Assessment of soil organic carbon stocks in Alberta using 2-scale sampling and 3D predictive soil mapping

Abstract

A three-dimensional predictive soil mapping approach for predicting soil organic carbon (SOC) stocks (t/ha) at high spatial resolution (30 m) for Alberta for 2020–2021 is described. A remote sensing data stack was first prepared covering Alberta’s agricultural lands. A total of 404 sampling locations were distributed across Alberta using 2-scale sampling: (1) 22 pilot farms representing main climatic zones and (2) conditioned Latin hypercube sampling at each farm. Soil samples were taken at four standard depths (0–15, 15–30, 30–60, 60–100 cm) using soil probes and analyzed for SOC. Predictive models for SOC content and bulk density were built separately and then used to predict at 0, 15, 30, 60, and 100 cm and calculate aggregated SOC stocks per pixel. The SOC content and bulk density models had R squares of 0.61 and 0.68, respectively. Based on these mapping results, grassland soils were consistently associated with higher SOC stocks across all soil types as compared to croplands. The average SOC stock increase for grassland soils compared to cropland soils was 2.1 Mg per hectare, ranging from 2.17 to 6.09 Mg per hectare depending on soil type. Results also showed that >15 % of total SOC stocks were located in subsoil, which was higher than expected.

Keywords

• predictive soil mapping
• soil organic carbon
• remote sensing
• ensemble machine learning