Spatial soil information is fundamental for environmental modelling and land use management. Spatial representation (maps) of separate soil attributes (both laterally and vertically) and of soil-landscape processes are needed at a scale appropriate for environmental management. The challenge is to develop explicit, quantitative, and spatially realistic models of the soil-landscape continuum to be used as input in environmental models, such as hydrological, climate or vegetation productivity (crop models) while addressing the uncertainty in the soil layers and its impact in the environmental modelling. Modern advances in soil sensing, geospatial technologies, and spatial statistics are enabling exciting opportunities to efficiently create soil maps that are more consistent, detailed, and accurate than previous maps while providing information about the related uncertainty. The production of high-quality soil maps is a key issue because it enables stakeholders (e.g. farmers, planners, other scientists) to understand the variation of soils at the landscape, field, and sub-field scales. The products of digital soil mapping should be integrated within other environmental models for assessing and mapping soil functions to support sustainable management. Examples of implementation and use of digital soil maps in different disciplines such as agricultural (e.g. crops, food production) and environmental (e.g. element cycles, water, climate) modelling are welcomed. All presentations related to the tools of digital soil mapping, the philosophy and strategies of digital soil mapping at different scales and for different purposes are also welcome.
vPICO presentations: Thu, 29 Apr
Since the establishment of Digital Soil Mapping (DSM) as a research field, the main focus has been on implementing new methods to improve the predictive performance of soil maps. However, considerably less effort has been invested in investigating the best way to communicate the quality of soil mapping products with users. This is essential for soil maps to be adopted by a broader community, future research guidance and most importantly, to ensure that they are used correctly. We introduce a high-resolution 3D soil modelling and mapping platform for the Netherlands (BIS-3D) using a quantile regression forest (QRF) for spatial interpolation approach that includes an assessment of the map quality using GlobalSoilMap (GSM) accuracy thresholds. Our objectives are twofold: a) providing accurate and high-resolution (25m) soil pH, soil organic carbon, and soil texture (clay, silt, and sand) maps over 3D space including prediction uncertainty; and b) providing an intuitive way to communicate accuracy of soil maps for users by means of accuracy thresholds. In this work, the first outputs of the modelling and mapping platform BIS-3D are being presented.
QRF models were trained and validated, yielding average predictions for each target location and depth as well as the 90% prediction interval. Predicted soil maps were evaluated using an independent validation data set based on a stratified random sampling design covering the entire Netherlands (1151 locations). Furthermore, at every validation location, predictions were assessed as A, AA or AAA quality using the GSM specifications.
First results for soil pH (KCl) using 15887 soil observations between depths 0-2 m and 180 covariates reveal a mean square error skill score (SSmse) = 0.88, RMSE = 0.49 and bias = 0.01 for out of bag predictions. Model evaluation using the independent validation set resulted in SSmse = 0.66, RMSE = 0.81 and bias = 0.12 across all depths. Prediction accuracy was highest for depths between 0-15 cm (SSmse = 0.66, RMSE = 0.76) and 60-100 cm (SSmse = 0.69, RMSE = 0.78) and lowest for 100-200 cm (SSmse = 0.61, RMSE = 0.86). The soil measurement (observation) was within the 90% prediction interval of model predictions in 83% of the cases, indicating that QRF is slightly over-optimistic in quantifying the prediction uncertainty. 61% of predictions that were independently validated over all depths were within the highest GSM accuracy threshold (AAA = +/- 0.5 pH), 23% were AA (+/- 1.0 pH), 9% were A (+/- 1.5 pH) and the remaining 7% were below A. A categorical physical geography map was the most important covariate, although other covariates associated with relief, geomorphology, land use and temperature were also effective. However, such variable importance measurements are merely indications and should be handled with care. The BIS-3D can easily be extended for predicting additional soil properties and it may provide a basis for decision makers to easily assess to what extent and in which areas soil maps can be used for their applications.
How to cite: Helfenstein, A., Mulder, V. L., Heuvelink, G. B. M., and Okx, J.: BIS-3D: high resolution 3D soil maps for the Netherlands using accuracy thresholds, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7836, https://doi.org/10.5194/egusphere-egu21-7836, 2021.
‘Strategic objective 1’ of the United Nations Convention to Combat Desertification (UNCCD) aims to improve conditions of affected ecosystems, combat desertification/land degradation, promote sustainable land management, and contribute to land degradation neutrality. The indicator ‘Proportion of land that is degraded over total land area’ (SO1) is compiled from three sub-indicators: ‘Trends in land cover’ (SO1-1), ‘Trends in land productivity or functioning of the land’ (SO1-2), ‘Trends in carbon stocks above and below ground’ (SO1-3).
Soil organic carbon (SOC) stock can be adopted as the metric of SO1-3, until globally accepted methods for estimating the total terrestrial system carbon stocks will be elaborated. SOC can be considered as one of the most important properties of soil, which shows not just spatial but temporal variability. According to our previous results in the topic, UNCCD default data of SOC stock for Hungary is strongly recommended to be replaced with country specific estimation of SOC stock.
SOC stock maps were compiled in the framework of DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) initiative, predicted by proper digital soil mapping (DSM) method. Reference soil data were derived from a countrywide monitoring system. The selection of environmental covariates was based on the SCORPAN model. The elaborated SOC stock mapping methodology have two components: (1) point support modelling, where SOC stock is computed at the level of soil profile, and (2) spatial modelling (quantile regression forest), where spatial prediction and uncertainty quantification are carried out using the computed SOC stock values.
We analyzed how SOC stock changed between 1998 and 2016. Nationwide SOC stock predictions were compiled for the years 1998, 2010, 2013, and 2016. For the intermediate years, we do not recommend to calculate SOC stock values, because we have no information on the dynamics of change in the intervening years. Based on the 1998 SOC stock prediction, we compiled a SOC stock map for 2018, using only land use conversion factors, according to the default data conversion values.
