Soil Map Data Research Articles

How geoecological components of a terroir can be altered by spatial changes of vineyards – A case study from Eger Wine District (Hungary)

Terroir is a concept referring interactions of natural (topography, lithology, climate, soil etc.) and human (economic conditions, traditions, cultivation practices, etc.) factors; therefore, terroir is spatially delimited and subjected to environmental, socio-economic, and temporal changes. The geoecological background of wine districts are considered more stable among them, but, because of its natural diversity and the spatial changes of production sites, changes in abiotic terroir components might occur too. In this study the spatial changes of grape production sites in Eger Wine District (Hungary) across two and a half centuries (1784 to 2018), and their consequences on the composition of the geoecological factors (lithology, topography, soil characteristics) were analyzed. Modernization of cultivation, urbanization and increase of built-up areas around the central settlement resulted in decreased concentration, i.e. increased spatial dispersion to more remote vineyards further from Eger. It also has consequences on the lithological and topographical composition of the production sites. Besides the slightly increasing extent of vineyards (from 5346 ha to 7413 ha) we found a distinct decrease of vineyards at higher elevations and a substantial increase at lower elevations. Distribution according to slope gradient changed also remarkably, with the share of vineyards on <5 % slopes from 38 % to 65 %. These changes resulted in transformations of pedological characteristics according to the comparison of vineyard’s extent with soil map data: vineyards shifted to slightly acidic, more fertile (i.e. deeper soil layer with higher organic carbon content) soils. The share of vineyards with different lithology and parent material also changed: loose, calcareous Tertiary sediments decreased almost to half, and the share of vineyards over acidic volcanics and their weathered regoliths almost doubled. Comparing these two dominant lithological types and soil profiles derived from them, different pedological characters and taxonomic status were found (Phaeozems and Vertisols). However, comparison of these two lithological types based on main topsoil characteristics (pH, SOC, carbonates, depth of fertile soil layer, N, P, K content) according to 25 randomly chosen surficial soil samples at production sites, showed no significant differences.. In the case of this particular wine district, spatial changes of the production sites affected mostly the distribution by elevation, by slope gradient, but did not alter significantly the surface soil character of the terroir.

A new approach for modeling stand height development of German forests under climate change

IntroductionWe developed a new approach for site index curve models that combines longitudinal height development patterns derived from state-space data with the broad environmental conditions covered by space-for-time data.MethodsFor this, we gathered dendrometry from both inventories and research plots. Concerning environmental variables, we included soil mapping data as well as atmospheric data, i.e., precipitation, temperature, and nitrogen deposition. The atmospheric data was included as a weighted mean over the stand life of the sums for the dynamically determined vegetation period or as yearly sums in the case of nitrogen deposition, respectively. As a weight, the values of a height increment function were used. Then, we derived the basic shape of a height development curve from research plot data and transferred said shape to a site index curve model.ResultsThe model represents a substantial advancement of a previous version and was fitted as a generalized additive model (GAM). All effects were of relevant size and showed biologically feasible patterns.DiscussionThough the model is biased for young ages, we could predict site index curves that, under constant environmental conditions, closely follow yield table curves and thus accurately depict stand height development. Moreover, the model does not require initial dendrometry, which broadens its applicability. Thus, the model represents a useful tool for forest management and planning under climate change.

GIS-based hydrodynamic modeling for urban flood mitigation in fast-growing regions: a case study of Erbil, Kurdistan Region of Iraq

Floods threaten urban infrastructure, especially in residential neighborhoods and fast-growing regions. Flood hydrodynamic modeling helps identify flood-prone locations and improve mitigation plans' resilience. Urban floods pose special issues due to changing land cover and a lack of raw data. Using a GIS-based modeling interface, input files for the hydrodynamic model were developed. The physical basin's properties were identified using soil map data, Land Use Land Cover (LULC) maps, and a Digital Elevation Model (DEM). So, the HEC-RAS 2-D hydrodynamic model was developed to estimate flood susceptibility and vulnerability in Erbil, Iraq. The case study examines the quality of flood modeling results using different DEM precisions. Faced with the difficulty, this study examines two building representation techniques: Building Block (BB) and Building Resistance (BR). The work presented here reveals that it is possible to apply the BR technique within the HEC-RAS 2-D to create urban flood models for regions that have a lack of data or poor data quality. Indeed, the findings confirmed that the inundated areas or areas where water accumulated in past rainfall events in Erbil are the same as those identified in the numerical simulations. The study's results indicate that the Erbil city is susceptible to flood hazards, especially in areas with low-lying topography and substantial precipitation. The study's conclusions can be utilized to plan and develop flood control structures, since it identified flood-prone areas of the city.

