SSSA 75th Anniversary Paper Digital Soil Mapping and...

SSSAJ: Volume 75: Number 4 • July–August 2011

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Soil Sci. Soc. Am. J. 75:1201-1213Posted online 23 June 2011doi:10.2136/sssaj2011.0025Received 20 Jan. 2011.*Corresponding author (sabgru@ufl .edu).© Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USAAll rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Digital Soil Mapping and Modeling at Continental Scales: Finding Solutions for Global Issues

SSSA 75th Anniversary Paper

The Big Bang, the point in space and time from which all matter and energy in the universe supposedly emanated, is thought to have occurred sometime

around 13.7 billion yr ago (Bloom, 2010). During the past 4.5 billion yr since the Earth coalesced and cooled, the planet has undergone alterations and transforma-tions via (for example) plate tectonics, volcanism, and orogenies, which spawned severe changes in the composition and structure of the atmosphere, the oceans, and the land surface—including the biosphere and pedosphere. Other major natural forcing factors have included solar processes and orbital and galactic variations, which changed the amount of solar energy the Earth received (Intergovernmental Panel on Climate Change, 2007). Solar insolation and the structure and composi-tion of the Earth’s surface drive many ecosystem processes that have formed the soils we observe on the landscapes of today.

VALUE OF SOILS IN THE ANTHROPOCENEDuring the last three centuries, human actions have produced profound shift s

in the Earth system, becoming the main driver of global environmental change. Crutzen (2002), Steff en et al. (2005), Rockström et al. (2009), and others have

S. Grunwald*Soil and Water Science Dep.2169 McCarty HallP.O. Box 110290Univ. of FloridaGainesville, FL 32611

J. A. ThompsonDivision of Plant and Soil Sciences1108 Agricultural Sciences BuildingP.O. Box 6108West Virginia Univ.Morgantown, WV 26506-6108

J. L. BoettingerPlants, Soils and Climate Dep.4820 Old Main Hill (AGS 354)Utah State Univ.Logan, UT 84322-4820

Profound shift s have occurred during the last three centuries in which human actions have become the main driver to global environmental change. In this new epoch, the Anthropocene, human-driven changes such as population growth, climate, and land use change are pushing the Earth system well outside of its normal operating range, caus-ing severe and abrupt environmental change. In the Anthropocene, soil change and soil formation or degradation have also accelerated, jeopardizing soil quality and health. Th us, the need for up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize the physicochemical, biological, and hydrologic conditions of ecosystems across continents has intensifi ed. Th ese needs are in sharp con-trast to available digital soil data representing continental and global soil systems, which only provide coarse-scale (1:1,000,000 or coarser) vector polygon maps with highly aggregated soil classes represented in the form of crisp map units derived from historic observations, lacking site-specifi c pedogenic process knowledge, and only indi-rectly relating to pressing issues of the Anthropocene. Furthermore, most available global soil data are snapshots in time, lacking the information necessary to document the evolution of soil properties and processes. Recently, major advancements in digital soil mapping and modeling through geographic information technologies, incor-poration of soil and remote sensing products, and advanced quantitative methods have produced domain-specifi c soil property prediction models constrained to specifi c geographic regions, which have culminated in the vision for a global pixel-based soil map. To respond to the challenges soil scientists face in the Anthropocene, we propose a space–time modeling framework called STEP-AWBH (“step-up”), explicitly incorporating anthropogenic forc-ings to optimize the soil pixel of the futurevv.

Abbreviations: DEM, digital elevation models; DSA, digital soil assessment; DSM, digital soil mapping; DSRA, digital soil risk assessment; GPS, global positioning system; LIBS, laser-induced breakdown spectroscopy; SSURGO, soil survey geographic database; VNIR, visible near-infrared spectroscopy.

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described this new epoch, named the Anthropocene, and ar-gued that human-driven changes are pushing the Earth system well outside of its normal operating range. About 30 to 50% of the planet’s land surface is exploited by humans (Crutzen, 2002). Global population, which was just under one billion in 1800, is currently about 6.9 billion (U.S. Census Bureau, 2010) and is projected to be approximately 9 billion by 2050 (United Nations, 2004), indicating exponential growth rates. Crossing beyond critical values, the so-called “planetary boundaries” that provide safe operating space for humanity with respect to the Earth system, could cause severe, abrupt, and untenable envi-ronmental change (Steff en et al., 2005). Out of nine planetary boundaries, three of the Earth-system processes—global climate change, the rate of biodiversity loss, and interference with the N cycle (i.e., the amount of reactive N)—have already transgressed their boundaries, thus jeopardizing the resilience of major com-ponents of Earth-system functioning (Rockström et al., 2009). In the Anthropocene, soil change and soil formation and degra-dation have also accelerated, jeopardizing soil quality and health. As such, the need for up-to-date, high-quality, high-resolution, spatiotemporal, and continuous soil and environmental data that characterize the physicochemical, biological, and hydrologic conditions of ecosystems across continents has intensifi ed.

The Need to Sustain Soil ResourcesAccording to the National Academy of Sciences (2001),

changes in climate, land use dynamics, biological diversity, eco-system functioning, biogeochemical cycles, and the quality of soil and water resources have accelerated during the past decades at a rate proportional to human-induced activities and popula-tion growth. To address such global challenges, the National Academy of Sciences (2010) has identifi ed four high-priority, in-terdisciplinary research initiatives in Earth surface processes: (i) interacting landscapes and climate; (ii) quantitative reconstruc-tion of landscape dynamics across time scales; (iii) coevolution of ecosystems and landscapes; and (iv) future of landscapes in the Anthropocene. Th e latter two priority initiatives target the development of integrated human–landscape systems that are undergoing rapid change under varying climate and land use conditions and projections. Clearly there are profound needs to better quantify and reconstruct spatial and temporal Earth sur-face (soil) patterns and landscape dynamics.

Humans have fundamentally altered global patterns of eco-system processes, biodiversity, pedodiversity, and landscape dy-namics. Ellis and Ramankutty (2008) linked global populations to land use and land cover, showing that >75% of Earth’s ice-free land has been altered as a result of human residence and land use, culminating in the delineation of anthropogenic biomes (anthromes). Between 1700 and 2000, the terrestrial biosphere made the critical transition from mostly wild to mostly anthro-pogenic biomes, passing the 50% mark early in the 20th century (Ellis et al., 2010). According to these researchers, anthropo-genic transformation of the biosphere during the Industrial Revolution resulted about equally from the expansion of urban

and agricultural land uses into wildlands and intensifi cation of land use within seminatural anthromes.

According to Scherr (1999), the land surface of the Earth to-tals 13.0 billion ha, of which about 8.7 billion ha are under human use, mostly suitable only for forest, woodland, grassland, or perma-nent vegetation. Only 3.2 billion ha are potentially arable. About half of this potentially arable land is currently cropped, but over-use threatens to severely degrade the soil quality. Th e International Soil Reference and Information Center (1990) concluded that 1.97 billion ha (23% of globally used land) was degraded between 1949 and 1990, primarily by water erosion, followed by wind ero-sion, soil nutrient depletion, and salinization. Overgrazing was the leading proximate cause, followed by deforestation, and agri-cultural activity. Of all degraded soils, 58% were in drylands and 42% in humid areas. Soil degradation was assessed as being high-est in Central America (31%), followed by Europe (20%), Africa (19%), Asia (16%), South America (9%), and North America (7%) (Scherr, 1999). According to the World Resources Institute (1990) about 25% of the globally used land is at risk for future deg-radation, including irreversible desertifi cation. Soil degradation translates into reduced crop productivity, diminished livelihood, famine, undernourishment, and other societal disasters. Th ere is a tremendous need for monitoring of soil degradation, which in-cludes tracking of spatially explicit soil change.

Globally, humanity uses the equivalent of 1.4 planets to provide the resources we use and to absorb our waste (Global Footprint Network, 2010). Ecological footprints in high-income countries (6.1 ha per capita) diff er tremendously from those of low-income countries (1.9 ha per capita), and of the world (2.6 ha per capita), indicating that humanity is consuming resources well beyond sustainability. Soils provide supporting, regulat-ing, provisioning, and cultural ecosystem services (Millennium Ecosystem Assessment, 2005) and play a major role in the global system regulating major biogeochemical cycles and energy and water fl uxes. To sustain soil resources and address the challenges of the Anthropocene at global and local scales, soil resource data are critical, requiring concerted eff orts that transcend social, economic, and political boundaries (Grunwald, 2006b). For example, the agrocentric approach to soil mapping in countries with food defi ciencies, which mainly targets soil fertility and soil degradation (Blum, 2006), must be integrated with the enviro-centric approach to soil mapping that has gained momentum in developed countries concerned with environmental qual-ity (Grunwald, 2009). Eff orts to map soil properties, quantify changes in the soil ecosystem, and assess soil–atmosphere, soil–hydrosphere, and soil–biosphere fl uxes at spatial and temporal scales matching other ecosystem components and processes have been hampered, however, by various factors as outlined here.

