@article{WehrhanSommer2021, author = {Wehrhan, Marc and Sommer, Michael}, title = {A parsimonious approach to estimate soil organic carbon applying Unmanned Aerial System (UAS) multispectral imagery and the topographic position index in a heterogeneous soil landscape}, series = {Remote sensing / Molecular Diversity Preservation International (MDPI)}, volume = {13}, journal = {Remote sensing / Molecular Diversity Preservation International (MDPI)}, number = {18}, publisher = {MDPI}, address = {Basel}, issn = {2072-4292}, doi = {10.3390/rs13183557}, pages = {20}, year = {2021}, abstract = {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.}, language = {en} } @article{WehrhanRaunekerSommer2016, author = {Wehrhan, Marc and Rauneker, Philipp and Sommer, Michael}, title = {UAV-Based Estimation of Carbon Exports from Heterogeneous Soil Landscapes-A Case Study from the CarboZALF Experimental Area}, series = {SENSORS}, volume = {16}, journal = {SENSORS}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s16020255}, pages = {24}, year = {2016}, abstract = {The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b(899). The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part.}, language = {en} } @article{WehrhanPuppeKaczoreketal.2021, author = {Wehrhan, Marc and Puppe, Daniel and Kaczorek, Danuta and Sommer, Michael}, title = {Spatial patterns of aboveground phytogenic Si stocks in a grass-dominated catchment}, series = {Biogeosciences : BG}, volume = {18}, journal = {Biogeosciences : BG}, number = {18}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1726-4170}, doi = {10.5194/bg-18-5163-2021}, pages = {5163 -- 5183}, year = {2021}, abstract = {Various studies have been performed to quantify silicon (Si) stocks in plant biomass and related Si fluxes in terrestrial biogeosystems. Most studies are deliberately designed on the plot scale to ensure low heterogeneity in soils and plant composition, hence similar environmental conditions. Due to the immanent spatial soil variability, the transferability of results to larger areas, such as catchments, is therefore limited. However, the emergence of new technical features and increasing knowledge on details in Si cycling lead to a more complex picture at landscape and catchment scales. Dynamic and static soil properties change along the soil continuum and might influence not only the species composition of natural vegetation but also its biomass distribution and related Si stocks. Maximum likelihood (ML) classification was applied to multispectral imagery captured by an unmanned aerial system (UAS) aiming at the identification of land cover classes (LCCs). Subsequently, the normalized difference vegetation index (NDVI) and ground-based measurements of biomass were used to quantify aboveground Si stocks in two Si-accumulating plants (Calamagrostis epige-jos and Phragmites australis) in a heterogeneous catchment and related corresponding spatial patterns of these stocks to soil properties. We found aboveground Si stocks of C. epige-jos and P. australis to be surprisingly high (maxima of Si stocks reach values up to 98 g Sim(-2)), i.e. comparable to or markedly exceeding reported values for the Si storage in aboveground vegetation of various terrestrial ecosystems. We further found spatial patterns of plant aboveground Si stocks to reflect spatial heterogeneities in soil properties. From our results, we concluded that (i) aboveground biomass of plants seems to be the main factor of corresponding phytogenic Si stock quantities, and (ii) a detection of biomass heterogeneities via UAS-based remote sensing represents a promising tool for the quantification of lifelike phytogenic Si pools at landscape scales.}, language = {en} } @article{ReicheFunkZhangetal.2012, author = {Reiche, Matthias and Funk, Roger and Zhang, Zhuodong and Hoffmann, Carsten and Reiche, Johannes and Wehrhan, Marc and Li, Yong and Sommer, Michael}, title = {Application of satellite remote sensing for mapping wind erosion risk and dust emission-deposition in Inner Mongolia grassland, China}, series = {Grassland science}, volume = {58}, journal = {Grassland science}, number = {1}, publisher = {Wiley-Blackwell}, address = {Malden}, issn = {1744-6961}, doi = {10.1111/j.1744-697X.2011.00235.x}, pages = {8 -- 19}, year = {2012}, abstract = {Intensive grazing leads to land degradation and desertification of grassland