Authors: Asa Gholizadeh, Nimrod Carmon, Aleš Klement, Luboš Borůvka, Eyal Ben-Dor, Sabine Chabrillat Contact: gholizadeh@af.czu.cz Effects of Measurement Protocols and Data Mining Techniques on Soil Proxy Model Extraction: A Czech Case Study Soil spectroscopy has shown to be a fast, cost-effective, environmentally-friendly, nondestructive, reproducible, and repeatable analytical technique. Soil components, as well as types of instruments, protocols, sampling methods, sample preparation, spectral acquisition techniques, and analytical algorithms have a combined influence on the final performance. Therefore, it’s important to characterize these differences and to establish a simple common standardization procedure in order to minimize the technical factors that alter reflectance spectra. To quantify this alteration, a joint project between Czech University of Life Sciences (CULS) and Tel-Aviv University (TAU) was conducted to estimate Cox, pH-H2O, pH-KCl, Fe-d, Fe-ox, Mn-d, and Mn-ox. Seventy-eight (78) soil samples from five agricultural lands in different parts of the Czech Republic, two different protocols for soil spectral and chemical measurement, as well as two data mining techniques were examined. The results showed that spectra based on the CULS setup demonstrated significantly higher albedo intensity and reflectance values relative to the TAU setup reflectance. The TAU protocol using Internal Soil Standard (ISS) also improved the analytical results as compared to the CULS (NON-ISS) protocol. Such initiative is not only a way to unlock current limitations of soil spectroscopy, but also helps the soil community in developing an agreed-upon protocol to contribute to the construction of the Soil Spectral Libraries (SSLs). Abstract Study Areas and Sampling Points Geographical position of the study area CULS Protocol TAU Protocol Modelling (PARACUDA II®) Modelling (PLSR) 120 preprocessing sequences using 8 different algorithms developing of 512 individual models Raw Spectra 2nd Derivative 1st Derivative + ISS Validation (k-fold cross-validation) Validation (Based on internal validation 25% technique) Results Soil Spectral Reflectance Comparison of Protocols and Data Mining Techniques Coefficient of determination (R2) of PLSR and PARACUDA II® performance for CULS and TAU spectral datasets The spectra of soil samples as measured by CULS (left) and TAU (right) setups Conclusions ISS aligned the TAU protocol to be more stable, and thus the protocol slightly improved the analytical results as compared to the CULS (NON-ISS) protocol. PARACUDA II® data mining engine provided better performance and proved to be a powerful and reliable tool in achieving the best prediction model. Adherence to a well-agreed-upon standardization quality, a common protocol, and a reliable data mining technique will enable the reliability and the comparability of results, in order to contribute to the construction of the SSLs in a newly beneficial way. Ongoing Research Based on GFZ Protocol Modelling Different pre-processing: Continuum Removal (CR), 1st and 2nd derivatives PLSR Spectral Features References Presented at Pedometrics 2017 26 June-1 July 2017, Wageningen, Netherlands