Journal articles: 'Soil maps'

1

Mueller, T. G., and F. J. Pierce. "Soil Carbon Maps." Soil Science Society of America Journal 67, no. 1 (2003): 258. http://dx.doi.org/10.2136/sssaj2003.0258.

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Mueller, T. G., and F. J. Pierce. "Soil Carbon Maps." Soil Science Society of America Journal 67, no. 1 (January 2003): 258–67. http://dx.doi.org/10.2136/sssaj2003.2580.

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Grodzynskyi, M. "Representation of soils in the landscapes maps." Visnyk of the Lviv University. Series Geography, no. 39 (December 11, 2011): 113–21. http://dx.doi.org/10.30970/vgg.2011.39.2169.

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Series (succession sequences) of soils that change each other over time and within the landscape units are proper objects for landscape mapping. The soil series give an idea of both retrospective state of a soil before its anthropogenic transformations and of tendencies of soil development in landscape complexes of various types. The names of soils as they are appeared in soil nomenclature of Soil science should not be duplicated in the legends of landscape maps. "Landscape" names for soils have to stress on their features and attributes that are of primary importance for vegetation, water, thermal and other ecological regimes of landscapes. The "landscape" names for different types of Albeluvisols and Phaeozems of Ukraine are suggested. Key words: soil, landscape, landscape map, landscape science.

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Hartemink, Alfred E., Birl Lowery, and Carl Wacker. "Soil maps of Wisconsin." Geoderma 189-190 (November 2012): 451–61. http://dx.doi.org/10.1016/j.geoderma.2012.05.025.

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Bogaert, Patrick, and Dimitri D'Or. "Estimating Soil Properties from Thematic Soil Maps." Soil Science Society of America Journal 66, no. 5 (September 2002): 1492–500. http://dx.doi.org/10.2136/sssaj2002.1492.

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Kondratyeva, Maria, and Natalya Bazhukova. "Experience of regional soil-geochemical mapping." InterCarto. InterGIS 26, no. 1 (2020): 584–94. http://dx.doi.org/10.35595/2414-9179-2020-1-26-584-594.

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The developed series of soil-geochemical maps reveals the ecological functions of soils and the soil cover associated with the processes of migration, transformation, and accumulation of chemicals substances in landscapes. The thematic basis for the maps was the electronic version of the soil map of the Russian Federation with a scale of 1 : 2 500 000 and the Unified State Register of Soil Resources of the Russian Federation developed on its basis, as well as regional sources and the database of soil properties of the Perm Territory. Prepared maps represented by two main blocks — basic and applied, each of which, in turn, includes constituent and assessment maps. The article discusses the methodological foundations, the compilation methodology and the content of the presented maps. Baseline maps reveal the most common soil-geochemical patterns of migration and accumulation of substances in soils. The block includes: maps of the thicknesses of organogenic and humus soil horizons, cation exchange capacities, and sorption capacity of soils. These maps make it possible to characterize the sorption properties of surface soil horizons as the most important geochemical barriers for technogenic substances. An analysis of the content of these maps allows us to conclude that the sorption capacity of the most common soils in the region is estimated to be very low and low, due to the low thickness of the humus horizons and low cation exchange capacity of podzolic soils. Podzolized chernozems, soddy-gley and soddy-carbonate soils have an increased sorption ability, but their distribution area is small. The high sorption capacity of soils is associated with a significant thickness of organogenic horizons in hydromorphic soils. The application block is devoted to the analysis of soil properties in relation to heavy metals as a priority group of pollutants for which the soil is a depositing medium. Two maps are included in this block — “Conditions for the migration of heavy metals in soils” and “Sensitivity of soils”. The conditions for the migration of heavy metals highlighted on the map of the same name are represented by 12 options. In the northern and central parts of the region, conditions prevail that combine constant or seasonal recovery conditions and low pH values. In the southern part of the region they are replaced by oxidative weakly acidic. The assessment of the sensitivity of soils to heavy metal pollution is given on the basis of expert assessment. The soils of the region are defined as sensitive and very sensitive, that is, they relatively quickly change their properties to a negative side under the influence of anthropogenic load.

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Yang, Lin, You Jiao, Sherif Fahmy, A.-Xing Zhu, Sheldon Hann, James E. Burt, and Feng Qi. "Updating Conventional Soil Maps through Digital Soil Mapping." Soil Science Society of America Journal 75, no. 3 (May 2011): 1044–53. http://dx.doi.org/10.2136/sssaj2010.0002.

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Oke, Satoshi, Masaharu Ookado, Takuo Kokuryu, and Sakae Shibusawa. "Soil Maps in Precision Farming." Agricultural Information Research 13, no. 1 (2004): 69–78. http://dx.doi.org/10.3173/air.13.69.

