High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia

Bin Wang; Cathy Waters; Susan Orgill; Jonathan Gray; Annette Cowie; Anthony Clark; De Li Liu

doi:10.1016/j.scitotenv.2018.02.204

High resolution mapping of soil organic carbon stocks using remote sensing variables in the semi-arid rangelands of eastern Australia

Sci Total Environ. 2018 Jul 15:630:367-378. doi: 10.1016/j.scitotenv.2018.02.204. Epub 2018 Feb 23.

Authors

Bin Wang¹, Cathy Waters², Susan Orgill³, Jonathan Gray⁴, Annette Cowie⁵, Anthony Clark², De Li Liu⁶

Affiliations

¹ NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, NSW 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
² NSW Department of Primary Industries, Orange Agricultural Institute, NSW 2800, Australia.
³ NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, NSW 2650, Australia.
⁴ Science Division, NSW Office of Environment and Heritage, PO Box 644, Parramatta, NSW 2124, Australia.
⁵ NSW Department of Primary Industries, Trevenna Rd, Armidale, NSW 2351, Australia.
⁶ NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, NSW 2650, Australia; Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, NSW 2052, Australia.

PMID: 29482145
DOI: 10.1016/j.scitotenv.2018.02.204

Abstract

Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are central in understanding the global carbon cycle and informing related land management decisions. However, mapping SOC stocks in semi-arid rangelands is challenging due to the lack of data and poor spatial coverage. The use of remote sensing data to provide an indirect measurement of SOC to inform digital soil mapping has the potential to provide more reliable and cost-effective estimates of SOC compared with field-based, direct measurement. Despite this potential, the role of remote sensing data in improving the knowledge of soil information in semi-arid rangelands has not been fully explored. This study firstly investigated the use of high spatial resolution satellite data (seasonal fractional cover data; SFC) together with elevation, lithology, climatic data and observed soil data to map the spatial distribution of SOC at two soil depths (0-5cm and 0-30cm) in semi-arid rangelands of eastern Australia. Overall, model performance statistics showed that random forest (RF) and boosted regression trees (BRT) models performed better than support vector machine (SVM). The models obtained moderate results with R² of 0.32 for SOC stock at 0-5cm and 0.44 at 0-30cm, RMSE of 3.51MgCha^-1 at 0-5cm and 9.16MgCha^-1 at 0-30cm without considering SFC covariates. In contrast, by including SFC, the model accuracy for predicting SOC stock improved by 7.4-12.7% at 0-5cm, and by 2.8-5.9% at 0-30cm, highlighting the importance of including SFC to enhance the performance of the three modelling techniques. Furthermore, our models produced a more accurate and higher resolution digital SOC stock map compared with other available mapping products for the region. The data and high-resolution maps from this study can be used for future soil carbon assessment and monitoring.

Keywords: Digital soil mapping; Machine learning; Remote sensing; Seasonal fractional cover; Soil organic carbon stocks.