Accuracy of regional-to-global soil maps for on-farm decision-making: are soil maps “good enough”?

Abstract. A major obstacle to selecting the most appropriate crops and closing the yield gap in many areas of the world is a lack of site-specific soil information. Accurate information on soil properties is critical for identifying soil limitations and the management practices needed to improve crop yields. However, acquiring accurate soil information is often difficult due to the high spatial and temporal variability of soil properties at fine scales and the cost and inaccessibility of laboratory-based soil analyses. With recent advancements in predictive soil mapping, there is a growing expectation that soil map predictions can provide much of the information needed to inform soil management. Yet, it is unclear how accurate current soil map predictions are at scales relevant to management. The main objective of this study was to address this issue by evaluating the site-specific accuracy of regional-to-global soil maps, using Ghana as a test case. Four web-based soil maps of Ghana were evaluated using a dataset of 6514 soil profile descriptions collected on smallholder farms using the LandPKS mobile application. Results from this study revealed that publicly available soil maps in Ghana lack the needed accuracy (i.e., correct identification of soil limitations) to reliably inform soil management decisions at the 1–2 ha scale common to smallholders. Standard measures of map accuracy for soil texture class and rock fragment class predictions showed that all soil maps had similar performance in estimating the correct property class. Overall soil texture class accuracies ranged from 8 %–14 % but could be as high as 38 %–64 % after accounting for uncertainty in the evaluation dataset. Soil rock fragment class accuracies ranged from 26 %–29 %. However, despite these similar overall accuracies, there were substantial differences in soil property predictions among the four maps, highlighting that soil map errors are not uniform between maps. To better understand the functional implications of these soil property differences, we used a modified version of the FAO Global Agro-Ecological Zone (GAEZ) soil suitability modeling framework to derive soil suitability ratings for each soil data source. Using a low-input, rain-fed, maize production scenario, we evaluated the functional accuracy of map-based soil property estimates. This analysis showed that soil map data significantly overestimated crop suitability for over 65 % of study sites, potentially leading to ineffective agronomic investments by farmers, including cash-constrained smallholders.

One-Dimensional Hydraulic Modelling of River Ona Bridges, Ibadan, South-Western Nigeria

Bridges are crucial for transporting people and goods, but their failures can be catastrophic, resulting in loss of life and property. This study assessed the flood stage of selected bridges (Alaafin Avenue, Seven-Up, and Oluyole) along the Ona River, Ibadan, Nigeria, using a one-dimensional model called the Hydraulic Engineering Center-River Analysis System (HEC-RAS). The model requires two major input data: geometric and steady flow data. The geometric features of the study area (stream centerline, riverbanks, flow paths, cross-sections, and bridges) were generated from the Digital Elevation Model (DEM) in HEC-GeoRAS, an extension of ArcMap GIS. The MapWindow Soil and Water Assessment Tool (MWSWAT) was used to predict flow. The input data for the model include DEM, Land use, soil map data, and meteorological data obtained from the Nigerian Meteorological Agency (NIMET). The flood stages of the three bridges were predicted in the HEC-RAS model and the results showed that Alaafin Avenue, Seven-Up and Oluyole bridges had predicted flood stages of 1.5 m, 1.6 m, and 2.5 m and a freeboard minimum clearance of 2 m, 0.3 m, and 0.5 m respectively. However, the predicted flow (75.25 m³/s) was compared with the corresponding value of the flow in ERA (2002) for a free board minimum clearance. It was shown that only Alaafin Avenue Bridge is safe while Seven-Up and Oluyole bridges are prone to inundation by approximated values of 0.3 m and 0.5 m respectively. Therefore, regular inspection and maintenance of bridges are recommended to prevent inundation.

Mapping of soil properties using geographic information systems (on the example of Tashkent region)

The article analyzes the options for mapping soil properties using GIS systems. The perspectives on the use of GIS in the efficient mapping of soil mechanical content, agrochemical, and salinization characteristics are given. The study is conducted for irrigated sierozem soils of the Parkent district of Tashkent region. The basis of the research is the analysis of soil map data of the study areas, and the generalization of the results of soil-cartographic, laboratory, and cameral-analytical studies, which were conducted by commonly use methodological instructions. As a result of the research, digital thematic maps were created for the selected areas, such as various thematic maps, such as soil mechanical composition, soil nutrient status, and salinity maps.