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Hartemink, Alfred E., and Marthijn P. W. Sonneveld. "Soil maps of The Netherlands." Geoderma 204-205 (August 2013): 1–9. http://dx.doi.org/10.1016/j.geoderma.2013.03.022.

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Hartemink, Alfred E., Pavel Krasilnikov, and J. G. Bockheim. "Soil maps of the world." Geoderma 207-208 (October 2013): 256–67. http://dx.doi.org/10.1016/j.geoderma.2013.05.003.

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11

Lembrechts, Jonas J., Johan Hoogen, Juha Aalto, Michael B. Ashcroft, Pieter De Frenne, Julia Kemppinen, Martin Kopecký, et al. "Global maps of soil temperature." Global Change Biology 28, no. 9 (February 11, 2022): 3110–44. http://dx.doi.org/10.1111/gcb.16060.

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12

Sisák, I., and A. Benő. "Probability-based harmonization of digital maps to produce conceptual soil maps." Agrokémia és Talajtan 63, no. 1 (June 1, 2014): 89–98. http://dx.doi.org/10.1556/agrokem.63.2014.1.10.

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Three centrally edited nationwide soil maps were published in Hungary between 1953 and 1988. Each of these soil maps has advantages, but serious drawbacks as well. Authors’ hypothesis was that the drawbacks of the individual soil maps are correctable with the help of other soil maps and with ancillary data. Therefore, the oldest soil map was digitized and a study was conducted for the harmonization of data on a 266 km2 area at Keszthely (near Lake Balaton) by using the CHAID classification tree method. CORINE land cover database, digital map of surface geology, digital elevation model and derived slope categories were used as ancillary data.The seven source maps contained 7–38 categories. After the intersection of all seven maps, the resulting file contained more than 50,000 polygons and nearly 14,000 category combinations. A variable — showing the probability of the category combinations in relation to the expected areas — was calculated. This was the target variable for classification by the CHAID method, using categories of the seven original maps as independent variables.0.5% of the total area was grouped into 13 less probable classes, which represent the inaccuracies of the initial maps. 99.5% of the total area was classified into 19 classes and some of them were further subdivided on the basis of the geological map. These classes were interpreted as eight WRB soil categories. The final soil map had much better spatial resolution than any of the initial soil maps, non-soil categories were interpreted as soil categories and spatial accuracy was successfully corrected with the proposed method.

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Basayigit, L., and S. Senol. "Comparison of soil maps with different scales and details belonging to the same area." Soil and Water Research 3, No. 1 (March 21, 2008): 31–39. http://dx.doi.org/10.17221/2097-swr.

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Two different soil maps prepared by different institutes at scales of 1:200 000 and 1:25 000 covering identical areas were compared to determine the accuracy of reconnaissance. These soil maps are widely used in land resources assessment studies in Turkey. For this purpose, the soil maps were digitised and performed a data set. Then the map layers were compared by using GIS technology in order to assess the soil properties and land characteristics. The reconnaissance soil map at the scale of 1:200 000 has the highest accuracy for the slope due to the fact that topographic maps have been used as basic maps for the field studies. The accuracy of other properties in descending order is as follows; slope > depth > salinity > texture > drainage > top soil texture. In addition, physiographic and topographic patterns of soils also affect the accuracy of maps. The reconnaissance soil map was found to be less accurate in flood plains where the slope does not affect other soil properties.

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Odgers, Nathan P., Karen W. Holmes, Ted Griffin, and Craig Liddicoat. "Derivation of soil-attribute estimations from legacy soil maps." Soil Research 53, no. 8 (2015): 881. http://dx.doi.org/10.1071/sr14274.

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It is increasingly necessary to apply quantitative techniques to legacy soil polygon maps given that legacy soil maps may be the only source of soil information over large areas. Spatial disaggregation provides a means of extracting information from legacy soil maps and enables us to downscale the original information to produce new soil class maps at finer levels of detail. This is a useful outcome in its own right; however, the disaggregated soil-class coverage can also be used to make digital maps of soil properties with associated estimates of uncertainty. In this work, we take the spatially disaggregated soil-class coverage for all of Western Australia and the agricultural region of South Australia and demonstrate its application in mapping clay content at six depth intervals in the soil profile. Estimates of uncertainty are provided in the form of the 90% prediction interval. The work can be considered an example of harmonisation to a common output specification. The validation results highlighted areas in the landscape and taxonomic spaces where more knowledge of soil properties is necessary.

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Rukhovich, D. I., A. D. Rukhovich, D. D. Rukhovich, M. S. Simakova, A. L. Kulyanitsa, A. V. Bryzzhev, and P. V. Koroleva. "Maps of averaged spectral deviations from soil lines and their comparison with traditional soil maps." Eurasian Soil Science 49, no. 7 (July 2016): 739–56. http://dx.doi.org/10.1134/s1064229316070085.