According to the elaborated scheme during the respective period, significant changes cannot be detected, only tendentious SOC stock changes appear. Based on our results, we recommend to use spatially predicted layers for all years when data are available, rather than calculating SOC stock change based on land use conversion factors.
Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820) and by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390) (Gábor Szatmári).
How to cite: Laborczi, A., Szatmári, G., Mészáros, J., Koós, S., Pirkó, B., and Pásztor, L.: Supporting land degradation neutrality assessment by soil organic carbon stock mapping in Hungary, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4832, https://doi.org/10.5194/egusphere-egu21-4832, 2021.
The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change and soil degradation. Reflectance spectroscopy has proven to be promising technique for SOC quantification in the laboratory and increasingly from air and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales.
The PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an earth-observation satellite with a medium spatial resolution hyperspectral radiometer onboard, developed and maintained by the Italian Space Agency.
The Pan-European Land Use/ Land Cover Area Frame Survey (LUCAS) topsoil database contains soil physical, chemical and spectral data for most European countries. Based on the LUCAS points located in Hungary, a synthetized spectral dataset was created and matched to the spectral characteristic of PRISMA sensor, later used for building up machine learning based models (random forest, artificial neural network). SOC levels for the sample area was predicted using generated models and mainly PRISMA imagery.
Our sample imagery data was generated from five consecutive, cloud-free PRISMA images covering 4500 km2 in the central part of the Great Plain in Hungary, which is one of the most important agricultural areas of the country, used mainly for crops on arable lands. The images were recorded in 2020 February when most croplands are not covered by vegetation therefore our tests were implemented on bare soils.
We tested the prediction accuracy of hyperspectral imagery data supplemented by various environmental datasets as additional predictor variables in four scenarios: (i) using solely hyperspectral imagery data (ii) spectral imagery data, elevation and its derived parameters (e.g. slope, aspect, topographic wetness index etc.) (iii) spectral imagery data and land-use information and (iv) all aforementioned data in fusion.
For validation two types of datasets were used: (i) measured data at the observation sites of the Hungarian Soil Information and Monitoring System and (ii) the recently compiled national SOC maps., which provides a suitable and formerly tested spatial representation of the carbon stock of the Hungarian soils.
Acknowledgment: Our research was supported by the Cooperative Doctoral Programme for Doctoral Scholarships (1015642) and by the OTKA thematic research projects K-131820 and K-124290 of the Hungarian National Research, Development and Innovation Office and by the Scholarship of Human Resource Supporter (NTP-NFTÖ-20-B-0022). Our project carried out using PRISMA Products, © of the Italian Space Agency (ASI), delivered under an ASI License to use.
How to cite: Kovács, Z. A., Mészáros, J., Árvai, M., Laborczi, A., Szatmári, G., László, P., and Pásztor, L.: Testing PRISMA hyperspectral satellite imagery in predicting soil carbon content based on synthetized LUCAS spectral data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15450, https://doi.org/10.5194/egusphere-egu21-15450, 2021.
Soil organic carbon (SOC) is the largest reservoir of organic carbon in the terrestrial biosphere and is the main constituent of soil organic matter, which underpins key soil functions such as storage and filtration of water, and nutrient cycling. SOC concentrations are controlled by several dynamic variables, ranging from micro-scale properties like particle aggregation, to larger-scale drivers such as climate and land cover. Hence, soils are vulnerable to climate change and human disturbances, with implications for ecosystem services such as agriculture and global warming mitigation. Recent decades have seen greater efforts to monitor SOC dynamics, such as the UKCEH Countryside Survey, and to predict concentrations of SOC where we have no measurements, using geostatistics or machine learning approaches. Yet, there is still much to be understood about what controls spatial patterns of SOC, and how effectively different modelling approaches can capture this. Here, we compare predictions by nine maps of the spatial distribution of topsoil SOC in Great Britain. We found broad similarities in SOC concentrations predicted by all maps, which each showed right-skewed distributions with similar median values (43 to 97 g kg-1). The greatest differences between maps occur at higher latitudes and are reflected in the upper ends of the SOC distributions. While the maps generally exhibit a sharp rise in SOC concentrations with increasing latitude from ~54oN, values predicted by the ISRIC-2017 and FAO-GSOC maps show weaker increases with increasing latitude, and peak at lower values of 332 g kg-1 and 354 g kg-1, respectively. We demonstrate that most of the maps, regardless of the modelling approach taken or the underlying data used, produced similar estimates of SOC concentration, including broad spatial patterns. This work will form the basis of more detailed future assessments of the sensitivity of SOC mapping to analytical methods versus the data used to drive these methods, and will be used to assess the importance of using stratified random field survey approaches for generating more accurate predictions of areas that cannot be sampled. Exploration of why and where different and coincident SOC predictions occur between maps should shed light on the utility of different modelling techniques and machine-learning meta-analyses of driving variables currently used to map SOC. Understanding how SOC predictions differ across all current national scale GB maps is a first step in improving modelling and assessment of SOC stock and change.
How to cite: Feeney, C., Cosby, J., Robinson, D., Thomas, A., and Emmett, B.: A comparison of soil organic carbon concentration maps of Great Britain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4700, https://doi.org/10.5194/egusphere-egu21-4700, 2021.
Parent material is an essential soil property, whose mapping is a challenging task. Usually, large scale geological maps are used if they are available. However, in many cases, especially in medium and large scale mapping, such source data are too old or not existing at all. In this project have been looking for a solution for this problem. Our aim is to create a new, large scale, lithological map of parent material in an old mining region.
The study area is the Dorogi Basin in northern central Hungary. It is known for coal mining, which ended in 2003 after more than two centuries. The latest large scale (1:10,000) geological map series from this area was made in the 1960’s, in the “golden age” of mining.
