Soil spectroscopy is a promising alternative to evaluate and monitor soil and water quality, particularly in mountainous agricultural lands characterized by intense degradation and limited soil tests reports; a few studies have evaluated the feasibility of VIS-NIR spectroscopy to predict Mehlich 3 (M3) extractable nutrients. This study aimed to (i) examine the potential of VIS-NIR spectroscopy in combination with partial least squares regression to predict M3-extractable elements (Ca, K, Mg, P, Fe, Cd, Cu, Mn, Pb, and Zn) and basic soil properties (clay, silt, sand, CaCO3, pH, and soil organic carbon-SOC), (ii) find optimal pre-processing techniques, and (iii) determine primary prediction mechanisms for spectrally featureless soil properties. Topsoil samples were collected from a representative area (114 samples from 525 ha) located in the mountainous region of NW Azerbaijan. A series of pre-processing steps and transformations were applied to the spectral data, and the models were calibrated and evaluated based on the coefficient of determination (R2), root mean square error (RMSE), and the residual prediction deviation (RPD). The leave-one-out cross-validated predictions showed that the first derivative spectra produce higher prediction accuracies (R2 = 0.51–0.91; RPD = 1.20–2.29) for most soil properties. The evaluation of the model performance with optimal pre-processing techniques revealed that both calibration and validation models produce considerable differences in RPD values associated with sample size and the random partition of the calibration or validation subsets. The prediction models were excellent or very good (RPD > 2.0) for CaCO3, SOC, sand, silt, Ca, and Pb, good or fair (1.4 < RPD < 2.0) for clay, K, Cd, pH, Fe, Mn, and Cu, and poor (1.0 < RPD < 1.4) for Mg, P, and Zn. Principal component and correlation, stepwise regression analysis, and variable importance in projection procedures allowed to elucidate the underlying prediction mechanisms. Unlike the previous studies, the spectral estimations of pH, Ca, Mg, P, Fe, Pb, and Cd concentrations were linked to their correlation with CaCO3 rather than soil organic matter, whereas Mg and P concentrations were also connected to Fe-oxides. Soil particle sizes contributed to predicting K concentration but confounded the prediction of P and Zn concentration. The weaker correlations of Mn, Cu or Zn with CaCO3, particle sizes, SOC, Fe, and spectral data yielded to their lower prediction accuracy. The major prediction mechanisms for M3-extractable elements relied on their relations with CaCO3, pH, clay content and mineralogy, and exchangeable cations in the context of their association with land use. The results can be used in mountain lands to evaluate and control the effect of management on soil quality indices and land degradation neutrality. Further studies are needed to develop most advantageous sampling schemes and modeling.

ABSTRACT This study aimed to predict soil properties using visible–near infrared (VIS-NIR) spectroscopy combined with partial least square regression (PLSR) modeling. Special emphasis was given to evaluating effect of pre-processing methods on prediction accuracy and important wavelengths. A total of 114 samples were collected and involved in chemical and spectral analyzes. PLSR model of each soil property was calibrated for all pre-processing methods using all samples, and leave-one-out cross-validation was used to make comparisons between them. Then, PLSR model of each best pre-processing method was calibrated using a 75% of all samples and correspondingly validated with the remaining a 25%. Model accuracy was evaluated based on coefficient of determination (R2), root mean-squared errors (RMSE), and residual prediction deviations (RPD). The high correlation coefficients were found between the tested soil properties and reflectance spectra. The pre-processing methods considerably improved prediction accuracy and filtering methods outperformed linearization methods, and the latter outperformed normalization methods. The performance of cross-validation, calibration and independent validation was similar. An excellent prediction (RPD>2.5) model was obtained for soil organic carbon (SOC) and calcium-carbonate (CaCO3), good quantitative (2.0< RPD<2.5) prediction for sand, silt, and clay, fair prediction (1.4< RPD<1.8) for pH, and poor prediction (1.0< RPD<1.4) for hygroscopic water content (WC). Important wavelengths varied depending on soil property, but some wavelengths were common. This study can be a precursor to building a pioneering soil spectral database, calibrating satellite data, and hyperspectral image mapping of soils as well as digital soil mapping, environmental, and erosion modeling in the Caucasus Mountains.