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16

Agbu, Patrick A., Donald J. Fehrenbacher, and Ivan J. Jansen. "Statistical Comparison of SPOT Spectral Maps with Field Soil Maps." Soil Science Society of America Journal 54, no. 3 (May 1990): 812–18. http://dx.doi.org/10.2136/sssaj1990.03615995005400030032x.

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17

LEENHARDT, D., M. VOLTZ, M. BORNAND, and R. WEBSTER. "Evaluating soil maps for prediction of soil water properties." European Journal of Soil Science 45, no. 3 (September 1994): 293–301. http://dx.doi.org/10.1111/j.1365-2389.1994.tb00512.x.

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18

Bogdanova, M. D., and M. I. Gerasimova. "Soil-ecological maps in national atlases." Geodesy and Cartography 912, no. 6 (July 20, 2016): 27–34. http://dx.doi.org/10.22389/0016-7126-2016-912-6-27-34.

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Cherlinka, V. R., and Y. M. Dmytruk. "Verification methods for predicative soil maps." Naukovij vìsnik Nacìonalʹnogo unìversitetu bìoresursìv ì prirodokoristuvannâ Ukraïni. Serìâ Bìologìâ, bìotehnologìâ, ekologìâ 2018, no. 287 (November 14, 2018): 160–73. http://dx.doi.org/10.31548/biologiya2018.287.160.

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20

Doolittle, James A., Fred E. Minzenmayer, Sharon W. Waltman, and Ellis C. Benham. "Ground Penetrating Radar Soil Suitability Maps." Journal of Environmental and Engineering Geophysics 8, no. 2 (June 2003): 49–56. http://dx.doi.org/10.4133/jeeg8.2.49.

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21

McCormick, Gerald W. "A Road Map to Soil Maps." Soil Horizons 29, no. 3 (1988): 100. http://dx.doi.org/10.2136/sh1988.3.0100.

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22

Møller, Anders Bjørn, Brendan Malone, Nathan P. Odgers, Amélie Beucher, Bo Vangsø Iversen, Mogens Humlekrog Greve, and Budiman Minasny. "Improved disaggregation of conventional soil maps." Geoderma 341 (May 2019): 148–60. http://dx.doi.org/10.1016/j.geoderma.2019.01.038.

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23

Mokarram, M., M. Najafi-Ghiri, and A. R. Zarei. "Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)." Soil and Water Research 13, No. 1 (January 24, 2018): 11–17. http://dx.doi.org/10.17221/139/2016-swr.

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Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other.

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Pindral, Sylwia, and Marcin Świtoniak. "The usefulness of soil-agricultural maps to identify classes of soil truncation." Soil Science Annual 68, no. 1 (March 28, 2017): 2–10. http://dx.doi.org/10.1515/ssa-2017-0001.

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Abstract Soil erosion led to the severe transformations of the soil cover of young morainic areas of northern Poland. Main alterations are connected with soil truncation on summits and in upper part of slopes, whereas at foot slopes and within depressions colluvial material is accumulated. Information and knowledge about the extent or intensity of erosion are mainly derived from sophisticated geospatial models or laborious field works. To reduce the effort associated with development of studies on erosion the use of easily available cartographic sources is required. The main aim of the paper is an elaboration of key to reinterpret information taken from soil-agricultural maps in the context of determining the degree of pedons truncation. The study is based on a comparison of the properties of soils representing various classes of erosional alterations with the data on existing maps. The correlation between descriptions recorded in the form of cartographic symbols with properties of pedons divided into several classes of vertical texturecontrast soil truncation and results from potential erosion maps was elaborated. The application of developed interpretative principles allows calculating the share of soil truncation classes within investigated area. The five test plots (each - 1 km2) were located along the north slopes of Noteć Middle Valley and Toruń Basin. The proposed interpretation of soil-agricultural maps reveals their significant value in studies on extent and degree of erosional alterations recorded in soil cover.

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Сабиров and Ayrat Sabirov. "ORGANIZATION OF FOREST SOILS MONITORING." Vestnik of Kazan State Agrarian University 11, no. 3 (October 31, 2016): 36–40. http://dx.doi.org/10.12737/22673.