Google Earth Engine was selected as main GIS platform, using mainly open source data and programs for mapping. We have used data originating from Earth Observation as ancillary information (e.g. satellite images, SRTM) and machine learning techniques to spatially predict parent material. The satellite images were used to calculate several geological indices, which can be used as indicators of chemical composition. We examined the use of multiple satellite platform (Sentinel-2, Landsat 8, ASTER) as it has different geological indices. The existing geological maps were used for training in the classification concerning the lithological composition.To predict the parent materials we have used random forest, using geomorphometric features and geological indices as predictors. The newly compiled map was validated by comparing it with the old one.
Acknowledgment: Our research was supported by the Hungarian National Research,Development and Innovation Office (NKFIH; K-131820) and from the part of G.A. financial support was provided from the NRDI Fund of Hungary, Thematic Excellence Programme no. TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.
How to cite: Takáts, T., Mészáros, J., Albert, G., and Pásztor, L.: Mapping parent material using data originating from Earth Observation as ancillary information, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4910, https://doi.org/10.5194/egusphere-egu21-4910, 2021.
Soil maps are an important source of data in monitoring natural resources and land use planning. However, in many countries, soil maps were prepared at a reconnaissance level. This detail is not enough for land use planning. Soil texture is one of the most important soil physical properties that affect water holding capacity, nutrient availability, and crop growth. The spatial distribution of soil texture at a high resolution is essential for crop planning and management. Digital soil mapping is the method of spatial data generation with the advantages of current technologies. It supplies fast, accurate, and reproducible results.
In this study, a soil texture map with 30 m spatial resolution was produced for an alluvial plain covering an area of approximately 10,000 ha. In the study, 11 Topographic Environmental Variables obtained from NASA's ASTER Global Digital Elevation model were used. Another input parameters were clay, silt, and sand values determined for 91 soil samples obtained through field studies.
R Core Environment (3.6.1) and related packages were used for environmental variable extraction, modeling, and spatial mapping. For model building, 70 % of data was used and the rest of the data was used for validation. Random Forest Algorithm offers interpretability for pedological information extraction by determining the importance of environmental variables in digital soil mapping. Random Forest Algorithm is preferred because of working in small data sets, harmoniously. The most important topographic environmental variables for clay were elevation, aspect, and slope. For sand, it was the elevation, aspect, and topographic wetness index. And for silt, it was the elevation, slope length, and planform curvature. Root Mean Square Error (RMSE), was used as a model performance measure. In the train data, R2 values for clay, sand and silt were 0.84, 0.75, 0.85 and RMSE values were 5.23 %, 3.03 %, 5.48 % respectively. In the test data, R2 and RMSE values were 0.26, 0.11, 0.10 and 11.8 %, 6.74 %, 13.71 % respectively.
There are high differences between RMSE values of training and test data sets. This event may be caused by the small sample size and to be discussed subject in different studies. High resolution (30 m) data of clay, silt, and sand contents can be useful for hydrological studies and for the preparation of land use plans. Digital soil maps can guide policymakers in creating site-specific land management plans. As well as it can be used for monitoring soil fertility and providing ecosystem services. This study revealed important results regarding the use of digital soil mapping in practice with its analytical and statistical accuracy.
How to cite: Kaya, F. and Başayiğit, L.: Digital Mapping of Soil Particle Size Distribution in an Alluvial Plain Using the Random Forest Algorithm, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7828, https://doi.org/10.5194/egusphere-egu21-7828, 2021.
High resolution, dynamic forest site classification is an innovative tool for decision making in forest management, in particular under the scope of climate change. For a high share of the Austrian forest area, forest soil/site maps are lacking, and, if available, they do not account for the fact that water, energy and nutrient supply may change over a forest rotation cycle. The project FORSITE aims at providing a dynamic site classification system for the Austrian province of Styria, covering 1 mio. hectares of forest area. High resolution maps of chemical and physical soil properties are a key requirement for describing water and nutrient supply, and for modelling scenarios of changing climatic conditions or the effects of management interventions. In order to provide the database for the creation of such maps, a stratified site description and soil sampling design was based on high resolution digital terrain models and lithological maps. The sampling included a detailed description of 1,800 soil pits down to a minimum of 80 cm depth or solid bedrock. Chemical and physical soil parameters (e.g. carbon content, grain size, bulk density, stone content) were determined for samples of the forest floor and up to five geometric horizons of 400 soil profiles. In addition, geologists developed a subsolum geological substrate (SGS) map describing the parent material for soil formation down to a depth of 150 cm. In the current presentation, we describe the steps of modelling maps which support the estimation of the water balance of forest sites. A first step was the development of pedotransfer-functions (PTFs) in order to upscale soil parameters like soil organic carbon content, bulk density, grain size distribution and plant available water storage capacity determined in the laboratory a. to the 1800 field sites and b. to a 10*10 m resolution grid for the whole of Styria. Subsequently, a number of published PTFs for Mualem van Genuchten values based on soil texture, bulk density and organic carbon content were compared to 100 water retention curves which were determined on a subset of the FORSITE soil profiles. These values are required for the parametrization of the lumped parameter hydrological model (Brook 90) which is used to characterize the water supply under present and future climatic conditions. The regionalisation of the single point measurements from the profiles was performed with a Neural Network. Spatial maps SGSs and derivatives of the Digital Elevation Model such as slope, elevation and curvature served as predictors. Information on SGS improves the predictions of soil properties in comparison to standard standard geological maps, because it describes in more detail the relevant layer between soil and bedrock. As Neural Networks were insufficient for describing waterlogging and groundwater influence, random forest models were applied to a dataset comprised of the ForSite profiles and 4,000 soil profiles from agricultural soil surveys in the region. The resulting high resolution maps of soil properties form the base for the hydrological characterisation of the sites and for the calculation of climate change scenarios.
How to cite: Gadermaier, J., Färber, V., Klebinder, K., and Katzensteiner, K.: High resolution soil hydrology maps as a decision tool for forest planning., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12775, https://doi.org/10.5194/egusphere-egu21-12775, 2021.
