| 摘 要： |
The rapid quantitative assessment of soil organic carbon (SOC) is essential for understanding SOC dynamics and developing management strategies in forest ecosystems. Compared with traditional laboratory methods, visible and near-infrared spectroscopy is an efficient and inexpensive technique widely used to predict SOC content. Herein, we compared three different spiking strategies. That is, a large-scale global soil spectral library (global-SSL; 3122 samples) was used as the basis for predicting SOC content in a small-scale local soil spectral library (local-SSL; 89 samples) in Wugong Mountain, Jiangxi Province, China. Partial least squares regression models using global-SSL 'spiking' with local samples did not necessarily achieve more accurate predictions than models using local-SSL. Using the developed strategy, a calibration set can be established by selecting the top N spectral samples from global-SSL with high similarity to each local sample, together with the 'spiking' set from local-SSL. It is possible to individually improve the prediction results based on local samples (R-2 = 0.90, RMSE = 7.19, RPD = 3.38) and still allow for quantitative prediction from fewer local calibration samples (R-2 = 0.83, RMSE = 8.71, RPD = 2.68). The developed method is cost-effective and accurate for local-scale SOC assessment in target forest areas using a large soil spectral library. |
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