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The impact of productive activity of human on the ecological balance of nature. Ecological functions of soils of forest biogeocenoses. Regional features of the ecosystems functioning, soil formation factors. Organization of the soil cover state monitoring. Environmental monitoring of forest soils. Objectives of soil monitoring of forest ecosystems. Collection of the available information on forest ecosystems. Choice of monitoring objects. Soil and environmental hospitals. Fixed trial areas. Long-term and seasonal observations of soil properties. Temporary trial areas. Soil monitoring on the route courses. The use of satellite imagery in the environmental assessment of erosive landscapes. Controlled soil indicators. Research methods of soil properties and composition of pollutants. Processing of experimental data using information technology. Mathematical models of the spread of pollutants, the interrelation between soil indicators (in the soil), between soil properties and indicators of the characteristic of forest, the evolution of forest soil. Small-scale and medium-scale regional maps of land erosion, soil contamination by chemicals. Large-scale maps of physical degradation of soils, the content of macronutrients and micronutrients, acidity, humus condition of soils. Maps are accompanied by an explanatory note (soil sketch). Maximum permissible amount of the chemicals (maximum allowable concentrations) polluting the soil. Maximum permissible loading on forest soils under anthropogenic impact. Rational use and protection of forest ecosystems.

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Ellili-Bargaoui, Yosra, Brendan Philip Malone, Didier Michot, Budiman Minasny, Sébastien Vincent, Christian Walter, and Blandine Lemercier. "Comparing three approaches of spatial disaggregation of legacy soil maps based on the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm." SOIL 6, no. 2 (August 14, 2020): 371–88. http://dx.doi.org/10.5194/soil-6-371-2020.

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Abstract. Enhancing the spatial resolution of pedological information is a great challenge in the field of digital soil mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially and are available at a coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining soil map units (SMUs), where each SMU can include one or several soil type units (STUs) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at a finer spatial resolution by machine-learning algorithms, using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of the spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithms (DSMART with extra soil profiles; DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at a 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on 135 independent soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1:25 000 soil maps. Pairwise comparisons were also performed, using the Shannon entropy measure, to spatially locate the differences between disaggregated maps. The main results show that adding soil landscape relationships to the disaggregation process enhances the performance of the prediction of soil type distribution. Considering the three most probable STUs and using 135 independent soil profiles, the overall accuracy measures (the percentage of soil profiles where predictions meet observations) are 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost 2 times higher when validated using 3×3 windows. They achieved 28.5 % for DSMART with soil landscape relationships and 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships and extra soil observations constraints allow the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial for obtaining consistent soil maps with a clear internal disaggregation of SMUs across the landscape.

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Tint, Zar Lee, Nyan Myint Kyaw, and Kyaw Kyaw. "Soil Liquefaction Potential Maps for Earthquake Events in Yangon, Myanmar." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2401–9. http://dx.doi.org/10.31142/ijtsrd12747.

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Balkovič, J., Z. Rašeková, V. Hutár, J. Sobocká, and R. Skalský. "Digital soil mapping from conventional field soil observations." Soil and Water Research 8, No. 1 (February 6, 2013): 13–25. http://dx.doi.org/10.17221/43/2012-swr.

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We tested the performance of a formalized digital soil mapping (DSM) approach comprising fuzzy k-means (FKM) classification and regression-kriging to produce soil type maps from a fine-scale soil observation network in Ri&scaron;ňovce, Slovakia. We examine whether the soil profile descriptions collected merely by field methods fit into the statistical DSM tools and if they provide pedologically meaningful results for an erosion-affected area. Soil texture, colour, carbonates, stoniness and genetic qualifiers were estimated for a total of 111 soil profiles using conventional field methods. The data were digitized along semi-quantitative scales in 10-cm depth intervals to express the relative differences, and afterwards classified by the FKM method into four classes A–D: (i) Luvic Phaeozems (Anthric), (ii) Haplic Phaeozems (Anthric, Calcaric, Pachic), (iii) Calcic Cutanic Luvisols, and (iv) Haplic Regosols (Calcaric). To parameterize regression-kriging, membership values (MVs) to the above A–D class centroids were regressed against PCA-transformed terrain variables using the multiple linear regression method (MLR). MLR yielded significant relationships with R2 ranging from 23% to 47% (P < 0.001) for classes A, B and D, but only marginally significant for Luvisols of class C (R2 = 14%, P < 0.05). Given the results, Luvisols were then mapped by ordinary kriging and the rest by regression-kriging. A “leave-one-out” cross-validation was calculated for the output maps yielding R2 of 33%, 56%, 22% and 42% for Luvic Phaeozems, Haplic Phaeozems, Luvisols and also Regosols, respectively (all P < 0.001). Additionally, the pixel-mixture visualization technique was used to draw a synthetic digital soil map. We conclude that the DSM model represents a fully formalized alternative to classical soil mapping at very fine scales, even when soil profile descriptions were collected merely by field estimation methods. Additionally to conventional soil maps it allows to address the diffuse character in soil cover, both in taxonomic and geographical interpretations.

