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Nondestructive proximal sensors can be an efficient source of information of N status in crops for localized and rapid adjustment of fertilization applications. The aim of this study was to compare two transmittance/reflectance-based sensors (SPAD, ASD) and a florescence-based sensor (Multiplex) in their ability to measure N content in corn (Zea mays L.), spring and winter barley (Hordeum vulgare L.), and rye (Secale cereale L.), both at the leaf and canopy level. Measurements of leaves and canopies from six fertilization field trials in 2019 and 2020 were analyzed to establish relationships between sensor information and laboratory-determined N content in crops. Analyses included linear regression for single sensor variables and machine learning for multivariate approaches, to assess the relative accuracy of the proximal sensors to measure N. The ASD is time-intensive and requires post hoc analyses of the spectra. However, the spectral outputs of this device were clearly correlated with the N status of leaves and canopies. At the leaf level, SPAD showed higher accuracy than any of the single Multiplex variables to predict plant N. Multiplex performance could be improved by combining three of its variables. At the canopy level, interpolated SPAD values and the best-performing Multiplex variables showed similar accuracy. It could be concluded that the relationship sensor-N status is species specific. Despite the high standard deviation recorded in some raw Multiplex variable, the derived indices showed a comparable low standard deviation. At both, leaf and canopy levels an integrated sensor solution would combine the multidimensionality of Multiplex and ASD, and the accuracy and practicality of SPAD.

Tillage erosion is known to be a major soil degradation process that is mainly associated with increasingly mechanised agriculture since the early 1950s. However, especially soil truncation on convex hilltops and slope shoulders can be already identified on historical aerial photos of our study region in Northeast Germany from the 1950s.The aim of the study is to better understand the effect of mechanised and especially long-term non-mechanised soil redistribution processes on soil organic carbon (SOC) dynamics over the past 1000 years since the beginning of widespread soil cultivation in our study region and their contribution to the question of soil being a carbon (C) sink or source.Therefore, a modified version of the spatially explicit soil redistribution and C turnover model SPEROS-C was applied on a large-scale catchment (approx. 200 km²) to simulate lateral soil and SOC redistribution, SOC turnover and erosion-induced vertical mixing within the profile (spatial and vertical resolution 5 m x 5 m and 0.1 m soil depth increments, respectively). The uncertainty of the modelling approach was estimated by varying the input variables according to different realisations of the development of agricultural management over the past 1000 years. The results were validated with an erosion classification derived from Sentinel-2 data and UAV based estimation of topsoil SOC. The lowest SOC stocks were found on hilltops, which points at tillage erosion as the major driver of soil degradation.Our results show that the beginning influence of tillage erosion on catchment wide vertical SOC fluxes can be traced back to around 500 years ago. This clearly indicates that non-mechanised tillage erosion from the early stage of cultivation affected the SOC patterns in the study area and hence impacts todays C cycling.

Peatlands ecosystem is one of the largest global terrestrial carbon pools. However, there is a shortness of its characterisation and information through new proximal sensing approaches. The visible and near-infrared spectroscopy is an inexpensive, quick, non-evasive, proximal sensing and low-cost analysis employed in field and/or laboratory. Despite that, there is another current issue in using this tool for creating global models, which is how it can retrieve local characteristics such as soil organic carbon (SOC) and total nitrogen (TN) in peatlands ecosystems. The aims in this study were to: (i) create a local model for quantifying SOC and TN finding the best pre-processing and machine learning methods in peatlands ecosystem, and (ii) evaluate the contribution of SOC and TN data collected in that ecosystem to global models in European Union. The hypothesis was that the SOC and TN data sampled in peatlands ecosystem can improve analytical quantification of those soil properties. The soil and spectral datasets were retrieved from the Land Use/Cover Area frame Statistical Survey with 21,771 observations at 0-20 cm depth and 63 soil cores in a degraded peatland in Germany with 262 observations up to 2 m depth. We evaluated three spectral pre-processing techniques with the Partial Least Square Regression (PLSR), Random Forest (RF), and Cubist machine learning algorithms. The best pre-processing technique was achieved applying Savitzky-Golay smoothing with a window size of 71 points, 2nd order polynomial, and zero derivative with Cubist algorithm for both SOC and TN predictions. Furthermore, merging the local with global data for global modelling demonstrated to improve SOC and TN predictions because of the local data representativeness and quality. Therefore, the SOC and TN data sampled in peatlands ecosystem can improve quantification of those soil properties in field and laboratory, which are crucial proxies for GHG emissions and climate change.

