DOI: 10.1016/j.jag.2014.04.007
Scopus记录号: 2-s2.0-84904738388
论文题名: Modeling soil parameters using hyperspectral image reflectance insubtropical coastal wetlands
作者: Anne N ; J ; P ; , Abd-Elrahman A ; H ; , Lewis D ; B ; , Hewitt N ; A
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 33, 期: 1 起始页码: 47
结束页码: 56
语种: 英语
英文关键词: Coastal wetlands
; Hyperspectral remote sensing
; Labile carbon
; Labile nitrogen
; Particulate organic matter
; Soil properties
Scopus关键词: coastal wetland
; Hyperion
; image analysis
; NDVI
; nitrogen
; particulate organic matter
; regression analysis
; remote sensing
; soil carbon
; soil property
; soil science
; spectral reflectance
; Florida [United States]
; United States
英文摘要: 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 (>53μm) 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 R2values ranging from 0.21to 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 (0.53μm, 2.11μm) 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. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79767
Appears in Collections: 气候变化事实与影响
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作者单位: School of Forest Resources and Conservation - Geomatics, University of Florida, Florida, United States; Department of Integrative Biology, University of South Florida, United States
Recommended Citation:
Anne N,J,P,et al. Modeling soil parameters using hyperspectral image reflectance insubtropical coastal wetlands[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,33(1)