Versioning of GlobalSoilMap.net raster property maps for the North American Node

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1 Digital Soil Assessments and Beyond Minasny, Malone & McBratney (eds) 2012 Taylor & Francis Group, London, ISBN Versioning of GlobalSoilMap.net raster property maps for the North American Node J.W. Hempel & Z. Libohova US Department of Agriculture, Natural Resources Conservation Service, Washington, D.C., US N.P. Odgers & J.M. Thompson West Virginia University, Morgantown, West Virginia, US S.S. Smith & G.W. Lelyk Agriculture and Agri-Food Canada, Canada ABSTRACT: The North American Node of the GlobalSoilMap.net consortium is composed of Canada, the United States and Mexico. Each country has abundant legacy soil map data at a variety of scales collected over multiple decades. Spatial soil information is provided as polygon maps at both coarse scales (Canada Soil Landscapes of Canada SLC 1:1,000,000; United States State Soil Geographic Data Base STATSGO2 1:250,000; Mexico Carta Edafologica escala 1:250,000) and finer scales (Canada Canada Land inventory CLI Detailed Soil Surveys 1:10,000 1:250,000; US Soil Survey Geographic Database SSURGO 1:12,0001:65,000; Mexico Cartas Edafológicas registradas 1:50,000). Due to the diverse soil information related to different spatial and temporal scales as well as type and completeness and consistency of information, several versions have been suggested for development of raster soil property maps from soil polygon maps that will utilize both legacy data and digital mapping techniques and meet the standards and specifications of the GlobalSoilMap.net project. Each version will successively utilize data of increased detail to produce new rasterized.information, with the final product being a fully modeled continuous digital soil map. The versioning concept will allow for initial data to be produced in a time efficient manner with continual product improvement providing greater compliance to GlobalSoilMap.net specifications over time. 1 INTRODUCTION The demand for up-to-date and relevant soil information is soaring (Sanchez, et al., 2009). The GlobalSoilMap.net consortium was established in response to such a demand. The consortium consists of leading soil science institutions from around the world. The consortium has undertaken the task of producing digital soil maps that will predict important soil properties at a fine resolution using state-of-the-art and emerging technologies for soil mapping. This raster based soil information allows for completely new ways of acquiring, disseminating and using soil information. This new digital information will be an essential component in geographic information systems to assist scientists, economists, sociologists, and policy makers in making better decisions for a range of issues from global to local such as food production, hunger reduction, climate change mitigation, energy security, ecosystem services, biodiversity and environmental degradation. The products generated will meet GlobalSoilMap. net standards and specifications (McMillian, et al., 2011), which the consortium has established through scientific rigor and peer review. A common set of soil properties at specific resolution with a defined spatial entity, specified depth increments and uncertainty calculations will be available consistently across the globe. Procedures and methodologies to produce this information will vary depending on the kinds and amounts of available data, but all information must meet the GlobalSoilMap.net standards and specifications. Data needed to produce this information will mainly come from archived legacy data, which could include polygon soil maps, point pedon data, climatic data, remote sensing information, geological data, and other forms of environmental co-variate information. Technology now exists to transform analog soils data into digital information. This information can be produced even with limited background information (Hartemink, et al., 2008). 429