The accurate representation of soil properties in the land component of Earth system models (land surface models; LSMs) remains a persistent challenge. The emergence of state-of-the-art continental-scale digital soil mapping (DSM) provides a unique opportunity to address this weakness (e.g., SoilGrids and POLARIS). However, it remains unclear whether these data are able to improve the modeling of land surface fluxes and states (e.g., latent heat flux). This presentation addresses this question by running and evaluating a field-scale resolving land surface model (HydroBlocks) at each of the eddy covariance sites in the NEON and Ameriflux networks over the Contiguous United States (~250 sites). More explicitly, the HydroBlocks LSM is run at a 30-meter spatial resolution in 5 km boxes centered around each of the NEON eddy covariance sites using both the POLARIS and Soilgrids soil properties databases. The model is also run using the CONUS-Soil (i.e., STATSGO) soil properties database as a baseline for comparison. Each simulation is run between 2002 and 2018 at a 1-hour resolution. The remaining datasets used to parameterize and force HydroBlocks includes the Princeton Climate Forcing meteorological dataset (PCF), USGS elevation data, and the National Land Cover dataset (NLCD) with a 5-year spin-up period. The simulated soil moisture and land surface fluxes are then evaluated using available in-situ and eddy covariance measurements in the NEON and Ameriflux networks using a suite of performance metrics over multiple temporal scales.
How to cite: Xu, C., Rojas, L. T., and Chaney, N. W.: Evaluating the added-value of state-of-the-art soil property maps in land surface modeling over the Contiguous United States, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13918, https://doi.org/10.5194/egusphere-egu21-13918, 2021.
Soil has the most important role in agriculture. For instance, it prevents run off and also through its capacity for storing water, it acts as a water reservoir and provide water resources for plant roots. Water retention characteristics, nutrient holding capacities and solute transport of soil can affect its productivity. So, the plant growth is directly associated with the type of soil drainage. The prediction of soil drainage classes is one of the major steps in developing crop modelling. Among different physical and chemical soil health indicators, soil magnetic susceptibility (MS) is a promising factor for soil surveying because it is strongly affected by soil drainage class. The extremely reducing conditions, present in hydric soils, significantly enhance dissolution of soil ferrimagnetic minerals such as magnetite and maghemite. Since the MS of soils is mainly controlled by magnetite and maghemite concentrations, therefore MS values are typically very low in hydric, i.e. poorly drained or gleyed, soils.
The common method for measuring soil MS is utilizing handheld or laboratory MS meters (e.g. Bartington MS2 sensors). Such MS meters are required soil specimen to be available to directly measure MS of that specimen. So, their application is limited to surface soils, soil exposures and sampled soils. Other types of instruments for quickly measuring soil properties are electromagnetic induction (EMI) instruments. Although the EMI instruments were primarily invented to measure electrical conductivity (EC) of the topsoil for assessment of soil salinity, they can also be utilized to measure absolute value of the volume MS of the topsoil. These volume MS values can be further processed and inverted to reveal MS variations of soil layers.
In this study, 1-D inversion of volume MS data, measured by Geonics EM38 instrument in both vertical and horizontal magnetic dipole configurations, was done to calculate MS of selected soil profiles in order to delineate soil drainage classes. Besides, laboratory measurements of volume and mass-specific MS of soil core samples, collected in the same soil profiles, were done using Bartington MS2B and MS2C sensors. Results show a strong and positive relationship between MS values measured in the laboratory and volume MS recovered from inversion technique. Furthermore, the results reveal that MS in a well drained profile is higher than that of a poorly drained profile. Since EMI measurements of soil MS are done quickly in the field, then using surface MS measurements facilitates hydric soil delineation in a faster and more precise way.
How to cite: Shirzaditabar, F., Heck, R., and Catalano, M.: Delineation of soil drainage class by electromagnetically measurements of soil magnetic susceptibility, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9232, https://doi.org/10.5194/egusphere-egu21-9232, 2021.
Open peatlands are a recognizable feature in the boreal landscape that are commonly mapped from aerial photographs. However, wet soils also occur on tree covered peatlands and in the riparian zones of forest streams and surrounding lakes. Comparisons between field data and available maps show that only 36 % of wet soils in the boreal landscape are marked on maps, making them difficult to manage. Wet soils have lower bearing capacity than dry soils and are more susceptible to soil disturbance from land-use management with heavy machinery. Topographical modelling of wet area indices has been suggested as a solution to this problem and high-resolution digital elevation models (DEM) derived from airborne LiDAR are becoming accessible in many countries. However, most of these topographical methods relies on the user to define appropriate threshold values in order to define wet areas. Soil textures, topography and climatic differences make any application difficult on a large scale. This complex landscape variability can be captured by utilizing machine learners that uses automated data mining methods to discover patterns in large data sets. By using soil moisture data from 20 000 field plots from the National Forest Inventory of Sweden, we combined information from 24 indices and ancillary environmental features using a machine learning known as extreme gradient boosting. Extreme gradient boosting used the field data to learn how to classify soil moisture and delivered high performance compared to many traditional single algorithm methods. With this method we mapped soil moisture at 2 m spatial resolution across the Swedish forest landscape in five days using a workstation with 32 cores. This new map captured 79 % (kappa 0.69) of all wet soils compared to only 36 % (kappa 0.39) captured by current maps. In addition to capture open wetlands this new map also capture riparian zones and previously unmapped cryptic wetlands underneath the forest canopy. The new maps can, for example, be used to plan hydrologically adapted buffer zones, suggest machine free zones near streams and lakes in order to prevent rutting from forestry machines to reduce sediment, mercury and nutrient loads to downstream streams, lakes and sea.
