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ARS Home » Southeast Area » Tifton, Georgia » Southeast Watershed Research » Research » Publications at this Location » Publication #178032
Title: IKONOS IMAGERY TO PREDICT SOIL PROPERTIES IN TWO PHYSIOGRAPHIC REGIONS OF ALABAMA

Author
item Sullivan, Dana
item SHAW, J - AUBURN UNIVERSITY
item RICKMAN, D - NASA-MARSHALL SPACE FLIGH

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/17/2005
Publication Date: 9/29/2005
Citation: Sullivan, D.G., Shaw, J.N., Rickman, D. 2005. IKONOS imagery to predict soil properties in two physiographic regions of Alabama. Soil Science Society of America Journal 69:1789-1798.

Interpretive Summary: Surface soil properties are often used to assess soil quality, establish soil survey map units, and determine agrochemical application rates. Current soil sampling methods include grid-based, management zone, and randomized sampling. Currently available high-resolution satellite imagery could be used to reduce the number of soil samples collected and facilitate soil survey mapping. Geostatistical methods of interpolation, fuzzy c-means clustering and regression analyses were used to estimate soil property variability in this study. Results indicate that advanced geostatistical analyses (co-kriging) provide the most accurate estimates of soil organic carbon and clay content. Fuzzy c-means, which does not quantify soil properties, can be used to cluster satellite data into soil zones and successfully reduce field scale variability.

Technical Abstract: 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 % sand (0.05 – 2 mm), silt (0.002 –0.05 mm), clay (< 0.002 mm), citrate dithionite extractable iron (Fed) and soil organic carbon (SOC). Four methods of evaluating variability in soil attributes were evaluated: 1) kriging of soil attributes, 2) co-kriging with soil attributes and reflectance data, 3) multivariate regression based on the relationship between reflectance and soil properties, and 4) fuzzy c-means clustering of reflectance data. Results indicate that co-kriging with remotely sensed data improved field scale estimates of surface SOC and clay content compared to kriging and regression methods. Fuzzy c-means worked best using RS data acquired over freshly tilled fields, reducing soil property variability within soil zones.