BackgroundThe stability of soil organic matter (SOM) can be characterized by thermal analysis. Methods to determine the thermal stability of SOM have recently been increasingly applied in soil analysis. Most studies focus on organic carbon (OC), whereas its subfractions, for example, microbial biomass carbon (MBC) or hot water-extractable carbon (HWEC), representing fast-reacting pools, have been less investigated.AimA set of 100 soil samples was analyzed for thermal mass losses and their relation to SOM and soil mineral phase properties.MethodThe temperature-dependent mass losses were determined by thermogravimetric analysis. For this purpose, soils differing in terms of parent material, soil texture, and land use were characterized and analyzed.ResultsTemperature ranges of mass losses and corresponding fractions of different thermal stability (thermolabile and thermostable) were defined. SOM-related parameters were highly correlated with mass losses of the thermolabile fractions. Mass losses of thermostable matter were significantly correlated with soil mineral phase parameters. The soil thermostability index (STSI) was calculated as the ratio of thermolabile and thermostable mass proportions, represented by the mass losses of selected temperature intervals. Regressions of STSI with ratios of mineral phase parameters to OC (e.g., clay/OC), representing the saturation degree of the mineral phase with OC, HWEC, or MBC, yielded strong relationships.ConclusionThe saturation of the mineral phase with OC determines the thermal stability of OC. Overall, relevant factors for OC thermal stability were identified. OC and HWEC were significantly correlated with thermal stability and mineral phase saturation. For MBC, no such relationship was found, indicating that its stability is driven by other factors.

Microplastics (MP) in soil have emerged as an environmental pollutant of increasing interest in recent years, emphasizing the need for efficient screening methods. Hyperspectral imaging in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) coupled with machine learning (ML) have shown potential for rapid, cost-effective MP detection in soils. However, key methodological challenges, including optimal ML algorithms for MP classification, detection limits dependent on MP type, and scaling relationships between area-based hyperspectral imaging and soil MP concentrations, should further be explored. In this study, we explored the potential of SWIR hyperspectral imaging to detect and quantify three MP types (polyamide - PA, polyethylene - PE, polypropylene - PP) at the (sub-)pixel level in soil-MP mixtures with concentrations ranging from 0.01 wt-% to 5.00 wt-% using Partial Least Squares - Discriminant Analysis (PLSDA), Random Forests (RF), 1D-Convolutional Neural Networks (1D-CNN) and a three-model ensemble. All machine learning algorithms achieved comparable classification accuracies in a calibration-validation approach on a large spectral library developed from pure material spectra. When applied to the independent SWIR image data, RF performance decreased markedly, whereas the ensemble proved beneficial to suppress individual model-specific random misclassifications. We found a close non-linear relationship between the SWIR image area-based MP quantification and the actual concentration (wt-%) of MP in the soil samples that depended on the MP type. Interpolated MP detection limits were also MP-type specific and corresponded to 0.05 wt-%, 0.46 wt-% and 1.15 wt-% for PE, PP and PA, respectively, with the larger PE particles having a lower detection limit than the more finely dispersed PA and PP particles. Our results show that hyperspectral SWIR imaging has the potential to enable screening applications where elevated MP levels can occur, such as landfill or industrial sites, but is likely not sensitive enough to detect current background concentrations.

Soil organic matter (SOM) is an indispensable component of terrestrial ecosystems. Soil organic carbon (SOC) dynamics are influenced by a number of well-known abiotic factors such as clay content, soil pH, or pedogenic oxides. These parameters interact with each other and vary in their influence on SOC depending on local conditions. To investigate the latter, the dependence of SOC accumulation on parameters and parameter combinations was statistically assessed that vary on a local scale depending on parent material, soil texture class, and land use. To this end, topsoils were sampled from arable and grassland sites in south-western Germany in four regions with different soil parent material. Principal component analysis (PCA) revealed a distinct clustering of data according to parent material and soil texture that varied largely between the local sampling regions, while land use explained PCA results only to a small extent. The PCA clusters were differentiated into total clusters that contain the entire dataset or major proportions of it and local clusters representing only a smaller part of the dataset. All clusters were analysed for the relationships between SOC concentrations (SOC %) and mineral-phase parameters in order to assess specific parameter combinations explaining SOC and its labile fractions hot water-extractable C (HWEC) and microbial biomass C (MBC). Analyses were focused on soil parameters that are known as possible predictors for the occurrence and stabilization of SOC (e.g. fine silt plus clay and pedogenic oxides). Regarding the total clusters, we found significant relationships, by bivariate models, between SOC, its labile fractions HWEC and MBC, and the applied predictors. However, partly low explained variances indicated the limited suitability of bivariate models. Hence, mixed-effect models were used to identify specific parameter combinations that significantly explain SOC and its labile fractions of the different clusters. Comparing measured and mixed-effect-model-predicted SOC values revealed acceptable to very good regression coefficients (R2=0.41–0.91) and low to acceptable root mean square error (RMSE = 0.20 %–0.42 %). Thereby, the predictors and predictor combinations clearly differed between models obtained for the whole dataset and the different cluster groups. At a local scale, site-specific combinations of parameters explained the variability of organic carbon notably better, while the application of total models to local clusters resulted in less explained variance and a higher RMSE. Independently of that, the explained variance by marginal fixed effects decreased in the order SOC > HWEC > MBC, showing that labile fractions depend less on soil properties but presumably more on processes such as organic carbon input and turnover in soil.

