Synergetic use of mobile and lab-based spectroscopic techniques (vis-NIR, lab and hand-held MIR, portable hyperspectral frame camera) to optimize the determination of soil properties with high variability in time and space

土壤有机碳（SOC）、氮（N）和磷（P）的总含量在土壤肥力管理（如施肥或耕作）研究中的应用有限，而SOC和N组分以及土壤微生物特性是更为敏感的指标。然而，高空间和时间密度的样本只能通过非破坏性采样技术来实现。在这方面，该项目研究了光谱技术的潜力，通过将近和中（可见-近红外和中红外）区域的非成像光谱与高光谱成像相结合，高精度地确定关键土壤特性（有机碳、氮、pH值、有机碳和氮、磷、硫、钾、铁的比例、阳离子交换能力、土壤质地、微生物和热水可溶性碳和氮）。除了实验室规模外，我们还专注于现场光谱测量的现场规模，这受到新仪器开发，便携式MIR光谱仪和便携式高光谱框架相机的青睐。MIR范围对于土壤光谱学来说是必不可少的，因为可以测量化学基团的基本波段（不同于只有组合波段和泛音的NIR范围）。对于总共八个耕地的土壤质地不同，表层土壤和土壤剖面将进行采样，以调查的潜力实验室光谱相比，现场光谱结合不同的光谱技术，用于估计上述土壤特性。为了提高获得的准确度，将通过使用例如支持向量机或随机森林而不是PLSR，通过应用光谱变量选择技术，通过用局部校准代替全局校准（即，执行适当的校准样本的样本方式选择），并通过使用加标的方法来局部地适配校准模型。然后将在现有数据集上验证优化的技术。此外，还将分析是否以及在何种程度上可以补偿源自不同土壤表面粗糙度或不同土壤含水量的干扰。已经存在的土壤光谱库（LUCAS，ICRAF-ISRIC）进行评估，以选择适当的样品，可以支持的定义和优化的校准模型。此外，还将分析潜在的光谱预测机制（例如，通过二维相关光谱），以阐明是否直接或仅间接光谱预测是可行的每一个研究的土壤性质。这对于澄清预测模型一旦校准，原则上是否可以在空间和时间上转移至关重要。

Total contents of soil organic carbon (SOC), nitrogen (N) and phosphorus (P) are only of limited use for studies of management (e.g. fertilizations or tillage) on soil fertility; SOC and N fractions as well as soil microbial properties are much more sensitive indicators. However, a high spatial and temporal density of samples can only be achieved with non-destructive sampling techniques. In this context, the project studies the potentials of spectroscopic techniques to determine key soil properties (SOC, N, pH, fractions of SOC and N, P, sulphur, potassium, iron, cation exchange capacity, soil texture, microbial and hot water-soluble C and N) with high accuracy by combining non-imaging spectroscopy in the near and middle (vis-NIR and MIR) domain with hyperspectral imaging. In addition to the lab scale, we focus on the field scale with on-site spectroscopic measurements, which is favoured by new instrumental developments, a portable MIR spectrometer and a portable hyperspectral frame camera. The MIR range is essential for soil spectroscopy, as fundamental bands of chemical groups can be measured (different from the NIR range with only combination bands and overtones). For a total of eight arable sites with soils of differing textures, top soils and soil profiles will be sampled to investigate the potentials of lab spectroscopy compared to on-site spectroscopy by combining the different spectroscopic techniques for the estimation of the soil properties mentioned above. To improve obtained accuracies, methods of multivariate calibration will be optimized by using e.g. Support Vector Machines or Random Forest instead of PLSR, by applying spectral variable selection techniques, by substituting global by local calibrations (i.e., a sample-wise selection of appropriate calibration samples is performed) and by using the approach of spiking to locally adapt calibration models. The optimized techniques will then be validated on existing data sets. Additionally, it will be analysed, whether and to what extent disturbances originating from different soil surface roughness or from different soil water contents can be compensated. Already existing soil spectral libraries (LUCAS, ICRAF-ISRIC) are evaluated to select appropriate samples which may support the definition and optimization of calibration models. In addition, the underlying spectral predictive mechanisms will be analysed (e.g., by 2D-correlation spectroscopy) to elucidate whether a direct or only indirect spectral prediction is feasible for each of the studied soil properties. This is fundamental to clarify whether a prediction model, once calibrated, may be in principle transferred in space and time.