1. KappaZeta at the Agroinsurance International Conference 2022
KappaZeta had a great opportunity to visit the AgroInsConf 2022 in beautiful Tbilisi in Georgia, which was designed to bring together agricultural insurance companies, reinsurance companies, brokers and insurtech companies. A lot of topics were covered during the conference from the overview of agricultural insurance markets to problems that insurance and reinsurance companies are facing and how technological applications of satellite imagery can help in solving them. The conference ended with a field day, where we had a chance to test out existing technological solutions for loss adjustment.
It was a pleasure to see that insurance companies understand the value that EO solutions can provide them and it’s a matter of time when satellite data will be used by most of the insurance companies in the world. KappaZeta’s expertise is in using SAR (radar) satellite data, incorporating it with optical satellite data and providing some of the most accurate AI models on the market. Radar 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. Having data is extremely important for insurance companies no matter what the weather is like. That is the main reason why a lot of the insurance companies are looking forward to cooperating with us and vice versa.
Tanel Kobrusepp, product manager of crop insurance oriented services
2. Spring Barley Growth Monitoring with SNDVI
To limit the effect of clouds in optical remote sensing for farm monitoring, we developed a generative model to synthesize NDVI images from radar and historical optical data, which we name Synthetic NDVI (SNDVI).
In the past we have modeled and reported SNDVI on the level of image sub-tiles (512 x 512 px.). However, NDVI values are as important as the parcels they’re measured on hence recent evaluations have focused on in-field changes. This newsletter article discusses one such, exploring spring barley growth with SNDVI images.
NDVI increases with crop growth for spring barley till after the flowering stage (where it peaks) and decreases as plant dries up in preparation for harvest. By categorizing some of these growth stages, we have discovered the model accuracies for monitoring early growth, especially between sowing, seed emergence and early tillering could be significant, if not practical for monitoring fields in the absence of cloud-free images.
The parcels in Figure 2 & Figure 3 compare historical NDVI (HNDVI) inputs, predicted SNDVI and target NDVI images as the fields move from bare soil to what could be an early tillering stage in spring barley. The synthesized images can identify zones or direction of growth (as growth is not even across the field).
Hudson Taylor Lekunze, data analyst
