Predicting Abiotic Soil Characteristics Using Sentinel-2 at Nature-Management-Relevant Spatial Scales and Extent

Abstract

Knowledge of local plant community characteristics is imperative for practical nature planning and management, and for understanding plant diversity and distribution drivers. Today, retrieving such data is only possible by fieldwork and is hence costly both in time and money. Here we used 9 bands from multispectral high-to-medium resolution (10–60 m) satellite data (Sentinel-2) and machine learning to predict the local vegetation plot characteristics at broad extent (approx. 30.000 km2) in terms of plants’ preferences for soil moisture, soil fertility, and pH, mirroring the levels of the corresponding actual soil factors. These factors are believed to be among the most important for local plant community composition. Our results showed that there are clear links between the Sentinel-2 data and plants abiotic soil preferences and using solely satellite data we achieved predictive powers between 26–59% improving to about 70% when habitat information was included as a predictor. This show that plants abiotic soil preferences can be detected quite well from space, but also that retrieving soil characteristics using satellites is complicated and that perfect detection of soil conditions using remote sensing – if at all possible – needs further methodological and data development.