Progress in the elaboration of GSM conform DSM products and their functional utilization in Hungary

Abstract The GlobalSoilMap initiative significantly inspired the DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) project, which was started intentionally for the renewal of the national spatial soil data infrastructure in Hungary. The main objectives of our work has been to broaden the possibilities, how demands on spatial soil related information could be satisfied in Hungary, how the gaps between the available and the expected could be filled with optimized digital soil (related) maps. During our activities, we have significantly extended the potential, how goal-oriented, map-based soil information could be created to fulfill the requirements. Primary and specific soil property, soil type and certain tentative functional soil maps were compiled. The set of the applied digital soil mapping techniques has been gradually broadened incorporating and eventually integrating geostatistical, machine learning and GIS tools. Soil property maps have been compiled partly according to GlobalSoilMap.net specifications, partly by slightly or more strictly changing some of their predefined parameters (depth intervals, pixel size, property etc.) according to the specific demands on the final products. The set of primary and derived soil properties specified by GSM for the required layers have been almost entirely prepared. The web publishing of the results was also elaborated creating a specific WMS environment. The map products are published on the www.dosoremi.hu website. The maps are serviced in two different ways. In the atlas version, map layouts are collected and published for application as graphical elements. Interactive maps are produced for browsing over alternative base map background. Most relevant information on the renewed Hungarian Spatial Soil Data Infrastructure, on its compilation and applicability are also communicated on the site. The nationwide, thematic digital soil maps compiled in the frame and spin-off of our research have been utilized in a number of ways. Programs or studies dedicated to the designation of areas suitable for irrigation; risk modelling of inland excess water hazard; mapping of potential habitats; spatial assessment and mapping of ecosystem services were heavily relied on the novel type spatial soil information. These programs however frequently required certain modifications of the standard GSM products due to various reasons. The paper presents the finalized GSM conform results of DOSoReMI.hu, together with their various national applications. Some reasons behind the application of modified GSM products are also presented.

[1]  Freddy Nachtergaele,et al.  State of the Art Report on Global and Regional Soil Information: Where are we? Where to go? , 2013 .

[2]  Budiman Minasny,et al.  Chile and the Chilean soil grid: A contribution to GlobalSoilMap , 2017 .

[3]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[4]  T. Hengl,et al.  3D soil hydraulic database of Europe at 250 m resolution , 2017 .

[5]  H. Keskin,et al.  Regression kriging as a workhorse in the digital soil mapper's toolbox , 2018, Geoderma.

[6]  Philippe Lagacherie,et al.  Evaluating Digital Soil Mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France) , 2015 .

[7]  Zoltán Botta-Dukát,et al.  A grid-based, satellite-image supported, multi-attributed vegetation mapping method (MÉTA) , 2007, Folia Geobotanica.

[8]  J. Ritchie,et al.  Cereal growth, development and yield , 1998 .

[9]  Dominique Arrouays,et al.  GlobalSoilMap France: High-resolution spatial modelling the soils of France up to two meter depth. , 2016, The Science of the total environment.

[10]  Paulo Pereira,et al.  Soil mapping, classification, and pedologic modeling: History and future directions , 2016 .

[11]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[12]  László Pásztor,et al.  Compilation of a national soil-type map for Hungary by sequential classification methods , 2018 .

[13]  Joe T. Ritchie,et al.  Soil water balance and plant water stress , 1998 .

[14]  László Pásztor,et al.  Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools , 2015 .

[15]  N. Zimmermann,et al.  Implementation and application of multiple potential natural vegetation models – a case study of Hungary , 2017 .

[16]  Elaboration and applications of spatial soil information systems and digital soil mapping at Research Institute for Soil Science and Agricultural Chemistry of the Hungarian Academy of Sciences , 2013 .

[17]  András Makó,et al.  Introduction of the Hungarian Detailed Soil Hydrophysical Database (MARTHA) and its use to test external pedotransfer functions , 2010 .

[18]  S. Baxter.  Guidelines for Soil Description. Rome: Food and Agriculture Organization of the United Nations. (2006), pp. 108, US$40.00. ISBN 92-5-1055-21-1 , 2007, Experimental Agriculture.

[19]  László Pásztor,et al.  Variations for the Implementation of SCORPAN’s “S” , 2016 .

[20]  G. Hoogenboom,et al.  Understanding Options for Agricultural Production , 1998, Systems Approaches for Sustainable Agricultural Development.

[21]  Hans-Jörg Vogel,et al.  Modeling Soil Processes: Review, Key Challenges, and New Perspectives , 2016 .

[22]  B. Minasny,et al.  Digital Soil Map of the World , 2009, Science.

[23]  W. Blum.  Functions of Soil for Society and the Environment , 2005 .