Can Low-Cost, Handheld Spectroscopy Tools Coupled with Remote Sensing Accurately Estimate Soil Organic Carbon in Semi-Arid Grazing Lands?

Soil organic carbon influences several landscape ecological processes, and soils are becoming recognized as a mechanism to mitigate the negative impacts of climate change. There is a need to define methods and technologies for addressing soils’ spatial variability as well as the time and cost of sampling soil organic carbon (SOC). Visible and near-infrared spectroscopy have been suggested as a sampling tool to reduce inventory cost. We sampled nineteen ranch properties totaling 17,347 ha across Oklahoma and Texas in 2019 to evaluate the effectiveness and accuracy of a handheld reflectometer (Our Sci, Ann Arbor, MI, USA) (370–940 nm) and existing remote sensing approaches to estimate SOC in semi-arid grazing lands. Our data suggest that the Our Sci Reflectometer estimated soil organic carbon with a precision of approximately (±0.3% SOC); however, it was least accurate at higher carbon concentrations. The Our Sci reflectometer, although consistently accurate at lower SOC concentrations, was still less accurate than a model built using only remote sensing and digital soil map data as predictors. Combining the two data sources was the most accurate means of determining SOC. Our results indicated that the Our Sci handheld Vis-NIR reflectometer tested may have only limited applications for reducing inventory costs at scale.

Development of a National Integrated Agricultural Field Data Application Programming Interface (API) that Combines Field Polygons, Agricultural Field Pins, and Soil Map Data

Development of a National Integrated Agricultural Field Data Application Programming Interface (API) that Combines Field Polygons, Agricultural Field Pins, and Soil Map Data

EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia

Abstract. To understand, model, and predict landscape evolution, ecosystem services, and hydrological processes, the availability of detailed observation-based soil data is extremely valuable. For the EstSoil-EH dataset, we synthesized more than 20 eco-hydrological variables on soil, topography, and land use for Estonia (https://doi.org/10.5281/zenodo.3473289, Kmoch et al., 2019a) as numerical and categorical values from the original Soil Map of Estonia, the Estonian 5 m lidar DEM, Estonian Topographic Database, and EU-SoilHydroGrids layers. The Soil Map of Estonia maps more than 750 000 soil units throughout Estonia at a scale of 1:10 000 and forms the basis for EstSoil-EH. It is the most detailed and information-rich dataset for soils in Estonia, with 75 % of mapped units smaller than 4.0 ha, based on Soviet-era field mapping. For each soil unit, it describes the soil type (i.e. soil reference group), soil texture, and layer information with a composite text code, which comprises not only the actual texture class, but also classifiers for rock content, peat soils, distinct compositional layers, and their depths. To use these as eco-hydrological process properties in modelling applications we translated the text codes into numbers. The derived parameters include soil layering, soil texture (clay, silt, and sand contents), coarse fragments, and rock content of the soil layers within the soil profiles. In addition, we aggregated and predicted physical variables related to water and carbon cycles (bulk density, hydraulic conductivity, organic carbon content, available water capacity). The methodology and dataset developed will be an important resource for the Baltic region, but possibly also for all other regions where detailed field-based soil mapping data are available. Countries like Lithuania and Latvia have similar historical soil records from the Soviet era that could be turned into value-added datasets such as the one we developed for Estonia.

Free, open, quantitative and adaptable digital soil map data and database for Nigeria.

Nigeria's digital soil map (DSM) and database is the most comprehensive and detailed source of quantitative information on the country's soil distribution. Digital cartography and geographic information system (GIS) operations were methods used in producing the DSM and database. We obtained analogue soil data in 8 hard copy maps (each at a displayable scale of 1: 650,000) from the Federal Department of Agricultural Land Resources (FDALR), which archived the result of Nigeria's reconnaissance soil survey. The survey started in 1985 and by 1990; it has completed the compilation of hard copy maps that outline Nigeria's major soil distribution. Our experimental design begins with electronic scanning of these paper maps. We set the scanning system to 500 dpi, creating high-resolution raster images, which were imported into ESRI ArcGIS software, for orthorectification by geo-referencing to WGS 1984 geographic coordinate system. We applied a spatial processing tool on the orthorectified images and created a geometrically-seamless mosaicked raster image for the soil data of the whole Nigeria. Using GIS on-screen digitization – with optimal snapping tolerance – we created vector polygons (spatial data) of soil components (mapping units). Finally, we coded the metadata (attributes) of Nigeria's soil distribution into Microsoft EXCEL spreadsheet, which we linked to the soil spatial data. The combined spatial and attribute soil data forms the soil database for Nigeria and provides, on-demand, vital soil information, such as thematic maps of soil characteristics. The department of Geoinformatics and surveying University of Nigeria, Enugu Campus (UNEC) is the major repository of Nigeria's DSM and database.

