Combined use of laboratory-based and portable infrared and x-ray fluorescence spectroscopy approaches for the spatial-vertical characterization of samples of selected soil groups

基于传感器的土壤监测，使用红外光谱（IR）方法（Vis/NIRS， MIRS）和能量色散x射线荧光光谱（XRF），可以量化土壤特性，这些特性对于在高空间和时间分辨率下导出和评估土壤功能至关重要。然而，基于实验室和便携式传感器的方法的收集和评估尚未得到优化，因此很难对这些方法的可能性和局限性进行批判性评估。本提案旨在利用基于实验室和便携式的方法，对选定土壤景观的代表性土壤类型复合体进行光谱活性土壤特性的红外和XRF数据收集。针对测量模式（MIRS的漫反射红外傅立叶变换与衰减全反射，XRF的粉末与颗粒），样品制备（原位，干燥，干燥和筛分，干燥和研磨），测量条件和所需的样品数量，对各个方法进行了优化。不同土壤性质的具体深度函数将在研究的样品剖面中定义。本文将对Vis/NIRS、MIRS和XRF用于光谱活性土壤特性定量的校准算法进行系统比较。线性和非线性校准方法以及光谱反褶积将被用作多个分区中不同样本量的函数。将使用现有数据库进行光谱变量选择和峰值处理。通过便携式和基于实验室的光谱方法表征土壤剖面的准确性，以及校准估计模型的潜在可转移性，将在所研究的土壤类型复合体的单个剖面上进行系统研究。光谱活性和光谱非活性或光谱不明确土壤特性之间的特定位点关系的分析将使用光谱活性土壤特性作为预测因子进行化学计量学和回归分析。使用基于实验室和移动方法的协同IR和XRF数据分析将基于低、中、高级以及混合数据融合方法，重点是外部产品分析，随后自动波长范围选择的低级融合，使用主成分的中级融合和使用Bates-Granger和Granger-Ramanathan方法的高级融合。本项目提供了优化基于传感器的土壤监测数据收集的工具，通过数据融合方法提高不同复杂土壤性质的确定精度，并评估基于空间和剖面相关的基于传感器的监测对选定土壤类型的适用性。

Sensor-based soil monitoring, which uses infrared spectroscopic (IR) approaches (Vis/NIRS, MIRS) and energy dispersive X-ray fluorescence spectroscopy (XRF), can quantify soil properties that are essential to derive and evaluate soil functions in high spatial and temporal resolutions. However, collection and evaluation in laboratory-based and portable sensor-based methods have not yet been optimised, making it difficult to critically assess possibilities and limitations of these methods.This proposal aims at optimising the IR and XRF data collection for spectrally-active soil properties using laboratory-based and portable methods for representative soil type complexes of selected soil landscapes. The individual methods are optimised with regard to measuring modes (diffuse reflectance infrared Fourier transform vs. attenuated total reflection for MIRS, powder vs. pellets for XRF), sample preparation (in-situ, dried, dried and sieved, dried and ground), measuring conditions and required sample numbers. Specific depth functions for the different soil properties will be defined in the studied sample profiles.A systematic comparison of calibration algorithms for the quantification of spectrally-active soil properties by Vis/NIRS, MIRS and XRF will be performed. Linear and non-linear calibration approaches and spectral deconvolution will be employed as functions of different sample sizes in multiple partitions. Spectral variable selection and spiking using existing databases will be used. The accuracy of soil profile characterisation by portable and laboratory-based spectroscopic methods as well as a potential transferability of calibrated estimation models will be systematically investigated on individual profiles of the studied soil type complexes. The analysis of site-specific relationships between spectrally-active and spectrally-inactive or spectrally poorly-defined soil properties will be performed both chemometrically and by regression analyses using the spectrally-active soil properties as predictors. The synergistic IR and XRF data analysis using the laboratory-based and mobile approaches will be based on low-, mid- and high-level as well as hybrid data fusion approaches with an emphasis on outer-product analysis, low-level fusion with subsequent automatic wavelength range selection, mid-level fusion using the principal components and high-level fusion using the approaches of Bates-Granger and Granger-Ramanathan.This project provides the tools with which to optimize the data collection of sensor-based soil monitoring, to improve the determination accuracy of differently complex soil properties by data fusion approaches, and to evaluate the applicability of a spatial and profile-related sensor-based monitoring for selected soil type complexes.