Are remote sensing images accurate enough to detect changes in soil carbon over time? An example from the North Wyke Farm Platform, UK

Soil scientists, farmers, and policy makers are seeking cost-effective alternatives for measuring, monitoring, and verifying changes in soil carbon as a result of land use change and adoption of sustainable management practices. The increasing interest is in line with the need to establish protocols for accurate representation of changes and variability in soil carbon, especially for sustainability metrics and carbon markets. As conventional soil sampling and laboratory analysis schemes are costly and labour-intensive at large scales, remote sensing-based approaches are promising. Digital soil mapping, which uses satellite images as explanatory variables in regression models, is increasingly used primarily to estimate and monitor changes in soil carbon over time and spatially. This study is intended to demonstrate how satellite images coupled with other environmental covariates and field measurements of soil carbon can be used to build models to explain soil carbon variability and change. The study was conducted at the North Wyke Farm Platform, an experimental farm located in Devon, in which different land-uses and management systems including permanent pasture, high-grass sugar and arable crops have been monitoring since 2012. Soil samples were collected in 2012 in a regular grid scheme and analysed for soil carbon, bulk density and other parameters. Subsequent measurements were also carried out in 2016, 2018, 2019, 2020, and 2021. Soil carbon values were then related to the spectral reflectance of Landsat-8 and terrain variables to build a baseline prediction model. A gradient boosting algorithm, which was parameterised to find the best parameters that minimise the square error of the prediction, was used. After the model was trained and tested for the baseline year, it was applied to satellite images and terrain attributes of the subsequent years and maps of soil carbon was obtained for each year. The predicted values for each year were compared with measured values in each field. Our results demonstrated that Landsat-8 images coupled with terrain attributes were able to explain 33% of the changes in soil carbon observed between 2012 and 2021. Future research is needed to improve these estimates and take full advantage of remote sensing and machine learning models in monitoring, measuring, reporting and verification of soil carbon stocks in agricultural systems.