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Tafasca, Salma, Agnès Ducharne, and Christian Valentin. "Weak sensitivity of the terrestrial water budget to global soil texture maps in the ORCHIDEE land surface model." Hydrology and Earth System Sciences 24, no. 7 (July 24, 2020): 3753–74. http://dx.doi.org/10.5194/hess-24-3753-2020.

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Abstract. Soil physical properties play an important role in estimating soil water and energy fluxes. Many hydrological and land surface models (LSMs) use soil texture maps to infer these properties. Here, we investigate the impact of soil texture on soil water fluxes and storage at different scales using the ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) LSM, forced by several complex or globally uniform soil texture maps. At the point scale, the model shows a realistic sensitivity of runoff processes and soil moisture to soil texture and reveals that loamy textures give the highest evapotranspiration and lowest total runoff rates. The three tested complex soil texture maps result in similar water budgets at all scales, compared to the uncertainties of observation-based products and meteorological forcing datasets, although important differences can be found at the regional scale, particularly in areas where the different maps disagree on the prevalence of clay soils. The three tested soil texture maps are also found to be similar by construction, with a shared prevalence of loamy textures, and have a spatial overlap over 40 % between each pair of maps, which explains the overall weak impact of soil texture map change. A useful outcome is that the choice of the input soil texture map is not crucial for large-scale modelling, but the added value of more detailed soil information (horizontal and vertical resolution, soil composition) deserves further studies.

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Brogi, Cosimo, Johan A. Huisman, Lutz Weihermüller, Michael Herbst, and Harry Vereecken. "Added value of geophysics-based soil mapping in agro-ecosystem simulations." SOIL 7, no. 1 (May 18, 2021): 125–43. http://dx.doi.org/10.5194/soil-7-125-2021.

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Abstract. There is an increased demand for quantitative high-resolution soil maps that enable within-field management. Commonly available soil maps are generally not suited for this purpose, but digital soil mapping and geophysical methods in particular allow soil information to be obtained with an unprecedented level of detail. However, it is often difficult to quantify the added value of such high-resolution soil information for agricultural management and agro-ecosystem modelling. In this study, a detailed geophysics-based soil map was compared to two commonly available general-purpose soil maps. In particular, the three maps were used as input for crop growth models to simulate leaf area index (LAI) of five crops for an area of ∼ 1 km2. The simulated development of LAI for the five crops was evaluated using LAI obtained from multispectral satellite images. Overall, it was found that the geophysics-based soil map provided better LAI predictions than the two general-purpose soil maps in terms of correlation coefficient R2, model efficiency (ME), and root mean square error (RMSE). Improved performance was most apparent in the case of prolonged periods of drought and was strongly related to the combination of soil characteristics and crop type.

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Lund, E. D., M. C. Wolcott, and G. P. Hanson. "Applying Nitrogen Site-Specifically Using Soil Electrical Conductivity Maps and Precision Agriculture Technology." Scientific World JOURNAL 1 (2001): 767–76. http://dx.doi.org/10.1100/tsw.2001.95.

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Soil texture varies significantly within many agricultural fields. The physical properties of soil, such as soil texture, have a direct effect on water holding capacity, cation exchange capacity, crop yield, production capability, and nitrogen (N) loss variations within a field. In short, mobile nutrients are used, lost, and stored differently as soil textures vary. A uniform application of N to varying soils results in a wide range of N availability to the crop. N applied in excess of crop usage results in a waste of the grower’s input expense, a potential negative effect on the environment, and in some crops a reduction of crop quality, yield, and harvestability. Inadequate N levels represent a lost opportunity for crop yield and profit. The global positioning system (GPS)-referenced mapping of bulk soil electrical conductivity (EC) has been shown to serve as an effective proxy for soil texture and other soil properties. Soils with a high clay content conduct more electricity than coarser textured soils, which results in higher EC values. This paper will describe the EC mapping process and provide case studies of site-specific N applications based on EC maps. Results of these case studies suggest that N can be managed site-specifically using a variety of management practices, including soil sampling, variable yield goals, and cropping history.

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CHENG, Wei, A.-xing ZHU, Cheng-zhi QIN, and Feng QI. "Updating conventional soil maps by mining soil–environment relationships from individual soil polygons." Journal of Integrative Agriculture 18, no. 2 (February 2019): 265–78. http://dx.doi.org/10.1016/s2095-3119(18)61938-0.

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Simakova, M. S., and S. V. Ovechkin. "Soil-forming materials and soil texture as shown on small-scale soil maps." Eurasian Soil Science 40, no. 7 (July 2007): 709–18. http://dx.doi.org/10.1134/s1064229307070010.

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Lázaro-López, Alberto, María Luisa González-SanJosé, and Vicente Gómez-Miguel. "Disaggregation of conventional soil maps: a review." Soil Research 59, no. 8 (2021): 747. http://dx.doi.org/10.1071/sr20288.

