- Soil data harmonisation and geostatistical modelling efforts in support of improved studies of global sustainabilityBatjes, NH, Kempen B, Leenaars JGB, and van den Bosch H, 2015
- Root zone plant-available water holding capacity of the Sub-Saharan Africa soil, version 1.0. Gridded functional soil informationLeenaars, JGB, Hengl T, Ruiperez Gonzalez M, Mendes de Jesus J, Heuvelink GBM, Wolf J, van Bussel L, Claessens L, Yang H, and Cassman, KG, 2015
- Mapping of soil properties and land degradation risk in Africa using MODIS reflectanceVågen,T-G, Winowiecki,LA, Tondoh,JE, Desta,LT, Gumbricht,T, 2015
- Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictionsHengl, T, Heuvelink GBM, Kempen B, Leenaars JGB, Walsh MG, Shepherd KD, Sila A, MacMillan RA, Mendes de Jesus J, Tamene L, and Tondoh JE, 2015
- Developing SoilML as a global standard for the collation and transfer of soil data and informationMontanarella, L, Wilson, P, Cox, S, McBratney, AB, Ahamed, S, McMillan, B, Jacquier, D, Fortner, J, 2015
- Soil map providing basic information for crop and site specific water and fertility recommendations in EthiopiaWösten, H, Leenaars JGB, Eyasu E, Keestra S, Mol G, Zaal A, Wallinga J, and Jansen B, 2015
- Comparative analysis of options for the spatial framework of yield gap analyses: a focus on soil dataClaessens, L, Cassman KG, van Ittersum MK, Leenaars JGB, van Bussel L, Wolf J, van Wart JP, Grassini P, Yang H, Boogaard H, de Groot H, Guilpart N, Heuvelink GBM, Stoorvogel JJ, Hendriks C, Keestra S, Mol G, Zaal A, Wallinga J, and Jansen B, 2015
- SoilGrids1km — Global Soil Information Based on Automated MappingHengl, T, Mendes de Jesus J, MacMillan RA, Batjes NH, Heuvelink GBM, Ribeiro E, Samuel-Rosa A, Kempen B, Leenaars JGB, Walsh MG, and Ruiperez Gonzalez M, 2014
- Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa (with datasetLeenaars, JGB, van Oostrum AJM, and Ruiperez Gonzalez M, 2014
- Africa Soil Profiles Database: a compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan AfricaLeenaars, JGB, Kempen B, van Oostrum AJM, and Batjes NH, 2014
- Mapping efficiency and information contentHengl, T, Nikolic, M, MacMillan, RA, 2013-06 [+]
This paper proposes two compound measures of mapping quality to support objective comparison of spatial prediction techniques for geostatistical mapping: (1) mapping efficiency – defined as the costs per area per amount of variation explained by the model, and (2) information production efficiency – defined as the cost per byte of effective information produced. These were inspired by concepts of complexity from mathematics and physics. Complexity i.e. the total effective information is defined as bytes remaining after compression and after rounding up the numbers using half the mapping accuracy (effective precision). It is postulated that the mapping efficiency, for an area of given size and limited budget, is basically a function of inspection intensity and mapping accuracy. Both measures are illustrated using the Meuse and Ebergötzen case studies (gstat, plotKML packages). The results demonstrate that, for mapping organic matter (Meuse data set), there is a gain in the mapping efficiency when using regression-kriging versus ordinary kriging: mapping efficiency is 7% better and the information production efficiency about 25% better (3.99 vs 3.14 EUR B−1 for the GZIP compression algorithm). For mapping sand content (Ebergötzen data set), the mapping efficiency for both ordinary kriging and regression-kriging is about the same; the information production efficiency is 29% better for regression-kriging (37.1 vs 27.7 EUR B−1 for the GZIP compression algorithm). Information production efficiency is possibly a more robust measure of mapping quality than mapping efficiency because: (1) it is scale-independent, (2) it can be more easily related to the concept of effective information content, and (3) it accounts for the extrapolation effects. The limitation of deriving the information production efficiency is that both reliable estimate of the model uncertainty and the mapping accuracy is required.
