A soil science renaissance

Hartemink, Alfred E.; McBratney, Alex B.

doi:10.1016/j.geoderma.2008.10.006

Cited by 183 publications

(103 citation statements)

References 41 publications

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“…There is a renewed awareness of the finite nature of the world's soil resources, growing concern about soil security, and significant uncertainties about the carrying capacity of the planet [1,2]. It has been answered with a growing number of soil policies and regulations around the world concerned with, e.g., increasing soil degradation and loss of organic carbon in top soils, and aiming at more soil management and soil protection.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

et al. 2016

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Abstract:With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral models for the mapping of common soil properties based on upcoming EnMAP (Environmental Mapping and Analysis Program) satellite data using semi-operational soil models. Iron oxide (Fe d ), clay, and soil organic carbon (SOC) content are predicted in test areas in Spain and Luxembourg based on a semi-automatic Partial-Least-Square (PLS) regression approach using airborne hyperspectral, simulated EnMAP, and soil chemical datasets. A variance contribution analysis, accounting for errors in the dependent variables, is used alongside classical error measurements. Results show that EnMAP allows predicting iron oxide, clay, and SOC with an R 2 between 0.53 and 0.67 compared to Hyperspectral Mapper (HyMap)/Airborne Hyperspectral System (AHS) imagery with an R 2 between 0.64 and 0.74. Although a slight decrease in soil prediction accuracy is observed at the spaceborne scale compared to the airborne scale, the decrease in accuracy is still reasonable. Furthermore, spatial distribution is coherent between the HyMap/AHS mapping and simulated EnMAP mapping as shown with a spatial structure analysis with a systematically lower semivariance at the EnMAP scale.

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Section: Introductionmentioning

confidence: 99%

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

et al. 2016

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“…The relationship between the different soil types and the potential yield of crops has long been known, for example for sugarcane (Joaquim et al, 1994), aside from the limitations to the land use (Lepsch, 1991). Soil is known to be a non-renewable resource in the short term, which is why depletion by overuse must be avoided and measures should be taken to preserve it (Hartemink & McBratney, 2008).…”

Section: Introductionmentioning

confidence: 99%

Comparison between detailed digital and conventional soil maps of an area with complex geology

Filho¹,

Rizzo

Lepsch

et al. 2013

Rev. Bras. Ciênc. Solo

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SUMMARYSince different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps (1) Part of the Dissertation of the first author, submitted to the Escola Superior de Agricultura "Luiz de Queiroz" -ESALQ/USP.Received for publication on November 1st, 2012 and approved on July 11, 2013. (2 RESUMO: COMPARAÇÃO ENTRE MAPAS DE SOLOS DETALHADOS OBTIDOS PELOS MÉTODOS CONVENCIONAL E DIGITAL EM

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“…In the current research environment funding to conduct soil research can be difficult to obtain, with shrinking budgets in many parts ofthe world since about the 1980s (Hartemink and McBratney, 2008;Brevik et al, 2016b). This situation makes it very important to make the best possible use of all information that is available to us, particularly when it involves little additional expense.…”

Section: Reinvigoration Ofmapping Datamentioning

confidence: 99%

Islands of biogeodiversity in arid lands on a polygons map study: Detecting scale invariance patterns from natural resources maps

Ibáñez

Pérez-Gómez

Brevik

et al. 2016

Science of The Total Environment

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Many maps (geology, hydrology, soil, vegetation, etc.) are created to inventory natural resources. Each of these resources is mapped using a unique set of criteria, including scales and taxonomies. Past research indicates that comparing results of related maps (e.g., soil and geology maps) may aid in identifying mapping deficiencies. Therefore, this study was undertaken in Almería Province, Spain to (i) compare the underlying map structures of soil and vegetation maps and (ii) investígate if a vegetation map can provide useful soil information that was not shown on a soil map. Soil and vegetation maps were imported into ArcGIS 10.1 for spatial analysis, and results then exported to Microsoft Excel worksheets for statistical analyses to evalúate fits to linear and power law regression models. Vegetative units were grouped according to the driving forces that determined their presence or absence: (i) climatophilous (ii) lithologic-climate; and (iii) edaphophylous. The rank abundance plots for both the soil and vegetation maps conformed to Willis or Hollow Curves, meaning the underlying structures of both maps were the same. Edaphophylous map units, which represent 58.5% of the vegetation units in the study área, did not show a good correlation with the soil map. Further investigation revealed that 87% of the edaphohygrophilous units were found in ramblas, ephemeral riverbeds that are not typically classified and mapped as soils in modern systems, even though they meet the definition of soil given by the most commonly used and most modern soil taxonomic systems. Furthermore, these edaphophylous map units tend to be islands of biodiversity that are threatened by anthropogenic activity in the región. Therefore, this study revealed áreas that need to be revisited and studied pedologically. The vegetation mapped in these áreas and the soils that support it are key components of the earth's critical zone that must be studied, understood, and preserved.

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A soil science renaissance

Cited by 183 publications

References 41 publications

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

Comparison between detailed digital and conventional soil maps of an area with complex geology

Islands of biogeodiversity in arid lands on a polygons map study: Detecting scale invariance patterns from natural resources maps

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