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
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.
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.
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.
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
