Abstract
Soil health is an environmental factor that impacts a range of important issues including food production, water retention and soil organic carbon storage. Agriculture relies on healthy soil for crop growth and animal grazing. Water retention reduces the risks of desertification and of flooding as the capacity to retain water reduces the rate of surface water flow. In addition, soil organic carbon represents the largest terrestrial carbon stock and is second only to the oceans. Yet, soil health is threatened by intensive farming practices and changes of land use such as deforestation. Thus, it is important to manage soil health to maintain food security, avoid desertification and maintain or ideally increase soil organic carbon storage. A useful tool to inform this management function would be a machine learning model that can predict soil health given land cover and parameters of the abiotic context, such as terrain elevation and historical weather data. The first step in developing such a model is to be able to identify the land cover for a chosen area. Satellites provide multi-spectral images that include the visual bands. Land cover databases provide the ground truth labels for a supervised learning approach to train an image semantic segmentation model. This chapter describes how Sentinel-2 satellite image data was combined with data from the UK Centre for Ecology and Hydrology Land Cover Map 2015, to train a convolutional neural network for land cover classification for the South of England.