A streamflow hydrograph analysis and simulation for a study case watershed

The streamflow hydrograph analysis and simulation for a study case watershed was carried out using the hydrologic engineering centre - geospatial hydrologic modelling system (HEC-GeoHMS) and the hydrologic engineering centre - hydrologic modelling system (HEC-HMS) software coupling. HEC-GeoHMS is integrated in a geographic information system (GIS) so as to develop a terrain's pre-processing tool and to generate output files which are inputted to the hydrologic model. Soil map data, land use map and digital elevation model (DEM) were used in order to delineate the physical watershed's characteristics. The runoff estimation was based on the soil conservation service curve number (SCS-CN) approach while the watershed's outflow estimation was carried out by employing SCS unit hydrograph method. The rainfall - runoff modelling is applied on a storm event which occurred on 01/10/2005 at Kastaneri watershed. The coupled hydrological models simulated runoff hydrograph for the latter storm event whilst the CN and the peak discharge values were computed equal to 69.5 and 72.8 m3/s respectively.

Geotecnologias aplicadas a determinação e análise do conflito da capacidade de uso da terra no município de Nova Europa (SP)

O presente estudo teve como objetivo usar geotecnologias para classificar a capacidade e o conflito do uso da terra em Nova Europa – SP. Dados da missão SRTM (Shuttle Radar Topography Mission) e do mapa pedológico do Estado de São Paulo foram usados para classificar a capacidade de uso da terra do município. O uso e a ocupação da terra foram determinados com base na classificação dos dados orbitais do sistema sensor Landsat 8/OLI. A análise do conflito da capacidade de uso foi realizada por meio da integração em Sistema de Informação Geográfica (SIG) entre os dados de uso e ocupação e capacidade de uso da terra. Os resultados demonstram que 85,8% do total da área de Nova Europa possuía uso adequado em 2013. Os locais subutilizados representavam 10,1% e os sobreutilizados 3,5% do total da área do município.

Sediment inflow estimation and mapping its spatial distribution at sub-basin scale: the case of Tendaho Dam, Afar Regional State, Ethiopia

Sedimentation is a major problem of reservoir operation in Ethiopia. Deforestation, overgrazing and poor land management practices are some that accelerate the rate of erosion. The topography of Ethiopia in general and TDW in particular is undulating and local farmers commonly cultivate on the hilly sides causing easy topsoil wash away. This study aims to determine the sediment yield at Tendaho dam reservoir, identify the high sediment source sub-basins and check the applicability of SWAT model in the area. To achieve these goals, the hydro-meteorological, sediment, topographic, land use and soil map data were used as inputs for the model. The model was successfully calibrated and validated for measured stream flow and sediment yield of AR at Wonji, Melka Werer and Tendaho stations. It was thus found applicable in this watershed with acceptable performance evaluation statistics values. From the model simulated output, sub-basins 27, 34, 29 and 50 were found to be the top four severely eroded sub-basins with average annual sediment yield of 26.66t/ha, 24.22t/ha, 23.79t/ha and 19.13t/ha, respectively. While, sub-basins 31, 18 and 21 were found to be the least sediment sources with annual averaged sediment yield of 0.02t/ha, 0.04t/ha and 0.04t/ha, respectively. In general, the results found in this study are very important inputs in selecting appropriate watershed management strategies and the methodologies used can also be valuable guides for the researchers in similar studies.Keywords: SWAT Model; Sediment yield modeling; Sub-basins; Tendaho reservoir

Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin

The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as model) and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situ G0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from-7 to-0.5K in LST amplitude and from-300 to 300J m-2 K-1 s-0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.

