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首页> 外文期刊>Journal of spectroscopy >Calibration Transfer of Soil Total Carbon and Total Nitrogen between Two Different Types of Soils Based on Visible-Near-Infrared Reflectance Spectroscopy
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Calibration Transfer of Soil Total Carbon and Total Nitrogen between Two Different Types of Soils Based on Visible-Near-Infrared Reflectance Spectroscopy

机译：基于可见-近红外反射光谱的两种不同类型土壤之间土壤总碳和总氮的标定传递

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Although visible-near-infrared spectroscopy can rapidly and accurately determine soil nutrients without sample destruction, some problems remain unresolved, such as the mismatch of the established spectral model with different types of samples, limiting the wide application of this technology. Here, we took riverside and mountain soils as examples to explore the calibration transfer between two different types of soils by the WMPDS-S/B algorithm (wavelet multiscale piecewise direct standardization combined with Slope/Bias correction method) and by adding new samples. The predicted TN and TC concentrations improved significantly after being transformed. Compared with adding new samples, the WMPDS-S/B algorithm obtained more accurate results. The average relative errors dropped from 440.2% (without transformation) to approximately 6% for TN and from 342.0% to approximately 7% for TC. The maximum relative errors were reduced from 538.1% to less than 20% for TN and from 403.7% to less than 20% for TC. The RMSEP decreased from 2.42 to approximately 0.04 for TN and from 15.74 to approximately 0.4 for TC. The WMPDS-S/B algorithm had advantages in selecting fewer known samples and obtaining better prediction results. In contrast to past studies, which resolved the calibration transfer between different spectrometers and the measurement environment for the same samples, our study resolved the calibration transfer between different types of samples under the same spectrometer and the measurement environment. The former could only be used for correction among instruments, while the latter fundamentally solved the problem of model sharing across different samples.
机译：尽管可见近红外光谱法可以快速，准确地测定土壤养分而不会破坏样品，但仍存在一些问题尚未解决，例如已建立的光谱模型与不同类型的样品不匹配，从而限制了该技术的广泛应用。在这里，我们以河床和山区土壤为例，通过WMPDS-S / B算法（小波多尺度分段直接标准化与Slope / Bias校正方法相结合）并添加新样本来探索两种不同类型土壤之间的标定传递。转化后，预测的TN和TC浓度显着提高。与添加新样本相比，WMPDS-S / B算法获得了更准确的结果。 TN的平均相对误差从440.2％（无变换）下降到大约6％，TC的平均相对误差从342.0％下降到大约7％。 TN的最大相对误差从538.1％降低到小于20％，TC的最大相对误差从403.7％降低到小于20％。 RMS的RMSEP从2.42降至约0.04，TC的RMSEP从15.74降至约0.4。 WMPDS-S / B算法在选择较少的已知样本并获得更好的预测结果方面具有优势。与以往的研究解决了不同光谱仪在同一样品的测量环境之间的校准转移相反，我们的研究解决了同一光谱仪和测量环境在不同类型的样品之间的校准转移。前者只能用于仪器之间的校正，而后者从根本上解决了不同样本之间模型共享的问题。

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