Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

Abstract

Abstract:Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, 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 is important to characterize these differences and to introduce an effective approach in order to minimize the technical factors that alter reflectance spectra and consequent prediction. To quantify this alteration, a joint project between Czech University of Life Sciences Prague (CULS) and Tel-Aviv University (TAU) was conducted to estimate Cox, pH-H 2 O, pH-KCl and selected forms of Fe and Mn. Two different soil spectral measurement protocols and two data mining techniques were used to examine seventy-eight soil samples from five agricultural areas in different parts of the Czech Republic. Spectral measurements at both laboratories were made using different ASD spectroradiometers. The CULS protocol was based on employing a contact probe (CP) spectral measurement scheme, while the TAU protocol was carried out using a CP measurement method, accompanied with the internal soil standard (ISS) procedure. Two spectral datasets, acquired from different protocols, were both analyzed using partial least square regression (PLSR) technique as well as the PARACUDA II ® , a new data mining engine for optimizing PLSR models. The results showed that spectra based on the CULS setup (non-ISS) demonstrated significantly higher albedo intensity and reflectance values relative to the TAU setup with ISS. However, the majority of statistics using the TAU protocol was not noticeably better than the CULS spectra. The paper also highlighted that under both measurement protocols, the PARACUDA II ® engine proved to be a powerful tool for providing better results than PLSR. Such initiative is not only a way to unlock current limitations of soil spectroscopy, but also offers considerable efficiency and cost-and time-saving possibilities, which lead to further improvements in prediction performance of spectral models.

Potential of VIS-NIR spectroscopy to characterize and discriminate topsoils of different soil types in the Triffa plain (Morocco)

“…Currently, visible and nearinfrared (VIS-NIR) spectroscopy represents an alternative method for soil analysis and quantification. The method is faster, non-destructive, environmentally friendly, has a repeatable analytical technique and requires minimal sample preparation (Gholizadeh et al 2017). Its implementation in the field and laboratory offers the possibility of repeating measurements and estimating the ranges of various soil properties such as OM content, texture and CaCO 3 (Lagacherie et al 2008, Liu et al 2017.…”

Examining the Performance of PARACUDA-II Data-Mining Engine versus Selected Techniques to Model Soil Carbon from Reflectance Spectra

“…Gholizadeh et al [11] also stated that one way of minimizing the undesirable impacts is by adopting advanced preprocessing methods as well as the appropriate selection of multivariate statistical analysis algorithms. These approaches can significantly decrease the differences between spectral measurements of samples assessed by different operators and systems under different conditions [12] and improve the obtained prediction accuracy [13].…”

Evaluating a low‐cost portableNIRspectrometer for the prediction of soil organic and total carbon using different calibration models

“…Among other factors that can influence the spectroscopy for soil properties determination are spectral resolution, which was shown not to have a significant effect on predictions (Knadel et al, 2013;Rossel et al, 2016). Another study indicated that scanning protocols may not have a significant impact on prediction if the standard is considered with enough care (Gholizadeh, Carmon, Klement, Ben-Dor, & Borůvka, 2017). However, some studies have shown that different calibration models, such as the most widely used models of Cubist regression tree, partial least squares regression (PLSR) and support vector machine (SVM), can produce different prediction accuracies for soil properties (Minasny & McBratney, 2008;Morellos et al, 2016;Rossel & Behrens, 2010).…”