How to cite: Lidberg, W., Larson, J., Paul, S., Laudon, H., and Ågren, A.: Using machine learning to generate high-resolution soil wetness maps for planning forest management, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2578, https://doi.org/10.5194/egusphere-egu21-2578, 2021.
To understand, model and predict landscape evolution, ecosystem services and hydrological processes the availability of detailed observation-based soil data is extremely valuable. Estonia has a national digitized soil map based on decades of Soviet era field mapping. It maps more than 750 000 soil units throughout Estonia at a scale of 1:10 000 - with 75% of mapped units smaller than 4.0 ha. However, due to the way it was recorded the data is not immediately useful for numerical modelling. We synthesized the EstSoil-EH dataset - more than 20 eco-hydrological variables on soil, topography and land use for Estonia as numerical and categorical values - using data fusion and machine learning.
As additional feature information we used a 5m DEM, the Estonian Topographic Database, and EU-SoilHydroGrids layers. For each soil unit, we analysed type, texture, and layer information from the originally recorded composite text-based soil information, which contains the actual texture class, classifiers for rock content, peat soils, distinct compositional layers, and their depths. Subsequently, we derived soil layering, clay, silt, and sand contents and coarse fragments of the soil layers. In addition, we aggregated and predicted physical variables related to water and carbon cycles (bulk density, hydraulic conductivity, organic carbon content, available water capacity). We validated our modelled data and achieved satisfying degrees of agreement depending on the variables type.
How to cite: Kmoch, A., Kanal, A., Astover, A., Kull, A., Virro, H., Helm, A., Pärtel, M., Ostonen, I., and Uuemaa, E.: EstSoil-EH - Developing a high-resolution eco-hydrological modelling parameters dataset for Estonia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-857, https://doi.org/10.5194/egusphere-egu21-857, 2021.
Soil water content is a key property for modelling the water balance in hydrological, eco-hydrological and agro-hydrological models. Currently available global maps of soil water retention are mostly based on pedotransfer functions applied to maps of other basic soil properties. We developed global maps of the volumetric water content at 10, 33 and 1500 kPa by direct mapping based on soil water content data derived from the WoSIS Soil Profile Database and covariates describing vegetation, terrain morphology, climate, geology and hydrology using the SoilGrids workflow. The preparation of the input soil data consisted of the verification of available volumetric water content data and conversion of gravimetric to volumetric data using measured and estimated bulk density. In total we had 9609, 41082 and 49224 soil water content observations at 10, 33 and 1500 kPa, respectively, and prepared around 200 covariates as candidate predictors. After covariates selection, model tuning and cross-validation and final model fitting for 3D spatial prediction, results were presented for the globe with uncertainty estimation. The results were also compared to other available global maps of water retention to evaluate differences between direct mapping against other types of approaches. Directly developing global maps of soil water content, with associated uncertainty, is a novel approach for this type of properties, and contributes to improving global soil data availability and quality.
How to cite: Turek, M. E., Heuvelink, G., Batjes, N., and Poggio, L.: Global mapping of volumetric water content at 10, 33 and 1500 kPa using the WoSIS global database, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15548, https://doi.org/10.5194/egusphere-egu21-15548, 2021.
The response of soils to different human forcings may vary among soil classes (in magnitude and direction of change) depending on their resistance and resilience. We propose a modelling framework for mapping soil-class specific references (i.e., genosoils) and their variants (i.e., phenosoils) that can be used for assessing changes in soil condition due to land use change and management practices. The methodology consists of a first step that creates groups characterized by homogeneous soil-forming factors for a given reference time, under the hypothesis that these groups represent soil classes resulting from multimillennial natural pedogenesis and historic anthropedogenesis (i.e., soil formation processes modified by human activities) (i.e., pedogenons). In this study we applied the methodology to New South Wales (Australia) at the time of the European settlement, because from 1788 onwards the intensification of land use may have accelerated the rate of change of soil properties. A thousand pedogenon classes were generated applying k-means clustering to a set of quantitative state variables that represent the soil-forming factors at the time of the European settlement. Hierarchical clustering was applied to the centroids of the pedogenon classes for assessing their similarities and organization. In a second step, information on native vegetation extent, status (cleared or intact), and current land use was combined for creating a categorical map distinguishing areas with different expected degree of human-induced soil change. The combination of both maps resulted in 5448 subclasses, ranging from remnant genosoils (located in protected areas of intact native vegetation), genosoils II, cleared, grazing and cropping phenosoils. For each pedogenon there was at least a 90-m grid cell classified as a remnant genosoil. The median of the proportion of the pedogenon of origin preserved as a remnant genosoil was 5.3%. Phenosoils grazing and cropping occupied larger areas, with mean values of 73 km2 and 153 km2 respectively. Finally, we tested differences in topsoil pH, as proxy for soil condition, by genosoil and phenosoil classes using legacy soil data accessed with the Soil Data Federator from the Terrestrial Ecosystem Research Network. A gls model indicated that the effects of pedogenon, genosoil/phenosoil and their interaction were statistically significant (p < 0.001). Paired mean comparisons suggested that mean pH did not differ between remnant genosoils and genosoil II, but the mean pH of both genosoil classes differed from phenosoils. Estimated pH means did not differ between phenosoil classes, although it followed the trend remnant genosoil < genosoil II < phenosoil cleared < phenosoil grazing < phenosoil cropping. The proposed methodology has several potential applications, including soil security and soil change assessment, and designing soil monitoring surveys.
How to cite: Roman Dobarco, M., McBratney, A., Minasny, B., and Malone, B.: Digital pedogenon mapping as basis for assessing changes in soil condition, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10460, https://doi.org/10.5194/egusphere-egu21-10460, 2021.
