AMARETTO: TOPIC 2
Detailing the Modeling of Atmospheric Ammonia for the Netherlands Using Livestock Housing Information and Sentinel-2 Derived Crop Map
Crop mapping through machine learning of Sentinel-2 multispectral multitemporal metrices
Crop mapping through machine learning of Sentinel-2 multispectral multitemporal metrices
We applied a random forest classification to the temporal features of the spectral metrics and vegetation indices derived from Sentinel-2 to generate a high-resolution crop map of 12 agricultural land cover classes. The crop statistics per region were used to calculate manure and mineral fertilizer and ammonia emission distribution based on nitrogen demand of different crop types using the INTEGRATOR model. Next, the crop map was used to spatially allocate the ammonia emissions to a high-resolution grid across the country. In addition, point sources emissions from animal housing and manure storage systems were introduced as point sources using data from the GIAB (Geographic Information Agricultural Business) system.