Soils—Part of the Global Biogeochemical CyclesAt local, regional, and global scales, the biogeo-

chemical reactor of the Earth’s surface responds to natu-ral and human-induced forcings through chemical weath-ering and erosion of bedrock or surface deposits, the

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availability of nutrients in soils, the fate of anthropogenic con-taminants, and the properties of ecosystems (National Academy of Sciences, 2010), which inherently provide positive or negative feedbacks to climate, water, and major biogeochemical cycles. Currently, major concern is focused on the infl uence of terrestrial C fl uxes on global climate change, particularly the role of humans in modifying storage and fl uxes in the global C cycle. In Norvig et al. (2010), David Montgomery pointed out that preserving the thin layer of minerals, living microorganisms, and dead plants blanketing the planet is critical to soil C sequestration as well as sustaining terrestrial life. Because the soil C is magnitudes of or-der larger than C in the atmosphere, even small increases in the rates of soil organic C loss could greatly enhance CO2 concentra-tions in the atmosphere, potentially creating a positive feedback on climate (Cox et al., 2000). On the other hand, the soil’s abil-ity to sequester large amounts of C is high, thus providing ample opportunity to counteract global climate change through mitiga-tion and adaptation strategies.

Global assessments of the soil organic C pool have dem-onstrated major uncertainties. Th e estimates for the global soil C pool in the upper 1-m profi le are vastly diff erent de-

pending on the data and method used to upscale soil C obser-vations, including 1395 Pg (Post et al., 1982), 1462 to 1548 Pg (Batjes, 1996), 1600 Pg plus 360 Pg in peat ( Jacobson et al., 2004), 2011 Pg (Intergovernmental Panel on Climate Change, 2000), 2500 Pg (Lal, 2004), and 3250 Pg (Field et al., 2007). Trumbore (1997) pointed out that soil C estimates in such glob-al studies are based on relatively few soil C inventories (pedon data) from important regions and the uncertainties involved in estimating total soil C stocks from coarse-scale soil map units are extremely high. Furthermore, these global studies do not specify which fraction of the total soil C pool is in active, intermedi-ate, or passive pools (Trumbore, 1997), thus they lack the link to processes involved in modulating soil C change. As Vasques et al. (2010a) pointed out, predictions of C pools and other dy-namic soil properties across large regions are still rare. Given the large uncertainty associated with current assessments of soil C stocks, which oft en rely on historic soil data, no reliable hind-cast and forecast estimates for soil C and other properties are available. Th is defi ciency is in contrast to estimates of climate change, biodiversity, or population dynamics compiled by other disciplines, which provide predictions into the past and future

Fig. 1. Timing of critical technologies, data management and mapping methods that have impacted digital soil mapping contrasted by temporal trends of select environmental and anthropogenic forcings that have accelerated in the Anthropocene. †Estimates of global soil carbon stocks (Pg C) from various sources (Post et al., 1982; Batjes, 1996; Jacobson et al., 2004; Intergovernmental Panel on Climate Change, IPCC, 2000; Lal, 2004; and Field et al., 2007 —U.S. Climate Change Science Program). ‡Past (U.S. Census Bureau) and projected population in billions (UN, 2004). §Past and projected nitrous oxide (N2O) concentrations (ppm) in the atmosphere under different scenarios for green house gas emissions (IPCC, 2001). ¶Departure in temperature (°C) from 1961 to 1990 average (Mann et al., 1999) and projected global average surface warming at the end of the 21st century in °C (relative to 1980–1999 temperature data) (IPCC, 2007). #Atmospheric CO2 (ppm) (Keeling et al., 1995) and projected CO2 (ppm) according to different best and worse case emission scenarios (IPCC, 2007).


(Fig. 1). Th ere are profound needs for better assessment of the global distributions of soil properties, such as soil C, N, P, mois-ture, and others, but also their linkages to ecosystem processes, biogeochemical cycles, and environmental and human-induced forcings. Accurate, high-resolution soil-environmental data spanning the globe are essential to assess ecosystem services, soil degradation, decoupling of major cycles (C, N, P, and S), soil–plant–water relationships, and soil contamination across spatial and temporal scales.

Signifi cance of Digital Soil Mapping and Modeling

Digital soil mapping (DSM) and modeling techniques have proliferated during the past decades to address these soil data and information needs (Grunwald, 2006a; Lagacherie et al., 2007; Hartemink et al., 2008; Boettinger et al., 2010) and have the potential to overcome some of the limitations imposed by labor-intensive and costly traditional soil surveys. But there is still terra incognita ahead of soil science to provide a universally accepted digital soil model responding to global societal needs and pres-sures and guiding society toward environmentally sustainable management. Th e notion that soil surveys should be more en-compassing than static soil maps was evoked earlier by Young (1973), who argued that soil surveys should include predictions of soil response to changes in land use, soil-specifi c crop-yield forecasts, and more soil interpretations than provided in tradi-tional soil maps. Albeit a major evolution in soil survey methods is taking place, most operational soil surveys are still focused on the generation of soil maps that delineate areas (polygons) of one or more taxonomic classes. Furthermore, the making of these maps usually does not use predictive quantitative models derived from mathematical algorithms, geostatistics, or statistics with as-sociated uncertainty and accuracy assessment for the estimated soil properties or processes (Grunwald, 2009). Grunwald (2009) also noted that current DSM research studies emphasize model-ing soils across space and rarely across both space and time. Th us the DSM challenge of describing the spatial and temporal vari-ability of physicochemical soil conditions in diverse ecosystems across continental and global scales is great.

Factors Limiting Soil Mapping and ModelingTh ere are several factors that have constrained soil mapping

and modeling at continental and global scales, including the costs and labor involved in developing inventories across large regions. Th e costs for soil mapping are dependent on the map scale (or spatial resolution), fi eld sampling, and the adopted pro-cessing methods to derive soil properties or soil classes. Current eff orts to maintain soil survey programs are expensive. For ex-ample, in the United States, the NRCS is responsible for soil sur-veys covering about 9.7 million km2 of the United States and its territories, disseminating vector-based soil map products at map scales of 1:12,000 to 1:24,000 (the soil survey geographic data-base, SSURGO), 1:250,000 (U.S. general soil map, STATSGO2; formerly the state soil geographic database, STATSGO), or

coarser. Most of the work is focused on maintenance, validation, and updates of surveys by about 450 staff members. Soil survey appropriations in the United States were US$43.46 million in 1980 and have risen to US$93.939 million in 2010 (Levin and Benedict, NRCS, personal communication, 2010). Th e current cost for soil surveying in the United States amounts to roughly US$10.30 ha−1.

Th is is in contrast to DSM projects, which have utilized en-vironmental covariates (e.g., derived from remote sensing, digital elevation models [DEM]) and predictive modeling techniques to map soils). For instance, MacMillan et al. (2010b) conducted predictive ecosystem mapping using a knowledge-based fuzzy semantic import model to assess soils across 8.2 million ha in Canada, costing about Canadian $0.34 ha−1, with an average ac-curacy of 69%. Even lower costs could be achieved by automatic predictive mapping across a 3 million ha forested area in British Columbia, Canada, at an eff ective map scale of 1:20,000 (grid resolution of 25 m) at US$0.20 ha−1 (MacMillan et al., 2007). Th e GlobalSoilMap.net project (www.globalsoilmap.net/; veri-fi ed 9 May 2011), which aims to predict 10 soil properties at six specifi c depth intervals at 90-m grid resolution across the globe (Sanchez et al., 2009; MacMillan et al., 2010a) (a land area of ?150 million km2), has estimated costs at US$0.20 ha−1. Th ese estimates suggest that spatially explicit DSM that utilizes lay-ers of globally available environmental covariates and modeling techniques may lower the cost for soil predictions at continental and global scales with spatial resolutions matching other envi-ronmental variables such as DEM and remote sensing images (≤30–90-m spatial resolution). Th ese fi ner spatial resolutions resemble more closely the inherent spatial variability of soil and environmental properties. In a meta analysis, McBratney and Pringle (1999) found that spatial autocorrelations were ≤300 m for soil C, NO3–N, and K, and ≤100 m for soil P, sand, clay, and pH, which suggests that soil map products should resemble these spatial resolutions (or pixel sizes). In addition to high-resolution soil models that represent the spatial variability and distribution of soil properties, monitoring of soil change will require a ma-jor investment to establish a soil monitoring network across the globe. Recent studies have demonstrated the capability to assess soil C change across large areas in Java (Minasny et al., 2010), China (Yan et al., 2010), and Belgium (Meersmans et al., 2011), which required space–time soil data sets. Such soil change analy-sis from regional to continental and global scales is needed to address emerging questions of the Anthropocene.

