Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands |
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| Institution: | 1. Western Geographic Science Center, United States Geological Survey, 345 Middlefield Road, MS-531, Menlo Park, CA 94025, USA;2. Department of Environmental Science, Policy and Management, University of California, Berkeley, 130 Mulford Hall, #3114, Berkeley, CA 94720-3114, USA;1. Global Environmental Change and Earth Observation Research Group, Geography and Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom;2. Palaeoecological Laboratory, Geography and Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom;3. Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YR, United Kingdom;1. Center for Geospatial Research, Department of Geography, University of Georgia, 210 Field Street, Rm. 204, Athens, GA 30605, USA;2. School of Natural Resources, University of Nebraska–Lincoln, USA;3. Department of Civil and Environmental Engineering, Israel Institute of Technology, Technion City, Haifa, Israel |
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| Abstract: | Developing spectral models of soil properties is an important frontier in remote sensing and soil science. Several studies have focused on modeling soil properties such as total pools of soil organic matter and carbon in bare soils. We extended this effort to model soil parameters in areas densely covered with coastal vegetation. Moreover, we investigated soil properties indicative of soil functions such as nutrient and organic matter turnover and storage. These properties include the partitioning of mineral and organic soil between particulate (>53 μm) and fine size classes, and the partitioning of soil carbon and nitrogen pools between stable and labile fractions. Soil samples were obtained from Avicennia germinans mangrove forest and Juncus roemerianus salt marsh plots on the west coast of central Florida. Spectra corresponding to field plot locations from Hyperion hyperspectral image were extracted and analyzed. The spectral information was regressed against the soil variables to determine the best single bands and optimal band combinations for the simple ratio (SR) and normalized difference index (NDI) indices. The regression analysis yielded levels of correlation for soil variables with R2 values ranging from 0.21 to 0.47 for best individual bands, 0.28 to 0.81 for two-band indices, and 0.53 to 0.96 for partial least-squares (PLS) regressions for the Hyperion image data. Spectral models using Hyperion data adequately (RPD > 1.4) predicted particulate organic matter (POM), silt + clay, labile carbon (C), and labile nitrogen (N) (where RPD = ratio of standard deviation to root mean square error of cross-validation RMSECV]). The SR (0.53 μm, 2.11 μm) model of labile N with R2 = 0.81, RMSECV= 0.28, and RPD = 1.94 produced the best results in this study. Our results provide optimism that remote-sensing spectral models can successfully predict soil properties indicative of ecosystem nutrient and organic matter turnover and storage, and do so in areas with dense canopy cover. |
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| Keywords: | Hyperspectral remote sensing Coastal wetlands Soil properties Particulate organic matter Labile carbon Labile nitrogen |
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