Predicting soil organic carbon using mixed conceptual and geostatistical models

The soil data demands required by the scientific community are increasing and the ability to supply these demands will be crucial in addressing global societal concerns. Often times, the scope of these data needs goes beyond the availability of ground referenced data. As soil scientists, we need to...

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Bibliographic Details
Published inDigital Soil Assessments and Beyond pp. 155 - 160
Format Book Chapter
LanguageEnglish
Published CRC Press 2012
Subjects
Digital Soil Mapping
SRTM DEM
Benton County
Soil Survey
USDA Soil Survey
National Soil Survey Center
Aquic Moisture Regimes
SOC
Soil Scientists
Soil Organic Carbon
SSURGO Database
Environmental Covariates
Soil Landscape Model
Soil Science Community
Digital Soil Assessments
Saga
Tippecanoe County
Multiple Linear Regression
Sil Prairie
Soil Carbon
RK
End Moraine
Terrain Attributes
Saga GIS
Online AccessGet full text
DOI10.1201/b12728-34

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Summary:The soil data demands required by the scientific community are increasing and the ability to supply these demands will be crucial in addressing global societal concerns. Often times, the scope of these data needs goes beyond the availability of ground referenced data. As soil scientists, we need to either produce new, typically costly data, through sampling, lab analysis, remote sensing, etc., or develop techniques for utilizing the limited data already available (McBratney et al., 2003). The challenge for the soil science community is to provide reliable estimates of soil properties based on limited point data, historical thematic maps and prior knowledge of soil and landscape relationships.
DOI:10.1201/b12728-34