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previous newsletters:

 Kappazeta NewsLetter, July 2022
 Kappazeta NewsLetter, June 2022
 Kappazeta NewsLetter, May 2022
 Kappazeta NewsLetter, April 2022
 Kappazeta NewsLetter, March 2022
 Kappazeta NewsLetter, February 2022
 Kappazeta NewsLetter, January 2022

See older newsletters here.

Newsletter, July 2022

1. ESA Latvia Workshop 2022

The 12th ESA Training Course on Earth Observation (EO) took place in Riga Technical University from June 27 to July 1. Targeting researchers and young professionals from in and around the Baltic area in EO, we participated (remotely and in-person) in theory and practical sessions covering (Interferometric) SAR, and applications of satellite data in Forestry, Agriculture and Marine observations.

Figure 1. ESA Workshop in Latvia.

It was heart-warming to see our Mowing and Grazing projects featured in different presentations, and exciting to learn more about the implementation (and challenges) of agricultural projects similar to our current portfolio, directly from ESA experts. The sessions were a blend of interactive theory discussions and hands-on data processing or analyses of radar and optical satellite data with old and new GIS tools. These new tools (including ForestryTEP, OpenEO Cloud) are mostly focused on aggregating satellite data sources and easing access to, and processing on this data.

Special thanks to the ESA, Ministry of Education and Science (Latvia), the Institute of Environmental Solutions and Riga Technical University for putting this workshop together. Details on the course outline, and materials used are available here.
 Hudson Taylor Lekunze, data analyst
2. Space Festival in Tallinn
On 12th of July KappaZeta took part in the Space Festival "100 km from space" event in Tallinn. Our EO Analyst Jelizaveta Vabištševitš & Geospatial Data Quality Specialist Olga Wold with KappaZeta intern Abdullah Toqeer were talking about the importance of satellites and Copernicus programme in agricultural sector.

Figure 2. KappaZeta at the Space Festival.

Many great meetings with young bright space enthusiasts, who learned a lot about the space sector in Estonia and how could the open-source satellite data be used. Many Estonian companies working in the field of space presented their challenging work and impressive research.

Figure 3. KappaZeta at the Space Festival.

Also, the main guest of the festival was the European Space Agency astronaut Matthias Maurer! He spent 177 days at the International Space Station and returned from space in May this year. His inspiring presentation was about the beauty of our planet and the importance of taking care of it, as it is our only home (for now).

Olga Wold, geospatial data quality specialist

Newsletter, June 2022

1. Webinar: “KappaOne – Sentinel-1 data layers for the subsidy checks under CAP”

Sentinel-1 is a radar satellite that works both day and night and can see through the clouds and is, therefore, an excellent source of data for monitoring changes in agricultural land. The KappaOne service is designed to make Sentinel-1 SAR data easily accessible and ready to be used and analysed. It is extremely helpful for the subsidy checks under the Common Agricultural Policy (CAP). For example, it is valuable for planning field visits to the most problematic areas and getting an initial overview of the field before leaving the office. Moreover, it is applicable for checking the parcels manually, where the machine learning model gives an uncertain or borderline result. Sentinel-1 images are also well suited for assessment of mowing, harvesting, ploughing and other markers.
Join the KappaOne webinar to learn more about the service and its use cases for the subsidy check under CAP. The webinar will be held on the 30th of June from 15:00 until 16:00 EEST. You can register here: https://forms.gle/dzYjorhjrPNxRYym7 
 Jelizaveta Vabištševitš, EO analyst
2. KappaMask in cloud masks comparison
KappaZeta is taking part in the Cloud Mask Intercomparison Exercise (CMIX) meeting in ESA/ESRIN, 20-21 June. It is a framework to compare alternative cloud masks for Sentinel-2, Landsat-8/-9 and other widely used optical missions. Cloud mask processor developers and Earth Observation experts discuss and (hopefully) agree on a common way how to label the reference data and measure the accuracy of cloud masks. Taken the hard work our team has done I’m sure KappaMask will stand out here as one of the most accurate cloud masks for Sentinel-2.

