Papers by Mogens Humlekrog Greve
GeoResJ, 2017
Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortuna... more Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117,000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of topdown and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1 km in 2014, followed by an update at a resolution of 250 m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications. Regardless of being national, continental and/or global, the following data rescue and grid map production steps are generally necessary, including references to GlobalSoilMap specific activities:
Soil mapping in Denmark has a long history and a series of soil maps based on conventional mappin... more Soil mapping in Denmark has a long history and a series of soil maps based on conventional mapping approaches have been produced. In this study, a national soil map of Denmark was constructed based on the FAO-Unesco Revised Legend 1990 using digital soil mapping techniques, existing soil profile observations and environmental data. This map was developed using soil-landscape models generated with a decision tree-based digital soil mapping technique. As input variables in the model, more than 1170 soil profile data and 17 environmental variables including geology, land use, landscape type, area of wetlands, digital elevation model and its derivatives were compiled. The predicted map showed that Podzols and Luvisols were the most frequent soil groups, covering almost two-thirds of the area of Denmark. Geographically, Podzols occupied a major portion of western Denmark, where the soils have developed on sandy parent material, whereas eastern Denmark mostly contained Luvisols developed on loamy basal till. The occurrence of the predicted soil groups was assigned using several variables, of the most important was clay content in the topsoil and subsoil, elevation, geology and landscape type. The overall prediction accuracy based on a 20% hold-back validation data was 60%, but increased to 76% when prediction accuracy of similar soil groups was considered. Podzoluvisols and Alisols were among the weakly predicted groups (b 48% prediction confidence), whereas Podzols and Luvisols had the highest accuracy of prediction (N 70%). Overall, the average prediction uncertainty was less than 34%. Compared to the existing conventional soil map, the new map showed promising predictions. Validation of the predicted map with different techniques (point validation, prediction confidence analysis, and map-to-map comparison) confirmed that the output is reliable and can be used in various soil and environmental studies without major difficulties. This study also verified the importance of GlobalSoilMap products and a priori pedological information that improved prediction performance and quality of the new FAO soil map of Denmark.
Recognition of magnetic anomalies in Ground Conductivity Meter soil surveys: a high-resolution field experiment
ABSTRACT
Soil pH influences a wide range of functionalities in soil system controlling ions mobility, solu... more Soil pH influences a wide range of functionalities in soil system controlling ions mobility, solubility and also microbial activities at extreme pH. So, a better land and crop-nutrient management plan needs detailed information on soil pH distribution especially in Denmark where 61% of total area is cultivated. Our research purpose is to investigate and visualize pH variability of Danish soils to 1m depth from the surface. Total 1950 profiles with pH data (1soil:5CaCl2) gathered from different sources (nation-wide 7km grid and other sources) were analyzed. Equal area splines were fitted to harmonize the pH depth function, and averaged for 0-5, 5-10, 10-20, 20-30, 30-50, 50-70 and 70-100cm depths and later on aggregated to 0-30 and 30-100cm to know the top and subsoil pH status. Rule-based regression method was applied to build prediction models on 75% training profiles and validated on the remaining profiles. The predictors used were elevation, slope, aspect, TWI, overland flow dist...
A Multievidence Approach for Crop Discrimination Using Multitemporal WorldView-2 Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
ABSTRACT Despite using multiple input datasets for effective crop classification, it is important... more ABSTRACT Despite using multiple input datasets for effective crop classification, it is important to select an appropriate method that efficiently integrates these multiple datasets to produce accurate classification results. In this paper, we present an endorsement theory-based crop classification approach that considers the qualitative information, in terms of prediction probabilities, from different input datasets and integrates them efficiently to produce final classification results. Three different input datasets are used in this study: 1) spectral; 2) texture; and 3) indices from multitemporal (spring, early summer) WorldView-2 multispectral imagery. A multilayer perceptron classifier is trained with the multitemporal datasets separately using a backpropagation learning algorithm, and prediction probabilities are produced for each pixel as evidence against each crop class. An integration rule based on endorsement theory is applied to these multiple evidence by considering their individual contribution, and the most probable class of a pixel is identified. Integration of the three multidate datasets by the proposed method is found to produce higher overall classification accuracy (91.2%) when compared to conventional winner-takes-all approach (89%). In order to determine which individual dataset is more useful for crop discrimination, the dataset's performance is compared using evidence and contributions produced in the proposed integration method for four selected crops, for both single- and multidate. The results of this analyses showed that seasonal textures information outperformed both spectral and indices. To verify this finding, results of individual dataset classification are examined. The highest overall classification accuracy of 88.8% is achieved by the use of multidate texture, where multidate spectral and indices resulted in 86.3% and 84.4%, respectively.
