Digital mapping of soil organic carbon using drone remote sensing

Abstract

Soil organic carbon (SOC) content is a key indicator of soil health informing about sustainable land management practices, but parcel-wide SOC mapping is challenging as it requires high-resolution data. Unoccupied Aerial Vehicles (UAVs) can collect data with cm-resolution but are not yet fully ready to be practically implemented. The aim of this study is to provide more insights in the explanatory capabilities of UAV-derived spectral and topographical variables. To this end, mixed models were employed to estimate the SOC content of three agricultural parcels with different crop types in Greece. Results showed variations in SOC content among parcels, with a vineyard and a kiwi orchard having higher values compared to a peach orchard. All models, containing topographical and/or spectral variables, explained 81% of SOC content variation of the training dataset. Besides crop type, other topographical and spectral variables were identified as significant predictors. The study emphasizes the feasibility of UAV data and specific modeling techniques for accurate SOC estimation at the parcel level, providing valuable insights for precision agriculture. The findings recommend further exploration, including machine-learning approaches in future studies.