Due to high rate of nutrient removal by cotton plants, the productive cotton-growing soils of Australia is becoming depleted of exchangeable (exch.) cations. For long-term development, data on exch. calcium (Ca), magnesium (Mg), potassium (K) and sodium (Na) throughout the soil profile is required. However, traditional laboratory analysis is tedious. The visible-near-infrared (Vis-NIR) spectroscopy is an alternative; whereby, spectral libraries are built which couple soil data and Vis-NIR spectra using models. While various models have been used to predict exch. cations, their performance was seldom systematically compared. Moreover, most previous studies have focused on prediction of topsoil (0–0.3 m) exch. cations while the effects of depth on applicability of topsoil spectral libraries are rarely investigated. Our first aim was to determine which model (i.e. partial least squares regression (PLSR), Cubist, random forest (RF), or support vector machine regression (SVMR)) produces the best prediction of topsoil exch. Ca, Mg, K and Na. The second aim was to evaluate if the best topsoil model can be used to predict subsurface (0.3–0.6 m) and subsoil (0.9–1.2 m) cations. The third aim was to explore the effect of spiking on the prediction in subsurface and subsoil. The fourth aim was to see if combining all depths to build a profile spectral library improved prediction. Based on independent validation, PLSR was superior for topsoil exch. cations prediction, while Cubist outperformed PLSR in some cases when spiking was applied, and the profile spectral library was considered. Topsoil PLSR could be applied to predict exch. Ca and Mg in the subsurface and subsoil, while spiking improved prediction. Moreover, a profile spectral library achieved equivalent results with when topsoil samples coupled with spiking were considered. We, therefore, recommended to predict exch. Ca and Mg throughout the profile using topsoil spectral library coupled with spiking approach.
How to cite: Zhao, D., Arshad, M., Wang, J., and Triantafilis, J.: Soil exchangeable cations estimation using Vis-NIR spectroscopy in different depths: Effects of multiple calibration models and spiking , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13575, https://doi.org/10.5194/egusphere-egu21-13575, 2021.
The EU Water Framework Directive (WFD) aims to target prevalent poor water quality status. Of the various contributing sources agriculture is particularly important due to the high loading rates of sediment and nutrient losses associated with fertilisation, sowing, and cropping regimes. Understanding soil nutrient status and the potential pathways for nutrient loss either through point or diffuse sources is an important step to improve water quality from an agricultural perspective. Research has demonstrated extensive in-field variability in soil nutrient status. A sampling regime that explores this variability at a sub-field scale is necessary. Traditional soil sampling consists of taking 20-30 cores per field in a W-shaped formation to produce a single bulked core, however, it generally fails to locate nutrient hotspots at finer resolutions. Inappropriate generalised fertilisation and management recommendations can be made in which nutrient hotspots or deficient zones are overlooked. Gridded soil sampling can reveal the full degree of in-field variability in nutrient status to inform more precise and site-specific nutrient applications. High soil phosphorus levels and the concept of legacy nutrient accumulation due to long-term over-application of phosphorus fertiliser in addition to animal slurry is a problem across the island of Ireland.
This research aims to locate and quantify the presence of soil nutrient hotspots at several field-scale locations in the cross-border Blackwater catchment in Northern Ireland / Republic of Ireland. Based on 35 m sampling grids, the nutrient content at unsampled locations in each field was determined using GIS interpolation techniques. Particular attention was paid to phosphorus, given its role in eutrophication. Gridded soil sampling enables the identification of nutrient hotspots within fields and when combined with an analysis of their location in relation to in-field landscape characteristics and knowledge of current management regimes, the risk of nutrient or sediment loss potential may be defined. This research concluded that traditional W soil sampling of producing one average value per field is not appropriate to uncover the degree of spatial variability in nutrient status and is inappropriate for catchment management of agricultural systems for controlling nutrient losses. Soil sampling at multiple locations per field is deemed to be cost-prohibitive for many farmers. However, sub-field scale soil sampling and appropriate geostatistical interpolation techniques can reveal the degree of variability and suggest an appropriate resolution for field-scale nutrient management that may be necessary to achieve measurable improvements in water quality.
How to cite: Hayes, E., Higgins, S., Mullan, D., and Geris, J.: Combining sub-field scale soil sampling and GIS interpolation techniques for mapping nutrient hotspots , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2653, https://doi.org/10.5194/egusphere-egu21-2653, 2021.
The digital soil mapping (DSM) approach predicts soil characteristics based on the relationship between soil observations and related covariates using machine learning (ML) models. In this research, we applied a wide range of machine learning models (12 base learners) to predict and map soil characteristics. To enhance accuracy and interpretability we combined the base learner predictions using super learning strategy. However, a major problem of using super learning and complex models is that the explicit share of individual covariates persons in the overall result cannot be explicitly quantified. To overcome this restriction and make the super learning models interpretable, we employed model-agnostic interpretation tools, for example, permutation feature importance. Particularly, we integrated the weight assigned to each ML base learner obtained by super learning and the ranked ML base learner’s covariates obtained by permutation feature importance to explore the contribution of covariates on the final prediction. We tested our super learning and permutation feature importance techniques to predict and mapping physicochemical soil characteristics of Urmia Playa Lake (UPL) sediments in Iran. As expected, our results indicated that super leaning could significantly improve the ML accuracies for predicting soil characteristics of single base learners. In terms of root mean square error, super learning improved over the performance of the linear regression by an average of 45.7%. Furthermore, the permutation feature importance allowed us to interpret our results better and prove the significant contribution of geomorphological features and groundwater data in predicting soil characteristics of UPL sediments.
How to cite: Taghizadeh-Mehrjardi, R., Hamzehpour, N., Hassanzadeh, M., Schmidt, K., and Scholten, T.: Enhancing accuracy and interpretability of machine learning models using super learning and permutation feature importance techniques in digital soil mapping , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9382, https://doi.org/10.5194/egusphere-egu21-9382, 2021.