Tillage erosion causes substantial soil redistribution that can exceed water erosion especially in hummocky landscapes under highly mechanized large field agriculture. Consequently, truncated soil profiles can be found on hill shoulders and top slopes, whereas colluvial material is accumulated at footslopes, in depressions, and along downslope field borders. We tested the hypothesis that soil erosion substantially affects in-field patterns of the enhanced vegetation index (EVI) of different crop types on landscape scale. The interrelation between the EVI (RAPIDEYE satellite data; 5 m spatial resolution) as a proxy for crop biomass and modeled total soil erosion (tillage and water erosion modeled using SPEROS-C) was analyzed for the Quillow catchment (size: 196 km2) in Northeast Germany in a wet versus normal year for four crop types (winter wheat, maize, winter rapeseed, winter barley). Our findings clearly indicate that eroded areas had the lowest EVI values, while the highest EVI values were found in depositional areas. The differences in the EVI between erosional and depositional sites are more pronounced in the analyzed normal year. The net effect of total erosion on the EVI compared to areas without pronounced erosion or deposition ranged from −10.2% for maize in the normal year to +3.7% for winter barley in the wet year. Tillage erosion has been identified as an important driver of soil degradation affecting in-field crop biomass patterns in a hummocky ground moraine landscape. While soil erosion estimates are to be made, more attention should be given toward tillage erosion., Land Degradation & Development, 32 (10), ISSN:1085-3278, ISSN:1099-145X

Fusarium head blight (FHB) is a global problem in small-grains agriculture that results in yield losses and, more seriously, produces harmful toxins that enter the food chain. This study builds on previous research identifying within-field humidity as an important factor in infection processes by Fusarium species and its mycotoxin production. Environmental variables describing topographic control of humidity (TWI), soil texture and related moisture by electrical conductivity (ECa), and canopy humidity by density (NDVI) were explored in their relationship to the fungal infection rates, the abundance of trichothecene-producing Fusarium spp. as determined by TRI 6 gene copies and mycotoxin accumulation. Field studies were performed at four field sites in northeastern Germany in 2009 and 2011. In the wet year 2011, a high Fusarium infection rate resulted in a high abundance of trichothecene-producing fungi as well as high concentrations of mycotoxins. Simultaneously, Fusarium spp. inhibited the development of other filamentous fungi. Overall, a very heterogeneous distribution of pathogen infections and mycotoxin concentrations were displayed in each field in each landscape. The NDVI serves as an important predictor of the occurrence of phytopathogenic Fusarium fungi and their mycotoxins in a field and landscape scale. In addition, the ECa reflects the distribution of the most frequently occurring mycotoxin deoxynivalenol within the fields and landscapes. In all cases, TWI was not found to be a significant variable in the models. All in all, the results extend our knowledge about suitable indicators of FHB infection and mycotoxin production within the field.

The conversion of point observations to a geographic field is a necessary step in soil mapping. For pursuing goals of mapping soil carbon at the landscape scale, the relationships between sampling scale, representation of spatial variation, and accuracy of estimated error need to be considered. This study examines the spatial patterns and accuracy of predictions made by different spatial modelling methods on sample sets taken at two different scales. These spatial models are then tested on independent validation sets taken at three different scales. Each spatial modelling method produced similar, but unique, maps of soil organic carbon content (SOC%). Kriging approaches excelled at internal spatial prediction with more densely spaced sample points. Because kriging depends on spatial autocorrelation, kriging performance was naturally poor in areas of spatial extrapolation. In contrast, the spatial regression approaches tested could continue to perform well in spatial extrapolation areas. However, the problem of induction allowed the potential for problems in some areas, which was less predictable. This problem also existed for the kriging approaches. Spatial phenomena occurring between sampling points could also be missed by kriging models. Use of covariates with kriging can help, but the requirement of capturing the full feature space in the map remains. Methods that utilize spatial association, such as spatial regression, can map soil properties for landscape scales at a high resolution, but are highly dependent on the inclusion of the full attribute space in the calibration of the model and the availability of transferable covariates.

Applying a data mining tool used regularly in digital soil mapping, this research focuses on the optimal inclusion of predictors for soil–landscape modeling by utilizing as wide of a pool of variables as possible. Predictor variables for digital soil mapping are often chosen on the basis of data availability and the researcher's expert knowledge. Predictor variables commonly overlooked include alternative analysis scales for land-surface derivatives and additional remote sensing products. For this study, a pool of 412 potential predictors was assembled, which included qualitative location classes, elevation, land-surface derivatives (with a wide range of analysis scales), hydrologic indicators, as well as proximal and remote sensing (from multiple sources with a variety of resolutions). Subsets of the full pool were also examined for comparison. The performance for the models built from the different starting predictor pools was analyzed for seven target variables. Results suggest that models with limited predictor pools can substitute other predictors to compensate for the missing variables. However, a better performing model was always found by considering predictor variables at multiple scales. Compared with baseline subsets with the most commonly used predictors for digital soil mapping at a single scale, the use of multi-scale predictor variables produced an improvement in model performance ranging from negligible to a 70% increase in the adjusted R2. Although the scale effect of the modifiable area unit problem is generally well known, this study suggests digital soil mapping efforts would be enhanced by the greater consideration of predictor variables at multiple analysis scales.