ecosystems followed by serious environmental and social problems. The Xilingol steppe grassland in Inner Mongolia, China, which has been a sink area for dust for centuries, is strongly affected by the negative effects of overgrazing and wind erosion. The aim of this study is the provision of a wind erosion risk map with a spatial high resolution of 25 m to identify actual source and sink areas. In an integrative approach, field measurements of vegetation features and surface roughness length z0 were combined with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image data for a land use classification. To determine the characteristics of the different land use classes, a field observation (ground truth) was performed in April 2009. The correlation of vegetation height and z0 (R2 = 0.8, n = 55) provided the basis for a separation of three main classes, grassland, non-vegetation and other. The integration of the soil-adjusted vegetation index (SAVI) and the spectral information from the atmospheric corrected ASTER bands 1, 2 and 3 (visible to near-infrared) led to a classification of the overall accuracy (OA) of 0.79 with a kappa () statistic of 0.74, respectively. Additionally, a digital elevation model (DEM) was used to identify topographical effects in relation to the main wind direction, which enabled a qualitative estimation of potential dust deposition areas. The generated maps result in a significantly higher description of the spatial variability in the Xilingol steppe grassland reflecting the different land use intensities on the current state of the grassland less, moderately and highly degraded. The wind erosion risk map enables the identification of characteristic mineral dust sources, sinks and transition zones.}, language = {en} } @article{PuppeHoehnKaczoreketal.2017, author = {Puppe, Daniel and H{\"o}hn, Axel and Kaczorek, Danuta and Wanner, Manfred and Wehrhan, Marc and Sommer, Michael}, title = {How big is the influence of biogenic silicon pools on short-term changes in water-soluble silicon in soils? Implications from a study of a 10-year-old soil-plant system}, series = {Biogeosciences}, volume = {14}, journal = {Biogeosciences}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1726-4170}, doi = {10.5194/bg-14-5239-2017}, pages = {14}, year = {2017}, abstract = {The significance of biogenic silicon (BSi) pools as a key factor for the control of Si fluxes from terrestrial to aquatic ecosystems has been recognized for decades. However, while most research has been focused on phytogenic Si pools, knowledge of other BSi pools is still limited. We hypothesized that different BSi pools influence short-term changes in the water-soluble Si fraction in soils to different extents. To test our hypothesis we took plant (Calamagrostis epigejos, Phragmites australis) and soil samples in an artificial catchment in a post-mining landscape in the state of Brandenburg, Germany. We quantified phytogenic (phytoliths), protistic (diatom frustules and testate amoeba shells) and zoogenic (sponge spicules) Si pools as well as Tironextractable and water-soluble Si fractions in soils at the beginning (t(0)) and after 10 years (t(10)) of ecosystem development. As expected the results of Tiron extraction showed that there are no consistent changes in the amorphous Si pool at Chicken Creek (Huhnerwasser) as early as after 10 years. In contrast to t(0) we found increased water-soluble Si and BSi pools at t(10); thus we concluded that BSi pools are the main driver of short-term changes in water-soluble Si. However, because total BSi represents only small proportions of water-soluble Si at t(0) (< 2 \%) and t(10) (2.8-4.3 \%) we further concluded that smaller (< 5 mu m) and/or fragile phytogenic Si structures have the biggest impact on short-term changes in water-soluble Si. In this context, extracted phytoliths (> 5 mu m) only amounted to about 16\% of total Si con-tents of plant materials of C. epigejos and P. australis at t(10); thus about 84\% of small-scale and/or fragile phytogenic Si is not quantified by the used phytolith extraction method. Analyses of small-scale and fragile phytogenic Si structures are urgently needed in future work as they seem to represent the biggest and most reactive Si pool in soils. Thus they are the most important drivers of Si cycling in terrestrial biogeosystems.