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Automatic Estimation of Soil Biochar Quantity via Hyperspectral Imaging
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Author(s): Lei Tong (Griffith University, Australia), Jun Zhou (School of Information and Communication Technology, Griffith University, Australia), Shahla Hosseini Bai (Griffith University, Australia), Chengyuan Xu (Griffith University, Australia), Yuntao Qian (Zhejiang University, China), Yongsheng Gao (Griffith University, Australia)and Zhihong Xu (Griffith University, Australia)
Copyright: 2019
Pages: 28
Source title:
Environmental Information Systems: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-7033-2.ch073
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Abstract
Biochar soil amendment is globally recognized as an emerging approach to mitigate CO2 emissions and increase crop yield. Because the durability and changes of biochar may affect its long term functions, it is important to quantify biochar in soil after application. In this chapter, an automatic soil biochar estimation method is proposed by analysis of hyperspectral images captured by cameras that cover both visible and infrared light wavelengths. The soil image is considered as a mixture of soil and biochar signals, and then hyperspectral unmixing methods are applied to estimate the biochar proportion at each pixel. The final percentage of biochar can be calculated by taking the mean of the proportion of hyperspectral pixels. Three different models of unmixing are described in this chapter. Their experimental results are evaluated by polynomial regression and root mean square errors against the ground truth data collected in the environmental labs. The results show that hyperspectral unmixing is a promising method to measure the percentage of biochar in the soil.
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