[24]  Zhou Shi,et al.  High-resolution three-dimensional mapping of soil organic carbon in China: Effects of SoilGrids products on national modeling. , 2019, The Science of the total environment.

[25]  László Pásztor,et al.  Comparison of various uncertainty modelling approaches based on geostatistics and machine learning algorithms , 2019, Geoderma.

[26]  Z. Libohova,et al.  Equal-area spline functions applied to a legacy soil database to create weighted-means maps of soil organic carbon at a continental scale , 2012 .

[27]  E. Dobos,et al.  Legacy Soil Data Harmonization and Database Development , 2010 .

[28]  Paulo Pereira,et al.  Goal Oriented Soil Mapping: Applying Modern Methods Supported by Local Knowledge , 2017 .

[29]  L. Pásztor,et al.  Comparison of soil texture maps synthetized from standard depth layers with directly compiled products , 2019, Geoderma.

[30]  Laura Poggio,et al.  3D mapping of soil texture in Scotland , 2017 .

[31]  Budiman Minasny,et al.  High‐Resolution 3‐D Mapping of Soil Texture in Denmark , 2013 .

[32]  László Pásztor,et al.  Compilation of functional soil maps for the support of spatial planning and land management in Hungary , 2017 .

[33]  L. Montanarella,et al.  European Soil Data Centre: Response to European policy support and public data requirements , 2012 .

[34]  David G. Rossiter,et al.  Technical Note: Statistical methods for accuracy assesment of classified thematic maps , 2004 .

[35]  Alfred E. Hartemink,et al.  Digital soil mapping: bridging research, environmental application, and operation , 2010 .

[36]  L. Pásztor,et al.  Digital processing and upgrading of legacy data collected during the 1:25 000 scale Kreybig soil survey , 2010 .

[37]  L. Pásztor,et al.  Mapping of topsoil texture in Hungary using classification trees , 2016 .

[38]  Gergely Tóth,et al.  Presentation of soil data from the National Pedological and Crop Production Database (NPCPD) and investigations on spatial representativeness , 2014 .

[39]  N. Fodor,et al.  The agro-ecological potential of Hungary and its prospective development due to climate change. , 2010 .

[40]  A. McBratney,et al.  The dimensions of soil security , 2014 .

[41]  Juan Pablo Gonzalez,et al.  A New Global Demand for Digital Soil Information , 2008 .

[42]  I. Sisák,et al.  Probability-based harmonization of digital maps to produce conceptual soil maps , 2014 .

[43]  Budiman Minasny,et al.  Constructing a soil class map of Denmark based on the FAO legend using digital techniques , 2014 .

[44]  D. C. Godwin,et al.  Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems , 1998 .

[45]  A. McBratney,et al.  GlobalSoilMap - Digital Soil Mapping from Country to Globe : Proceedings of the Global Soil Map 2017 Conference, July 4-6, 2017, Moscow, Russia , 2017 .

[46]  Eszter Illyés,et al.  A NEW HABITAT CLASSIFICATION AND MANUAL FOR STANDARDIZED HABITAT MAPPING , 2007 .

[47]  László Pásztor,et al.  Compilation of 1:50,000 scale digital soil maps for Hungary based on the digital Kreybig soil information system , 2012 .

[48]  A. Makó,et al.  Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics , 2019, Hydrology and Earth System Sciences.

[49]  Béla Pirkó,et al.  Spatio-temporal assessment of topsoil organic carbon stock change in Hungary , 2019 .

[50]  David Clifford,et al.  The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project , 2015 .

[51]  Colby Brungard,et al.  Soil Property and Class Maps of the Conterminous United States at 100-Meter Spatial Resolution , 2018 .

[52]  Dominique Arrouays,et al.  Evaluating large-extent spatial modeling approaches: A case study for soil depth for France , 2016 .

[53]  The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region , 2019, Aerobiologia.

[54]  L. Kiss,et al.  Early distribution and spread of Ambrosia artemisiifolia in Central and Eastern Europe , 2010, Botanica Helvetica.

[55]  Alfred E. Hartemink,et al.  Digital Mapping of Soil Organic Carbon Contents and Stocks in Denmark , 2014, PloS one.

[56]  László Pásztor,et al.  Coupling the 4M crop model with national geo-databases for assessing the effects of climate change on agro-ecological characteristics of Hungary , 2014, Int. J. Digit. Earth.

[57]  László Pásztor,et al.  Digital mapping of selected WRB units based on vast and diverse legacy data , 2014 .

[58]  Michael Bock,et al.  System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 , 2015 .

[59]  R. Pongrácz,et al.  Estimation of agro-ecosystem services using biogeochemical models , 2020, Időjárás.

[60]  L. Montanarella.  Need for interpreted soil information for policy making , 2010 .