CURRENTLY AVAILABLE DIGITAL SOIL DATA AT GLOBAL SCALES

Most of the Earth’s land area is covered by existing soil maps at various scales, from low-resolution (e.g., the 1:5,000,000 FAO-UNESCO Soil Map of the World), to moderate resolution (e.g., 1:24,000 NRCS soil survey maps), to high resolution (e.g., the 1:5000 pedologic map of Belgium). Furthermore, some of these maps and their associated databases have been digitized and are available in digital form. However, digital soil mapping is more


than digitizing existing soil maps. Finke (2007) described several means of assessing the accuracy of digital soil maps, in terms of both producer accuracy and user accuracy. When evaluating cur-rently available digital soil maps, or when considering the poten-tial for new digital soil map products, data quality can be viewed as a function of positional quality, attribute quality, complete-ness, semantic quality, currency, logical consistency, and lineage (Finke, 2007).

Th e FAO-UNESCO Soil Map of the World (Nachtergaele, 1999), originally published as paper maps between 1971 and 1981, has been digitized, generalized, modifi ed, and updated to produce several global digital soil databases (Table 1). While there is no current alternative to these digital versions of the FAO-UNESCO Soil Map of the World at the global scale, defi nite shortcomings to this map are recognized (Sanchez et al., 2009). In particular, the map does not adequately represent the current condition of soils, nor the current state of knowledge about soils and soil classifi cation. All of the digital versions of the Soil Map of the World are at coarse scales (1:25,000,000 or 1:5,000,000) and represent information from soil classes. Th e earliest ver-sions—the World Soil Resources Map (produced in 1990) and the World Reference Base (WRB) World Soil Resources Map (produced in 2003)—were digitized as generalized versions of the paper map and presented at a scale of 1:25,000,000. Th e pri-mary diff erence between these two databases is that the legend for the WRB World Soil Resources Map was updated to con-form to the WRB classifi cation system. Th e more recent Digital Soil Map of the World (produced in 2007) provides a digital ren-dering of the FAO-UNESCO map at the original resolution of 1:5,000,000 and, like its predecessors, represents the dominant soil types within each soil map unit polygon. It may be noted that soil types (classes) only indirectly relate to soil-environmental change induced by the Anthropocene. Th e other permutations of the Soil Map of the World, the Global Data Set of Derived Soil Properties (from 2005) and the Derived Soil Properties Database (from 2006), were created by the International Soil Reference and Information Center (ISRIC), and combine spa-tial data from the Digital Soil Map of the World with measured soil property data from the World Inventory of Soil Emission potentials global soil profi le data set (Table 1). Both databases

are of coarse spatial resolution (e.g., at the equator, the 0.5° reso-lution raster of the Global Data Set of Derived Soil Properties is approximately equivalent to a horizontal resolution of 55 km, whereas the 5 arc-min resolution of the Derived Soil Properties Database is approximately 9 km). Both data sets report approxi-mately 20 soil properties for two to fi ve depth intervals (Table 1), including available water capacity, base saturation, bulk den-sity, cation exchange capacity, coarse fragment content, drainage class, electrical conductivity, organic C, particle size distribution, pH, and total N. Th e digital Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009) is the most recent spatial data set derived from the Soil Map of the World. It is a raster data set with a 30 arc-sec resolution (?1 km) and it pro-vides data on soil classes and 13 selected soil properties. While the Harmonized World Soil Database is also derived from the FAO-UNESCO map, it incorporates regional and national soil information from around the globe to update both the spatial and tabular data in the database to produce a more seamless and consistent representation of world soil resources (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009).

Table 2 lists several digital soil map products available at regional to continental scales but does not include non-digital products (e.g., atlas publications such as the Soil Atlas of the Northern Circumpolar Region, eusoils.jrc.ec.europa.eu/library/maps/Circumpolar/; verifi ed 9 May 2011), nor does it include non-map products (e.g., soil profi le databases such as the NRCS National Soil Characterization Database). It is apparent that most of these digital map products were produced by digitization of older paper maps without sampling of current soil resources, and in most cases, the digital database represents a compilation of multiple paper maps created at diff erent times, by diff erent individuals, at diff erent scales, and for diff erent purposes. Such compilations have varied currency and lineage and oft en contain logical inconsistencies (most evident where two or more separate maps have been joined, e.g., at political boundaries), all of which diminish the data quality (Finke, 2007). Eff orts to harmonize the multiple data sets are required to improve both the attribute quality and the semantic quality of the resulting digital soil map. With the exception of digital soil survey databases from Canada and the United States, all of the regional and continental data-

Table 1. Overview of available digital global soil data sets.Product Date of release Currency of data Scale Data model Variables

Map of World Soil Resources May 1990(version 1) 1981 + updates 1:25,000,000 vector Soil classes (using 1988

revised legend)

World Reference Base (WRB) Map of World Soil Resources Jan. 2003 1:25,000,000 vector Soil classes (using WRB)

Digital Soil Map of the World Feb. 2007(version 3.6) 1:5,000,000 vector Soil classes (using WRB)

Global Data Set of Derived Soil Properties

December 2005(version 3.0) 1:5,000,000 raster

(0.5° resolution)22 soil properties at 0–30 and

30–100 cm

Derived Soil Properties Database

June 2006(version 1.1) 1:5,000,000

raster(5 arc-min resolution)

19 soil properties at 20-cm depth intervals to 100 cm

Harmonized World Soil Database

Mar. 2009(version 1.1) 1981 + updates 1:5,000,000

raster(30 arc-s

resolution)

harmonized soil class and 13 soil properties


bases are at coarse scales (1:1,000,000 or coarser). Th e most de-tailed data—the Soil Landscapes of Canada (version 3.1.1) and the SSURGO database—have incomplete spatial coverage.

Th e Soil and Terrain Digital Database (SOTER) project has been a source for multiple national and regional digital soil maps and databases (Table 2). Th e goal of the SOTER project was to develop a global soil database coverage at 1:1,000,000 scale (Batjes, 1990). Th rough cooperation among the United Nations Environmental Program, FAO, and ISRIC, soil class maps that represent standardized soil and terrain attributes have been de-veloped for many regions of the globe, including South America, southern and central Africa, and eastern and central Europe at scales of 1:2,000,000 to 1:5,000,000 (Table 2). Th e map units of the SOTER databases delineate land areas with distinct patterns of soils and associated landforms and parent materials. Th ese SOTER products are expected to have greater positional accu-racy, improved attribute accuracy, greater semantic accuracy, and improved logical consistency than the Digital Soil Map of the World. However, they are still at relatively coarse scales.

In summary, it is evident that the available digital global soil data do not meet the profound needs of the Anthropocene. If such coarse-scale soil data are included in global ecological biodiversity models, global climate change simulation models, or ecosystem service assessments, major uncertainties arise with unknown outcomes. Global soil monitoring networks describ-ing soil change have not been implemented at this point in time. Assessing the impact of anthropogenic forcings on soil health,

quality, services, degradation, and change requires higher spatial- and temporal-resolution soil data, which are currently not avail-able at continental and global scales.

ARE PEOPLE READY FOR RASTER DIGITAL SOIL DATA?

As illustrated above, most spatial soil data are available as generalized vector polygon maps—either as paper or down-loadable digital products. Given the relative dearth of publi-cally available pixel-based soil data, we asked the question, “Are people ready for raster digital soil maps?” To address this, we informally assessed people’s awareness of existing soil survey in-formation and their preferences for a future soil map format. We interviewed 65 participants with a wide variety of ages (18–60+) and backgrounds (12% agriculture, 23% natural resources, 65% other), with about half of the participants indicating that they were familiar with soil as a natural resource. To establish the same minimum level of basic understanding for all participants, each interview began with a brief review of a customized soil survey and interpretations for building site development for a site near the Utah State University campus in Logan, UT, generated from Soil Survey Staff (2011a). We asked the participants to compare vector polygon and digital pixel products for a 140-km2 area of northern Utah (centered at 41°32′37″ N, 112°19′31″ W): poly-gon lines over a black-and-white air photo base, a colored pixel-based map over an air photo base, and a colored pixel-based map only. Participants were asked to rate the maps as high, medium,

Table 2. Select regional and continental digital soil data sets and national data sets of broad geographic extent.