2 2 BACKGROUND The GlobalSoilMap.net consortium is made up of lead and supporting institutions. The consortium is responsible for developing overall policies and technical procedures that can be applied throughout the project, along with exploring fund raising potentials and building partnerships and developing data. The consortium partnership is developed on the basis of regional nodes (Figure 1). Each node has a designated lead institution that is responsible for building partnerships and leading the development of the data within the individual node. Figure 1 displays the nodal boundaries for the GlobalSoilMap.net project. The North American Node of the GlobalSoil- Map.net Consortium consists of Canada, Mexico and the United States. Agencies representing these countries respectively are Agriculture and Agri-Food Canada (AAFC), Instituto Nacional de Estadística y Geografía-National Institute of Statistic and Geography (INEGI) and the US Department of Agriculture, Natural Resources Conservation Service (NRCS). Other partners within each of these countries make up the North American Node Consortium and assist with data collection, data preparation and development of information to meet the GlobalSoilMap.net Standards and Specifications (GlobalSoilMap.net, 2011). Examples of these partners are universities, other federal/local agencies and private non-governmental organizations. The agencies listed are responsible for the soil survey data holdings for each of the countries. Canada, Mexico and the US have extensive legacy soil survey data that has been collected and archived over many decades. This legacy information consists of point pedon data, polygon maps at a variety of scales and other environmental covariate information. This information will be utilized in the preparation of the final raster based soil property maps. Figure 1. GlobalSoilMap.net nodal boundaries. The North American Node will develop data based on versions of progressively greater detail. These versions will allow the node to take full advantage of the available data and produce the information in a timely manner that will meet the GlobalSoilMap.net standards and specifications. Figure 2 (Minasny and McBratney, 2010) is the Digital Soil Mapping Workflow diagram that documents available soil data and the possible modeling options for producing raster based soil property information. The North American Node legacy soil survey information is mainly at the far left of the diagram. Each country in the North American Node also has ample soil point data holdings and soil maps at various scales that can be used for modeling or validation of modeled information. 3 DATA AVAILABLE Soil legacy data for the North American Node is available in different scales. Small Scale information for the 3 countries are as follows: 3.1 Small scale CANADA-Soil Landscapes of Canada (SLC- 1:1,000,000) (Soil Landscapes of Canada Working Group, 2010); MEXICO-Carta Edafologica escala (1:250,000) (no citation available); USA-US General Soil Map (STATSGO2-1:250,000) (Soil Survey Staff, 2012a). Small scale maps are available for 100 percent of the North American Node land surface area. Tabular data needed to model soil properties is available for US General Soil Map areas and the cropland portions of the Soil Landscapes of Canada. All other areas within the node need Figure 2. Digital Soil Mapping Workflow to be used in creating versions of North American Node GlobalSoil- Map.net information soil maps available for the entire continent. 430

3 to have tabular data attributed to the spatial information before soil properties can be modeled. Much of Canada and all of Mexico need to have this process completed. 3.2 Medium to large scale CANADA Canada Land inventory (CLI)- (Detailed Soil Surveys-1:10,000 1:250,000). MEXICO-Cartas Edafológicas registradas (no citation available). USA-SSURGO Soil Survey Geographic Database (SSURGO-1:12,000 1:65,000) (Soil Survey Staff, 2012b). Medium to large scale digital polygon soil maps are available for much of the United States. Availability for these scales is less in Canada and Mexico. 4 VERSIONING OF NORTH AMERICAN NODE DATA The North America Node of GlobalSoilMap. net has developed a versioning concept where each successive version of map products incorporates improvements over the previous version. In Canada, Mexico and the United States, initial versions will rely heavily on the use of existing area-class maps as held in SLC, Carta Edafologica escala, STATSGO2, CLI, Cartas Edafológicas registradas and SSURGO, with the understanding that later versions will make better use of spatial disaggregation of these maps and point observations and will eventually lead to truly continuous raster property maps. For the three countries, the proposed versioning system is as follows: 4.1 Versions Essentially rasterised versions of the existing STATSGO2, SLC and the Carta Edafologica escala of Mexico map products will be used. Soil components will be fitted with equal-area splines (Bishop et al., 1999; Malone et al., 2009), which allows property estimates to be made at the GlobalSoilMap.net standard depth increments (GlobalSoilMap.net, 2011). (Figure 3). Weighted means will then be calculated within map units where more than one soil component possesses data, otherwise the single soil component s property estimate is reported. The products may have gaps where soil exists but property values were not recorded. Map units will not have been harmonized, so artifacts (data discontinuities) may exist at political boundaries. The products are not truly continuous. Pre-existing scale discrepancies are inherited Figure 3. Conceptual diagram illustrating the progression of versions from rasterizing soil polygon maps to truly 4 dimensional continuous property maps as more point data (black dots) and other soil and auxiliary soil data become available. from each of the countries small scale maps. For initial versions, the upper and lower values reported in STATSGO2 are assumed to be the 95% confidence limits for uncertainty estimation. Confidence intervals for Canada and Mexico are being developed. Later versions may estimate probability distribution functions and hence confidence intervals from legacy point observations in each of the countries National Soil Characterization Databases. 4.2 Versions Essentially as per versions but the greaterdetail SSURGO, CLI and Cartas Edafológicas registradas map products will be used. These versions will begin to incorporate harmonization of map units at political boundaries, which may necessitate the use of spatial disaggregation and other predictive techniques. Techniques will need to be developed to fill gaps in SSURGO, CLI and Cartas Edafológicas registradas map products where no data currently exists. The use of STATSGO2, SLC and the Carta Edafologica escala of Mexico map products can be used to fill in blank areas in the higher detailed map products. The disaggregation of the spatial components of these maps may be a potential solution for providing higher resolution data. Uncertainty predictions will be documented as per versions , or other potential quantitative techniques currently being researched may also be used. 4.3 Versions 1.0+ These versions will be produced using digital soil mapping predictive techniques that will provide soil property estimates based on the environmental continuum. Products will not contain gaps other than where bedrock or water exists. Products will 431

