Recently, forest management faces new challenges resulting from increasing temperatures and drought occurrences. For sustainable, site-specific management strategies, the availability of up to date soil information is crucial. Proximal soil sensing techniques are a promising approach for rapid and inexpensive collection of data, and could facilitate the provision of the necessary information. This study evaluates the potential of visual and near-infrared spectroscopy (vis-NIRS) for estimating soil parameters relevant for humus mapping in Saxon forests. Therefore, soil samples from the organic layer are included. So far there is little knowledge about the applicability of vis-NIRS in the humus layer of forests. We investigate the spectral behaviour of samples from organic (Oh) and mineral (0–5 cm, Ah) horizons, pointing out differences in the occurring absorption features. Further, we identify and assess the accuracy of selected soil properties based on vis-NIRS for forest sites, compare the outcome of different regression methods, investigate the implications for forest soils due to the presence and different composition of the humus layer and organic horizons and interpret the results regarding their usefulness for soil mapping and monitoring purposes. For this, we used retained humus soil samples of forests from Saxony. Regression models were built with Partial Least Squares Regression, Support Vector Machine and Cubist. Investigated properties were carbon (C) and nitrogen (N) content, C/N ratio, pH value, cation exchange capacity (CEC) and base saturation (BS) due to their importance for assessing humus conditions in forests. In organic Oh horizons, prediction results for C and N content achieved R² values between 0.44 and 0.58, with corresponding RPIQ ranging from 1.58 to 2.06 depending on the used algorithm. Estimations of C/N ratio were more precise with R² = 0.65 and RMSE = 2.16. Best results were reported for pH value, with R² = 0.90 and RMSE = 0.20. Regarding BS, the best model accuracy was R² = 0.71, with RMSE = 13.97. In mineral topsoil, C and N content models achieved higher values of R² = 0.59 to 0.72, with RPIQ values between 2.22 and 2.54. However, prediction accuracy was lower for C/N ratio (R² = 0.50, RMSE = 3.52) and pH values (R² = 0.62, RMSE = 0.29). Models for CEC achieved R² = 0.65, with RPIQ = 2.81. In general, prediction precision varied dependent on the used algorithm, without showing clear tendencies. Classification into pH classes was exemplified since this offers a new perspective for humus mapping on forest soils. Balanced accuracy for the defined classes ranged from 0.50 to 0.87. We show that vis-NIR spectroscopy is suitable for assessing humus conditions in Saxon forests (Germany), in particular not only for mineral horizons but also for organic Oh horizons.
<p>Soil water content in the unsaturated zone is a key parameter of the environmental system. The understanding of soil moisture plays a major role with regard to questions of water and nutrient supply to plants, groundwater recharge, soil genesis and climatic interactions.</p><p>In our study we aim to test a new technology for the non-invasive measurement of soil moisture profiles, the so-called Surface-NMR (Nuclear Magnetic Resonance). The instrument applies magnetic fields to the ground and detects its changes caused by mobile and immobile hydrogen atoms in the soil column. Using four different frequencies, the data may provide insights into the water content of four distinct soil layers between the surface and 20 cm depth.</p><p>We carried out multiple NMR measurements at four different field sites in Germany and compared the data with conventional methods, such as gravimetric soil samples, Time Domain Reflectometry (TDR), and Cosmic-Ray Neutron Sensing (CRNS).</p><p>The dataset will be used to investigate the following research questions:</p><ol><li>Is the Surface-NMR method suitable to provide depth-resolved information of soil moisture under field conditions?</li> <li>Does Surface-NMR have the potential to replace or complement conventional methods of soil moisture measurement in the field?</li> <li>What can we learn about the spatial variability and scale dependency of soil moisture by combining three measurement methods of different scale (TDR, NMR, CRNS)?</li> </ol>
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