Abstract
Accurate remote estimation of the soil organic carbon (SOC) content can be useful for site-specific soil management and precision agriculture. However, spectral reflectance is affected by properties such as the soil moisture content in addition to the SOC content, so these properties will affect and limit the accuracy of SOC estimates. The aim of this study was to determine whether the soil-moisture-index spectrum reconstruction (SSR) method combined with the partial least squares regression (PLSR) method could improve SOC estimation accuracy by decreasing the effect of soil moisture. Soil spectra were acquired using an ASD FieldSpec 3 spectrometer, and the SOC contents of soil samples were determined using the potassium dichromate capacity method. Soil moisture contents inferred using the normalized difference soil moisture index were used to construct the optimal model for use in the SSR-PLSR method. The SSR-PLSR model performed better (root mean square error 2.114 g/kg, ratio of performance to deviation 2.851, ratio of performance to interquartile range 4.289, and bias − 0.028 for the model validation process) than did the PLSR model. The SSR-PLSR method has great potential for improving the accuracy of SOC estimates and is a new quantitative soil spectroscopy tool.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant Nos. 51574221, 4157011325 and 42107491) and the Nanjing University of Information Science & Technology (NUIST) Startup Foundation for Introducing Talent (Grant No. 2016r071). We thank Gareth Thomas, PhD, from Liwen Bianji (Edanz) (https://www.liwenbianji.cn), for editing the language of a draft of this manuscript.
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Lin, L., Liu, X. Soil-moisture-index spectrum reconstruction improves partial least squares regression of spectral analysis of soil organic carbon. Precision Agric 23, 1707–1719 (2022). https://doi.org/10.1007/s11119-022-09905-3
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DOI: https://doi.org/10.1007/s11119-022-09905-3