Product Date of release Scale Data model Variables

Continental and regional

European soil database (ESDB), including soil geographical database of Eurasia (SGDBE)

Mar.–Nov. 2006 (version 2) 1:1,000,000 raster,

vectorassociations of soil typological units

with associated soil properties

Soil map of the European Communities June 1986 1:1,000,000 vector taxonomic classes (associations)

Soil and terrain database (SOTER), land degradation status and soil vulnerability assessment for central and eastern Europe (SOVEUR)

2000 (version 1.1) 1:25,000,000 vector soil classes + terrain elements,

with measured soil property data

Soil and terrain database for Latin America and the Caribbean (SOTERLAC) December 1998 1:5,000,000 vector

soil classes + terrain elements, with measured soil property data

for 1800 soil profi les

Soil and physiographic database for north and central Eurasia December 1999 1:5,000,000 vector soil classes + terrain elements

Digital atlas of Australian soils 1:2,000,000 vector soil–landscape units (consisting of multiple soil types)

SOTER for northeastern Africa 1998 1:1,000,000–1:2,000,000 vector 1200 homogeneous agro-ecological

mapping units

SOTER for central Africa (SOTER-CAF) September 2006 1:2,000,000 (Congo), 1:1,000,000 (others) vector dominant soil type, number of soil

components

SOTER for southern Africa (SOTERSAF) 2003 1:2,000,000 vector

National

U.S. general soil map (STATSGO2) 2006 1:250,000 vector taxonomic classes with estimated soil properties

Soil survey geographic database (SSURGO) multiple 1:12,000–1:24,000 vector taxonomic classes with estimated soil properties

CONUS-Soil 1998 1:250,000 raster (1 km)

estimated soil properties at six depth increments

Soil landscapes of Canada (SLC) December 1996 (version 2.2) 1:1,000,000 vector taxonomic classes with estimated

soil properties

Soils of Russia 1:25,000,000 vector soil classes with associated properties


or low in terms of (i) visual appeal, (ii) convey-ing information about the spatial distribution of soils, and (iii) determining the soil type mapped for a specifi c location.

Th e visual appeal of the colored pixel maps was rated much higher than the polygon map; however, all the maps were rated similar-ly for concisely conveying soil spatial distribu-tion and determining the soil type at a specifi c location (Fig. 2). Interestingly, the participants who had previously used soil surveys (32%) rated the polygon map much higher in all as-pects than those who had no prior experience with soil maps. Th e most common response by participants choosing to off er comments was that the pixel maps would be useful in an interactive environment or web application where spatially explicit soil information can be accessed. We concluded that soil data and information can be eff ectively represented by a variety of map types and that pixel map prod-ucts would be favorably received by the public, regardless of age and background.

ADVANCES IN SOIL MAPPINGSoil Mapping Paradigms

Pivotal events and paradigm shift s in soil mapping were described by Grunwald (2006a) and are summarized in Fig. 1. Technologies introduced to soil science in the early 1980s, such as global positioning systems (GPS), soil and remote sensing, and geographic in-formation systems, have facilitated the upscal-ing of site-specifi c soil observations to larger landscape scales. In particular, soil sensors, such as visible near-infrared spectros-copy (VNIR) (Shepherd and Walsh, 2002; Vasques et al., 2009, 2010b), mid-infrared spectroscopy, and laser-induced break-down spectroscopy (LIBS) (Harmon et al., 2005; Martin et al., 2010), off er new opportunities for rapid, accurate, and dense collection of soil properties. National soil survey programs have focused on the mapping of soil classes based on pedon descrip-tions in the fi eld and laboratory characterization of soil morpho-logical and physicochemical parameters of selected pedons at map scales of 1:24,000 and 1:100,000 to very coarse map scales of 1:1,000,000 and coarser (Fig. 3). Th ese soil maps are “double crisp” because they use crisp map unit boundaries and crisp soil classes that ignore the internal heterogeneity of properties within them. Considering that fi ne-scale variability of many soil prop-erties and processes has created intricate spatial patterns across the soil-landscape continuum, such double-crisp polygon-based soil maps impose major constraints. As Hartemink et al. (2010) pointed out, polygon-based soil maps have been useful for gener-alized land use planning and management, but their drawbacks are numerous, including that they are (i) static without represent-

ing the dynamics of soil conditions (e.g., nutrient depletion), (ii) infl exible for quantitative studies (e.g., C balance) because they require functional soil properties, (iii) losing information because soil classes provide a summarized account of the soils of a region, (iv) providing soil data oft en represented at a scale that is seldom useful for particular questions, and (v) diffi cult to integrate with other grid-based resource data (e.g., satellite images and DEM). In addition, crisp soil maps do not provide uncertainty and error as-sessments. Th is is in contrast to pixel-based soil prediction models that provide error metrics from cross-validation or validation pro-cedures. With the advent of advanced computational capabilities (e.g., cloud computing), large soil grids can be processed, over-coming the limitations of the pre-digital era, which constrained soil maps to vector-based formats.

Implications of Soil Mapping and Modeling across Space and Time

Th e phenomena of space and time, the distributions of soil properties and processes at escalating spatial scales, and the pre-dominant current DSM approaches are summarized in Fig. 3. Some inherent soil and environmental phenomena on Earth can-

Fig. 2. Summary of ratings of polygon vs. pixel soil maps for all participants, participants who had previously used soil surveys, and participants who had not used soil surveys; Vis App = visual appeal, Sp Distr = conveying information about the spatial distribution of soils; Spec Loc = determining soil type mapped for a specifi c location.


not be changed, such as geographic domain space and entropy of a soil ecosystem, whereas mapping of soil properties and pro-cesses and soil representation models can be adapted. As the spa-tial scale increases from fi ne (fi eld) to coarser scales (landscapes, continents, and global), the increasing extent and geographic domain space translates into increased variance of soil attributes (McBratney, 1992, 1998) and increased Shannon’s information entropy, which measures the diversity or disorder (unpredictabil-ity) of a system (Vieux, 1993; Culling, 1988; Martin and Rey, 2000; Seuront, 2010). If a landscape exhibits constant soil prop-erty values, the probability to predict them is high, at 1.0, result-ing in zero entropy and zero uncertainty; however, many research studies have documented the high variability of soil properties (Cambardella et al., 1994; McBratney and Pringle, 1999; Lin et al., 2005). Increasing variances also mean that spatial and tempo-ral autocorrelations of soil and environmental landscape proper-ties increase at escalating spatial scales (Vasques et al., 2011).

Upscaling of Site-Specifi c Soil Observations to Global Scales

Soils have been mapped based on genetic horizons or fi xed depth intervals within soil profi les because the representation of soil properties and processes requires time and space to be turned into discrete units. As the spatial scale increases, larger pixel (grain) sizes or map unit polygons have been used to repre-sent soil properties. Th is increase in pixel or grain size inherently infl uences the upscaling behavior of soil models, which are de-pendent on the grain, extent, and variance of soil-environmental observations (Vasques et al., 2011). Th e three sampling scales of spacing, extent, and sample support, termed the scale triplet by

Blöschl and Sivapalan (1995), impact up- and downscaling of soil models. Scale-independent behavior (self-similar or fractal behavior) assumes that the coarser scale system behaves like the average fi ner scale system, which implies that processes are lin-ear. Such linear behavior of soil processes may only occur across a specifi c range of scales, reaching a threshold (the “tipping point”) at which processes become nonlinear, resulting in multifractal behavior. Nonlinear dynamics with thresholds, hysteresis, and alternate states are well known in ecological systems (deYoung et al., 2008; Contamin and Ellison, 2009) but poorly investigated in the soil science discipline. Such inherent scaling behavior sug-gests that it is incorrect to assume that simple aggregation of soil property or process data at fi ne grain (or map units) to coarser grain will represent the soil system at global or continental scales. In essence, summing soil map units or pixels may not correctly represent the whole soil system because of the nonsimilarity of soil property or process behavior across spatial scales. In the past, however, simple aggregation methods have commonly been adopted to generalize regional soil maps (e.g., a map scale of 1:250,000 or coarser) to global maps (e.g., 1:1,000,000) to pro-vide global soil estimates lacking uncertainty assessment and an understanding of the scaling behavior of soils.