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 since decades. However, while most research has been focused on phytogenic Si pools, knowledge on other BSi pools is still limited. We hypothesized different BSi pools to influence short-term changes of 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 Tiron extractable and water soluble Si fractions in soils at the beginning (t0) and after ten years (t10) of ecosystem development. As expected the results of Tiron extraction showed, that there are no consistent changes of the amorphous Si pool at Chicken Creek as early as after ten years. In contrast, compared to t0 we found increased water soluble Si and BSi pools at t10, thus we concluded BSi pools to be the main driver of short-term changes of water soluble Si. However, because total BSi represents only small proportions of water soluble Si at t0 (

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.

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.

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 25°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.

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.

Detailed information on soil textural heterogeneity is essential for land management and conservation. It is well known that in individual fields, measurement of the soil's apparent electrical conductivity (EC a ) offers an opportunity to map the clay content of soils with free drainage under a humid climate. At the catchment scale, however, units of different land management and differing sampling dates add variation to EC a and constrain the mapping across field boundaries. We analyzed their influence and compared three approaches for applying electromagnetic induction (EM v ) to clay-content mapping at the landscape scale across the boundaries of individual fields and different sampling dates. In the study region, a separate calibration of the relation between clay and EC a for each field and sampling date (fieldwise calibration) yielded satisfactory clay-content predictions only if the costly precondition of sufficient calibration points for each field was fulfilled. We propose a method (nearest-neighbors EC a correction) for unifying EC a across boundaries based only on the EC a data themselves, and the assumption of continuity of textural properties at field boundaries, which was fulfilled in the landscape studied. Prediction is calibrated once for the entire landscape, which allows a reduced set of calibration points. The coefficient of determination for predicting clay content (here, including silt

We analyzed a highly complex soilscape of fluvial sediments by a hierarchical expert system. Using (i) inquiries, (ii) relief analysis on basis of a DEM 5, and (iii) soils' apparent electrical conductivity (EM38) as a database, we first defined zones of identical pedogenic context. Next, multi-temporal remote sensing data of winter wheat were obtained by an airborne multi-spectral scanner (Daedalus-ATM), which gives radiometric information with a geometric (ground) resolution of 1 m2 (pixel size). Leaf area index (LAI) was semi-physically modeled using red and near-infrared canopy reflectances and related to above-ground biomass. Further, the resulting spatial patterns of vegetation parameters were processed by image analysis methods, i.e. an opening–closing procedure using a circular element with a radius of 5 m. These coarser patterns of LAI and biomass, respectively, were interpreted as patterns of site quality within each zone of pedogenic context. By our multi-temporal approach we were able to distinguish between stationary and time-variant pattern. Combined with point calibration on basis of a 50-m raster we identified available water capacity (AWC) and O2 deficiency due to stagnant water as the most important soil properties constituting site quality for plant growth. Our results will be used for precision agriculture practices in future.

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m−2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Publisher Summary Recent developments of methods such as remote sensing, terrain analysis on digital elevation models and geophysical measures (ground-penetrating radar, electromagnetic induction [EMI]) allow prediction of soil variability from field to landscape scale, the so-called digital soil mapping. This chapter presents the results of different noninvasive methods to detect soil patterns, focusing on the methodological aspects. Most methods are only proxies for relevant soil properties and soil types. Additional pedotransfer functions or soil inference systems are needed for data interpretation. In addition, most of the studies using noninvasive methods focus on single soil properties, such as water content, clay content or soil organic carbon. Interpretation of remote sensing data seems to be the most promising way to get information on soil (properties) because of its high spatial resolution and (potential) high areal coverage of agricultural landscapes. Soils are four-dimensional natural bodies with the key characteristic of variation in time and space. Different approaches have been developed to handle spatial variability of soils from field to regional scale. A functional analysis of the soilscape is needed to decipher the exact site-specific causes for differences in plant growth first, before any inference from canopy data to soils can be made. A hierarchy of soil forming factors generally explains most of the variability observed at landscape scale. Hierarchy in this sense means that a change in a factor on higher hierarchical level has a greater impact on soil pattern, regardless of differences in low-level factors.