In this study, we tested the potential of visible-near infrared (VNIR, 350–2500nm) and mid-infrared spectroscopy (MIR, 2500–15,000nm) for quantification and prediction of soil parameters to support the Saxon Permanent Soil Monitoring Program. As Saxony is characterised by a large variety of soil metal concentrations which can negatively affect essential soil functions, the presented study is strongly focused on the forecast of soil metal contents. As data basis, a total of 203 soil samples of the Saxon Permanent Soil Monitoring Program, collected between 1998 and 2013 at 48 representative locations with respect to soil type, parent material, land use and climate conditions, were used. The chemical analysis provided information regarding total soil organic carbon content (TOC) and pH-value as well as element concentrations (Al, As, Ca, Cu, Fe, K, Mn, Na, Ni, Pb, Zn). VNIR-spectra were collected utilising an ASD FieldSpec Pro FR while an Agilent 4300 Handheld FTIR spectrometer was applied to cover the MIR wavelength region. Spectra pre-processing comprised the application of multi scatter correction (MSC), standard normal variate (SNV), continuum removal (CR) and the first and second derivatives. To model the relationship between soil spectral and chemical properties, and to predict element concentration, the spectra and chemical data were used as input for Partial Least Square Regression (PLSR) models. The definition of training data was realised on Kennard-Stone sampling algorithm and we selected 103 samples for model calibration and 100 samples for model validation. In general, the MIR-spectra and the MSC- and SNV-pre-processing improved the model performance. We obtained promising model results for TOC, Al, Fe, K and Ni with R2-values between 0.70 and 0.88. Moderate results were obtained for Ca (R2=0.61) and Mn (R2=0.43). We conclude that VNIR and MIR spectroscopy has the potential to quickly provide reliable information regarding major soil parameters and metal contents and is thus a promising alternative approach to support soil analysis within the Saxon Permanent Soil Monitoring Program.

The focus of this study was the comparative analysis and evaluation of reflectance measurements (350 nm–2500 nm) of a chlorite rock sample, which were collected by 26 institutions in 42 different spectroscopic set-ups as a part of an international measurement comparison designed to document the plurality in laboratory reflectance spectroscopy. The impacts of the different set-ups on the chlorite spectra were determined by analysing the parameter variations of two characteristic chlorite absorption features at 1400 nm and 2345 nm and interpretation based on user-provided metadata. The positions of the 1400 nm absorption features showed a standard deviation of 1.4 nm. Larger deviations were observed for the shoulders, widths, depths and areas. Here, the strongest deviations could be clearly related to impacts of unfavourable background materials and unsuitable illumination types. The positions of the absorption feature at 2345 nm showed a standard deviation of 16 nm and the variations in absorption width were stronger compared to the 1400 nm feature. In contrast, the variations of depths and areas of the feature at 2345 nm were comparable to the variations we observed for the 1400 nm feature but could not be assigned to singular influencing factors. Although the majority of the spectra showed the typical shapes and specific features of chlorite, strong deviations were present in a few spectra which are likely to hamper the spectral identification of chlorite and quantitative spectral analysis. Thus, the results of this study underline the necessity to define measurement standards and protocols and to provide basic information for future standards.

This study focuses on the derivation of an urban surface material map to parameterize a 3D numerical microclimate model. For this purpose, fusion of airborne hyperspectral and light detection and ranging (LiDAR) remote sensing data is performed. In a first step, surface materials are extracted from the preprocessed input datasets using a hybrid, three-stage classification approach. The resulting map is then utilized in combination with the LiDAR object height information data to parameterize the microclimate model. To demonstrate the potential of data-driven microclimate modeling, two case studies are presented for selected test sites in the City of Houston, Texas. The results of this study highlight that the synergistic combination of hyperspectral and LiDAR data enables reliable mapping of some of the key input parameters required for urban microclimate modeling. Moreover, classification-based microclimate simulations can reveal the thermal properties of urban neighborhoods under varying conditions and, thus, facilitate the identification of hot spot areas and critical land cover configurations.