Mapping soil organic carbon stocks and trends with satellite-driven high resolution maps over South Africa

Zander S Venter; Heidi-Jayne Hawkins; Michael D Cramer; Anthony J Mills

doi:10.1016/j.scitotenv.2021.145384

Mapping soil organic carbon stocks and trends with satellite-driven high resolution maps over South Africa

Sci Total Environ. 2021 Jun 1:771:145384. doi: 10.1016/j.scitotenv.2021.145384. Epub 2021 Jan 27.

Authors

Zander S Venter¹, Heidi-Jayne Hawkins², Michael D Cramer³, Anthony J Mills⁴

Affiliations

¹ Terrestrial Ecology Section, Norwegian Institute for Nature Research - NINA, 0349 Oslo, Norway; ZSV Consulting, Unit 104, Sunstone, Ruby Estate, Marquise Drive, Burgundy Estate, South Africa. Electronic address: zander.venter@nina.no.
² Conservation South Africa, 301 Heritage House, 20 Dreyer Street, 7735, Claremont, Cape Town, South Africa; Department of Biological Sciences, University of Cape Town, Private Bag X1, 7701, Rondebosch, Cape Town, South Africa.
³ Department of Biological Sciences, University of Cape Town, Private Bag X1, 7701, Rondebosch, Cape Town, South Africa.
⁴ C4 EcoSolutions, 18 Gerrie Avenue, Dennendal, 7945 Cape Town, South Africa.

PMID: 33540160
DOI: 10.1016/j.scitotenv.2021.145384

Abstract

Estimation and monitoring of soil organic carbon (SOC) stocks is important for maintaining soil productivity and meeting climate change mitigation targets. Current global SOC maps do not provide enough detail for landscape-scale decision making, and do not allow for tracking carbon sequestration or loss over time. Using an optical satellite-driven machine learning workflow, we mapped SOC stocks (topsoil; 0 to 30 cm) under natural vegetation (86% of land area) over South Africa at 30 m spatial resolution between 1984 and 2019. We estimate a total topsoil SOC stock of 5.6 Pg C with a median SOC density of 6 kg C m^-2 (IQR: interquartile range 2.9 kg C m^-2). Over 35 years, predicted SOC underwent a net increase of 0.3% (relative to long-term mean) with the greatest net increases (1.7%) and decreases (-0.6%) occurring in the Grassland and Nama Karoo biomes, respectively. At the landscape scale, SOC changes of up to 25% were evident in some locations, as evidenced from fence-line contrasts, and were likely due to local management effects (e.g. woody encroachment associated with increased SOC and overgrazing associated with decreased SOC). Our SOC mapping approach exhibited lower uncertainty (R² = 0.64; RMSE = 2.5 kg C m^-2) and less bias compared to previous low-resolution (250-1000 m) national SOC mapping efforts (average R² = 0.24; RMSE = 3.7 kg C m^-2). Our trend map remains an estimate, pending repeated measures of soil samples in the same location (time-series); a global priority for tracking SOC changes. While high resolution SOC maps can inform land management decisions aimed at climate mitigation (natural climate solutions), potential increases in SOC are likely limited by local climate and soils. It is also important that climate mitigation efforts such as planting trees balance trade-offs between carbon, biodiversity and overall ecosystem function.

Keywords: Carbon stocks; Land degradation; Natural climate solutions; Remote sensing; Soil mapping; Spatial prediction.