Spatial variability of soil and relationship with satellite digital data in eastcentral Illinois
Agbu, Patrick Aniweta
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https://hdl.handle.net/2142/23048
Description
Title
Spatial variability of soil and relationship with satellite digital data in eastcentral Illinois
Author(s)
Agbu, Patrick Aniweta
Issue Date
1989
Department of Study
Agronomy
Discipline
Agronomy
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Agriculture, Agronomy
Remote Sensing
Language
eng
Abstract
Past studies have suggested that satellite imagery is of little use in large scale soil mapping, because they found few relationships between satellite-derived maps and field soil maps. Rather than evaluate imagery-derived maps by their degree of spatial correspondence with a field soil map, it seems preferable to judge the field soil map and imagery-derived map by a common objective standard: the degree to which each map groups similar soils and separates different ones based on significant soil properties.
Two areas in Ford County, Mona Township in the north, and Drummer Township to the southwest of the County, each covering an area of 31.08 Km$\sp2$, with apparently low and high spatial variability respectively, were selected for the study. Recent field soil maps of the areas were compared to the photo-interpretion maps, and four Spot satellite spectral maps prepared by different techniques of image classification, using an April, 1987 Spot digital image.
There was significant relationship between soil property variability and satellite digital data. Partitioning variability among and within units showed that most of the variation was left within mapping units. Comparison of the various map products with regard to soil property variability, showed that the published soil survey (field map) and the aerial photo-interpretion (API) refinement thereof were similar in effectiveness, according to the parameters used to evaluate them. The Spot texture map was the best among the spectral maps, and various parameters used indicate that although it compared reasonably well with the field map, the field map showed superiority in some parameters.
The data clearly indicate that computer classification of Spot imagery is a potentially useful approach to soils investigations. Most likely, its greatest utility will be in supporting field soil surveys rather than direct application of computer-derived maps to land use and management problems. At the very least, field work to characterize the spectral mapping units in terms of significant soil properties would be a necessary next step before these maps would be useful for direct application in land management or land use planning.
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