 processing chain. KappaSet, the Sentinel-2 cloud and cloud shadow dataset, distributed geographically throughout all seasons over the globe. Go to KappaMask (GitHub) Go to KappaSet
AI-based Cloud Mask Processor for Sentinel-2 Develop new and better cloud mask than existing ones Duration: 2020–2021 Funder: European Space Agency Planned outcomes: Reliable cloud mask processor for Northern Europe region, which is compatible with ESA Sentinel-2 L2 processing chain. Create high quality reference dataset for future developments. Use innovative deep learning techniques in cloud masking.
Grazing detection from Copernicus data for agricultural subsidy checks A complete satellite-based grassland monitoring service Duration: 2020–2021 Funder: European Space Agency Partners: Gisat s.r.o Planned outcomes: Develop grazing detection methodology based on Copernicus data (Sentinel-1 and Sentinel-2 imagery time series). Provide the NPA operators means to carry out checks on grasslands using EO data and substituting on the spot checks of grassland grazing activity with new EO data based grazing detection methodology. Close the grasslands subsidy checks case for CAP satellite monitoring. Grazing detection project website
Harvesting Time Recommendation for maximum crop Yield (HaTRY) Predicting the best time to harvest crop using remotely sensed data Duration: 2020–2021 Funder: European Space Agency Partners: local farmers in Estonia Planned outcomes: Prototype service for Precision Farming application to predict and recommend the most favorable harvesting time for maximum crop yield. Focus on Northern Europe region and three most common crops: winter wheat, spring barley and winter rapeseed. Learn and collect farmers requirements for a fully operational service.
National Programme for Addressing Socio-Economic Challenges through R&D (RITA). Using remote sensing data in favour of the public sector services Monitoring the use of agricultural land Duration: 2019–2020 Funder: This study was financially supported by the European Regional Development Fund within National Programme for Addressing Socio-Economic Challenges through R&D (RITA). Partners: Consortium team: University of Tartu, Tallinn University of Technology, Estonian University of Life Sciences, KappaZeta Ltd Planned outcomes: Mature, reliable and tested crop classification methodology development specifically suited for Estonian agricultural, ecological and climatic conditions. Multi-year country-wide testing and error analysis about vegetative seasons 2018–2020. Crop classification model prototype with test datasets.
ESA Business Incubation Improving our software quality during the incubation phase Duration: 2018–2019 Funder: European Space Agency Partners: University of Tartu (Institute of Computer Science), Sookolli Kaardid OÜ, Elmer SKB OÜ, Codeborne OÜ Outcomes: Satellite imagery model development Software architecture definition and review Software implementation Market analysis and business plan development Improved web map for visualizing our analysis results
Detection of mowing events on grasslands from Sentinel-data The first nation-wide system for automated monitoring of agricultural practices in EU Duration: 2016-2018 Customer: Estonian Agricultural Registers and Information Board (ARIB) Partners: CGI Estonia, Tartu Observatory Outcomes: Nation-wide fully automated mowing detection system operational in Estonia from 2018 Sentinel-1 and Sentinel-2 time-series for operational near real-time monitoring 85% of detection accuracy of the mowing events on grasslands Automated “early warning” reminders to applicants	An isolated grassland – status “not mowed”
Grassland mowing detection for agricultural subsidy checks with Sentinel-1 and Sentinel-2 Looking over the borders to Denmark, Sweden and Poland Duration: 2017-2019. Customer: European Space Agency, Industry Incentive Scheme (ESA IIS) Partners: The Danish Agrifish Agency, the Swedish Board of Agriculture, The Agency for Restructuring and Modernisation of Agriculture in Poland, Reach-U Ltd, KappaZeta Ltd Outcomes: Validate the service by performing user trials in Sweden and Denmark Enhance the existing cutting and grazing detection methodology Study Earth Observation Community Platform (EO CP) service providers Perform Viability Analysis
Data Analytics for Optimizing Agricultural Monitoring Going beyond the mowing detection powered by high-level data analytics expertise Duration: 2017-2018. Funder: Enterprise Estonia, project No. EU48684 Partners: Software Technology and Applications Competence Centre (STACC), KappaZeta Outcomes: Develop a scientifically validated methodology for ploughing and grazing events detection from Sentinel-1 and Sentinel-2 time series Bring to customers a new cultivation and grazing detection product prototype Involve high-level data analytics expertise powered by STACC.
Home page and demo application KZ branding, home page and demo application Duration: 2017 Funder: Enterprise Estonia, project No. EU51738; amount of the grant 14,960 €. Main partners: Reach-U, Kiften, Jon & Pun, KappaZeta Outcomes: KZ branding, home page and mowing detection demo application

Research

Relating Sentinel-1 Interferometric Coherence to Mowing Events on Grasslands the effect of grassland mowing on interferometric coherence calculated for a Sentinel-1 pair separated by 12 days The coherence increases after a mowing event, but the effect wears off in a couple of weeks Precipitation counteracts this effect Go to paper (MDPI)
Monitoring of Agricultural Grasslands With Time Series of X-Band Repeat-Pass Interferometric SAR Using COSMO-SkyMed acquisition pairs separated by one day, the relationship between grassland mowing and interferometric coherence was noticed Precipitation and further agricultural activity on parcels after mowing may negate the coherence increase Go to paper (IEEE)
Observations of Cutting Practices in Agricultural Grasslands Using Polarimetric SAR SAR polarimetry also provides means to detect mowing events from TerraSAR-X data products Polarimetric parameters such as HH/VV polarimetric coherence and alpha angle of the H/alpha decomposition are sensitive to mowing Go to paper (MDPI)
Sensitivity of Sentinel-1 backscatter to characteristics of buildings Sentinel-1 backscatter statistics respect to the physical parameters of the buildings Backscatter dependence on building height, material, orientation angle and shape Alignment effects due to Sentinel-1 orbit and look angle Go to paper (Taylor & Francis)