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

The Science of the Total Environment
Bin WangDe Li Liu

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 an...Continue Reading

Citations

Mar 18, 2021·Environmental Monitoring and Assessment·Kingsley JohnSamuel K Ahado
May 26, 2021·The Science of the Total Environment·Eduardo López-SenespledaRicardo Ruiz-Peinado
Oct 13, 2021·Environmental Monitoring and Assessment·Kamal Khosravi AqdamZohreh Mosleh
Nov 16, 2021·Environmental Monitoring and Assessment·Omosalewa OdebiriMbulisi Sibanda
Nov 18, 2021·Environmental Monitoring and Assessment·Mahmood RostaminiaZiba Maghsodi

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