Chair of Plant Nutrition, Technical University Munich, Emil-Ramann-Str. 2, D-85350 Freising, Germany.
Chair of Agricultural Systems Engineering, Technical University Munich, Dürnast 4, D-85354 Freising, Germany.
Sensors (Basel). 2021 Feb 18;21(4):1423. doi: 10.3390/s21041423.
Near-infrared reflectance spectroscopy (NIRS) was successfully used in this study to measure soil properties, mainly C and N, requiring spectral pre-treatments. Calculations in this evaluation were carried out using multivariate statistical procedures with preceding pre-treatment procedures of the spectral data. Such transformations could remove noise, highlight features, and extract essential wavelengths for quantitative predictions. This frequently significantly improved the predictions. Since selecting the appropriate transformation was not straightforward due to the large numbers of available methods, more comprehensive insight into choosing appropriate and optimized pre-treatments was required. Therefore, the objectives of this study were (i) to compare various pre-processing transformations of spectral data to determine their suitability for modeling soil C and N using NIR spectra (55 pre-treatment procedures were tested), and (ii) to determine which wavelengths were most important for the prediction of C and N. The investigations were carried out on an arable field in South Germany with a soil type of Calcaric Fluvic Relictigleyic Phaeozem (Epigeoabruptic and Pantoclayic), created in the flooding area of the Isar River. The best fit and highest model accuracy for the C (Ct, Corg, and Ccarb) and N models in the calibration and validation modes were achieved using derivations with Savitzky-Golay (SG). This enabled us to calculate the Ct, Corg, and N with an R higher than 0.98/0.86 and an ratio of performance to the interquartile range (RPIQ) higher than 10.9/4.1 (calibration/validation).
近红外反射光谱(NIRS)技术成功地应用于本研究,用于测量土壤特性,主要是 C 和 N,需要光谱预处理。本评估中的计算使用多元统计程序,对光谱数据进行了预处理。这些变换可以去除噪声,突出特征,并提取用于定量预测的基本波长。这通常可以显著提高预测精度。由于由于可用方法数量众多,因此选择适当的变换并不简单,因此需要更全面地了解选择适当和优化的预处理方法。因此,本研究的目的是:(i)比较光谱数据的各种预处理变换,以确定它们是否适合使用近红外光谱建模土壤 C 和 N(测试了 55 种预处理方法);(ii)确定对于 C 和 N 的预测,哪些波长最重要。研究在德国南部的一个耕地进行,土壤类型为钙质富铁淋溶灰壤(层状和潘托粘壤土),是在伊萨尔河泛滥区形成的。在 Calcaric Fluvic Relictigleyic Phaeozem(Epigeoabruptic 和 Pantoclayic)土壤中,使用 Savitzky-Golay(SG)衍生法对 C(Ct、Corg 和 Ccarb)和 N 模型进行了最佳拟合和最高的模型精度校准和验证模式。这使我们能够计算 Ct、Corg 和 N 的 R 值高于 0.98/0.86,性能与四分位距比(RPIQ)高于 10.9/4.1(校准/验证)。