Knowing our soils well, is the base for a sound land use management, and thus for a worldwide sustainable food production and safe drinking water supply. Especially in countries of the Global South, high quality digital information on soil properties on regional level are rare. While conventional soil inventories are time consuming, digital mapping of soil properties is a promising approach to close the gap more quickly. For this purpose, a reliable method is developed within the BGR project “ReCharBo” (Regional Characterisation of Soil Properties) to minimize field and laboratory work by combining remote sensing techniques like hyperspectral and thermal analyses as well as geophysical methods (e.g. gamma spectrometry) with conventional soil survey from different scales. At local and field-scale the data acquisition is done by drones, portable equipment and soil sampling, complemented at regional level by helicopter and satellite supported methods. In a corresponding talk in the same session Mommertz et al. (2021) give a detailed technical overview of the selected methods and the research concept of the project. To deploy the method including the concept of ground-truthing on arable land, areas in Germany were selected from Soil Maps of Germany at scale 1:1.000.000 (BÜK1000), 1:200.000 (BÜK200) and 1:50.000 (BK50) depending on representative soil types and region. In a first attempt, the research concept was carried out with simultaneous field and air borne analyses at two sites in autumn 2020. The results of this first attempt will be presented at the conference.
How to cite: Konen, L., Mommertz, R., Rückamp, D., Ibs-von Seht, M., and Möller, A.: Regional Characterisation of soil properties by combining remote sensing, geophysical and pedological methods, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4596, https://doi.org/10.5194/egusphere-egu21-4596, 2021.
Due to certain socio-economic processes and technical pressure, the number of potential data sources targeting the Earth’s surface increases rapidly as well as the data generated by them. Soil mapping heavily relied on these changes in the paradigm shift, which took place in the population and interpretation of spatial soil information in the last decade. In digital soil mapping practice, auxiliary, environmental co-variables, which are related to soil forming factors and processes, have been taken into account in spatially exhaustive form. However, the potential hidden in spatially non-exhaustive (most frequently point-like), ancillary information – originating from observations also targeting the soil mantle – is far from being exploited. In their thematic features, accuracy and reliability they are inferior to primary field and/or laboratory measurements collected directly, but they are generated in more facile, cheaper way, in greater volume, with denser temporal and spatial coverage and characteristically they are available in significantly easier form. Data sequences of various installed field sensors, data collections by proximal sensing techniques, information supply by farmers and land managers as well as citizen science are considered as possible information sources. Essentially, the (soft) data supplied by them don’t provide spatially exhaustive coverage, neither direct pedological reference, nevertheless they are hypothesized to be utilized as auxiliary information within DSM framework. In a recently started project we started to investigate, (i) in which way and with what efficiency these ancillary information originating from different secondary sources can be applied, furthermore (ii) in what manner their application influences (hopefully improves) the results, accuracy and reliability of goal-specific spatial predictions. The elaborated digital mapping procedures, which are based on (i) large amount of data with differing quality and (ii) integrated geostatistical and data mining methods can be absorbed in various earth and environmental science applications.
Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820) and Gábor Szatmári by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390).
How to cite: Pásztor, L., Szatmári, G., Laborczi, A., Mészáros, J., Takáts, T., Kovács, Z., Árvai, M., László, P., Koós, S., and Benő, A.: Application of information originating from spatially non-exhaustive ancillary observations in digital soil mapping, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5116, https://doi.org/10.5194/egusphere-egu21-5116, 2021.
High quality global soil maps are crucial to face several challenges such as reducing soil erosion, climate change adaptation and mitigation, ensuring food and water security, and biodiversity conservation planning. To obtain accurate and robust soil properties maps, research and development are necessary to identify the most appropriate prediction models and to develop efficient and robust workflows. A few recent studies used Artificial Neural Networks (ANN) in Digital Soil Mapping, in some cases improving the accuracy of the predicted maps compared to other methods like Random Forest (RF). In this study we tested different ANN architectures on a global top-soil dataset of ca. 110 000 samples, comparing the results for the different architectures with the more traditional approach of RF. The target variables considered are pH, Soil Organic Carbon, Sand, Silt, and Clay. We selected 40 environmental covariates from a pool of over 400 to represent the most important soil forming factors. We tried simpler architectures (single input – single target) using point observations for one target variable with corresponding raster cell values for spatially explicit environmental covariates. We also used more complex architectures (multi input - multi target) incorporating contextual information surrounding an observation (convolutional) and with multiple target variables. Preliminary results show that increasing the number of hidden layers in the neural network does not significantly influence the results, while changing the type of architecture can play a bigger role in the overall accuracy of the model. The overall prediction accuracy of the ANN was comparable with the RF model. We conclude that ANN are a promising, relatively new, approach for Global Digital Soil Mapping and that further research is needed to improve performance.
How to cite: Genova, G., de Sousa, L., Mimmo, T., Borruso, L., and Poggio, L.: Global soil mapping with Neural Networks , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15379, https://doi.org/10.5194/egusphere-egu21-15379, 2021.
Cropmarks are a major factor in the effectiveness of traditional aerial archaeology. The positive and negative features shown up by cropmarks are the role of the different cultivated plants and the importance of precipitation and other elements of the physical environment. In co-operation with the experts of the Eötvös Loránd University a new research was initiated to compare the pedological features of cropmark plots (CMP) and non-cropmark plots (nCMP) in order to identify demonstrable differences between them. For this purpose, the spatial soil information on primary soil properties provided by DOSoReMI.hu was employed. To compensate for the inherent vagueness of spatial predictions, together with the fact that the definition of CMPs and nCMPs is somewhat indefinite, the comparisons were carried out using data-driven, statistical approaches. In the first round three pilot areas were investigated, where Chernozem and Meadow type soils proved to be correlated with the formation of cropmarks. Kolmogorov-Smirnov tests and Random Forest models showed a different relative predominance of pedological variables in each study area. The geomorphological differences between the study areas explain these variations satisfactorily. In the next round, the identified relationships between cropmarking and soil features are planned to be utilized in the spatial inference of soil properties, where crop-marking sites will represent a unique, spatially non-exhaustive auxiliary information.
