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Reconstructing rates and patterns of colluvial soil redistribution in agrarian (hummocky) landscapes
(2019)
Humans have triggered or accelerated erosion processes since prehistoric times through agricultural practices. Optically stimulated luminescence (OSL) is widely used to quantify phases and rates of the corresponding landscape change, by measuring the last moment of daylight exposure of sediments. However, natural and anthropogenic mixing processes, such as bioturbation and tillage, complicate the use of OSL as grains of different depositional ages become mixed, and grains become exposed to light even long after the depositional event of interest. Instead, OSL determines the stabilization age, indicating when sediments were buried below the active mixing zone. These stabilization ages can cause systematic underestimation when calculating deposition rates. Our focus is on colluvial deposition in a kettle hole in the Uckermark region, northeastern Germany. We took 32 samples from five locations in the colluvium filling the kettle hole to study both spatial and temporal patterns in colluviation. We combined OSL dating with advanced age modelling to determine the stabilization age of colluvial sediments. These ages were combined with an archaeological reconstruction of historical ploughing depths to derive the levels of the soil surface at the moment of stabilization; the deposition depths, which were then used to calculate unbiased deposition rates. We identified two phases of colluvial deposition. The oldest deposits (similar to 5 ka) were located at the fringe of the kettle hole and accumulated relatively slowly, whereas the youngest deposits (<0.3 ka) rapidly filled the central kettle hole with rates of two orders of magnitude higher. We suggest that the latter phase is related to artificial drainage, facilitating accessibility in the central depression for agricultural practices. Our results show the need for numerical dating techniques that take archaeological and soil-geomorphological information into account to identify spatiotemporal patterns of landscape change, and to correctly interpret landscape dynamics in anthropogenically influenced hilly landscapes. (c) 2019 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.
Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils' SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R-2) = 0.91; root mean square error (RMSE) = 0.11% and R-2 = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R-2 = 0.88, RMSE = 0.07%; R-2 = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.