Advanced monitoring of crop traits with hyperspectral and multispectral time series

One part for tackling agricultural challenges such as the need for increased food production, reduced environmental impacts, and adaptation to climate change is the optimization of agricultural management. This requires improved monitoring of agricultural areas with respect to vegetation status over dense time series. The combined use of different types of satellite missions has the potential for a more detailed monitoring of agricultural fields. The increasing availability of spaceborne hyperspectral data (from EnMAP, PRISMA and upcoming CHIME) calls for fast processing chains and efficient retrieval algorithms. Therefore, this contribution aims to (i) accelerate and improve existing retrieval workflows, (ii) evaluate currently available hyperspectral EnMAP/PRISMA retrievals to monitor crops, and (iii) test the combined use of hyperspectral and multispectral Sentinel-2 data for crop monitoring.