}, language = {en} } @article{KuehnBrenningWehrhanetal.2009, author = {K{\"u}hn, J{\"u}rgen and Brenning, Alexander and Wehrhan, Marc and Koszinski, Sylvia and Sommer, Michael}, title = {Interpretation of electrical conductivity patterns by soil properties and geological maps for precision agriculture}, issn = {1385-2256}, doi = {10.1007/s11119-008-9103-z}, year = {2009}, abstract = {Precision farming needs management rules to apply spatially differentiated treatments in agricultural fields. Digital soil mapping (DSM) tools, for example apparent soil electrical conductivity, corrected to 25A degrees C (EC25), and digital elevation models, try to explain the spatial variation in soil type, soil properties (e.g. clay content), site and crop that are determined by landscape characteristics such as terrain, geology and geomorphology. We examined the use of EC25 maps to delineate management zones, and identified the main factors affecting the spatial pattern of EC25 at the regional scale in a study area in eastern Germany. Data of different types were compared: EC25 maps for 11 fields, soil properties measured in the laboratory, terrain attributes, geological maps and the description of 75 soil profiles. We identified the factors that influence EC25 in the presence of spatial autocorrelation and field-specific random effects with spatial linear mixed-effects models. The variation in EC25 could be explained to a large degree (R (2) of up to 61\%). Primarily, soil organic matter and CaCO3, and secondarily clay and the presence of gleyic horizons were significantly related to EC25. Terrain attributes, however, had no significant effect on EC25. The geological map unit showed a significant relationship to EC25, and it was possible to determine the most important soil properties affecting EC25 by interpreting the geological maps. Including information on geology in precision agriculture could improve understanding of EC25 maps. The EC25 maps of fields should not be assumed to represent a map of clay content to form a basis for deriving management zones because other factors appeared to have a more important effect on EC25.}, language = {en} } @article{KaiserWehrhanWerneretal.2012, author = {Kaiser, Thomas and Wehrhan, Marc and Werner, Armin and Sommer, Michael}, title = {Regionalizing ecological moisture levels and groundwater levels in grassland areas using thermal remote sensing}, series = {Grassland science}, volume = {58}, journal = {Grassland science}, number = {1}, publisher = {Wiley-Blackwell}, address = {Malden}, issn = {1744-6961}, doi = {10.1111/j.1744-697X.2011.00240.x}, pages = {42 -- 52}, year = {2012}, abstract = {Site-specific soil moisture and groundwater levels are key input parameters for ecological modeling. Obtaining such information in a comprehensive manner is difficult for large regions. We studied a floodplain region in the Federal State of Brandenburg, Germany, to examine the degree to which the average depth of groundwater tables can be derived from surface temperatures obtained by the ASTER radiospectrometer (spatial resolution of 90 m per pixel). A floristic ecological indicator representing the site-specific moisture level was applied to develop a proxy between the thermal satellite data and groundwater table depth. The use of spring scenes (late April to early May) from 2 years proved to be well suited for minimizing the effects of weather and land use. Vegetation surveys along transects that were 2 m wide across the pixel diagonals allowed for the calculation of average ecological moisture values of pixel-sites by applying Ellenberg-numbers. These values were used to calibrate the satellite data locally. There was a close relationship between surface temperature and the average ecological moisture value (R2 = 0.73). Average ecological moisture values were highly indicative of the average groundwater levels during a 7-year measurement series (R2 = 0.93). Satellite-supported thermal data from spring were suitable for estimating the average groundwater levels of low-lying grasslands on a larger scale. Ecological moisture values from the transect surveys effectively allowed the incorporation of relief heterogeneity within the thermal grid and the establishment of the correlation between thermal data and average groundwater table depth. Regression functions were used to produce a map of groundwater levels at the study site.}, language = {en} }