To upscale our understanding of pedogenic processes from the pedon to regional and global scales, it is critical to quantify the scaling behavior between soil properties–processes and natu-ral and anthropogenic drivers, which may become nonlinear and lack stationarity at escalating spatial and temporal scales. Our un-derstanding of the scaling of soil properties and processes is still limited, partially due to the soil models and representations as described above. At fi ne spatial scale, pedogenic, hydrologic, and

Fig. 3. Overview of phenomena of space and time, distributions of soil properties and processes at escalating spatial scales, and predominant current digital soil mapping approaches.


many ecosystem processes have been represented at high temporal resolution for biogeochemical speciation and in-depth soil pro-cess studies, whereas time steps for monitoring usually increase at escalating spatial scales. Th ese spatial and temporal discretiza-tion phenomena illustrate how the relationships between soil and environmental properties, which are used in many factorial-based DSM projects, tend to decrease in strength as the spatial scale in-creases from the fi eld to large landscape scales. Worldwide, opera-tional digital soil map products have been focused on soil classes, whereas research-oriented digital soil map products have empha-sized quantitative factorial modeling using CLORPT (CLimate, Organisms, Relief, Parent material, and Time) ( Jenny, 1941) or SCORPAN (S, soils; C, climate; O, organisms, biotic factor; R, relief; P, parent material; A, age; and N, space) (McBratney et al., 2003) and statistical and geostatistical methods to estimate soil properties and classes (Grunwald, 2009).

Digital Soil Mapping Projected into the FutureBesides the provision of soil taxonomic and property data at

coarse scales, there is need for interpretations of soil–environ-mental relationships and formation of higher level soil biogeo-chemical and soil process models formalizing knowledge of pedo-genic and ecosystem processes. Th e latter have been developed at local (site-specifi c) scales (Minasny et al., 2008; Rasmussen et al., 2010); however, upscaling these models to larger spatial and temporal scales is hampered by the large amounts of soil and environmental input data required to run them. Th is drawback has created opportunities for global climate change simulation models (Intergovernmental Panel on Climate Change, 2007) to address critical questions pertaining to the Anthropocene, which are used at coarse spatial resolutions to model ecosystem changes and forcings but oft en rely on generalized (historic) soil data in-puts with high uncertainties. Other studies have used meta anal-ysis, which synthesizes disparate soil data to derive new knowl-

edge addressing critical questions of the Anthropocene but oft en relies on scarce data sets. For example, global meta analysis was used by Post and Kwon (2000) to assess soil C sequestration and land use change across various ecosystem types, by Guo and Giff ord (2002) to estimate soil C stocks and land use change, and by Bond-Lamberty and Th omson (2010) to assess global soil respiration.

Digital soil mapping can advance in the future if the win-dow of perception widens, as the density of observations in space and time increases, and mapping techniques advance (Fig. 4). Improved soil sensors and in situ soil data collection methods would allow the collection of soil data at higher spatial and tem-poral resolution and at denser grids at escalating spatial scales. We envision modeling continuous soil depth functions in soil profi les, rather than crisp soil horizons, and developing three-dimensional soil-landscape models (Grunwald, 2006c; Malone et al., 2009). Denser soil data sets would allow us to more accu-rately quantify the relationships between soil and environmental attributes (SCORPAN approach), where the density and scale of soil observations resemble more closely the spatial resolution of the SCORPAN factors, and to elucidate the scaling behav-ior of soil properties and processes across spatial and temporal scales. Because the assessment of spatial and temporal autocor-relations are dependent on the density, amount, and distribution of soil observations within a landscape, advanced soil collection methods would also advance predictions of digital soil models in space and time. Th us, major advancements to quantify soil properties and processes are highly dependent on improvements in inferential delineation of soil observations and less so on im-proved methodologies or models. It has been demonstrated in various studies that soil predictions can be made at ≤30-m pixel resolutions (Th ompson and Kolka, 2005; Grunwald et al., 2007; Rivero et al., 2007; Vasques et al., 2010a) but within limited geo-graphic domains, i.e., smaller study areas.

Fig. 4. Envisioned future of digital soil mapping and modeling.


VISION FOR GLOBAL DIGITAL SOIL MAPPING AND MODELING: THE FUTURE SOIL PIXEL

Given the constraints and issues related to currently available digital soil data at continental and global scales, the urge to iden-tify an ideal soil pixel is high. Such a soil pixel (i) is knowledge rich (i.e., provides detailed pedogenic information), (ii) provides an ideal dimension (width by length) to represent the spatial variability of multiple soil properties and outcomes of soil eco-system processes, and (iii) is contiguous in space and time across continents and the globe. Th is future soil pixel is probably not uniform and universal across the globe, depending on the geo-graphic location, inherent soil variability, relationships among soil-SCORPAN factors, and disparately acting anthropogenic and natural forcings, which transform and reshape the soil pixel as time evolves. Although this soil pixel of the Anthropocene is unknown, we propose the following conceptual modeling frame-work to explicitly account for anthropogenic and natural forc-ings that determine and modulate soils:

SA c

c c

c

z p t

f S z p t T p t

E p t P p

x

j x j xj

n

j x j

, ,

, , , , ,

, ,

( )=

( ) ( )⎡⎣⎧⎨⎪

⎩⎪

( )

∑

xx

j x i j x ij

n

i

m

j x i j

t

A p t W p t

B p t H

, ;

, , , ,

, ,

c( )⎤⎦

( ) ( )⎡⎣⎧⎨⎪

⎩⎪

( )

∑∫=0

pp tx i,( )⎤⎦ ⎧⎨⎪

⎩⎪

⎧⎨⎪

⎩⎪

[1]

where SA is the target soil property (e.g., soil organic C), S repre-sents ancillary soil properties (e.g., soil texture, soil spectral data), T represents topographic properties (e.g., elevation, slope gradi-ent, slope curvature, compound topographic index), E represents ecological and geographic properties (e.g., physiographic region, ecoregion), P is the parent material and geologic properties (e.g., geologic formation), A represents atmospheric properties (e.g., precipitation, temperature, solar radiation), W represents water properties (e.g., surface runoff , infi ltration rate), B represents bi-otic properties (e.g., vegetation or land cover, land use, land use change, spectral indices derived from remote sensing, organisms), H is human-induced forcings (e.g., contamination, greenhouse gas emissions), j is the number of properties from j = 1, 2, …, n, px is a pixel with size x (width = length = x) at a specifi c location on Earth, tc is the current time, ti is the time to tc with time steps i = 0, 1, 2, …, m, and z is depth. (Note: the H factor includes human activities that force the change, such as greenhouse gas emissions, which may alter other factors. For example, global climate change is the result of feedbacks of anthropogenic and natural forcings and processes. Th e actual change in climate is represented by A.)

Th e envisioned STEP-AWBH (phonetically, “step-up”) model is spatially and temporally explicit and enhances previous factorial modeling frameworks. Th e soil property of interest, SA,

is estimated from various spatially explicit environmental vari-ables (STEP) that tend to be static within a human time frame and thus is represented in the model at one time (tc or, if available, ti). Soil properties (S) may be derived from existing soil maps or databases, fi eld or laboratory investigations, and include soil tax-onomic classes, physicochemical and biological properties, and soil spectral or other soil properties derived from sensors (e.g., VNIR, LIBS, electromagnetic induction, or γ radiometrics). Geologic (P) and ecological (E) properties usually show orders-of-magnitude higher spatial variation than soil properties and stratify a given landscape into subsets. Th e T factor represents a variety of primary and secondary topographic properties derived from DEM as described by Wilson and Gallant (2000). Th e pixel size (px) may vary among STEP factors because of disparate spatial autocorrelations and the variability of STEP attributes across a given landscape.

Th e AWBH factors explicitly account for space (i.e., pixel location) and time, whereby the time component may be aggre-gated to represent diff erent time vectors. Atmospheric (A) prop-erties (e.g., temperature and precipitation) include the current cli-mate at time tc at a location (px) derived from regional and global climate monitoring networks; the seasonal impacts of climate on SA(z,px,tc) represented by the aggregation of climatic properties across weeks or months preceding tc; and the longer term trends in warming or cooling, droughts, or other climate oscillations, such as the El Niño Southern Oscillation, on SA, which can be represented by annual or decadal climatic aggregates. Similarly, the W factor represents time-dependent knowledge of water quantity and quality related features (e.g., soil moisture, water table amplitude, or mean suspended sediment values within a drainage basin). Biotic (B) properties, such as natural or human-induced vegetation or land use change can be assessed through remote sensing imagery or aerial photographs across a specifi c period of time (ti). Remote sensing imagery is globally available, such as the European Space Agency’s Earth Observation data on soil moisture (soil moisture and ocean salinity mission), the Moderate Resolution Imaging Spectroradiometer (MODIS, 250–1000 m), Landsat (30–60 m), Advanced Spaceborne Th ermal Emission and Refl ection Radiometer (ASTER, 15–90 m), and regionally available at fi ne spatial resolutions, such as Quickbird (0.61-m panchromatic, 2.44-m multispectral), GeoEye1 (0.41-m panchromatic, 1.65-m multispectral), and light detection and ranging (LIDAR) at submeter resolutions. Remote sensing images have been used widely to infer the biophysical state and composition of vegetation, land use, and aboveground features (e.g., biomass and C content). Th e H factor represents diff erent anthropogenic forcings that can act across shorter or longer periods of time on SA(z,px,tc) to shift SA into a diff erent state, such as greenhouse gas emissions, contamination (e.g., an oil spill), disturbances, overgrazing, and others. Th e modeling framework represented by Eq. [1] can be adapted to model soil degradation, losses in fertility, functions and values of soils, and more. Depending on the geographic setting and history of a soil landscape, one (or more) of the factors in Eq. [1] impart(s) major


control on SA, which may shift into a diff erent state due to scal-ing up of models to coarser landscape scales, crossing geographic or attribute domain boundaries, or disproportional impact of an-thropogenic forcings. Th e STEP-AWBH model can be adapted to forecast and hindcast soil properties; however, answering criti-cal questions of the Anthropocene will require investment in the spatially explicit collection and monitoring of soil data to popu-late such models and test and validate results.