Estimating urban flooding potential near the outlet of an arid catchment in Saudi Arabia

ABSTRACTThe aim of this study is to build a flood simulation model for the city of Hafr Al-Batin catchment area. The model consists of a hydrologic model, a hydraulic model and pre- and post-processing tools. A geographic information system-based modelling interface was used to pre-process the terrain and rainfall data and generate input files for hydrologic and hydraulic models. Soil map data, land cover map, land use map and digital elevation model was used to delineate the physical watershed's characteristics. The runoff estimation was based on the widely known Soil Conservation Service curve number approach. The hydrologic/hydraulic model simulations simulated the runoff hydrograph corresponding to different design storms and helped to delineate the resulting flood inundation maps. The results indicate good agreement between the delineated inundation map and the hazard map developed by the municipality. They also show that the location of the city complicates the runoff response for small storms creating two distinct peaks. The results of this study can be utilized for planning purposes and in the design of flood control structures as the study has estimated the runoff corresponding to different design storms and used hydraulics and geospatial data in delineating the flood zones.

Mapping peat soils in Ireland: updating the derived Irish peat map

Determining the spatial extent of peat soils is essential for measuring soil carbon (C) stocks. The Derived Irish Peat Map (DIPM) estimated the spatial extent of peat soils as 13.8% of the national area. The DIPM was derived from the Peatland Map of Ireland, CORINE land cover database (CORINE) 1990 and the General Soil Map of Ireland. This paper presents an updated version, the Derived Irish Peat Map Version 2 (DIPMV2), using the same rules-based decision tree methodology, but CORINE 2000 and the Indicative Soil Map of Ireland (ISMI) replaced CORINE 1990 and the General Soil Map of Ireland. The DIPMV2, a best-estimate, illustrates that peat soils occur more extensively than previously mapped, especially in the ‘drumlin belt’ of counties Cavan, Monaghan and Louth. The producer, user and overall accuracies are 88, 91 and 85%, respectively. This is an improvement on the DIPM (87, 84, and 85%). The DIPMV2, estimates that peat soils cover 1,466,469 ha, or 20.6%, of the national land area. The DIPM methodology may provide a long-term tool for updating national peat soil map data. It can be concluded that data for peat soils will become more important because peat has high carbon density and modifications to peat soils have implications for climate change.

Characterization of some Middle European soil textures by gamma‐spectrometry

AbstractInformation about the variability of different soil attributes within a field is essential for sustainable land management and precision agriculture. Mobile proximal gamma‐ray spectrometry can map soil characteristics of vast areas at different scales rapidly and cost‐effectively. This study aims at investigating reliability and capability of mobile‐gamma‐spectrometry (radiometrics) data to map typical soils of Middle Europe. In this paper, we investigate relationships between the radioelement concentrations (K, U, Th, and dose rate) and soil parameters (texture, CEC, pH, and organic‐C content) at four different field sites and soil textures. The data reliability is confirmed at the survey start. Mobile data have an excellent linear correlation (nearly 1:1) with the stationary readings (of identical devices, acquisition setups, and soil conditions) but moderate correlation with laboratory data (of different devices, setups, and sample conditions). Dried lab samples have systematically higher radioelement concentrations than the field soils (normally wet). Consequently, the mobile‐gamma‐spectrometric data is sufficiently accurate for soil mapping, and its calibration by laboratory data is less useful due to the varying environmental conditions. Single absolute radioelement concentrations show only moderate correlations with the different soil parameters, particularly clay content and CEC. This may be related to varying environmental conditions (soil moisture, soil structure, vegetation, land use, etc.) between the study sites. Investigations of the ratios of radioelement concentrations yield a clear improvement of their correlations to soil parameters, especially for sand and clay contents, CEC, and organic C. Additionally, multiple‐linear‐regression models were established using the element concentrations of potassium and thorium to predict silt content and pH. The results of the highly correlated models were confirmed by comparing with clay and silt content and pH value, respectively, to six additional independent field samples. Briefly, applications of gamma‐ray data for soil mapping offers the possibility of the development of quantitative relationships regarding soil parameters like sand and clay contents, CEC, and organic C. Classification of soil textures by gamma‐ray data seems to be promising, though a broader database of soils is needed for further research. We recommend gamma‐ray mapping as a complementary or even an alternative to common mapping techniques.

Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale

Legacy data in the form of soil maps, which often have typical property measurements associated with each polygon, can be an important source of information for digital soil mapping (DSM). Methods of disaggregating such information and using it for quantitative estimation of soil properties by methods such as regression kriging (RK) are needed. Several disaggregation processes have been investigated; preferred methods include those which include consideration of scorpan factors and those which are mass preserving (pycnophylactic) making transitions between different scales of investigation more theoretically sound. Area to point kriging (AtoP kriging) is pycnophylactic and here we investigate its merits for disaggregating legacy data from soil polygon maps. Area to point regression kriging (AtoP RK) which incorporates ancillary data into the disaggregation process was also applied. The AtoP kriging and AtoP RK approaches do not involve collection of new soil measurements and are compared with disaggregation by simple rasterization. Of the disaggregation methods investigated, AtoP RK gave the most accurate predictions of soil organic carbon (SOC) concentrations (smaller mean absolute errors (MAEs) of cross-validation) for disaggregation of soil polygon data across the whole of Northern Ireland.Legacy soil polygon data disaggregated by AtoP kriging and simple rasterization were used in a RK framework for estimating soil organic carbon (SOC) concentrations across the whole of Northern Ireland, using soil sample data from the Tellus survey of Northern Ireland and with other covariates (altitude and airborne radiometric potassium). This allowed direct comparison with previous analysis of the Tellus survey data. Incorporating the legacy data, whether from simple rasterization of the polygons or AtoP kriging, substantially reduced the MAEs of RK compared with previous analyses of the Tellus data. However, using legacy data disaggregated by AtoP kriging in RK resulted in a greater reduction in MAEs. A jack-knife procedure was also performed to determine a suitable number of additional soil samples that would need to be collected for RK of SOC for the whole of Northern Ireland depending on the availability of ancillary data. We recommend i) if only legacy soil polygon map data are available, they should be disaggregated using AtoP kriging, ii) if ancillary data are also available legacy data should be disaggregated using AtoP RK and iii) if new soil measurements are available in addition to ancillary and legacy soil map data, the legacy soil map data should be first disaggregated using AtoP kriging and these data used along with ancillary data as the fixed effects for RK of the new soil measurements.

Soil Types Extraction Based on MODIS Image

Abstract In the 1990s, the emergence of MODIS has caused to try new something in soil. This paper explores the soil extraction of secondary classification and accuracy based on computer supervised classification with MOIDIS data, topographic map and soil map data in fuyu country, heilongjiang province. The research indicates it's effective for using MODIS to extract soil speies classification and the extraction of homogeneous dividation can be realized and cultivates high accuracy, which indicates that MODIS spectral differences on soil target faint dividation have certain inversion ability and soil information extraction can be attained certain value of the classification results based on the spectral characteristics and auxiliary knowledge. It is a useful supplement for black soils secondary classification based on MODIS data and it is an effective means for black soil monitoring and protection.

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

Soil organic carbon (SOC) content is an important soil quality indicator that plays an important role in regulating physical, chemical and biological properties of soil. Field assessment of SOC is time consuming and expensive. It is difficult to obtain high-resolution SOC distribution maps that are needed for landscape analysis of large areas. An artificial neural network (ANN) model was developed to predict SOC based on parameters derived from digital elevation model (DEM) together with soil properties extracted from widely available coarse resolution soil maps (1:1 000 000 scale). Field estimated SOC content data extracted from high-resolution soil maps (1:10 000 scale) in Black Brook Watershed in northwestern New Brunswick, Canada, were used to calibrate and validate the model. We found that vertical slope position (VSP) was the most important variable that determines distributions of SOC across the landscape. Other variables such as slope steepness, and potential solar radiation (PSR) also had significant influence on SOC distributions. The prediction of the selected two-input-node SOC model (VSP and coarse resolution soil map recorded SOC as inputs) had a correlation coefficient of 0.92 with measured values, and model predicted SOC values had 47.9% of the total points within ±0.5% of the measured values and 70.6% within ±1% of the measured values. The prediction od the selected four-input-node model (VSP, slope steepness, PSR and coarse resolution SOC values as inputs) had a correlation coefficient of 0.98 with measured values and model predicted SOC values had 75% of the total points within ±0.5% of the measured values and 87% within ±1% of the measured values. The prediction of the five-input-nodes model with soil drainage as additional input had a correlation coefficient of 0.99 with measured values, and model predicted SOC values had 87% of the total points within ±0.5% of the measured values and 98% of the total points within ±1% of the measured values. The calibrated SOC prediction model was used to produce a high-resolution SOC map for the Black Brook Watershed and the resulting SOC distribution map is considered to be realistic. Results indicated that DEM-derived hydrological parameters together with widely available coarse resolution soil map data could be used to produce high-resolution SOC maps with the ANN method.Key words: Soil organic carbon, artificial neural network model, high-resolution soil maps, digital elevation model, vertical slope position