Mapping soil organic carbon with hyperspectral spaceborne images

Monitoring of soil quality and its degradation is essential to face the big challenges of food security and climate change. One of the main soil parameters to observe is the content of soil organic carbon (SOC) which is linked to soil fertility and other ecosystem services. We investigate the potential of spaceborne hyperspectral images to estimate the content of SOC in the uppermost soil layer. Therefore, we use the spectral information of bare soil pixels in multiple PRISMA images together with chemically analyzed SOC contents of a range of local soil samples and a large-scale soil spectral library. The study site is located in the North-East of Germany within the long-term observatory of TERENO-NE and near the village of Demmin. We compare different machine learning and regression algorithms (Partial Least Squares Regression, Random Forest, Gaussian Process Regression, spectral SOC indices) for each of the images separately and for a synthetic multitemporal image. The best performing models are applied to all bare soil pixels to produce SOC content maps. The preliminary results show medium to high quality models for most cases. With an increasing number of hyperspectral satellites in orbit the outcomes of this case study can provide valuable information for future SOC mapping and monitoring.