Kaupo Voormansik, CEO, SAR expert

3. NISAR is coming
There are several new and interesting upcoming SAR missions including Copernicus ROSE-L, Sentinel-1 NG, ALOS-4 and NISAR. Perhaps the most interesting in the near future is NISAR jointly developed by NASA and ISRO (Indian Space Research Organisation). The satellite is almost ready with the testing and qualification procedure yet to be concluded before the launch, which is expected to early 2024. NISAR is very interesting mission because of three reasons, firstly it is a dual-band mission acquiring SAR data simultaneously in S- and L-band. It will be a Sentinel-1 like data factory imaging the whole planet several times per week and its data policy is planned to be free and open.

NISAR, image by NASA.

The last two points are very important for an operational service development point of view. KappaZeta will keep an eye on NISAR developments and help the world to make the most of this beautiful up-and-coming SAR data factory. If everything goes well people can access and use NISAR data in a simple way via our KappaOne service starting from 2024.
Kaupo Voormansik, CEO, SAR expert

Newsletter, May 2022

1. KappaZeta in the Living Planet Symposium
It is 23rd of May 2022 and we are glad to share that we are present at European Space Agency Living Planet Symposium in Bonn!

On Friday 27th of May, our CEO Kaupo Voormansik will give an oral presentation on “High quality food for AI - Sentinel-1 analysis-ready data (ARD) with interferometric coherence”. The presentation will focus on freshly launched KappaOne service. The SAR expertise of KZ helps to take full advantage of the interferometric and polarimetric data content of Sentinel-1. And for the end-user, it means calibrated and noise corrected imagery products with the highest possible spatial resolution using advanced speckle suppression methods.

On Tuesday our EO analyst Jelizaveta Vabistsevits will give a session on “KappaOne fresh Sentinel-1 data layers’ use case examples for the subsidy checks in Common Agricultural Policy”. She will tell you about KappaOne and its use case for the CAP subsidy checks through a hands-on experience!

On Friday there will also be a poster presentation on “AI-based Cloud Mask Processor for Sentinel-2” by Olga Wold, our Geospatial Data Quality Specialist. This presentation will describe the KappaMask model details and its progress on going global!
See you at the Living Planet Symposium 2022!
Olga Wold, geospatial data quality specialist
2. Crop Damage Detection
In KappaZeta we are among other things investigating the possibilities to use remotely sensed time-series data to detect crop damage on agricultural parcels.

The initial stage of the research was carried out with the cooperation of local farmers, who provided information about the general condition of agricultural parcels and granted access to the croplands for field surveying. During the in-person visits that took place early in July 2021, a visual assessment of various winter and spring vegetation types was performed and georeferenced images from across the fields were taken. Extra attention was paid to the difference between crop condition within damaged and healthy areas.

Figure 1. Damaged and healthy areas of winter wheat.

The results of the preliminary analysis on a test subset of agricultural parcels vary. The number of parcels in the current stage of analysis was quite limited and it caused several issues. Nevertheless, using simple unsupervised classification techniques it is possible to obtain a spatial representation of the damaged areas through the analysis of specific features of winter rapeseed at particular stages of its development.

At the next stage of the analysis, the work will be continued in the scope of supervised classification techniques combined with individual approaches to various agricultural crop types.

Anton Kostiukhin, software developer

Newsletter, April 2022

1. 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. Although the filter adds significant load to the computations, the output raster will become much sharper with it as the edge structures are preserved much better.

To demonstrate the impact of a new filter to the backscatter images, we chose an area that has comb-shaped parcel on it (see the bottom right corner of figure 1). The narrow “teeth” of the comb are about 20 meters in width. Although “teeth” were also visible previously, their sharpness on KappaOne output is drastically higher.

Figure 1: Backscattering in VV (red) and VH (green) polarization previously (left) and with KappaOne (right) speckle filter.

The newly created KappaOne filter allows improving the quality of coherence images as well. On figure 2 the same area is used for demonstration as previously. Now the comb-like structure is gone, as for coherence more averaging is needed to produce meaningful output. There are large high coherence areas on the previous coherence image, which are created by objects that create extremely high coherence. One of those areas is marked by an ellipse on figure 2. Our novel filter helps to significantly shrink the area where those coherence values dominate. As a result, we can produce an image with much finer details on it.