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The disaggregation of conventional soil maps is an active research line inside the Digital Soil Mapping framework that aims to generate new cartographies by disclosing the non-explicit soil distribution pattern within the polytaxic or multi-component cartographic units. This article shows a comprehensive review of methodologies found after a bibliographic search in the Web of Science and Scopus databases. They are analysed regarding common factors such as the conventional soil map, environmental data sources and covariates, classification methods, and evaluation; likewise, those specific to the leveraging of conventional maps as the main source of soil information such as sampling scheme and assignment of soil categories for the classification. The applications were frequently carried out in small and medium areas with intensive and extensive conventional soil maps and featuring supervised classification methods. The definition of the training sets is a critical task that has a strong influence on their performance. The comparative analysis noted the potential of the reviewed disaggregation methodologies that adopted two-stage strategies: first, areas potentially associated with soil categories are delimited; and second, supervised models are built on them. Ultimately, the development of new disaggregation methodologies is possible by combining those strategies within each factor that yielded the best results in terms of accuracy.

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Mosimann, Thomas, and Philipp Herbst. "Flächenhafte Modellierung von Waldboden-eigenschaften in der Nordwestschweiz." Schweizerische Zeitschrift fur Forstwesen 164, no. 1 (January 1, 2013): 10–22. http://dx.doi.org/10.3188/szf.2013.0010.

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Spatial modeling of forest soil properties in Northwestern Switzerland Forest soils are an important natural resource. However, up to now almost no area-wide forest soil information was available for Switzerland. Since 2006, model-based, high-resolution maps of forest soil properties in the cantons Basel-Landschaft and Basel-Stadt were generated, depicting soil depth, stone content, soil wetness, acidity and water storage capacity. These maps are based on all available point information on soils, and on 95 recently analyzed pedological forest soil profiles. Two different methods were applied in parallel: 1) the development of decision trees based on frequency statistics combined with expertise and 2) the semi-automated Random Forest modeling approach. Highly branched hierarchical decision trees were used to derive soil properties from 24 predictors (relief forms, parent material, vegetation, forest type, location, climate, etc.). This article describes the approaches and portrays the mapped results of soil depth, top soil acidity and water storage capacity. Our project shows that it is basically feasible to predict soil properties with a high spatial resolution, classifying them into 4–5 categories. However, depending on soil type, for 10–30% of the area no predictions are possible, especially because of high soil heterogeneity, inadequate morphographic slope differentiation in the terrain models and the implausibility of predictor information. Soil property maps provide basic information for set up forestry maps for forest development, forest management and risk assessment.

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Greiner, Lucie, Madlene Nussbaum, Andreas Papritz, Stephan Zimmermann, Andreas Gubler, Adrienne Grêt-Regamey, and Armin Keller. "Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources." SOIL 4, no. 2 (May 29, 2018): 123–39. http://dx.doi.org/10.5194/soil-4-123-2018.

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Abstract. Spatial information on soil function fulfillment (SFF) is increasingly being used to inform decision-making in spatial planning programs to support sustainable use of soil resources. Soil function maps visualize soils abilities to fulfill their functions, e.g., regulating water and nutrient flows, providing habitats, and supporting biomass production based on soil properties. Such information must be reliable for informed and transparent decision-making in spatial planning programs. In this study, we add to the transparency of soil function maps by (1) indicating uncertainties arising from the prediction of soil properties generated by digital soil mapping (DSM) that are used for soil function assessment (SFA) and (2) showing the response of different SFA methods to the propagation of uncertainties through the assessment. For a study area of 170 km2 in the Swiss Plateau, we map 10 static soil sub-functions for agricultural soils for a spatial resolution of 20 × 20 m together with their uncertainties. Mapping the 10 soil sub-functions using simple ordinal assessment scales reveals pronounced spatial patterns with a high variability of SFF scores across the region, linked to the inherent properties of the soils and terrain attributes and climate conditions. Uncertainties in soil properties propagated through SFA methods generally lead to substantial uncertainty in the mapped soil sub-functions. We propose two types of uncertainty maps that can be readily understood by stakeholders. Cumulative distribution functions of SFF scores indicate that SFA methods respond differently to the propagated uncertainty of soil properties. Even where methods are comparable on the level of complexity and assessment scale, their comparability in view of uncertainty propagation might be different. We conclude that comparable uncertainty indications in soil function maps are relevant to enable informed and transparent decisions on the sustainable use of soil resources.

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Schreiner, Simon, Dubravko Culibrk, Michele Bandecchi, Wolfgang Gross, and Wolfgang Middelmann. "Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors." at - Automatisierungstechnik 69, no. 4 (April 1, 2021): 325–35. http://dx.doi.org/10.1515/auto-2020-0042.

