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Laser-induced breakdown spectroscopy (LIBS) and visible near-infrared spectroscopy (vis-NIRS) are spectroscopic techniques that offer promising alternatives to traditional laboratory methods for the rapid and cost-effective determination of soil properties on a large scale. Despite their individual limitations, combining LIBS and vis-NIRS has been shown to enhance the prediction accuracy for the determination of soil properties compared to single-sensor approaches. In this study, we used a comprehensive Danish national-scale soil dataset encompassing mostly sandy soils collected from various land uses and soil depths to evaluate the performance of LIBS and vis-NIRS, as well as their combined spectra, in predicting soil organic carbon (SOC) and texture. Firstly, partial least squares regression (PLSR) models were developed to correlate both LIBS and vis-NIRS spectra with the reference data. Subsequently, we merged LIBS and vis-NIRS data and developed PLSR models for the combined spectra. Finally, interval partial least squares regression (iPLSR) models were applied to assess the impact of variable selection on prediction accuracy for both LIBS and vis-NIRS. Despite being fundamentally different techniques, LIBS and vis-NIRS displayed comparable prediction performance for the investigated soil properties. LIBS achieved a root mean square error of prediction (RMSEP) of <7% for texture and 0.5% for SOC, while vis-NIRS achieved an RMSEP of <8% for texture and 0.5% for SOC. Combining LIBS and vis-NIRS spectra improved the prediction accuracy by 16% for clay, 6% for silt and sand, and 2% for SOC compared to single-sensor LIBS predictions. On the other hand, vis-NIRS single-sensor predictions were improved by 10% for clay, 17% for silt, 16% for sand, and 4% for SOC. Furthermore, applying iPLSR for variable selection improved prediction accuracy for both LIBS and vis-NIRS. Compared to LIBS PLSR predictions, iPLSR achieved reductions of 27% and 17% in RMSEP for clay and sand prediction, respectively, and an 8% reduction for silt and SOC prediction. Similarly, vis-NIRS iPLSR models demonstrated reductions of 6% and 4% in RMSEP for clay and SOC, respectively, and a 3% reduction for silt and sand. Interestingly, LIBS iPLSR models outperformed combined LIBS-vis-NIRS models in terms of prediction accuracy. Although combining LIBS and vis-NIRS improved the prediction accuracy of texture and SOC, LIBS coupled with variable selection had a greater benefit in terms of prediction accuracy. Future studies should investigate the influence of reference method uncertainty on prediction accuracy. [ABSTRACT FROM AUTHOR]

Soil water repellency (WR) is ubiquitous across Greenlandic cultivated fields, which may constrain agricultural production. Fine‐grained glacial rock flour (GRF) is available in the surrounding landscape, which could serve as a soil amendment. We tested whether the application of GRF (rates of 0, 50, 100, 300, and 500 ton ha−1) reduced the WR across two field trials in South Greenland. The field trials, Upernaviarsuk (UP) and South Igaliku (SI), differed in clay (UP: 0.05–0.11 kg kg−1; SI: 0.03–0.05 kg kg−1) and organic carbon (OC) contents (UP: 0.04–0.13 kg kg−1; SI: 0.01–0.03 kg kg−1). We measured WR across gravimetric water contents (W) from oven‐dry to the W where WR ceased (WNON) to obtain whole WR‐W curves. Most soils became hydrophilic around air‐dry conditions at application rates of ≥300 ton ha−1, likely due to increased clay:OC ratios. Application rates of ≥300 ton ha−1 generally reduced the trapezoidal integrated area of the WR‐W curve (WRAREA), WNON, and WR after heat treatments at 105°C (WR105) and 60°C (WR60). The WR105 was significantly reduced in both fields at 500 ton ha−1, while WR60 was significantly reduced in UP at application rates of ≥300 ton ha−1. The GRF effects were masked by texture and OC variations. Normalizing WRAREA to the water vapor sorption isotherms (utilizing the Campbell‐Shiozawa model) revealed that GRF consistently reduced the normalized WRAREA. The SI field showed the largest reduction in the normalized WRAREA, likely due to its lower OC and clay contents. Thus, GRF could reduce WR across two Greenlandic field trials. Core Ideas: Applying fine‐grained glacial rock flour (GRF) may alleviate highly hydrophobic subarctic soils.Moisture‐dependent hydrophobicity was assessed in two field trials in South Greenland.Parameters evaluated included both total area under the hydrophobicity curve and single‐point values.Hydrophobicity was reduced at GRF applications ≥300 ton ha−1, particularly in the less clayey field.Normalizing the level of hydrophobicity to the level of water retention enabled comparisons between soils. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2010.01.001 Byline: Rania Bou Kheir, Mogens H. Greve, Peder K. BA[cedilla]cher, Mette B. Greve, Rene Larsen, Keith McCloy Abstract: Soil organic carbon (SOC) is one of the most important carbon stocks globally and has large potential to affect global climate. Distribution patterns of SOC in Denmark constitute a nation-wide baseline for studies on soil carbon changes (with respect to Kyoto protocol). This paper predicts and maps the geographic distribution of SOC across Denmark using remote sensing (RS), geographic information systems (GISs) and decision-tree modeling (un-pruned and pruned classification trees). Seventeen parameters, i.e. parent material, soil type, landscape type, elevation, slope gradient, slope aspect, mean curvature, plan curvature, profile curvature, flow accumulation, specific catchment area, tangent slope, tangent curvature, steady-state wetness index, Normalized Difference Vegetation Index (NDVI), Normalized Difference Wetness Index (NDWI) and Soil Color Index (SCI) were generated to statistically explain SOC field measurements in the area of interest (Denmark). A large number of tree-based classification models (588) were developed using (i) all of the parameters, (ii) all Digital Elevation Model (DEM) parameters only, (iii) the primary DEM parameters only, (iv), the remote sensing (RS) indices only, (v) selected pairs of parameters, (vi) soil type, parent material and landscape type only, and (vii) the parameters having a high impact on SOC distribution in built pruned trees. The best constructed classification tree models (in the number of three) with the lowest misclassification error (ME) and the lowest number of nodes (N) as well are: (i) the tree (T1) combining all of the parameters (ME=29.5%; N =54); (ii) the tree (T2) based on the parent material, soil type and landscape type (ME=31.5%; N =14); and (iii) the tree (T3) constructed using parent material, soil type, landscape type, elevation, tangent slope and SCI (ME=30%; N =39). The produced SOC maps at 1:50,000 cartographic scale using these trees are highly matching with coincidence values equal to 90.5% (Map T1/Map T2), 95% (Map T1/Map T3) and 91% (Map T2/Map T3). The overall accuracies of these maps once compared with field observations were estimated to be 69.54% (Map T1), 68.87% (Map T2) and 69.41% (Map T3). The proposed tree models are relatively simple, and may be also applied to other areas. Author Affiliation: Department of Agroecology and Environment, Faculty of Agricultural Sciences (DJF), Aarhus University, Blichers Alle 20, P.O. Box 50, DK-8830 Tjele, Denmark Article History: Received 12 July 2009; Revised 9 December 2009; Accepted 3 January 2010