Soil moisture retrieval in infrared remote sensing: a physically-based inversion algorithm

1. INTRODUCTION

Optical scattering from soils is a complex phenomenon that depends on wave properties such as the wavelength and polarization state of incident and reflected waves as well as soil properties (Planet, 1970). The presence of water in soil drastically modifies the properties measured in the thermal infrared (IR) and microwave (MW) domain (Prigent et al., 2005). Typically, applications involving the thermal IR band are based on the difference between air temperature and soil temperature (Bryant et al., 2003). Most of the satellite IR sensors such as the moderate resolution imaging spectroradiometer (MODIS), atmospheric infrared sounder (AIRS), and infrared atmospheric sounding interferometer (IASI) measure the unpolarized radiances.

In this paper, we present a unique soil moisture algorithm that exploits the dielectric contrast and the polarizations over land surfaces in the IR wavelength band.

2. METHODS

 

The first step is to estimate the unpolarized reflectivity based on the radiances measured by satellite and a radiative transfer calculation. In this study, we use the method proposed by Prigent et al. (1997), which is

                                           (1)

where  is the surface reflectivity,  is the observed radiance,  is the upwelling radiance,  is the down-welling radiance,  is the Planck surface radiance,  is the surface temperature, and is the total transmittance.

The AIRS level L1 and L2 acquired on April 4, 2007 and the community radiative transfer model (CRTM) (Han et al., 2005) are used.

The second step is to decompose the unpolarized reflectivity. Not preferring either polarization allows us to define the unpolarized reflectivity as a simple average of the vertically (V) - and horizontally (H)-polarized reflectivities (Wu and Smith, 1997). Most randomly generated rough surfaces may be represented by a Gaussian distribution about the mean (Bennett, 1963; Ohlidal et al., 1974). In this study, the surface is assumed to be slightly rough due to the difficulty of the roughness estimation in the IR bands using                                                       (2)

where the subscripts and  indicate the vertical and horizontal polarizations, respectively.

However, Hong (submitted to Int. J. Rem. Sens.) has presented a method, called the Hong approximation, to decompose the unpolarized reflectivity using the approximate relationship between  and  of

                                                           (3)

Thus, the unpolarized reflectivity can be decomposed using Eqs. (2) and (3).  Figure 1 illustrates the decomposed emissivities and polarizations of the AIRS 10.8 ¥ìm channel. The low emissivity indicates the cloud contamination.

The third step consists of solving for the dielectric constants by directly inverting the generalized Fresnel equations (Querry, 1969; Armaly et al., 1972).

Finally, we estimate the volumetric soil moisture. The effective volumetric soil moisture for this study is defined as:

                                                   (4)

In this study, the dielectric constants at 11 ¥ìm of the quartz, sand, and water of 4.052+0.070i, 4.060+0.069i (Hess et al., 1988), and 1.320+0.223i (Hale and Querry, 1973), respectively.

(a)(b)

(c)  (d)

Figure 1. Polarization and decomposed emissivity for AIRS data at 10.8 ¥ìm. (a) AQUA MODIS RGB image, (b) retrieved emissivity, (c) decomposedand (d) decomposed.

3. RESULTS

Figure 3 shows the estimated and. The retrieved emissivityis between 1 (air) and 4 (soil). The desert region near Mongolia exhibits lower emissivity as compared to the vegetation regions due to the effect of soil moisture. The retrieved  also exhibits a similar spatial distribution, spanning from roughly 0.2 over Manchuria to more than 0.2 over the Korean peninsular (excepting for the northern Manchuria region). Screening processes are not included.  

(a)   (b)

Figure 3. (a)  and (b)  at the AIRS 10.8 ¥ìm channel.

4. DISCUSSION

The proposed soil moisture algorithm offers the advantage that it does not depend on the complicated forward surface model. Therefore, the proposed method is appropriate for operational implementation.

Regarding the accuracy of the algorithm, the representative soil types should be predetermined to estimate the regional dielectric constants and soil moisture. Validating the accuracy of the retrieved soil moisture is left for future work due to the various sources of errors.

REFERENCES

 

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Bennett, H. E. 1963. Specular reflectance of aluminized ground glass and the height distribution of surface irregularities. J. Opt. Soc. Am. 53: 1389-1394.

Bryant, R., D. Thoma, S. Moran, C. Holifield,   D. Goodrich, T. Keefer, G. Paige, D. Williams, and S. Skirvin. 2003. Evaluation of Hyperspectral, Infrared Temperature and Radar Measurements for Monitoring Surface Soil Moisture. Proceedings of FICR in the Watersheds.

Hale, G. M. And M. R. Querry, 1973. Optical constants of water in the 200-nm to 200-¥ìm wavelength region. Appl. Opt., 12: 555-563.

Han, Y., P. van Delst, Q. Liu, F. Weng, and J. C. Derber. 2005. User¢®¯s guide to the JCSDA community radiative transfer model (beta version). Joint Cent. for Satellite Data Assimilation, Camp Springs, Maryland.

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Prigent, C., W. Rossow, and E. Mattews. 1997. Microwave land surface emissivities estimated from SSM/I observations. J. Geophys. Res., 102: 867-890.

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Wu, X. and W. L. Smith. 1997. Emissivity of rough sea surface for 8–13 mm: modeling and verification. Appl. Opt. 36:2609-2619.