1. Modeling NDVI for Estonian Fields
With painstaking work on extracting and processing data steadily progressing, our latest successes have come from modeling NDVI for agricultural areas in Estonia. Training data was prepared from 1088 land patches from select areas in Estonia and Latvia due to similarities of agricultural land. Each land patch represents a 5242.88 km2 area.
We were able to predict correct NDVI values with an accuracy of 93% and equally significant improvements in other structural image qualities and sharpness. Pictured below is a synthetic image compared with its target, visualized with contrast enhancement. Try guessing the synthetic image!
There are still many questions we are exploring, including the generalizability of models over other areas in Northern Europe. We are currently considering Poland, Germany, Denmark, Sweden and Finland, and the effects of crop types from the mapped fields.
We are excited to share some updates on these questions in the following issues.
Hudson Taylor Lekunze, Data analyst
2. Grazing Detection with Satellite Data
The importance of the grazing detection project lies in the reform of the Common Agricultural Policy, where the current method of on-the-spot checks will be replaced with satellite monitoring. As a significant part of grasslands (about 20%) in Europe is being grazed, complete grassland maintenance checks cannot be performed due to the complexity and high costs. Thus, satellite monitoring will take over the on-the-spot checks.
At the beginning of September, we conducted an international meeting with agricultural agencies from Denmark, Sweden, Estonia and Czechia. It is clear that this project is valuable for many countries, considering the lower costs of satellite monitoring and the impossibility of full coverage by the on-the-spot checks. Thus, to reach this challenging goal, a lot of development work is still to be done in future.
The following figure shows the time series of two different grasslands during the vegetative season. There is a clear NDVI drop, and coherence increase in the upper time series, which corresponds to the mowing event. The lower time series has no abrupt change, instead, there is a gradual NDVI decrease during the whole period, which illustrates grazing activity. Since these time series are highly different, grazing cannot be detected with the same model as mowing. Therefore, a different approach is needed, and the improvement of the existing mowing and grazing methodology is planned to be done.
Figure 1. S1 coherence and S2 NDVI time series of two Danish grasslands during the vegetative season in 2018. Red – grazing signature.
We are also pleased to report that our weekly fieldwork in Estonia is finished! We have collected continuous data about grazing activity and changes in the grassland condition, which is now being analyzed.
Figure 2. A member of KappaZeta team doing fieldwork
Figure 3. Cattle doing fieldwork
Olga Wold, Geospatial data quality specialist
