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Light Absorption Model for Water Content to Improve Soil Mineral Estimates in Hyperspectral Imagery

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Light Absorption Model for Water Content to Improve Soil Mineral Estimates in Hyperspectral Imagery LIGHT ABSORPTION MODEL FOR WATER CONTENT TO IMPROVE SOIL MINERAL ESTIMATES IN HYPERSPECTRAL IMAGERY Michael L. Whiting, Postdoctoral Researcher Susan L. Ustin, Professor California Space Institute Center of Excellence, One Shields Ave. The Barn University of California, Davis, CA 95616-8527, United States. [email protected]; [email protected] Alicia Palacios Orueta, Professor Titular Escuela Tecnica Superior de Ingenieros de Montes, Universidad Politecnica de Madrid, Madrid, Spain. [email protected] Lin Li, Assistant Professor Department of Geology, Indiana University - Purdue University, Indianapolis, IN, United States. [email protected] ABSTRACT High spectral resolution of hyperspectral images promise mineral identification and abundance estimates of bare soils for advancing precision farming and carbon cycle modeling. Under desiccated conditions, investigators have shown that band parameter measurements (position, depth, width, and derivatives) can be used as reliable estimators of mineral type and contents. With moisture, even air-dried soil, the water absorption fundamental and combination absorptions reduce accuracy of mineral estimates as soil albedo declines and mineral absorption band-depths diminish. The effects of water on soil albedo were accurately modeled by extrapolating the shortwave infrared (SWIR, 1.0 to 2.4 m) continuum to the water fundamental center at 2.8 m using an inverted Gaussian function, that is located beyond the range of common airborne and field hyperspectral instruments. This improves accuracy of water estimates over modeling the water content using specific water bands (principally, 1.4 and 1.9 m) since these bands are susceptible to saturation by atmospheric water vapor beneath airborne and satellite platforms. In contrast, our soil moisture Gaussian model (SMGM) provides an accurate measure of soil moisture for use with other band metrics in spectral mixture analysis or classification and regression classification models for mapping mineral abundance of surface soils. The improvement contributed by the SMGM parameter to estimates of clay and carbonate mineral contents is demonstrated through a simple linear regression analysis of Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and HyMap images from Kings County, California, United States, and La Mancha, Spain, respectively. INTRODUCTION Soil classification, soil health inventories, reduction of erosion and desertification are all examples where increased resolution in soil surface mapping will improve our understanding of the implications of soil variability. Greater spatial resolution in mapping clay minerals and organic matter contents will contribute to improved modeling of plant and soil responses to various resource management and global changes. With anticipated changes in rainfall, soil acidification and greenhouse gas exchange, the accuracy of soil carbon sequestration modeling will benefit from greater spatial resolution of clay mineral content due to the close relationship between organic matter and clay. Advances in precision farming require greater detail in the mineral components in developing prescriptions for water, nutrient, herbicide and pesticide applications. Accurate mapping of the mineral and organic matter contents will improve farm management of nitrate and irrigation applications to reduce ground water pollution. In the right circumstances, multi-spectral photography and satellite imagery has been useful in differentiating organic matter and clay contents (Curran, 1979; Sudduth and Hummel, 1991). Research on soil classifications (Horvath et al., 1984; Palacios-Orueta and Ustin, 1996) and soil salinity (Csillag et al., 1993; Metternicht and Zinck, 2003) described spectral characteristics of different soils. In alluvium and lacustrine soils Pecora 16 “Global Priorities in Land Remote Sensing” October 23 – 27, 2005 * Sioux Falls, South Dakota with greater homogeneity, techniques developed in spectroscopy are more useful. In these cases, hyperspectral imagery has been successful in discriminating the secondary clay mineral types that influence the soil shrink-swell potential important for urban development and other engineering structures (Chabrillat et al., 2002). Water is a principal component among the many compositional variables in the soil surface interacting with the incident light. The complex nature of soils in situ, as organic matter and mineral particles and films, results in non- linear light absorptions related to the component proportions. Primary and secondary mineral grains are coated with films of water, clay and iron chelated-organic matter (Clark, 1999). Pore