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A Linear Physically-Based Model for Remote Sensing of Soil Moisture using
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Short Wave Infrared Bands
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*Morteza Sadeghi1, Scott B. Jones1, William D. Philpot2
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1- Dept. Plants, Soils and Climate, Utah State University, Logan, UT 84322-4820
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2- School of Civil & Environmental Engineering, Cornell University, Ithaca, NY.
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*Corresponding author at: 4820 Old Main Hill, Logan, UT 84322-4820, USA. Tel.: +1 435-
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797-2175; fax: +1 435 797 3376.
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E-mail addresses:
[email protected] (M. Sadeghi),
[email protected] (S.B. Jones),
[email protected] (W.D. Philpot).
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Acknowledgements
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Financial support was provided by Utah State University Office of Research and Graduate Studies
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Research Catalyst (RC) Grant and by the Utah Agricultural Experiment Station, Utah State
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University, Logan, Utah 84322-4810, approved as journal paper no. 8746. We also express
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appreciation to Michael Whiting and David Lobell for sharing their valuable data.
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Abstract
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Technological advances in satellite remote sensing have offered a variety of techniques for
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estimating surface soil water content as a key variable in numerous environmental studies. Optical
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methods are particularly valuable for remote sensing of soil moisture since reflected solar radiation
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is the strongest passive signal available to satellites and thus observations at optical wavelengths
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are capable of providing high spatial resolution data. Since remote sensors do not measure soil
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water content directly, mathematical algorithms that describe the connection between the
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measured signal and surface water content must be derived. Here, we present a physically-based
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soil moisture retrieval model in the solar domain (350 – 2500 nm) that is based on the Kubelka-
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Munk two-flux radiative transfer theory. The model is designed to describe diffuse reflectance
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from a uniform, optically thick, absorbing and scattering medium. The theory suggests a linear
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relationship between a transformed reflectance and soil water content in the short wave infrared
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bands (e.g. band 7 of Landsat and MODIS satellites) providing an easy-to-use algorithm in these
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bands. Accuracy of this model was tested and preliminarily verified using laboratory-measured
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spectral reflectance data of different soils. Further studies on potentials and challenges of this
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model for large-scale application using optical satellites data remain a topic of ongoing research.
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Key words: Remote sensing, soil moisture, reflectance, absorption, scattering.
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1. Introduction
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Surface soil moisture is a fundamental state variable controlling a wide range of processes
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occurring at the land-atmosphere interface including water infiltration and runoff, evaporation,
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heat and gas exchange, solute infiltration, erosion, etc. The importance of measuring and 2
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monitoring surface soil moisture at various spatial scales has been highlighted by many authors
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(e.g. Vereecken, et al., 2008; Robinson et al., 2008; Wang and Qu, 2009; Ochsner et al., 2013).
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Despite recent advances in electromagnetic (EM) sensing capability, whether ground-based (Jones
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et al., 2002; Jones et al., 2005; Bogena et al., 2007; Vaz et al., 2013) or remotely deployed
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(Huisman et al., 2003; Njoku et al., 2003; de Jeu et al., 2008; Kornelsen and Coulibaly, 2013),
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accurate assessment of surface water content is still a challenging task due to complex
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spatiotemporal variability of soil moisture in addition to scaling issues (Western et al. 2002;
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Western et al. 2004; Wu and Li, 2009; Brocca et al., 2010; Crow et al., 2012; Landrum, 2013; Ojha
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et al., 2014).
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The interaction of EM radiation with soils at various wavelengths has been shown to be
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significantly correlated with surface moisture content. Hence, various remote sensing (RS)
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methods have been developed in different regions of the EM spectrum. These methods may be
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classified into three major groups (Verstraeten et al., 2008; Wang and Qu, 2009):
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i) Optical methods: Methods using optical (or solar domain) bands (wavelengths between 0.35
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and 2.5 μm) in which the reflected radiation of the sun from the Earth’s surface, known as
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reflectance, is measured (Ben-Dor et al., 2009). Reflectance is commonly formulated as a function
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of soil moisture content using empirical (regression) analysis (Wang and Qu, 2009).
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ii) Thermal infrared methods: Methods in which the thermal emission of the Earth (wavelengths
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between 3.5 and 14 μm) is measured (Wang and Qu, 2009). The estimation of surface soil moisture
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using remotely sensed thermal wavebands primarily relies on the use of soil surface temperature
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measurements, either singly by the thermal inertia method (Verstraeten et al., 2006) or in
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combination with vegetation indices, for example using the so-called “triangle method” (Carlson
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et al., 1994; Carlson, 2007).
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iii) Microwave methods: Methods in which the intensity of microwave emission (wavelengths
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between 5 and 1000 mm) is measured. The fundamental basis of microwave RS of soil moisture
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is the large contrast between the dielectric properties of water (~ 81) and other soil constituents (