4 not contain scale discrepancies. Uncertainty will be represented as 95% confidence interval estimated by quantitative techniques and will be refined as more point observations become available. 5 SPATIALLY WEIGHTED MEAN CALCULATION The existence of many components within soil map unit polygons along with the lack of explicit spatial representation of these components necessitates the use of spatially weighted means. The weighted mean is spatial only in that it is based on the relative contribution of each component within the spatial limits of that soil polygon map unit. Within each map unit, a weighted mean is calculated as follows: m μ * = w i z i i= 1 (1) where z i is the estimated soil property and w i is the areal proportion of component i within the map unit. m is the number of soil components in the map unit that have data. The weighted mean thus reflects only the proportion of the map unit represented by the available components. Rock outcrops and water bodies are excluded from the spatially weighted mean calculation. 6 EXAMPLES The following figures are examples of Versioning of North American Node information. Figure 4 is an example of a Version 0.1 for organic carbon Figure 4. Soil Organic Carbon, United States, 0 to 5 cm, based on STATSGO2 derived using spatially weighted means calculation. Figure 5. Soil Organic Carbon (g m 2 ) for USA, 0 5 cm from SSURGO and Statsgo2 derived using spatially weighted means calculation (courtesy of N. Bliss and S. Waltman). Red indicates low SOC, blue indicated high SOC values. Figure 6. Soil Organic Carbon for the agricultural areas of Canada, 0 to 5 cm derived from the Soil Landscapes of Canada using spatially weighted means calculation. (OC) based on STATSGO2 derived using spatially weighted means calculations. Figure 5 is an example of Version 0.5 for organic carbon based on SSURGO and STATSGO2 derived using spatially weighted means calculations. Figure 6 is an example of Version 0.1 for OC for Canada based on SLC. 7 CONCLUSIONS In an effort to provide timely data and information that meets the GlobalSoilMap.net project standards and specifications, the versioning concept will prove of be a valuable tool for the North American Node. The intention is that all versions will meet the GlobalSoilMap.net standards and specifications. Information derived with spatially weighted mean calculations can be done relatively easily 432

5 with a small amount of resources. Functional soil property maps and data can be produced with existing legacy information (area class maps) and made available to the user community for application and feedback on the usability. The early versions can be a stopgap for providing needed initial information quickly. These early versions are a rudimentary digital soil mapping procedure. The modeled information mirrors the data from existing polygon maps which do not model the entirety of the landscape but provide an aggregated value for each polygon area. Later versions will be more labor intensive and use more technologically challenging methodologies that will model the landscape on the natural continuum. These methodologies are currently under research and development. In early versions which are based on existing correlated and accepted soil category maps there should be minimal need for rigorous validation measures as quality control and assurance processes have already been completed and the data accepted as official. The versions procedure can be used in other GlobalSoilMap.net nodes where there are polygonal maps that have been digitized and atributed to provide information that meets the GlobalSoilMap. net standards and specifications. REFERENCES Bishop, T.F.A., McBratney, A.B. & Laslett, G.M Modelling soil attribute depth functions with equalarea quadratic smoothing splines. Geoderma 91: GlobalSoilMap.net, Specifications, Version 1 GlobalSoilMap.net products. Release 2.1. Hartemink, A.E., McBratney A.B. & Mendonça-Santos M.L., Eds Digital Soil Mapping with Limited Data Springer, New York. Malone, B.P., McBratney, A.B., Minasny, B. & Laslett, G.M Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma 154: Sanchez, P.A., Ahamed, S., Carre, F., Hartemink, A.E., Hempel, J., Huising, J., Lagacherie, P., McBratney, A.B., McKenzie, N.J. & de Mendonca-Santos, M.L. et al., Digital Soil Map of the World, Science, vol. 325, no. 5941, pp , asoil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. U.S. General Soil Map (STATSGO2). Available online at Accessed January, bsoil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Soil Survey Geographic (SSURGO) Database. Available online at Accessed January, Soil Landscapes of Canada Working Group, Soil Landscapes of Canada version 3.2. Agriculture and Agri-Food Canada. (digital map and database at 1:1 million scale). 433