Figure 2: Previously computed coherence image in VV polarization (left) and the one with KappaOne speckle filter (right).
Mihkel Veske, software developer
2. Towards production: Synthetic NDVI (SNDVI) rasters
The ultimate test of any machine learning model waits in production. Hence in the last month, we have focused on operationalizing the NDVI model as a layer of a new Analysis-Ready Data (ARD) product – KappaOne. In the ARD Germany demo alone, we prepared 35 rasters (each spanning 2500 sqkm) with a newly implemented semi-automated pipeline, that takes collocated Sentinel-1 and -2 products through preprocessing, inference, and post-processing tasks, to create NDVI or SNDVI rasters. 31 of these rasters in Northern Germany were entirely or partially created by our generative model due to cloudy Sentinel-2 images. For larger demo areas such as these, the production objective is to have a new NDVI or SNDVI image within 24 hours of newly acquired data from Sentinel-2.

Figure 3: Rasters created for Germany ARD demo (NDVI – green circles, SNDVI – white circles)

However, we are tackling new challenges at this stage, foremost of which is validating the SNDVI rasters. Validation involves evaluating the quality of images generated by our model, and the accuracy of parcel-level SNDVI compared with available NDVI. We have made significant progress in the former as reported in previous issues, however by the next issue on SNDVI we plan to have a comparable SNDVI timeseries (in addition to partial NDVI timeseries) in the demo map to validate newly created rasters. Most importantly, we want to learn more about the model as we scale and validate on new data and unseen areas, to improve the accuracy and reliability of predictions.
Hudson Taylor Lekunze, data analyst

Newsletter, March 2022

1. KappaMask for Sentinel-2
Phase II of the KappaMask development has been going in full power for a few months already! We started introducing the model new areas such as deserts in Africa and Australia, tropics in South America and even Arctic regions. Previously KappaMask was trained only for Northern terrestrial summer conditions, but now we are also preparing ground truth data for all the seasons. And thanks to the hard and precise work of our labellers, the first results of the new KappaMask model on the previously unseen areas are out and you can examine them on the figures below.

Figure 1. KappaMask predictions in water conditions. Yellow – clouds, blue – semi-transparent clouds.
Previously, KappaMask misclassified water as cloud shadows or semi-transparent clouds, or even as invalid pixels. Now, the model predictions look reasonable, however errors still occur.

On Figure 2, KappaMask predictions are presented in desert areas. You can see that most of the clouds and shadows are predicted correctly. Although, some of the terrain conditions are misclassified as cloud shadows and missed semi-transparent clouds are presented. Therefore, there is a lot of work still to be done!

Figure 2. KappaMask predictions in desert areas. Yellow – clouds, green – cloud shadows, blue – semi-transparent clouds.

Olga Wold, geospatial data quality specialist
2. Success at Kemira and Valtra Hackathons
KappaZeta participated in Kemira and Valtra hackathons on 9-10 and 22-23 of March in Finland. We teamed up with Lauri Karp and the partnership resulted in brilliant results. Our joint-team won the Kemira Hackathon with a concept of monitoring the end-to-end supply chains of next generation bio-based renewable materials with radar satellite data.

Few weeks later the same team got an honorable mention in Valtra Hackathon with a concept of automatic log generation of farming events (ploughing, sowing, mowing, harvesting etc.) for non-smart tractors.

More info at:

Kaupo Voormansik, CEO, SAR expert

Newsletter, February 2022

1. Grazing detection based on Copernicus data
Grazing detection from Copernicus data for agricultural subsidy checks is one of our recently finished projects, carried out in collaboration with the European Space Agency (ESA) and with our partner, Czech EO company Gisat s.r.o. We developed 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.
Fieldwork was a significant part of ground reference data collection required for the development of grazing detection methodology. We have collected the data about grazing and mowing events and grass height weekly during summer 2021 from several grasslands in central Estonia. Summary of the data together with S2 NDVI, NDWI, and PSRI indices and calculated grazing intensity for two grasslands is visualized in the figure below.

Figure 1. Time series of two grasslands in central Estonia for the May - September period.

On these grasslands, cows moved from one field to another throughout the summer, staying on each grassland for up to 5 weeks. In Figure 2 it is visible that grassland V2 was separated by the farmer into two parts. The larger part was mowed on the 30th of June for silage, which is visible from the NDVI, NDWI, and grass height drop on the time series. Animals were located on the smaller part of the V2 before and after the mowing event. Due to the mowing event in between, it is quite hard to distinguish changes caused by grazing activity, even though the grazing intensity is high (4.2 LU/ha).

