Metzger Konrad, Liebisch Frank, Herrera Juan M, Guillaume Thomas, Bragazza Luca
Field-Crop Systems and Plant Nutrition, Agroscope, Route de Duillier 60, 1260 Nyon, Switzerland.
Water Protection and Substance Flows, Agroscope, Reckenholzstrasse 191, 8046 Zurich, Switzerland.
Sensors (Basel). 2024 May 31;24(11):3556. doi: 10.3390/s24113556.
One challenge in predicting soil parameters using in situ visible and near infrared spectroscopy is the distortion of the spectra due to soil moisture. External parameter orthogonalization (EPO) is a mathematical method to remove unwanted variability from spectra. We created two different EPO correction matrices based on the difference between spectra collected in situ and, respectively, spectra collected from the same soil samples after drying and sieving and after drying, sieving and finely grinding. Spectra from 134 soil samples recorded with two different spectrometers were split into calibration and validation sets and the two EPO corrections were applied. Clay, organic carbon and total nitrogen content were predicted by partial least squares regression for uncorrected and EPO-corrected spectra using models based on the same type of spectra ("") as well as using laboratory-based models to predict in situ collected spectra (""). Our results show that the within-domain prediction of clay is improved with EPO corrections only for the research grade spectrometer, with no improvement for the other parameters. For the cross-domain predictions, there was a positive effect from both EPO corrections on all parameters. Overall, we also found that in situ collected spectra provided an equally successful prediction as laboratory-based spectra.
利用原位可见和近红外光谱预测土壤参数面临的一个挑战是土壤水分导致的光谱失真。外部参数正交化(EPO)是一种从光谱中去除不必要变异性的数学方法。我们基于原位采集的光谱与分别经过干燥、筛分以及干燥、筛分和精细研磨后的相同土壤样品采集的光谱之间的差异,创建了两种不同的EPO校正矩阵。用两种不同光谱仪记录的134个土壤样品的光谱被分为校准集和验证集,并应用了两种EPO校正。通过偏最小二乘回归,使用基于相同类型光谱的模型以及基于实验室的模型来预测原位采集光谱,对未校正和EPO校正光谱的粘土、有机碳和总氮含量进行了预测。我们的结果表明,仅对研究级光谱仪而言,EPO校正改善了粘土的域内预测,而其他参数没有改善。对于跨域预测,两种EPO校正对所有参数都有积极影响。总体而言,我们还发现原位采集的光谱与基于实验室的光谱提供了同样成功的预测。