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IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies
Author(s) -
Sullivan Dana G.,
Shaw J. N.,
Rickman D.
Publication year - 2005
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2005.0071
Subject(s) - environmental science , soil science , loam , soil water , silt , soil texture , hydrology (agriculture) , soil test , soil series , digital soil mapping , soil map , geology , soil classification , geomorphology , geotechnical engineering
Knowledge of surface soil properties is used to assess past erosion and predict erodibility, determine nutrient requirements, and assess surface texture for soil survey applications. This study was designed to evaluate high resolution IKONOS multispectral data as a soil‐mapping tool. Imagery was acquired over conventionally tilled fields in the Coastal Plain and Tennessee Valley physiographic regions of Alabama. Acquisitions were designed to assess the impact of surface crusting, roughness, and tillage on our ability to depict soil property variability. Soils consisted mostly of fine‐loamy, kaolinitic, thermic Plinthic Kandiudults at the Coastal Plain site and fine, kaolinitic, thermic Rhodic Paleudults at the Tennessee Valley site. Soils were sampled in 0.20‐ha grids to a depth of 15 cm and analyzed for percentages of sand (0.05–2 mm), silt (0.002–0.05 mm), clay (<0.002 mm), citrate‐dithionite extractable Fe, and total C (TC). Four methods of evaluating variability in soil attributes were evaluated: (i) kriging of soil attributes, (ii) cokriging with soil attributes and reflectance data, (iii) multivariate regression based on the relationship between reflectance and soil properties, and (iv) fuzzy c ‐means clustering of reflectance data. Results indicate that cokriging with remotely sensed (RS) data improved field scale estimates of surface TC and clay content compared with kriging and regression methods. Fuzzy c ‐means worked best using remotely sensed data acquired over freshly tilled fields, reducing soil property variability within soil zones compared with field scale soil property variability.