Abstract
The monitoring and quantification of soil carbon provide a better understanding of soil and atmosphere dynamics. Visible-near-infrared-short-wave infrared (VIS-NIR-SWIR) reflectance spectroscopy can quantitatively estimate soil carbon content more rapidly and cost-effectively compared to traditional laboratory analysis. However, effective estimation of soil carbon using reflectance spectroscopy to a great extent depends on the selection of a suitable preprocessing sequence and data-mining algorithm. Many efforts have been dedicated to the comparison of conventional chemometric techniques and their optimization for soil properties prediction. Instead, the current study focuses on the potential of the new data-mining engine PARACUDA-II®, recently developed at Tel-Aviv University (TAU), by comparing its performance in predicting soil oxidizable carbon (Cox) against common data-mining algorithms including partial least squares regression (PLSR), random forests (RF), boosted regression trees (BRT), support vector machine regression (SVMR), and memory based learning (MBL). To this end, 103 soil samples from the Pokrok dumpsite in the Czech Republic were scanned with an ASD FieldSpec III Pro FR spectroradiometer in the laboratory under a strict protocol. Spectra preprocessing for conventional data-mining techniques was conducted using Savitzky-Golay smoothing and the first derivative method. PARACUDA-II®, on the other hand, operates based on the all possibilities approach (APA) concept, a conditional Latin hypercube sampling (cLHs) algorithm and parallel programming, to evaluate all of the potential combinations of eight different spectral preprocessing techniques against the original reflectance and chemical data prior to the model development. The comparison of results was made in terms of the coefficient of determination (R2) and root-mean-square error of prediction (RMSEp). Results showed that the PARACUDA-II® engine performed better than the other selected regular schemes with R2 value of 0.80 and RMSEp of 0.12; the PLSR was less predictive compared to other techniques with R2 = 0.63 and RMSEp = 0.29. This can be attributed to its capability to assess all the available options in an automatic way, which enables the hidden models to rise up and yield the best available model.
Highlights
Soil carbon content is a valuable indicator of soil fertility and is a critical parameter in directing the soil and atmosphere dynamics of different agrotechnical processes
The current study focuses on the potential of the new data-mining engine PARACUDA-II®, recently developed at Tel-Aviv University (TAU), by comparing its performance in predicting soil oxidizable carbon (Cox) against common data-mining algorithms including partial least squares regression (PLSR), random forests (RF), boosted regression trees (BRT), support vector machine regression (SVMR), and memory based learning (MBL)
The performance of five data-mining techniques (PLSR, RF, BRT, SVMR, and MBL) was compared against the PARACUDA-II® engine in order to predict Cox in the Pokrok dumpsite located in the northeastern part of the Czech Republic
Summary
Soil carbon content is a valuable indicator of soil fertility and is a critical parameter in directing the soil and atmosphere dynamics of different agrotechnical processes. Concerns about the influence of soil-carbon-decline influences on soil quality have encouraged research on the expansion of accurate and effective methods of evaluating soil carbon [1]. The development of more rapid, accurate, and cost-effective methodologies for soil analysis, and for carbon content estimation, is a major desire. The technique has become a well-recognized, rapid, non-destructive, and low-cost [3] method with minimal sample preparation requirements that can be applied in both the laboratory and the field using point and imaging spectral measurements [4,5,6]. The method does not use any chemicals, and it has capability to measure several soil properties using a single scan and a large number of samples in a very short time [7]
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