How to cite: Árvai, M., Czajlik, Z., Mészáros, J., Nagy, B., and Pásztor, L.: Cropmarks used in aerial archaeology as special spatial indicators of soil features potentially applicable in soil mapping, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5204, https://doi.org/10.5194/egusphere-egu21-5204, 2021.
Recently, FAO and Global Soil Partnership (GSP) launched the Global Map of Salt-affected Soils (GSSmap) international initiative, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The objective of our study is to present how Hungary contributed to this international initiative by preparing its own SAS maps according to the GSSmap specifications. For this purpose, we used not just a combination of advanced machine learning and multivariate geostatistical techniques for predicting the spatial distribution of the selected SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and for the subsoil (30–100 cm), but also a number of image indices exploiting a huge amount of relevant information contained in Sentinel-2 satellite images. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling of SAS indicators was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier.
Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820 and K-124290) and by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390) (Gábor Szatmári).
How to cite: Szatmári, G., Bakacsi, Z., Laborczi, A., Petrik, O., Pataki, R., Tóth, T., and Pásztor, L.: Elaborating Hungarian segment of the Global Map of Salt-Affected Soils (GSSmap), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-902, https://doi.org/10.5194/egusphere-egu21-902, 2021.
Parent material is widely recognised as an important factor for soil formation. Thus, quantitative information on the lithogenetic, geochemical, and physical characteristics of the subsolum geological substrates (SSGS) are essential input parameters for digital soil mapping (DSM). Forming the interface between bedrock – the domain of geologists, and soil – the domain of soil scientists, spatial information on SSGS is however scarce. Recognising these shortcomings, a novel geochemical-physical classification system for subsolum geological substrates has been developed, in order to support DSM at a regional scale. The units of the classification system reflect the properties of the SSGS also considering multilayering structure of quaternary deposits. The basis for the classification are mineral component groups, namely dolomite, calcite, and felsic, mafic, and clay minerals. In order to test the relevance of SSGS for the prediction of spatially continuous physical and chemical soil properties, Generalized Additive Models (GAMs) were applied to the forested area of Tyrol, Austria. The plant-available water storage capacity, as a physical soil property, was predicted with r² = 0.56. The Ellenberg´s mean soil reaction indicator value for vegetation turned out to be a suitable proxy for soil pH value and was predicted with r² = 0.75. Topography and associated morphometric terrain features are formative characteristics of mountain areas and, due to its various effects on redistribution processes as well as on water and energy budget of forest sites, are considered as the most essential soil forming factors. Thus, variables derived from digital terrain models, which are available in high spatial resolution, are assumed to be one of the most important predictors for digital soil mapping. In our study we could show however, that SSGS information is the most relevant predictor for both investigated soil properties. In the plant-available water storage capacity model, the predictor variables related to SSGS account for around 76% of the variance explained. Accordingly, a special focus should be placed on the predictive relevance of parent material and the frequently unlocked potential of quantitative geological substrate information. Thus, the newly developed subsolum geological substrate information could stimulate further developments in digital soil mapping, especially in mountain environments.
How to cite: Simon, A., Wilhelmy, M., Klosterhuber, R., Geitner, C., and Katzensteiner, K.: On the role of parent material for predictive mapping of soil properties in mountain forests , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9374, https://doi.org/10.5194/egusphere-egu21-9374, 2021.
Municipal solid waste (MSM) has been increasingly difficult to deal with, especially for cities of developing countries. In these cities, the increase in waste generation leads to open dumping and the development of landfills without the consideration of environmental assessment and monitoring, which may result in environmental disturbance and risk to human health. Therefore, the main goal of this study was to access the adequacy of the placement of new landfills for Maputo city, Mozambique. The study used the geographic information system (GIS) based on a multi-criteria decision approach that combined environmental, social, and technical variables to aid in the assessment of potential landfill sites. Results indicate that approximately 50% of the area is suitable for landfill placement. A further on-site evaluation is important to validate the obtained results, nonetheless, this preliminary site selection can be integrated into the MSW landfill selection to optimize waste management.
How to cite: Langa, C., Wang, J., Nakamura, K., Watanabe, N., and Takeshi, K.: Geographic information system and multi-criteria decision analysis as an assessment method for landfill site selection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10769, https://doi.org/10.5194/egusphere-egu21-10769, 2021.
Novel estimations of burn severity consequences are relevant to improve the understanding of spatial ecosystem dynamics between soil and vegetation. In this study, we implemented digital soil mapping (DSM) with Random Forest (RF) and generalized additive model (GAM) as internal statistical models, to generate maps for spatial prediction of chemical parameters of post-fire litter (N, P, C and OM) in the Purapel River basin, Maule region of Chile. Response variables were the chemical characterization of 67 samples of litter collected in different hillslopes of the basin during the first post-fire winter. The predictive variables that fed the RF model were spectral, topographic, and vegetation structure derivations, obtained from free and private use satellite products (Sentinel 1, Sentinel 2, LiDAR and TanDEM-X). As a result, we generated maps of post-fire spatial distribution of N, P, C and OM with acceptable adjustment (R2 0.52-0.61, nRMSE 54-72, pbias 0.35-1.20). The uncertainty associated with the predictions of these variables was successfully evaluated with the prediction interval coverage probability (PICP). A clear decrease on the concentration of litter elements is observed respect to the degree of burn severity, and this relationship depends on the type of cover and the environmental gradient where they are distributed.
How to cite: Banda Carrasco, D., Tolorza, V., and Galleguillos, M.: Spatial prediction of soil parameters after wildfire and their relationship with ecological process of soil and vegetation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13726, https://doi.org/10.5194/egusphere-egu21-13726, 2021.
We are sorry, but presentations are only available for users who registered for the conference. Thank you.
We are sorry, but presentations are only available for users who registered for the conference. Thank you.