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Abstract This work describes an approach to calculate pedological parameter maps using hyperspectral remote sensing and soil sensors. These maps serve as information basis for automated and precise agricultural treatments by tractors and field robots. Soil samples are recorded by a handheld hyperspectral sensor and analyzed in the laboratory for pedological parameters. The transfer of the correlation between these two data sets to aerial hyperspectral images leads to 2D-parameter maps of the soil surface. Additionally, rod-like soil sensors provide local 3D-information of pedological parameters under the soil surface. The goal is to combine the area-covering 2D-parameter maps with the local 3D-information to extrapolate large-scale 3D-parameter maps using AI approaches.

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Dai, Yongjiu, Wei Shangguan, Nan Wei, Qinchuan Xin, Hua Yuan, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, Dagang Wang, and Fapeng Yan. "A review of the global soil property maps for Earth system models." SOIL 5, no. 2 (July 5, 2019): 137–58. http://dx.doi.org/10.5194/soil-5-137-2019.

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Abstract. Soil is an important regulator of Earth system processes, but remains one of the least well-described data layers in Earth system models (ESMs). We reviewed global soil property maps from the perspective of ESMs, including soil physical and chemical and biological properties, which can also offer insights to soil data developers and users. These soil datasets provide model inputs, initial variables, and benchmark datasets. For modelling use, the dataset should be geographically continuous and scalable and have uncertainty estimates. The popular soil datasets used in ESMs are often based on limited soil profiles and coarse-resolution soil-type maps with various uncertainty sources. Updated and comprehensive soil information needs to be incorporated into ESMs. New generation soil datasets derived through digital soil mapping with abundant, harmonized, and quality-controlled soil observations and environmental covariates are preferred to those derived through the linkage method (i.e. taxotransfer rule-based method) for ESMs. SoilGrids has the highest accuracy and resolution among the global soil datasets, while other recently developed datasets offer useful compensation. Because there is no universal pedotransfer function, an ensemble of them may be more suitable for providing derived soil properties to ESMs. Aggregation and upscaling of soil data are needed for model use, but can be avoided by using a subgrid method in ESMs at the expense of increases in model complexity. Producing soil property maps in a time series still remains challenging. The uncertainties in soil data need to be estimated and incorporated into ESMs.

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Ellili Bargaoui, Yosra, Christian Walter, Didier Michot, Nicolas P. A. Saby, Sébastien Vincent, and Blandine Lemercier. "Validation of digital maps derived from spatial disaggregation of legacy soil maps." Geoderma 356 (December 2019): 113907. http://dx.doi.org/10.1016/j.geoderma.2019.113907.

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Zhao, Z., T. L. Chow, Q. Yang, H. W. Rees, G. Benoy, Z. Xing, and F. R. Meng. "Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps." Canadian Journal of Soil Science 88, no. 5 (November 1, 2008): 787–99. http://dx.doi.org/10.4141/cjss08012.

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High-resolution soil drainage maps are important for crop production planning, forest management, and environmental assessment. Existing soil classification maps tend to only have information about the dominant soil drainage conditions and they are inadequate for precision forestry and agriculture planning purposes. The objective of this research was to develop an artificial neural network (ANN) model for producing soil drainage classification maps at high resolution. Soil profile data extracted from coarse resolution soil maps (1:1 000 000 scale) and topographic and hydrological variables derived from digital elevation model (DEM) data (1:35 000 scale) were considered as candidates for inputs. A high-resolution soil drainage map (1:10 000) of the Black Brook Watershed (BBW) in northwestern New Brunswick (NB), Canada, was used to train and validate the ANN model. Results indicated that the best ANN model included average soil drainage classes, average soil sand content, vertical slope position (VSP), sediment delivery ratio (SDR) and slope steepness as inputs. It was found that 52% of model-predicted drainage classes were exactly the same as field assessment observations and 94% of model-predicted drainage classes were within ±1 class. In comparison, only 12% of maps indicated drainage classes were the same as field assessment observations based on coarse resolution soil maps and only 55% of points were within ±1 class of field assessed drainage classes. Results indicated that the model could be used to produce high-resolution soil drainage maps at relatively low cost. Key words: Soil drainage, artificial neural network model, ANN model, high-resolution soil maps, DEM, hydrology model

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Ellehoj, Erik A., and Michael R. C. Coulson. "Legend Design for Soil Maps: An Experiment." Cartography and Geographic Information Systems 17, no. 3 (January 1990): 225–35. http://dx.doi.org/10.1559/152304090783814131.

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Doolittle, J., R. Dobos, S. Peaslee, S. Waltman, E. Benham, and W. Tuttle. "Revised Ground-Penetrating Radar Soil Suitability Maps." Journal of Environmental & Engineering Geophysics 15, no. 3 (September 1, 2010): 111–18. http://dx.doi.org/10.2113/jeeg15.3.111.

