HR: 0800h
AN: B41A-0178    [Abstracts]
TI: Remote Sensing of Soil Surface Texture, Carbon and Water Contents using Bare Soil Imagery
AU: Iqbal, J
EM: ji1@ra.msstate.edu
AF: Mississippi State University, Dept. of Ag and Biological Eng. PO Box 9632, Mississippi State, MS 39762 United States
AU: * Owens, P R
EM: prowens@purdue.edu
AF: Purdue University, Dept. of Agronomy 915 W. State St., West Lafayette, IN 47906 United States
AB: Knowledge of spatial soil diversity and landscape dynamics is fundamental to understanding of global biogeochemical cycles. Remote sensing data are increasingly being used for large-scale quantification of land-based measurements such as soil texture, carbon and water content. These regional estimates of surface soil properties through remote sensing can be used as input for global biogeochemical models. The objective of this study was to explore the relationship between bare soil reflectance and surface soil texture (sand, silt, and clay), organic matter, and soil moisture. High spatial (2 m) and spectral resolution (414-920 nm) hyperspectral /multispectral aerial imageries were collected over the Mississippi Delta and Mississippi Blackland Prairie Regions. Major soils included Commerce (fine-silty, mixed, superactive, nonacid, thermic Fluvaquentic Endoaquepts), Robinsonville (coarse-loamy, mixed, superactive, nonacid, thermic Typic Udifluvents), and Convent (coarse-silty, mixed, superactive, nonacid, thermic Fluvaquentic Endoaquepts) and Brooksville (Fine, smectitic, thermic Aquic Hapluderts). Over three hundred surface soil samples were collected within the study area and analyzed for particle size analysis, organic matter, moisture and hydraulic properties. ArcView GIS was used to generate sampling locations which included random, transect, and target soil sampling. Each soil sample represented a composite of six sub-samples collected within a two meter square area. These sample sites were selected to represent the range of aspect, slope, elevation, and parent materials within the site. To reduce the dimensionality of the hyperspectral data set, PCA analysis was applied. The selected bands were used in generating the statistical relationships between spectral reflectance and surface soil properties data. Stepwise (backward & forward) and partial least square statistical methods were used to generate surface maps of soil texture, organic matter, and surface soil moisture. The multivariate analysis including partial least squares and stepwise linear regression reveal that the near infrared band (NIR950 nm) was the best predictor of percent clay (R2 = 0.683) and silt (R2 = 0.634), while the combination of Red band (RED650 nm) and Green band (Green550 nm) were the best predictors of organic matter. Surface soil moisture dynamic was highly spatially correlated with soil texture maps. Once these relationships were established, ERDAS Imagine Spatial Module was used to generate surface maps for percent clay, percent silt and percent organic matter. These final products not only could be used for management purposes but also to quantify the spatial patterns and temporal dynamics of soils and their impact on climate change.
DE: 0480 Remote sensing
DE: 0486 Soils/pedology (1865)
DE: 1615 Biogeochemical cycles, processes, and modeling (0412, 0414, 0793, 4805, 4912)
DE: 1632 Land cover change
SC: Biogeosciences [B]
MN: Fall Meeting 2005