Accurate information about soil organic carbon (SOC), presented in a spatially form, is prerequis... more Accurate information about soil organic carbon (SOC), presented in a spatially form, is prerequisite for many land resources management applications (including climate change mitigation). This paper aims to investigate the potential of using geomorphometrical analysis and decision tree modeling to predict the geographic distribution of hydromorphic organic landscapes at unsampled area in Denmark. Nine primary (elevation, slope angle, slope aspect, plan curvature, profile curvature, tangent curvature, flow direction, flow accumulation, and specific catchment area) and one secondary (steady-state topographic wetness index) topographic parameters were generated from Digital Elevation Models (DEMs) acquired using airborne LIDAR (Light Detection and Ranging) systems. They were used along with existing digital data collected from other sources (soil type, geological substrate and landscape type) to statistically explain SOC field measurements in hydromorphic landscapes of the chosen Danish area. A large number of tree-based classification models (186) were developed using (1) all of the parameters, (2) the primary DEM-derived topographic (morpholog-HESSD
Water and solute transport in agricultural soils predicted by volumetric clay and silt contents
Journal of Contaminant Hydrology, 2016
Solute transport through the soil matrix is non-uniform and greatly affected by soil texture, soi... more Solute transport through the soil matrix is non-uniform and greatly affected by soil texture, soil structure, and macropore networks. Attempts have been made in previous studies to use infiltration experiments to identify the degree of preferential flow, but these attempts have often been based on small datasets or data collected from literature with differing initial and boundary conditions. This study examined the relationship between tracer breakthrough characteristics, soil hydraulic properties, and basic soil properties. From six agricultural fields in Denmark, 193 intact surface soil columns 20cm in height and 20cm in diameter were collected. The soils exhibited a wide range in texture, with clay and organic carbon (OC) contents ranging from 0.03 to 0.41 and 0.01 to 0.08kgkg(-1), respectively. All experiments were carried out under the same initial and boundary conditions using tritium as a conservative tracer. The breakthrough characteristics ranged from being near normally distributed to gradually skewed to the right along with an increase in the content of the mineral fines (particles ≤50μm). The results showed that the mineral fines content was strongly correlated to functional soil structure and the derived tracer breakthrough curves (BTCs), whereas the OC content appeared less important for the shape of the BTC. Organic carbon was believed to support the stability of the soil structure rather than the actual formation of macropores causing preferential flow. The arrival times of 5% and up to 50% of the tracer mass were found to be strongly correlated with volumetric fines content. Predicted tracer concentration breakthrough points as a function of time up to 50% of applied tracer mass could be well fitted to an analytical solution to the classical advection-dispersion equation. Both cumulative tracer mass and concentration as a function of time were well predicted from the simple inputs of bulk density, clay and silt contents, and applied tracer mass. The new concept seems promising as a platform towards more accurate proxy functions for dissolved contaminant transport in intact soil.
Geoderma, 2013
The purpose of this paper is to investigate the geographical distribution of pH values in Danish ... more The purpose of this paper is to investigate the geographical distribution of pH values in Danish soils of different ages representing the main Saalian and Weichselian ice advances. The investigation is based on soil sampling from top-and subsoils in soil profiles located in a nationwide 7-km grid. The data have been analysed using statistical spatial analysis methods, and a model has been erected demonstrating areas of homogeneous low, high, or inhomogeneous pH values relative to deposits from different ice advances and regional variations in land use. The investigation shows that the major part of Jutland is characterized by low pH values in the topsoils and subsoils compared to the islands east of the peninsula. This corresponds with the maximum extension of the Weichselian Young Baltic Ice Cap. A Hot Spot analysis carried out on regional and local scales shows that most of the Danish islands form a homogeneous area of high pH values except Northeast Zealand. And in Jutland a huge area east and north of the maximum extension of the Young Baltic Ice Cap formed a homogeneous area of low pH values. Exceptions are the areas around Mors in the western part of the Limfjord and in the eastern part of Himmerland and on Djursland.
Generating a Danish raster-based topsoil property map combining choropleth maps and point information
Geografisk Tidsskrift, 2007
Geografisk Tidsskrift, Danish Journal of Geography 107(2) 1 Denmark is characterized by an intens... more Geografisk Tidsskrift, Danish Journal of Geography 107(2) 1 Denmark is characterized by an intensive agricultural sy-stem with a large animal production. ... Geografisk Tidsskrift Danish Journal of Geography 107(2):1-12, 2007 Generating a Danish raster-based topsoil property ...