- Africa Soil Profiles Database, Version 1.1. A compilation of geo-referenced and standardized legacy soil profile data for Sub Saharan AfricaLeenaars, JGB, 2013
- Soil hydraulic information for river basin studies in semi-arid regionsWösten, JHM, Verzandtvoort SJE, Leenaars JGB, Hoogland T, and Wesseling JG, 2013
- Soil property maps of Africa at 1 kmISRIC – World Soil Information, 2013
- Africa Soil Profiles Database, Version 1.0. A compilation of geo-referenced and standardized legacy soil profile data for Sub Saharan Africa (with dataset).Leenaars, JGB, 2012
- The challenges of collating legacy data for digital mapping of Nigerian soilsOdeh, I.O.A.; Leenaars, J.G.B.; Hartemink, A.E.; Amapu, I., 2012
- Sampling for validation of digital soil mapsBrus, DJ, Kempen, B, Heuvelink, GBM, 2011
- Methodologies for global soil mappingMinasny, B, McBratney, AB, 2010 [+]
The Global Digital Soil Properties Map consortium (http://www.GlobalSoilMap.net) has been formed with an objective to create a digital map of the world’s soil properties. The methods for mapping soil properties globally are not straightforward as different parts of the world have varying data sources of varying qualities. This paper presents a set of methodologies for global digital soil mapping. The first stage involves a set of methodologies based on legacy soil data. The second stage comprises a set of methodologies to obtain new soil samples based on the available information or soil data. We present two decision trees for the methodologies and discuss each of the methods.
- GlobalSoilMap.net - From planning, development and proof of concept to full-scale production mappingMacMillan, RA, Hartemink, AE, McBratney, AB, 2010
- 2010 GlobalSoilMap.net – a new digital soil map of the world.Hartemink, AE, Hempel, J, Lagacherie, P, McBratney, AB, McKenzie, NJ, MacMillan, RE, Montanarella, L, Santos, ML, Sanchez, PA, Sanchez, M, M.Zhang, 2010
- Digital Soil Mapping: Bridging Research, Production, and Environmental ApplicationBoettinger, JL, Howell, DW, Moore, AM, Hartemink, AE, Kienast-Brown, S, 2010
- Digital Soil Mapping: Bridging Research, Production, and Environmental ApplicationHartemink, AE, Hempel, J, McBratney, AB, McKenzie, NJ, Montanarella, L, Mendon, L, 2010 [+]
Progress in Soil Science series aims to publish books that contain novel approaches in soil science in its broadest sense – books should focus on true progress in a particular area of the soil science discipline. The scope of the series is to publish books that enhance the understanding of the functioning and diversity of soils in all parts of the globe. The series includes multidisciplinary approaches to soil studies and welcomes contributions of all soil science subdisciplines such as: soil genesis, geography and classification, soil chemistry, soil physics, soil biology, soil mineralogy, soil fertility and plant nutrition, soil and water conservation, pedometrics, digital soil mapping, proximal soil sensing, soils and land use change, global soil change, natural resources and the environment.
- Digital Soil Map of the WorldSanchez, PA, Ahamed, S, Carr, F, Hartemink, AE, Hempel, J, Huising, J, Lagacherie, P, McBratney, AB, McKenzie, NJ, Mendon, MDL, Minasny, B, Montanarella, L, Okoth, P, Palm, CA, Sachs, JD, Shepherd, KD, Vågen, T-G, Vanlauwe, B, Walsh, MG, Winowiecki, LA, Z, 2009-08 [+]
Soils are increasingly recognized as major contributors to ecosystem services such as food production and climate regulation (1, 2), and demand for up-to-date and relevant soil information is soaring. But communicating such information among diverse audiences remains challenging because of inconsistent use of technical jargon, and outdated, imprecise methods. Also, spatial resolutions of soil maps for most parts of the world are too low to help with practical land management. While other earth sciences (e.g., climatology, geology) have become more quantitative and have taken advantage of the digital revolution, conventional soil mapping delineates space mostly according to qualitative criteria and renders maps using a series of polygons, which limits resolution. These maps do not adequately express the complexity of soils across a landscape in an easily understandable way.
