Analysis of Mid-Infrared and X-Ray Fluorescence Data Fusion Methods for Field-Scale Soil Property Prediction

Gianlua Toandi^*
*Correspondence: Gianlua Toandi, Department of Biosciences, Paris-Lodron-University Salzburg, Salzburg, Austria, Email:

Introduction

Multi-sensor fusion for the collection of soil information has been the subject of varying improvements in previous studies, but the underlying prediction mechanisms for spectrally active and inactive properties are still poorly understood. By measuring Mid-Infrared (MIR) and X-ray Fluorescence (XRF) spectra, texture, total and labile Organic Carbon (OC) and Nitrogen (N) content, pH, and Cation Exchange Capacity (CEC) for n=117 soils from an arable field in Germany, our goal was to investigate the prediction mechanisms and benefits of model fusion. Using MIR spectra or elemental concentrations derived from XRF spectra, partial least squares regression models went through a three-step training and testing process. Two high-level fusion and two sequential hybrid strategies were also tested. MIR outperformed XRF when it came to inorganic properties (RPIQV for clay=3.4, silt=3.0, and sand=1.8) in the field under investigation, while MIR was superior for organic properties (RPIQV for total OC=7.7 and N=5.0). For these properties, there was little to no improvement in accuracy with even the optimal fusion approach. The large number of elements with variable importance in the projection scores >1 (Fe, Ni, Si, Al, Mg, Mn, K, Pb (clay only), and Cr) and strong spearman correlations (0.57 rs 0.90) with clay and silt account for the high XRF accuracy for these materials.

Description

The foundation of global food production is soil, which also serves as a habitat, regulates the hydrological cycle, and mitigates climate change through carbon sequestration. However, precision agriculture, soil mapping, contamination monitoring, and documentation of soil C sequestration all require a high spatial and temporal density of soil information due to the heterogeneous and dynamic nature of soils. In this context, sensors that make use of various parts of the electromagnetic spectrum offer a quicker, less expensive and non-destructive alternative to conventional laboratory procedures. Models can be used to predict a variety of soil properties after they have been calibrated with paired reference data and spectral measurements. However, the prediction mechanisms for the soil property of interest determine the accuracy of the resulting model.

In the field of soil science, the use of Mid-Infrared Spectroscopy (MIRS) is well-established. The fundamental vibrations of many organic molecules containing soil Organic Carbon (OC) and nitrogen (N) as well as minerals in the clay (such as kaolinite, smectite) and sand (such as quartz) particle size fractions are captured by MIRS using radiation in the range of 2500-25,000 nm (4000-400 cm^-1). Quantitative spectral models are based on the proportionality and specificity of spectrally-active molecules in relation to the soil property of interest, and as a result, model accuracy is affected. Reviews, for instance, have found that complex properties related to both organic and inorganic soil fractions, such as pH and CEC, have lower and more inconsistent estimation accuracy than OC, total N, clay, and sand content. Secondary soil properties that are indirectly estimated by covariations with primary soil properties (such as OC and clay) are described in other infrared studies.

We examined evolutionary patterns of physiology in this article, which may be relevant to comprehending amphibian responses to climate change dangers. To evaluate local physiology in conjunction with phylogenetic and local environmental factors, this study uses a novel small spatial-scale approach that ensures that the environmental variables measured represent the species' actual environment. Importantly, our study includes 12 species, or 63% of the known alpha diversity at our study site, when looking at the entire dataset. Frog body length was positively correlated with CT_max, but no environmental factor was found to be driving local CT_max. In addition, we discovered a significant phylogenetic signal in the species' CT_max, indicating that this physiological trait is constrained by evolution. As a result, this group has very little chance of locally adapting to keep up with changes in temperature because such changes in CT_max would necessitate the use of evolutionary processes.

Conclusion

This field-scale case study's findings should be tested at larger spatial scales in subsequent research. In situ MIR and XRF measurements must also be thoroughly tested because the presence of soil moisture, coarse particles, and surface roughness may alter the advantages of using these sensors separately and together. A tannin–furanic polymer was tested against mimosa tannin to see how it affected the in vitro growth of two white-rot fungi (Trametes versicolor and Agrocybe aegerita) and two strains of bacteria in the family Bacillaceae and Pseudomonaceae. The ability of tested bacteria and fungi to tolerate or eventually degrade condensed tannins was the criteria for selection. For the fungal experiments, the effect of the tannin content was looked at, and for the bacteria, the concentration chosen (1%) provided additional data for this study. The antimicrobial properties of the mimosa tannin caused bacteria to grow faster when combined with glucose than when glucose was used alone.