space concentrates light absorbing gases such as CO2 that may be 10 to 100 times greater than the above ground atmosphere (Brady and Weil, 1996), as well as water vapor. Accurately measuring mineral contents from bare soil images is significantly influenced by the presence of water in the pore spac and as particle film (Kruse and Clark, 1986; Liu et al., 2002). For remote sensing, spring or irrigated soil moisture increases water content causing a decline in the albedo and low contrast of absorptions from the spectral continuum (Bowers and Hanks, 1965), inhibiting accurate mineral identification and abundance. Although highly variable spatially, the soil moisture parameter is one that is easily measured. The estimate of clay and carbonate contents are physically based on the combination and overtone bands of Al- OH and CO3 bonds, respectively, in the SWIR (Clark, 1999). Effective measures of mineral abundance include band-depth (Clark and Roush, 1984; Van der Meer, 2004) and shape of the absorption (Ben-Dor and Banin, 1995) in desiccated materials. The depth and width of the mineral absorptions is an indication of the quantity of the electronic transitions and molecular bond stretching and vibrations. In addition, the overlap of adjacent absorptions should be considered at each wavelength as demonstrated in modeling mineral constituents (Mustard, 1992), such as the Modified Gaussian Model (Sunshine et al., 1990) algorithms. The exponential of the sum of all apparent absorptions defines reflectance for optically thick materials (Clark and Roush, 1984), where the apparent absorption is the product of the optical depth and absorption coefficient (Kortum, 1969). This general principle is not mathematically sensitive to accurately decomposing the spectra into endmember content estimates (Hapke, 1993), although understanding the sum of absorptions does lead toward understanding a spectrum by the absorptions of the contributing components. The most commonly used technique for determining absorption depth is continuum removal (Clark and Roush, 1984; Van der Meer, 2004). Continuum removal calculates normalized spectra by dividing the reflectance within an absorption region by an interpolated reflectance straight-line datum between the two local maxima that bound the region. The maximum band-depth is found by subtracting this normalized spectral reflectance from 1.0 (Clark and Roush, 1984). In Figure 1, the transformation is shown from the original spectrum in 1a through the continuum removal process as it appears in 1b. The band center at the minimum normalized-reflectance position and the asymmetry of the absorption shape can be used to determine mineral type (Van der Meer, 2004). After the water bands at 1.4 nd 1.9 m, the next strongest absorptions in the SWIR are assigned to silt and secondary clay minerals near 2.2 m, and carbonate minerals in the 2.3 m region (Clark, 1999). Organic matter in soil introduces cellulose absorption bands near 2.1 and 2.3 Figure 1. (a) Soil spectrum with fitted straight-line continuum to m. These band-depth and convex hull boundary points, and (b) spectrum after normalizing with absorption shapes are influenced by the continuum, or continuum removed (Whiting, 2004). the presence of soil moisture (Liu et al., 2002; Lobell and Asner, 2002; Whiting et al., 2004). Pecora 16 “Global Priorities in Land Remote Sensing” October 23 – 27, 2005 * Sioux Falls, South Dakota Presented here is our method for characterizing the amount of soil water as a step to developing a water content parameter for mineral abundance models. The soil moisture Gaussian model (SMGM) is calculated simultaneously with the continuum removal band-depth. To demonstrate the effectiveness of the SMGM to improve clay and carbonate estimates, we present a simple multiple linear regression analysis using the SMGM and mineral band- depths calculated using spectra extracted from hyperspectral images over Tomelloso, Castilla-La Mancha, Spain, and Lemoore, Kings County, California, United States. The SMGM and Its Physical Basis The Soil Moisture Gaussian Model (SMGM) fits an inverted Gaussian function to the convex hull boundary points in the SWIR region of a bare soil spectrum (Whiting et al., 2004). The function’s variables with the greatest predictive value are the depth (amplitude) of the function and the area of one side of the Gaussian function, above the spectral continuum. Sunshine et al. (1990) describes a modified Gaussian distribution for modeling the broadening of the range of energy frequencies that are absorbed with increasing mineral abundance. The length and rate of stretching and bending increase randomly and symmetrically with additional shorter and longer wavelength bonds absorbing energy. The nominal bond length can be associated with the central absorption peak. In spectra- moisture trials, Whiting et al. (2004) found fitting
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