- Using additional criteria for measuring the quality of predictions and their uncertainties in a digital soil mapping frameworkMalone, BP, McBratney, AB, Gruijter, D, J., J, Minasny, B, Brus, DJ [+]
In this paper we introduce additional criteria to assess the quality of digital soil property maps. Soil map quality is estimated on the basis of validating both the accuracy of the predictions and their uncertainties (which are expressed as a prediction interval [PI]). The first criterion is an accuracy measure that is different in form to the usual mean square error (MSE) because it accounts also for the prediction uncertainties. This measure is the spatial average of the statistical expectation of the mean square error of a simulated random value (MSES). The second criterion addresses the quality of the uncertainties which is estimated as the total proportion of the study area where the (1−α)–PI covers the true value. Ideally, this areal proportion equals the nominal value (1 − α). In the Lower Hunter Valley, NSW, Australia, we used both criteria to validate a soil pH map using additional units collected from a probability sample at five depth intervals: 0 to 5, 5 to 15, 15 to 30, 30 to 60, and 60 to 100 cm. For the first depth interval (0–5 cm) in 96% of the area, the 95% PI of pH covered the true value. The root mean squared simulation error (RMSES) at this depth was 1.0 pH units. Generally, the discrepancy between the nominal value and the areal proportion in addition to the RMSES increased with soil depth, indicating largely a growing imprecision of the map and underestimation of the uncertainty with increasing soil depth. In exploring this result, conventional map quality indicators emphasized a combination of bias and imprecision particularly with increasing soil depth. There is great value in coupling conventional map quality indicators with those which we propose in this study as they target the decision making process for improving the precision of maps and their uncertainties. For our study area we discuss options for improving on our results in addition to determining the possibility of extending a similar sampling approach for which multiple soil property maps can be validated concurrently.
- The upsurge of soil science and the new global soil map.Hartemink, AE [+]
Proceedings 9th International Conference of the East and Southeast Asia Federation of Soil Science Societies, Seoul, South Korea
- Homosoil, a methodology for quantitative extrapolation of soil information across the globeMallavan, BP, Minasny, B, McBratney, AB [+]
In many places in the world, soil information is difficult to obtain and can be non-existent. When no detailed map or soil observation is available in a region of interest, we have to extrapolate from other parts of the world. This chapter will discuss the Homosoil method, which assumes homology of soil-forming factors between a reference area and the region of interest. This includes: climate, physiography, and parent materials. The approach will involve seeking the smallest taxonomic distance of the scorpan factors between the region of interest and other reference areas (with soil data) in the world. Using the digital information of soil climate from the Climate Research Unit (CRU) (solar radiation, rainfall, temperature, and evapo-transpiration), topography from the HYDRO1k (elevation, slope, and compound topographic index), and lithology of the world on a 0.5°× 0.5° grid, we calculated Gower’s similarity index between an area of interest and the rest of the world. The rules calibrated in the reference area can be applied in the region of interest realising its limitations and extrapolation consequences.
- Digital Soil Mapping: Bridging Research, Production, and Environmental ApplicationBoettinger, JL, Howell, DW, Moore, AM, Hartemink, AE, (Eds.), K-BS
- Empirical estimates of uncertainty for mapping continuous depth functions of soil attributesMalone, BP, McBratney, AB, Minasny, B [+]
We use an empirical method where model output uncertainties are expressed as a prediction interval (PI) of the underlying distribution of prediction errors. This method obviates the need to identify and determine the contribution of each source of uncertainty to the overall prediction uncertainty. Conceptually, in the context of digital soil mapping, rather than a single point estimate at every prediction location, a PI, characterised by upper and lower prediction limits, encloses the prediction (which lies somewhere on the interval) and ideally the true but unknown value 100(1 − α)% of times on average the target variable (typically 95%). The idea is to partition the environmental covariate feature space into clusters which share similar attributes using fuzzy k-means with extragrades. Model error for predicting a target variable is then estimated from which cluster PIs are constructed on the basis of the empirical distribution of errors associated with the observations belonging to each cluster. PIs for each non-calibration observation are then formulated on the basis of the grade of membership each has to each cluster.
We demonstrate how we can apply this method for mapping continuous soil depth functions. First, using soil depth functions and digital soil mapping (DSM) methods, we map the continuous vertical and lateral distribution of organic carbon (OC) and available water capacity (AWC) across the Edgeroi district in north-western NSW, Australia. From those predictions we define a continuous PI for each prediction node, generating upper and lower prediction limits of both attributes. From an external validation dataset, preliminary results are encouraging where 91% and 93% of the OC and AWC observations respectively fall within the bounds of their 95% PIs. Ideally, 95% of instances should fall within these bounds.
- GlobalSoilMap.net - From planning, development and proof of concept to fullscale production mappingMacMillan, RA, Hartemink, AE, McBratney, AB [+]
Proceedings 19th World Congress of Soil Science, Brisbane, Australia