Soil–air phase characteristics: Response to texture, density, and land use in Greenland and Denmark
Soil Science Society of America Journal
Agronomy
It is vital for farmers to know if their land is suitable for the crops that they plan to grow. A... more It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies have systematically tested this assumption. We aimed to test the assumption for specialty crops in Denmark. First, we mapped suitability for 41 specialty crops using machine learning. Then, we compared the predicted land suitabilities with the mechanistic model ECOCROP (Ecological Crop Requirements). The results showed that there was little agreement between the suitabilities based on machine learning and ECOCROP. Therefore, we argue that the methods represent different phenomena, which we label as socioeconomic suitability and ecological suitability, respectively. In most cases, machine learning predicts socioeconomic suitability, but the am...
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Compression and rebound characteristics of agricultural sandy pasture soils from South Greenland
Geoderma
Downscaling digital soil maps using electromagnetic induction and aerial imagery
Coarse-resolution soil maps at regional to national extents are often inappropriate for mapping i... more Coarse-resolution soil maps at regional to national extents are often inappropriate for mapping intra-field variability. At the same time, sensor data, such as electromagnetic induction measurements and aerial imagery, can be highly useful for mapping soil properties that correlate with electrical conductivity or soil color. However, maps based on these data nearly always require calibration with local samples, as multiple factors can affect the sensor measurements. In this study, we present a method, which combines coarse-resolution, large extent soil maps with sensor data in order to improve predictions of soil properties. We test this method for predicting clay and soil organic matter contents at five agricultural fields located in Denmark. We test the method for one field at a time, using soil samples from the four other fields to predict soil properties. Results show that the method generally improves predictions over the predictions from the coarse-resolution maps, especially ...
Mapping soil phosphorus sorption capacity in four depths with uncertainty propagation
<p&amp... more <p>Phosphorus (P) is one of the most important plant nutrients, and farmers regularly apply P as mineral fertilizer and with animal manures. Typically, reactions with amorphous aluminum and iron oxides or carbonates retain P in the soil. However, if P additions exceed the soil’s ability to bind them, P may leach from soil to surface waters, where it causes eutrophication. The phosphorus sorption capacity (PSC) is thus an inherent soil property that, when related to bound P, can describe the P saturation of the soil. Detailed knowledge of the spatial distribution of the PSC is therefore important information for assessing the risk of P leaching from agricultural land.</p><p>In weakly acidic soils predominant in Denmark, the PSC depends mainly on the oxalate-extractable contents of aluminum and iron. In this study, we aimed to map PSC in four depth intervals (0 – 25; 25 – 50; 50 – 75; 75 – 100 cm) for Denmark using measurements of oxalate-extractable aluminum and iron from 1,623 locations.</p><p>We mapped both elements using quantile regression forests. Predictions of oxalate-extractable aluminum had a weighted RMSE of 13.9 mmol kg<sup>-1</sup>. For oxalate-extractable iron, weighted RMSE was 33.5 mmol kg<sup>-1</sup>.</p><p>We included depth as a covariate and therefore trained one model for each element. For each element in each depth interval, we predicted the mean prediction value as well as 100 quantiles ranging from 0.5% to 99.5% in 1% intervals. The maps had a 30.4 m resolution. We then calculated PSC by convoluting the prediction quantiles of the two elements, using every combination of quantiles, in order to obtain the prediction uncertainty for PSC.</p><p>Oxalate-extractable aluminum was roughly normal distributed, while oxalate-extractable iron had a large positive skew. The age and origin of the parent material had a large effect on oxalate-extractable aluminum, and soil-forming processes such as weathering and podzolization had clear effects on the distribution in depth. Meanwhile, organic matter, texture and wetland processes were the main factors affecting oxalate-extractable iron, so much so that they obscured any trends with depth.</p><p>The weighted RMSE of the predicted PSC was 19.1 mmol kg<sup>-1</sup>. PSC was highest in wetland areas and lowest in young upland deposits, such as aeolian deposits and the loamy Weichselian moraines of eastern Denmark. The sandy glaciofluvial plains and Saalian moraines of western Denmark had intermediate PSC. In most cases, PSC was highest in the top soil, but in the sandy soils of western Denmark, PSC was highest in the depth interval 25…
Agronomy
Predicting wheat yield is crucial due to the importance of wheat across the world. When modeling ... more Predicting wheat yield is crucial due to the importance of wheat across the world. When modeling yield, the difference between potential and actual yield consistently changes because of advances in technology. Considering historical yield potential would help determine spatiotemporal trends in agricultural development. Comparing current and historical yields in Denmark is possible because yield potential has been documented throughout history. However, the current national winter wheat yield map solely uses soil properties within the model. The aim of this study was to generate a new Danish winter wheat yield map and compare the results to historical yield potential. Utilizing random forest with soil, climate, and topography variables, a winter wheat yield map was generated from 876 field trials carried out from 1992 to 2018. The random forest model performed better than the model based only on soil. The updated national yield map was then compared to yield potential maps from 1688 ...