Soil spectroscopy in the visible-to-near infrared (VNIR) and mid-infrared (MIR) is a cost-effective method to determine the soil organic carbon content (SOC) based on predictive spectral models calibrated to analytical-determined SOC reference data. The degree to which uncertainty in reference data and spectral measurements contributes to the estimated accuracy of VNIR and MIR predictions, however, is rarely addressed and remains unclear, in particular for current handheld MIR spectrometers. We thus evaluated the reproducibility of both the spectral reflectance measurements with portable VNIR and MIR spectrometers and the analytical dry combustion SOC reference method, with the aim to assess how varying spectral inputs and reference values impact the calibration and validation of predictive VNIR and MIR models. Soil reflectance spectra and SOC were measured in triplicate, the latter by different laboratories, for a set of 75 finely ground soil samples covering a wide range of parent materials and SOC contents. Predictive partial least-squares regression (PLSR) models were evaluated in a repeated, nested cross-validation approach with systematically varied spectral inputs and reference data, respectively. We found that SOC predictions from both VNIR and MIR spectra were equally highly reproducible on average and similar to the dry combustion method, but MIR spectra were more robust to calibration sample variation. The contributions of spectral variation (ΔRMSE < 0.4 g·kg−1) and reference SOC uncertainty (ΔRMSE < 0.3 g·kg−1) to spectral modeling errors were small compared to the difference between the VNIR and MIR spectral ranges (ΔRMSE ~1.4 g·kg−1 in favor of MIR). For reference SOC, uncertainty was limited to the case of biased reference data appearing in either the calibration or validation. Given better predictive accuracy, comparable spectral reproducibility and greater robustness against calibration sample selection, the portable MIR spectrometer was considered overall superior to the VNIR instrument for SOC analysis. Our results further indicate that random errors in SOC reference values are effectively compensated for during model calibration, while biased SOC calibration data propagates errors into model predictions. Reference data uncertainty is thus more likely to negatively impact the estimated validation accuracy in soil spectroscopy studies where archived data, e.g., from soil spectral libraries, are used for model building, but it should be negligible otherwise.

Portable visible to near-infrared (VNIR) and mid-infrared (MIR) soil spectroscopy holds great potential to support field applications in soil science and management by complementing conventional soil analytical methods. Under field conditions, however, soil moisture can critically affect the quality of reflectance measurements. In this study, we examined the effects of soil moisture on VNIR and MIR soil spectra and how its magnitude and variation impact the accuracy and robustness of predictive spectral models. We carried out a systematic re -wetting experiment on two soil datasets of different scale and origin that were measured at four gravimetric moisture levels (air-dried, 5 %, 10 %, 15 %) with portable VNIR and MIR instruments. The spectral data of each moisture class, as well as randomized combinations of different moisture contents, were then used to calibrate VNIR, MIR and combined PLSR models to estimate soil organic carbon (SOC) and clay content, where combined models included spectra concatenation (VNMIR) and model output average (MOA). The overall shape of MIR spectra was more significantly distorted by soil moisture than VNIR spectra, while the general impact of soil water content in both spectral domains was texture-dependent. In terms of predictive accuracy, MIR models were generally superior for air-dried sample material, while VNIR models fared better for uniformly moist samples. With increasing soil moisture variability, comparative estimation accuracies between individual VNIR and MIR models were dependent on the underlying dataset. VNMIR and MOA models proved beneficial and yielded the most accurate and robust predictions for SOC and clay content when soil moisture was variable, irrespective of the considered dataset (regional dataset: RMSESOC = 0.22-0.27 %, RMSECLAY = 2.67-3.14 %; field dataset: RMSESOC = 0.09-0.11 %, RMSECLAY = 0.87-1.15 %). Predictive mechanisms, as evaluated by variable impor-tance in the projection (VIP) of PLSR models, changed substantially with variation in soil water content, espe-cially in the MIR, where important absorption bands for SOC and clay minerals could be heavily attenuated or completely masked. Our study highlights the advantages of employing both VNIR and MIR instruments for spectral data collection on soils in field condition and the potential of integrating VNIR and MIR spectra collected at different soil moisture levels into soil spectral libraries.

Abstract. Soil organic matter (SOM) is an indispensable component of terrestrial ecosystems. Soil organic carbon (SOC) dynamics are influenced by a number of well-known abiotic factors such as clay content, soil pH or pedogenic oxides. These parameters interact with each other and vary in their influence on SOC depending on local conditions. To investigate the latter, the dependence of SOC accumulation on parameters and parameter combinations was statistically assessed that vary on a local scale depending on parent material, soil texture class and land use. To this end, topsoils were sampled from arable and grassland sites in southwestern Germany at four regions with different soil parent material. Principal component analysis (PCA) revealed a distinct clustering of data according to parent material and soil texture that varied largely between the local sampling regions, while land use explained PCA results only to a small extent. The obtained global and the different local clusters of the dataset were further analyzed for the relationships between SOC and mineral phase parameters in order to assess specific parameter combinations explaining SOC and its labile fractions. Analyses were focused on soil parameters that are known as possible predictors for the occurrence and stabilization of SOC (e.g. fine silt plus clay and pedogenic oxides). Regarding the global dataset, we found significant correlations between SOC and its labile fractions hot water-extractable C (HWEC) and microbial biomass C (MBC), respectively and the predictors, yet correlation coefficients were partially low. Mixed effect models were used to identify specific parameter combinations that significantly explain SOC and its labile fractions of the different clusters. Comparing measured and mixed effect models-predicted SOC values revealed acceptable to very good regression coefficients (R² = 0.41–0.91). Thereby, the predictors and predictor combinations clearly differed between models obtained for the whole data set and the different cluster groups. At a local scale site specific combinations of parameters explained the variability of organic matter notably better, while the application of global models to local clusters resulted in less sufficient performance. Independent from that, the overall explained variance generally decreased in the order SOC > HWEC > MBC, showing that labile fractions depend less on soil properties than on organic matter input and turnover in soil.