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ARS Home » Southeast Area » Tifton, Georgia » Southeast Watershed Research » Research » Publications at this Location » Publication #199242
Title: USING SATELLITE REMOTE SENSING FOR SITE SPECIFIC MANAGEMENT OF SOILS

Author
item Sullivan, Dana
item SHAW, J - AUBURN UNIV
item RICKMAN, D - NASA-HUNTSVILLE,AL

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2006
Publication Date: 9/20/2006
Citation: Sullivan, D.G., Shaw, J.N., Rickman, D. 2006. Using satellite remote sensing for site specific management of soils [abstract]. International Conference on Biohydrology, Prague, Czech Republic. 20-22 Sept. 2006.

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: Until recently, soil scientists have emphasized stable, subsurface soil properties by which to classify and manage soil resources. However, dynamic surface soil features may also provide critical information regarding erodibility, surface soil texture and soil organic carbon content. Because of the dynamic nature of surface soil properties, quantification at any scale can be time and labor intensive. However, currently available, high-resolution satellite imagery shows promise as a method to streamline estimation of the variability in surface soil properties. This study was designed to evaluate high resolution IKONOS multispectral satellite imagery as a soil-mapping tool in two physiographic regions of the Southeastern United States: Coastal Plain and Tennessee Valley. The IKONOS satellite acquires imagery in three visible and one near-infrared spectral band with a spatial resolution of 4-m. Satellite data were acquired over two farm-sites and were designed to assess surface crusting and tillage effects on our ability to depict soil texture, soil organic matter and iron oxide content. The soils studied 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 and total carbon. 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 soil organic carbon and clay content compared to 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 to field scale soil property variability. Results from this study show promise as a rapid method for determining soil management zones. Knowledge of the variability in surface soil features can then be used to target remediation strategies, direct soil sampling and site specifically apply agrochemicals.