Figure 2. Sentinel-2 NDVI raster time-series. Base map: Estonian Land Board

Changes in biomass on the grasslands are mostly visible on Sentinel-2 NDVI rasters, as the increase or decrease of NDVI values are more distinguishable by the human eye (Figure 2). In comparison, the visual analysis of Sentinel-1 raster parameters is more complex. The changes in VV or VH coherence values are difficult to detect if the exact location of animals is missing (Figure 3). However, the machine learning approach can be implemented in future works. As grazing is not distinguishable for the human eye on Sentinel-1 images, some of the signatures can be detected by the AI model.

Figure 3. Sentinel-1 coherence VV raster time-series. Base map: Estonian Land Board

Jelizaveta Vabištševitš, EO analyst, project manager
2. KappaZeta is going to Kemira and Valtra hackathons in Finland
KappaZeta and CarbonEye Global are to deliver new breed innovative solutions that fuse Carbon, Space and Agricultural markets to Kemira and Valtra hackathons in Finland in March 2022. Hackathons are organized by Jyväskylä University (JAMK) in cooperation with global segment leader Kemira Oyj (Chemicals company) and Valtra Inc. (Smart Tractors company). More here. For further enquiries please contact jurgen.lina@kappazeta.ee and lauri.karp@carboneye.ai.

Figure 4. Biomass View in KappaZeta ARD Demo.

Read more about the hackathons:

Kaupo Voormansik, CEO, SAR expert

Newsletter, January 2022

1. KappaOne
We are happy to announce KappaOne, a WMS service for selected Sentinel 1 based raster images and an API for parcel statistics. The WMS service gives an opportunity to either look at your particular area of interest in a web browser, like many mapping solutions currently work, or use the WMS directly in analytics program like QGIS.

Figure 1. The logo of KappaOne.

Currently, one can see backscattering in VH, a coherence image of VV in 6/12 interval, and RGB composite of previous two plus backscatter in VV. In addition, we provide a custom product – synthetic NDVI, a product where an AI algorithm derives current NDVI from Sentinel 1 image, using history of Sentinel 1 and Sentinel 2 data. This way, an NDVI can be given for dates when clouds do not permit optical satellites to get useful data.

Figure 2. KappaOne ARD demo.

For parcels, by which we mean a continuous agricultural area with size larger than 0.5 hectares, we provide statistical information, which can be viewed either in the web map or accessed directly via an API. The statistics uses the same information that is available in raster but aggregated over the parcel area. This data, represented as time series, allows detection of biomass changes and farming events like haying, harvesting and ploughing, but also natural events of significant influence, like flooding or hail damage.

Figure 3. KappaOne ARD demo - parcel view.

We provide two examples for the service in our initial demo, one for Germany https://demodev2.kappazeta.ee/ard_demo/ and one for Estonia https://demodev2.kappazeta.ee/ard_ee/. We encourage you to test the services and let us know what you think.
The KappaOne service development is funded by the InCubed program of ESA ɸ-lab.
Andres Luhamaa, KappaOne product owner
2. sNDVI rasters now available on our Demo Map
Since the last issue, we have prepared NDVI images for a 50×50km demo area in Northern Germany spanning 5 months (May to September). We replace cloudy image tiles with inferred synthetic NDVI (sNDVI) images, using historical NDVI and Synthetic Aperture Radar (SAR) data from the same area as inputs for the model. A sub-section of this demo area is pictured below (Figure 4), with the color scheme (ranging from brown through yellow to green) showing increasing areas of healthy vegetation, estimated by NDVI.
Figure 4. Synthetic NDVI Map of subarea in Germany Demo. Brown through yellow to Green, shows increasing healthy vegetation. Image source: KappaZeta Germany ARD Layers (September 29, 2021)

This demo is available in KappaZeta web map environment and includes other SAR data - backscatter and coherence - over the same area.

Interaction with red-outlined parcels, reveals rich temporal information of selected fields, as averaged parcel statistics over the entire demo period (April – December 2021) and can be used to compare accuracy of synthetic NDVI images. For now, not all areas have NDVI images for the entire period since historical inputs are required for image synthesis and some areas did not have sufficient cloud-free historical images for prediction. Months with dense NDVI coverage are May, June, and September.

An evaluation of model accuracy over some test images (212 images sized at 512px) from this period, shows synthetic images have a mean structural similarity (or reconstruction accuracy) of 82% and absolute pixel error of 6.86%. The next challenge is making all synthetic images available, even for areas with insufficient historical data.
Hudson Taylor Lekunze, Data analyst