Decision tree algorithms, such as random forest, have become a widely adapted method for mapping ... more Decision tree algorithms, such as random forest, have become a widely adapted method for mapping soil properties in geographic space. However, implementing explicit spatial trends into these algorithms has proven problematic. Using x and y coordinates as covariates gives orthogonal artifacts in the maps, and alternative methods using distances as covariates can be inflexible and difficult to interpret. We propose instead the use of coordinates along several axes tilted at oblique angles to provide an easily interpretable method for obtaining a realistic prediction surface. We test the method on four spatial datasets and compare it to similar methods. The results show that the method provides accuracies better than or on par with the most reliable alternative methods, namely kriging and distance-based covariates. Furthermore, the proposed method is highly flexible, scalable and easily interpretable. This makes it a promising tool for mapping soil properties with complex spatial variation.
Soil Science Society of America Journal
The soil specific surface area (SSA) affects soil physical and chemical properties. Numerous stud... more The soil specific surface area (SSA) affects soil physical and chemical properties. Numerous studies applied visible near-infrared spectroscopy (Vis-NIRS) to estimate clay content (particles < 2 µm). Since SSA is better defined and more directly related to particle size distribution and mineralogy than clay content, predictions of SSA from Vis-NIRS are expected to be better than that for clay. Thus, the aims of this study were to (i) test the feasibility of using Vis-NIRS for SSA determination, (ii) compare the predictive ability of Partial Least Squares (PLS) model of SSA with that of clay, (iii) identify important wavelengths using interval Partial Least Squares (iPLS) regression, and to test if the application of iPLS improves the predictive ability of the models. A total of 550 soil samples with a wide range in SSA (3-437 m 2 g-1) and clay content (1-83%) was divided into a calibration and a validation set. The PLS models had similar predictive ability for SSA (ratio of performance to interquartile range, RPIQ = 1.7) and clay content (RPIQ = 1.6). utilizing iPLS led to only limited improvement in the prediction accuracy (RPIQ of 1.8 and 1.7 for SSA and clay content, respectively), yet decreased the number of relevant wavelengths and indicated a higher specificity of SSA over the broader spectral response of clay. The important wavelengths for SSA and clay predictions were indicative of the organo-mineral content and its interactions, including spectral response from not only iron oxides and minerals but also organic matter due to masking effect of the non-complexed organic carbon on the mineral phases of some of the soils. Abbreviations: EGME, ethylene glycol monoethyl ether; iPLS, interval partial least squares; OM, organic matter; PC, principal component; PCA, principal component analysis; PLS, partial least squares; RPIQ, ratio of performance to interquartile range; RMSEP, root mean squared error of prediction; SOC, soil organic carbon; SSA, soil specific surface area; vis-NIRS, visible near-infrared spectroscopy. T he soil specific surface area (SSA) is a basic soil property and indicates the surface area per unit mass of soil. It affects physical and chemical properties of the soil and is important for processes such as water retention and movement, ion exchange reactions, contaminant adsorption, microbial attachment, nutrient dynamics, soil aggregation, and irrigation management (Pennell, 2002). The SSA can vary greatly depending on the soil type and the method used for its determination. Different soils present differences in mineralogical and organic composition as well as in particle-size distribution resulting in large variations in the amount of available reactive surfaces. For instance, soils rich in clay and organic matter can have SSA of up to 8 × 10 5 m 2 kg-1 , whereas the SSA for sandy soils can be as low as 1 × 10 3 m 2 kg-1 (Pennell, 2002). Due to the importance of SSA, there is a worldwide interest and need for its accurate determination. Although SSA is measured routinely by scientists, agronomists, and engineers around the world, there is no common standard method available for its determination. The existing methods measure different surfaces of the
Estimating Soil Particle Density using Visible Near-infrared Spectroscopy and a Simple, Two-compartment Pedotransfer Function
Soil Science Society of America Journal
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Papers by Mogens Humlekrog Greve