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Brus, D. J., B. Kempen, and G. B. M. Heuvelink. "Sampling for validation of digital soil maps." European Journal of Soil Science 62, no. 3 (April 12, 2011): 394–407. http://dx.doi.org/10.1111/j.1365-2389.2011.01364.x.

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Zhogolev, A. V., and I. Yu Savin. "Automated updating of medium-scale soil maps." Eurasian Soil Science 49, no. 11 (November 2016): 1241–49. http://dx.doi.org/10.1134/s1064229316110120.

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Eisenhauer, Nico, and Carlos A. Guerra. "Global maps of soil-dwelling nematode worms." Nature 572, no. 7768 (July 24, 2019): 187–88. http://dx.doi.org/10.1038/d41586-019-02197-0.

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FISHER, PETER F. "Visualizing Uncertainty in Soil Maps by Animation." Cartographica: The International Journal for Geographic Information and Geovisualization 30, no. 2-3 (October 1993): 20–27. http://dx.doi.org/10.3138/b204-32p4-263l-76w0.

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Bockheim, J. G., and A. N. Gennadiyev. "General state soil maps in the USA." Geoderma 253-254 (September 2015): 78–89. http://dx.doi.org/10.1016/j.geoderma.2015.04.013.

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Szucs, Mihaly. "Soil trace-elements availability maps of Hungary." Computers, Environment and Urban Systems 19, no. 2 (March 1995): 117–22. http://dx.doi.org/10.1016/0198-9715(95)00011-v.

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Pásztor, László, J. Szabó, Zs Bakacsi, P. László, and M. Dombos. "Large-scale Soil Maps Improved by Digital Soil Mapping and GIS-based Soil Status Assessment." Agrokémia és Talajtan 55, no. 1 (March 1, 2006): 79–88. http://dx.doi.org/10.1556/agrokem.55.2006.1.9.

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A key issue of the applicability of both traditional soil maps and soil information systems (SSISs) is their accuracy. Essentially, the main practical aim of soil surveys/mapping and spatial soil information is prediction. A traditional tool of this information extension is the classical (crisp) soil map (using soil mapping units), which generally constitute the geometric basis of SSISs, too. Numerous novel methods have been developed for producing more accurate soil maps, however traditional crisp soil maps are still extensively applied, as they offer the most easily interpretable results for the majority of users. On the other hand, accuracy of this kind of soil maps can be increased in several ways: with the refinement of soil contours; with the subdivision of mapping units taking into consideration smaller, within patch inhomogeneities; and with the refinement of attribute information (more recent data, more precise measurement, up-to-date methodology, more appropriate classification etc.). The GIS adaptation of soil information originating from the 1:25,000 scale practical soil mapping of Hungary is under construction. Compilation of the Kreybig Digital Soil Information System (KDSIS) involves both its integration within appropriate spatial data infrastructure and updating with efficient field correlation, which make an inherent refinement and upgrading of the system possible. The first attempts for the field-based updating of KDSIS have been done, using field GIS technology. Processes of desktop and field reambulation of the detailed, complex, national spatial soil information system are presented in this paper.

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Holmes, K. W., E. A. Griffin, and N. P. Odgers. "Large-area spatial disaggregation of a mosaic of conventional soil maps: evaluation over Western Australia." Soil Research 53, no. 8 (2015): 865. http://dx.doi.org/10.1071/sr14270.

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Conventional soil maps may be the best available source for spatial soil information in data-limited areas, including individual soil properties. Spatial disaggregation of these maps, or mapping the unmapped soil components, offers potential for transforming them into spatially referenced soil class distributions. We used an automated, iterative classification tree approach to spatially disaggregate a patchwork of soil surveys covering Western Australia (2.5 × 106 km2) to produce raster surfaces of soil class occurrence. The resulting rasters capture the broad spatial patterns of dominant soils and harmonise soil class designations across most survey boundaries. More than 43 000 archived profiles were used to evaluate the accuracy of the rasters. In 20% of cases, the predicted soil class with the highest probability matched that recorded for the profile; when any of the three highest probability soil classes predicted were considered correct, the global accuracy was 40%. The accuracy increased to 71% when the rasters were reassembled to represent a higher level in the soil classification system. The predicted surfaces retained features related to the mapping intensity of the original surveys and generally had higher prediction accuracy of profile soil class where the surface geochemistry was more homogeneous. The best indicator of prediction accuracy was how common the profile soil class was in the original mapping (94% variance explained); profile observations collected during soil survey may be biased towards rare soils, making them less suitable for validation or modelling directly from point data.

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Journal articles on the topic 'Soil maps'

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