Carré et al. (2007) proposed to go beyond DSM and out-lined the concepts of digital soil assessment (DSA) and digital soil risk assessment (DSRA). Digital soil assessment is the quan-titative modeling of diffi cult-to-measure soil attributes, nec-essary for assessing threats to the soil (e.g., the decline of soil organic matter or biodiversity and erosion) and soil functions (e.g., biomass production) using DSM outputs. Digital soil risk assessment is the quantitative evaluation of soil-related scenari-os for providing policy guidance using the outputs from DSA fused with socioeconomic data and more general information on the environment (Carré et al., 2007). Th e main advantages of a DSM–DSA–DSRA chain are reduced costs; formalized, consistent, and transparent methods; and models that are eas-ily updated and allow assessment of error propagation for soil risk assessment. Fused soil systems facilitate higher order model-ing of complex soil-environmental systems that are exposed to natural and human-induced stressors. Predictions of SA pixels are viewed not as the endpoint but as a stepping stone toward the assessment of soil functions, soil quality, the risk of soil deg-radation, and ecosystem services. Ecosystem services assess “the benefi ts human populations derive, directly or indirectly from ecosystem functions or ecosystems” (Costanza et al., 1997; Millennium Ecosystem Assessment, 2005); thus, they add the so-cioeconomic dimension to products derived from DSM. Bouma (1997, 2001) has advocated the role of soil science (and soil sci-entists) in environmental, social, and economic policy, particu-larly the application of soil maps and quantitative methods to land use management (e.g., Wösten et al., 1985; Droogers and Bouma, 1997; Sonneveld et al., 2002). In the future, closer link-ages between soil prediction maps or models and environmental and socioeconomic applications are desirable to assess the value of soils as “soil natural capital” (e.g., Hewitt et al., 2010)—and of equal importance, as “monetary capital” or “social capital” in a global, interconnected world.

VISION FOR GLOBAL DIGITAL SOIL MAPPING AND MODELING: THE MODERN SOIL SCIENTIST

Th e future of DSM and modeling depends not only on the scientifi c expertise of researchers but also on stakeholder needs, people’s perception of soil information, and the training and ed-ucation of the modern soil scientist. For instance, current qualifi -cations for a soil scientist in the NRCS, the agency responsible for leading and coordinating activities of the National Cooperative Soil Survey and for advancing soil survey technology for global applications (Soil Survey Staff , 2011b), are “a bachelor’s degree

or higher in soil science or a closely related discipline that in-cludes 30 semester hours or equivalent in biological, physical, or earth science with a minimum of 15 semester hours in such subjects as soil genesis, pedology, soil chemistry, soil physics, and soil fertility” (NRCS, 2011a). While these are sound fundamen-tal qualifi cations, better education and training are required for DSM and modeling at multiple spatial scales. In addition to the theory and fi eld application of pedology, the modern soil scien-tist should (i) be able to access and manipulate geospatial, topo-graphic, remotely sensed spectral, and proximally sensed spectral data to represent soil and other environmental covariates, and (ii) have strong quantitative skills, particularly in statistics and spatial analysis. Along with an increasing public awareness and use of geospatial information (e.g., online mapping and Earth viewing tools, portable GPS units), there is an increasing number of college-level courses available in geographic information sys-tems and analysis. It is oft en diffi cult, however, to fi nd advanced courses in nonparametric statistics, spatial analysis, and sampling design useful in DSM and modeling. We propose that the edu-cation and training of the modern soil scientist can be achieved via the development and delivery of short courses that focus on specifi c knowledge and skills. Th e short-course model for train-ing and education of the modern soil scientist can help (i) in-novate curriculum development, particularly for the education of graduate students and professionals, (ii) accelerate curriculum revision, and (iii) provide opportunities for professional societies (e.g., the SSSA and the International Union of Soil Sciences) to be actively involved in advancing DSM and modeling by facili-tating short-course off erings in conjunction with international, national, and regional meetings.

SUMMARYTh e challenges facing human societies in the Anthropocene

will require spatial information about soil resources that can be used by planners, modelers, scientists, and policymakers. Th is in-formation must be digital, be compatible with other geospatial re-source and environmental data, and convey knowledge of specifi c soil properties and processes across three-dimensional space and time with estimates of uncertainty. Existing soil maps are gener-ally inadequate because of limitations that include scale, currency, completeness, logical consistency, and lineage. Future digital soil maps must be pixel-based, multiresolution representations of spa-tial and temporal patterns of soil properties. Our vision for global DSM provides a modeling framework that will support the devel-opment of this future soil pixel based on STEP-AWBH, as well as a context for the training and education of the modern soil scien-tist to use DSM and advance toward DSA and DSRA.

ACKNOWLEDGMENTSWe thank Dr. R.A. MacMillan for providing valuable resources on costs for digital soil mapping projects, and Amy Rohman and Suzann Kienast-Brown for conducting interviews to assess perceptions of soil maps.

REFERENCESBatjes, N.H. 1990. Macro-scale land evaluation using the 1:1M world soils and


terrain digital database: Identifi cation of a possible approach and research needs. SOTER Report 5. Int. Soil Sci. Soc., Wageningen, the Netherlands.

Batjes, N.H. 1996. Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sci. 47:151–163. doi:10.1111/j.1365-2389.1996.tb01386.x

Bloom, A.J. 2010. Global climate change: Convergence of disciplines. Sinauer Assoc. Publ., Sunderland, MA.

Blöschl, G., and M. Sivapalan. 1995. Scale issues in hydrological modeling: A review. Hydrol. Processes 9:251–290. doi:10.1002/hyp.3360090305

Blum, W.E.H. 2006. Th e future of soil science. p. 16–18. In A.E. Hartemink (ed.) Th e future of soil science. Int. Union of Soil Sci., Wageningen, the Netherlands.

Boettinger, J.L., D.W. Howell, A.C. Moore, A.E. Hartemink, and S. Kienast-Brown (ed.). 2010. Digital soil mapping: Bridging research, environmental application, and operation. Springer-Verlag, Dordrecht, the Netherlands.

Bond-Lamberty, B., and A. Th omson. 2010. Temperature-associated increases in the global soil respiration record. Nature 464:579–583. doi:10.1038/nature08930

Bouma, J. 1997. Th e role of quantitative approaches in soil science when interacting with stakeholders. Geoderma 78:1–12. doi:10.1016/S0016-7061(97)00014-1

Bouma, J. 2001. Th e role of soil science in the land negotiation process. Soil Use Manage. 17:1–6. doi:10.1111/j.1475-2743.2001.tb00001.x

Cambardella, C.A., T.B. Moorman, J.M. Novak, T.B. Parkin, D.L. Karlen, R.F. Turco, and A.E. Konopka. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Sci. Soc. Am. J. 58:1501–1511. doi:10.2136/sssaj1994.03615995005800050033x

Carré, F., A.B. McBratney, T. Mayr, and L. Montanarella. 2007. Digital soil assessment: Beyond DSM. Geoderma 142:69–79. doi:10.1016/j.geoderma.2007.08.015

Contamin, R., and A.M. Ellison. 2009. Indicators of regime shift s in ecological systems: What do we need to know and when do we need to know it? Ecol. Appl. 19:799–816. doi:10.1890/08-0109.1

Costanza, R., R. d’Arge, R. de Groot, S. Farber, M. Grasso, B. Hannon, et al. 1997. Th e value of the world’s ecosystem services and natural capital. Nature 387:253–260. doi:10.1038/387253a0

Cox, P.M., R.A. Betts, C.D. Jones, S.A. Spall, and I.J. Totterdell. 2000. Acceleration of global warming to carbon-cycle feedbacks in a coupled climate model. Nature 408:184–187. doi:10.1038/35041539

Crutzen, P.J. 2002. Geology of mankind. Nature 415:23.Culling, W.E.H. 1988. Dimension and entropy in the soil-covered

landscape. Earth Surf. Processes Landforms 13:619–648. doi:10.1002/esp.3290130706

deYoung, B., M. Barange, G. Beaugrand, R. Harris, R.I. Perry, M. Scheff er, and F. Werner. 2008. Regime shift s in marine ecosystems: Detection, prediction and management. Trends Ecol. Evol. 23:402–409. doi:10.1016/j.tree.2008.03.008

Droogers, P., and J. Bouma. 1997. Soil survey input in exploratory modeling of sustainable soil management practices. Soil Sci. Soc. Am. J. 61:1704–1710. doi:10.2136/sssaj1997.03615995006100060023x

Ellis, E.C., K.K. Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecol. Biogeogr. 19:589–606.

