Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. For Denmark, we modeled the vertical distribution of soil organic carbon (SOC) and bulk density, and mapped its spatial distribution at five standard soil depth intervals (0−5, 5−15, 15−30, 30−60 and 60−100 cm) using 18 environmental variables as predictors. SOC distribution was influenced by precipitation, land use, soil type, wetland, elevation, wetness index, and multi-resolution index of valley bottom flatness. The highest average SOC content of 20 g kg−1 was reported for 0−5 cm soil, whereas there was on average 2.2 g SOC kg−1 at 60−100 cm depth. For SOC and bulk density prediction precision decreased with soil depth, and a standard error of 2.8 g kg−1 was found at 60−100 cm soil depth. Average SOC stock for 0−30 cm was 72 t ha−1 and in the top 1 m there was 120 t SOC ha−1. In total, the soils stored approximately 570 Tg C within the top 1 m. The soils under agriculture had the highest amount of carbon (444 Tg) followed by forest and semi-natural vegetation that contributed 11% of the total SOC stock. More than 60% of the total SOC stock was present in Podzols and Luvisols. Compared to previous estimates, our approach is more reliable as we adopted a robust quantification technique and mapped the spatial distribution of SOC stock and prediction uncertainty. The estimation was validated using common statistical indices and the data and high-resolution maps could be used for future soil carbon assessment and inventories.

Soil texture which is spatially variable in nature, is an important soil physical property that governs most physical, chemical, biological, and hydrological processes in soils. Detailed information on soil texture variability both in vertical and lateral dimensions is crucial for proper crop and land management and environmental studies, especially in Denmark where mechanized agriculture covers two thirds of the land area. We modeled the continuous depth function of texture distribution from 1958 Danish soil profiles (up to a 2‐m depth) using equal‐area quadratic splines and predicted clay, silt, fine sand, and coarse sand content at six standard soil depths of GlobalSoilMap project (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) via regression rules using the Cubist data mining tool. Seventeen environmental variables were used as predictors and their strength of prediction was also calculated. For example, in the prediction of silt content at 0 to 5 cm depth, factors that registered a higher level of importance included the soil map scored (90%), landscape types (54%), and landuse (27%), while factors with lower scores were direct insolation (17%) and slope aspect (14%). Model validation (20% of the data selected randomly) showed a higher prediction performance in the upper depth intervals but increasing prediction error in the lower depth intervals (e.g., R2 = 0.54, RMSE = 33.7 g kg−1 for silt 0–5 cm and R2 = 0.29, RMSE = 38.8 g kg−1 from 100–200 cm). Danish soils have a high sand content (mean values for clay, silt, fine sand, and coarse sand content for 0‐ to 5‐cm depth were 79, 84, 324, and 316 g kg−1, respectively). Northern parts of the country have a higher content of fine sand compared to the rest of the study area, whereas in the western part of the country there was little clay but a high coarse sand content at all soil depths. The eastern and central parts of the country are rich in clay, but due to leaching, surface soils are clay eluviated with subsequent accumulation at lower depths. We found equal‐area quadratic splines and regression rules to be promising tools for soil profile harmonization and spatial prediction of texture properties at national extentacross Denmark.

Abstract:Synthetic aperture radar (SAR) sensors are often used to characterize the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configurations of the sensors (polarization, incidence angle and radar wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction.This article reviews the potential of currently operational SAR sensors and those planned for the near future to characterize soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal radar configurations and the precision associated with the estimation of soil surface characteristics.The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (<35°). This configuration, which enables an estimated moisture accuracy greater than 6% is possible several times a month taking into account all the current and future sensors. Concerning soil roughness, it is best mapped using three classes (smooth, moderately rough, and rough). Such mapping requires high-incidence data, which is possible with certain current sensors (RADARSAT-1 and ASAR both in band C). When L-band sensors (ALOS) become available, this mapping accuracy should improve because the sensitivity of the radar signal to Soil Surface Characteristics (SSC) increases with wavelength. Finally, the polarimetric mode of certain imminent sensors (ALOS, RADARSAT-2, TerraSAR-X, etc.), and the possibility of acquiring data at very high spatial resolution (metre scale), offer great potential in terms of improving the quality of SSC mapping.

This paper predicts the geographic distribution and size of gullies across central Lebanon using a geographic information system (GIS) and terrain analysis. Eleven primary (elevation; upslope contributing area; aspect; slope; plan, profile and tangential curvature; flow direction; flow width; flow path length; rate of change of specific catchment area along the direction of flow) and three secondary (steady-state; quasi-dynamic topographic wetness; sediment transport capacity) topographic variables were generated and used along with digital data collected from other sources (soil, geology) to statistically explain gully erosion field measurements. Three tree-based regression models were developed using (1) all variables, (2) primary topographic variables only and (3) different pairs of variables. The best regression tree model combined the steady-state topographic wetness and sediment transport capacity indices and explained 80% of the variability in field gully measurements. This model proved to be simple, quick, realistic and practical, and it can be applied to other areas of the Mediterranean region with similar environmental conditions, thereby providing a tool to help with the implementation of plans for soil conservation and sustainable management.The study area was chosen because it represents the environmental diversity of Lebanon in terms of geology, soil, hydrography, land cover and climate. It covers 676 km 2 , or 6·5% of the total area of Lebanon. It extends 33 km from west to east across the middle of Lebanon ( Figure 1) and can be divided into two major geomorphic units, Mount Lebanon and the Bekaa.Mount Lebanon, which comprised 76% of the study area, runs parallel to the shoreline, dipping steeply seaward, with an east-west gradient of 7·5-10%. It can be divided into three major parts: the lower slopes (100-500 m altitude), the upper sloping plateaus (500-1500 m altitude) and the elevated crests (>1500 m altitude). The lower slopes, consisting of clastic and oolithic limestone, sandstone and clayey rocks of the Lower Cretaceous and Upper Jurassic formations (Dubertret, 1945), are dominated by bare soils and residential/commercial urban areas. The upper sloping plateaus are covered with coniferous (mainly Pinus pinea), oak (mainly Quercus calliprinos) and broadleaf (Quercus infectoria) forests and shrub lands on dolomites, limestone and dolomitic limestone rocks with patches of basalts, sandstone and clay materials. The elevated crests are covered by grass and herbaceous vegetation on limestone and marly limestone Cenomanian rocks and dolomitic limestone Jurassic rocks. Mount Lebanon is structurally affected by faults running parallel to one another, cutting in a SW-NE direction and separated from the Bekaa by the shed line of the Dead Sea Fault Zone and the 'Yammounah Fault' with a NE-SW strike. The Bekaa comprises the hills (1000-1500 m altitude; 6% of total area) located between the crests of Mount Lebanon and the Bekaa valley, and the valley bottom (500-1000 m altitude; 18% of total area). The h...

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After decades of mining and industrialization in Qatar, it is important to estimate their impact on soil pollution with toxic metals. The study utilized 300 topsoil (0-30 cm) samples, multi-spectral images (Landsat 8), spectral indices and environmental variables to model and map the spatial distribution of arsenic (As), chromium (Cr), nickel (Ni), copper (Cu), lead (Pb) and zinc (Zn) in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R 2 and the ratio of performance to interquartile distance (RPIQ), the models showed good predictive capabilities for all elements. Of all of the prediction results, Cu had the highest R 2 = 0.74, followed by As > Pb > Cr > Zn > Ni. This study found that all of the models only chose images from January and February as predictors, which indicates that images from these two months are important for soil toxic metals' monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental/socio-economic conditions and pollution causes.

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