Open and Free Sentinel-2 Mowing Event Data for Austria
2025
Petra Miletich | Marco Kirchmair | Janik Gregory Deutscher | Alexander Schippl | Manuela Hirschmugl
The accurate detection of mowing events is important in many applications, including in agricultural contexts such as yield and fodder production, as well as biodiversity assessments, habitat modeling, and protected area monitoring. This work presents the first free and open dataset of mowing events covering the entire Austrian territory for the year 2023 at a spatial resolution of 10 ×: 10 m. We use the Sentinel-2 time series of the Normalized Difference Vegetation Index (NDVI) to detect mowing events, and additionally, we use the mean of the two ShortWave InfraRed (SWIR) bands to exclude misclassification caused by remaining cloud artifacts and shadows. The validation procedure builds on a visual interpretation of the Panomax webcam archive complemented by a selection of field observations. The final validation dataset consists of 211 mowing events recorded in 85 different locations across Austria. In total, 77.73% of these mowing events were detected with a mean time delay of 4 days. The detection delay in summer was smaller than the values recorded in spring and fall. The pixel-based approach exhibited superior efficacy, especially for meadows with three or more mowing events, compared to the polygon-based approach. The results of our study are consistent with those of previous works demonstrating the capacity to produce high-quality mowing event data for various grassland areas in a fully automated manner, independent from training datasets. The results could be used in research on biodiversity or in practical applications such as agricultural policy support and control, fodder supply evaluation, or impact assessment in nature restoration efforts.
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