Ellis, E.C., and N. Ramankutty. 2008. Putting people in the map: Anthropogenic biomes of the world. Front. Ecol. Environ 6:439–447. doi:10.1890/070062

FAO/IIASA/ISRIC/ISS-CAS/JRC. 2009. Harmonized world soil database (version 1.1). IIASA, Laxenburg, Austria.

Field, C.B., J. Sarmiento, and B. Hales. 2007. Th e carbon cycle of North America in a global context. p. 21–28. In A.W. King et al. (ed.) Th e fi rst state of the carbon cycle report (SOCCR): North American carbon budget and implications for the global carbon cycle. Synthesis and Assessment Product 2.2. Natl. Climatic Data Ctr., Asheville, NC.

Finke, P.A. 2007. Quality assessment of digital soil maps: Producers and users perspectives. p. 523–541. In P. Lagacherie et al. (ed.) Digital soil mapping: An introductory perspective. Elsevier, Amsterdam.

Global Footprint Network. 2010. Just how big is the human footprint on Earth? Living Planet Rep. 2010. Available at www.footprintnetwork.org. Global Footprint Network, Oakland, CA.

Grunwald, S. 2006a. What do we really know about the space–time continuum of soil-landscapes. p. 3–36. In S. Grunwald (ed.) Environmental

soil-landscape modeling: Geographic information technologies and pedometrics. CRC Press, Boca Raton, FL.

Grunwald, S. 2006b. Future of soil science. p. 51–53. In A.E. Hartemink (ed.) Th e future of soil science. Int. Union of Soil Sci., Wageningen, the Netherlands.

Grunwald, S. 2006c. Th ree-dimensional reconstruction and scientifi c visualization of soil-landscapes. p. 373–392. In S. Grunwald (ed.) Environmental soil-landscape modeling: Geographic information technologies and pedometrics. CRC Press, Boca Raton, FL.

Grunwald, S. 2009. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 152:195–207. doi:10.1016/j.geoderma.2009.06.003

Grunwald, S., K.R. Reddy, J.P. Prenger, and M.M. Fisher. 2007. Modeling of the spatial variability of biogeochemical soil properties in a freshwater ecosystem. Ecol. Modell. 201:521–535. doi:10.1016/j.ecolmodel.2006.10.026

Guo, L.B., and R.M. Giff ord. 2002. Soil carbon stocks and land use change: A meta analysis. Global Change Biol. 8:345–360. doi:10.1046/j.1354-1013.2002.00486.x

Harmon, R.S., F.C. De Lucia, A.W. Miziolek, K.L. McNesby, R.A. Walters, and P.D. French. 2005. Laser-induced breakdown spectroscopy (LIBS): An emerging fi eld-portable sensor technology for real-time, in-situ geochemical and environmental analysis. Geochem. Explor. Environ. Anal. 5:21–28. doi:10.1144/1467-7873/03-059

Hartemink, A.E., J. Hempel, P. Lagacherie, A.B. McBratney, N. McKenzie, R.A. MacMillan, B. Minasny, L. Montanarella, M.L. Mendonça Santos, P. Sanchez, M. Walsh, and G.L. Zhang. 2010. GlobalSoilMap.net: A new digital soil map of the world. p. 423–427. In J.L. Boettinger et al. (ed.) Digital soil mapping: Bridging research, environmental application, and operation. Springer-Verlag, Dordrecht, the Netherlands.

Hartemink, A.E., A.B. McBratney, and M.L. Mendonça-Santos (ed.). 2008. Digital soil mapping with limited data. Springer-Verlag, Dordrecht, the Netherlands.

Hewitt, A.E., C. Hedley, and B. Rosser. 2010. Evaluation of soil natural capital in two soilscapes. p. 17–20. In R.J. Gilkes and N. Prakongkep (ed.) Soil solutions for a changing world: Proc. World Congr. of Soil Sci., 19th, Brisbane, QLD, Australia. 1–6 Aug. 2010. Int. Union of Soil Sci., Wageningen, the Netherlands.

Intergovernmental Panel on Climate Change. 2000. Global carbon cycle overview. In R.T. Watson et al. (ed.) Land use, land-use change and forestry. Available at www.ipcc.ch/ipccreports/sres/land_use/index.php?idp=3 (verifi ed 8 May 2011). Cambridge Univ. Press, UK.

Intergovernmental Panel on Climate Change. 2001. Climate change 2001: Th e scientifi c basis. Contributions of Working Group I to the Th ird Assessment Report. Cambridge Univ. Press, Cambridge, UK.

Intergovernmental Panel on Climate Change. 2007. Climate change 2007: Synthesis report. Contribution of Working Groups I, II and III to the Fourth Assessment Report. Available at www.ipcc.ch/publications_and_data/ar4/syr/en/contents.html (verifi ed 8 May 2011). IPCC, Geneva, Switzerland.

International Soil Reference and Information Center. 1990. Global assessment of the status of human induced soil degradation (GLASOD). Available at gcmd.nasa.gov/records/GCMD_GNV00018_171.html. ISRIC, Wageningen, the Netherlands.

Jacobson, M.C., R.J. Charlson, H. Rodhe, and G.H. Orians. 2004. Earth system science: From biogeochemical cycles to global change. Int. Geophys. Ser. 72. Elsevier Acad. Press, New York.

Jenny, H. 1941. Factors of soil formation. McGraw-Hill, New York.Keeling, C.D., T.P. Whorf, M. Wahlen, and J. van der Pfl icht. 1995. Interannual

extremes in the rate of rise of atmospheric carbon dioxide since 1980. Nature 375:666–670. doi:10.1038/375666a0

Lagacherie, P., A.B. McBratney, and M. Volz (ed.). 2007. Digital soil mapping: An introductory perspective. Elsevier, Amsterdam.

Lal, R. 2004. Soil carbon sequestration to mitigate climate change. Geoderma 123:1–22. doi:10.1016/j.geoderma.2004.01.032

Lin, H., D. Wheeler, J. Bell, and L. Wilding. 2005. Assessment of soil spatial variability at multiple scales. Ecol. Modell. 182:271–290. doi:10.1016/j.ecolmodel.2004.04.006

MacMillan, R.A., A.E. Hartemink, and A.B. McBratney. 2010a. GlobalSoilMap.net: From planning, development and proof of concept to fullscale production mapping. In R.J. Gilkes and N. Prakongkep (ed.) Soil solutions for a changing world: Proc. World Congr. of Soil Sci., 19th, Brisbane, QLD,


Australia. 1–6 Aug. 2010. Available at www.iuss.org/19th%20WCSS/symposium/pdf/1589.pdf (verifi ed 8 May 2011). Int. Union of Soil Sci., Wageningen, the Netherlands.

MacMillan, R.A., D.E. Moon, and R.A. Coupé. 2007. Automated predictive ecological mapping in a forest region of B.C., Canada, 2001–2005. Geoderma 140:353–373. doi:10.1016/j.geoderma.2007.04.027

MacMillan, R.A., D.E. Moon, and N. Phillips. 2010b. Predictive ecosystem mapping (PEM) for 8.2 million ha of forestland, British Columbia, Canada. p. 337–356. In J.L. Boettinger et al. (ed.) Digital soil mapping: Bridging research, environmental application, and operation. Springer-Verlag, Dordrecht, the Netherlands.

Malone, B.P., A.B. McBratney, B. Minasny, and G.M. Laslett. 2009. Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma 154:138–152. doi:10.1016/j.geoderma.2009.10.007

Mann, M.E., R.S. Bradley, and M.K. Hughes. 1999. Northern hemisphere temperatures during the past millennium: Inferences, uncertainties, and limitations. Geophys. Res. Lett. 26:759–762. doi:10.1029/1999GL900070

Martin, M.A., and J.-M. Rey. 2000. On the role of Shannon’s entropy as a measure of heterogeneity. Geoderma 98:1–3. doi:10.1016/S0016-7061(00)00049-5

Martin, M.Z., N. Labbé, N. André, S.D. Wullschleger, R.D. Harris, and M.H. Ebinger. 2010. Novel multivariate analysis for soil carbon measurements using laser-induced breakdown spectroscopy. Soil Sci. Soc. Am. J. 74:87–93. doi:10.2136/sssaj2009.0102

McBratney, A.B. 1992. On variation, uncertainty and informatics in environmental soil management. Aust. J. Soil Res. 30:913–935. doi:10.1071/SR9920913

McBratney, A.B. 1998. Some considerations on methods for spatially aggregating and disaggregating soil information. Nutr. Cycling Agroecosyst. 50:51–62. doi:10.1023/A:1009778500412

McBratney, A.B., M.L. Mendonça Santos, and B. Minasny. 2003. On digital soil mapping. Geoderma 117:3–52. doi:10.1016/S0016-7061(03)00223-4

McBratney, A.B., and M.J. Pringle. 1999. Estimating average and proportional variograms of soil properties and their potential use in precision agriculture. Precis. Agric. 1:125–152. doi:10.1023/A:1009995404447

Meersmans, J., B. Van Wesemael, E. Goidts, M. Van Molle, S. De Baets, and F. De Ridder. 2011. Spatial analysis of soil organic carbon evolution in Belgian croplands and grasslands, 1960–2006. Global Change Biol. 17:466–479. doi:10.1111/j.1365-2486.2010.02183.x

Millennium Ecosystem Assessment. 2005. Ecosystems and human well-being: Synthesis. Island Press, Washington, DC.

Minasny, B., A.B. McBratney, and S. Salvador-Blanes. 2008. Quantitative models for pedogenesis: A review. Geoderma 144:140–157. doi:10.1016/j.geoderma.2007.12.013

Minasny, B., Y. Sulaeman, and A.B. McBratney. 2010. Is soil carbon disappearing? Th e dynamics of soil organic carbon in Java. Global Change Biol. 17:1917–1974. doi:10.1111/j.1365–2486.2010.02324.x.

Nachtergaele, F.O. 1999. From the soil map of the world to the digital global soil and terrain database: 1960–2002. p. 5–17. In M.E. Sumner (ed.) Handbook of soil science. CRC Press, Boca Raton, FL.

National Academy of Sciences. 2001. Grand challenges in environmental sciences. Natl. Acad. Press, Washington, DC.

National Academy of Sciences. 2010. Landscapes on the edge: New horizons for research on Earth’s surface. Natl. Acad. Press, Washington, DC.

Norvig, P., D.A. Relman, D.B. Goldstein, D.M. Kammen, D.R. Weinberger, L.C. Aiello, et al. 2010. 2020 Visions. Nature 463:26–32. doi:10.1038/463026a

NRCS. 2011. Challenging careers in the Natural Resources Conservation Service. Available at www.nrcs.usda.gov/about/challengingcareers.html (accessed 16 Jan. 2011; verifi ed 8 May 2011). NRCS, Washington, DC.

Post, W.M., W.R. Emanuel, P.J. Zinke, and A.G. Stangenberger. 1982. Soil carbon pools and world life zones. Nature 298:156–159. doi:10.1038/298156a0

Post, W.M., and K.C. Kwon. 2000. Soil carbon sequestration and land-use change: Processes and potential. Global Change Biol. 6:317–327. doi:10.1046/j.1365-2486.2000.00308.x

Rasmussen, C., P.A. Troch, J. Chorover, P. Brooks, J. Pelletier, and T.E. Huxman. 2010. An open system framework for integrating critical zone structure and function. Biogeochemistry 102:15–29.

Rivero, R.G., S. Grunwald, and G.L. Bruland. 2007. Incorporation of spectral data into multivariate geostatistical models to map soil phosphorus

variability in a Florida wetland. Geoderma 140:428–443. doi:10.1016/j.geoderma.2007.04.026

Rockström, J., W. Steff en, K. Noone, A. Persson, F.S. Chapin III, E.F. Lambin, et al. 2009. A safe operating space for humanity. Nature 461:472–475. doi:10.1038/461472a

Sanchez, P.A., S. Ahamed, F. Carré, A.E. Hartemink, J. Hempel, J. Huising, et al. 2009. Digital soil map of the world. Science 325:680–681. doi:10.1126/science.1175084

Scherr, S.J. 1999. Soil degradation: A threat to developing-country food security by 2020? Food, Agric. and the Enviro. Discuss. Pap. 27. Int. Food Policy Res. Inst., Washington, DC.

Seuront, L. 2010. Fractals and multifractals in ecology and aquatic science. CRC Press, Boca Raton, FL.

Shepherd, K.D., and M.G. Walsh. 2002. Development of refl ectance spectral libraries for characterization of soil properties. Soil Sci. Soc. Am. J. 66:988–998. doi:10.2136/sssaj2002.0988

Soil Survey Staff . 2011a. Web soil survey. Available at websoilsurvey.nrcs.usda.gov/ (last accessed 25 Mar. 2011; verifi ed 8 May 2011). Natl. Soil Surv. Ctr., Lincoln, NE.

Soil Survey Staff . 2011b. National soil survey handbook. Title 430-VI. Available at soils.usda.gov/technical/handbook/ (accessed 16 Jan. 2011; verifi ed 8 May 2011). Natl. Soil Surv. Ctr., Lincoln, NE.

Sonneveld, M.P.W., J. Bouma, and A. Veldkamp. 2002. Refi ning soil survey information for a Dutch soil series using land use history. Soil Use Manage. 18:157–163. doi:10.1111/j.1475-2743.2002.tb00235.x

Steff en, W., A. Sanderson, P.D. Tyson, J. Jäger, P.A. Matson, B. Moore III, F. Oldfi eld, K. Richardson, H.J. Schellnhuber, B.L. Turner II, and R.J. Wasson. 2005. Global change and the Earth system. Springer-Verlag, Berlin.

Th ompson, J.A., and R.K. Kolka. 2005. Soil carbon storage estimation in a central hardwood forest watershed using quantitative soil-landscape modeling. Soil Sci. Soc. Am. J. 69:1086–1093. doi:10.2136/sssaj2004.0322

Trumbore, S.E. 1997. Potential responses of soil organic carbon to global environmental change. Proc. Natl. Acad. Sci. 94:8284–8291. doi:10.1073/pnas.94.16.8284

United Nations. 2004. World population to 2300. ST/ESA/SER.A/236. UN Dep. of Econ. and Social Aff airs, Population Div., New York.

U.S. Census Bureau. 2010. U.S. & world population clocks. Available at www.census.gov/main/www/popclock.html (verifi ed 8 May 2011). U.S. Census Bureau, Washington, DC.

Vasques, G.M., S. Grunwald, N.B. Comerford, and J.O. Sickman. 2010a. Upscaling of dynamic soil organic carbon pools in a north-central Florida watershed. Soil Sci. Soc. Am. J. 74:870–879. doi:10.2136/sssaj2009.0242

Vasques, G.M., S. Grunwald, and W.G. Harris. 2010b. Building a spectral library to estimate soil organic carbon in Florida. J. Environ. Qual. 39:923–934. doi:10.2134/jeq2009.0314

Vasques, G.M., S. Grunwald, and D.B. Myers. 2011. Spatial patterns of soil carbon and associated processes impacting scaling. Landscape Ecol. (in press).

Vasques, G.M., S. Grunwald, and J.O. Sickman. 2009. Visible/near-infrared spectroscopy modeling of dynamic soil carbon fractions. Soil Sci. Soc. Am. J. 73:176–184. doi:10.2136/sssaj2008.0015

Vieux, B.E. 1993. DEM aggregation and smoothing eff ects on surface runoff models. J. Comput. Civ. Eng. 7:310–338. doi:10.1061/(ASCE)0887-3801(1993)7:3(310)

Wilson, J.P., and J.C. Gallant (ed.). 2000. Terrain analysis: Principles and applications. John Wiley & Sons, New York.

World Resources Institute. 1990. World resources 1990–1991: A guide to the global environment. Oxford Univ. Press, Oxford, UK.

Wösten, J.H.M., J. Bouma, and G.H. Stoff elsen. 1985. Th e use of soil survey data for regional soil water simulation models. Soil Sci. Soc. Am. J. 49:1238–1245. doi:10.2136/sssaj1985.03615995004900050033x

Yan, X., Z. Cai, S. Wang, and P. Smith. 2010. Direct measurement of soil organic carbon content change in the croplands of China. Global Change Biol. 17:1487–1496. doi:10.1111/j.1365–2486.2010.02286.x.

Young, A. 1973. Soil survey procedures in land development planning. Geogr. J. 139:53–64. doi:10.2307/1795795

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