Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing
Introduction
Sea ice covers a significant fraction of the global oceans (5% to 8%) and is one of the most important parameters in the global climate system (Comiso et al., 2003). Quantitative information on the annual variability of the sea ice is vital in understanding the surface energy budget (Gorodetskaya et al., 2008), atmospheric circulation (Tjernström & Mauritsen, 2009), precipitation and moisture fluxes (Gutowski et al., 2007), clouds (Abbot & Tziperman, 2009), the Earth's fresh water (Salminen et al., 2009), and global temperature (Qu & Hall, 2005).
Aircraft observations have showed that the emissivity of bare soil, dense vegetation, wet snow, and new ice is sufficiently uniform on a scale of kilometers to allow retrievals of surface information from satellite microwave instruments (Hewison, 2001). However, dry snow and older ice types have highly variable emissivity. The spectrum of new ice is similar to that of land surfaces. The emissivity is relatively high and little changes with frequency, although emissions are more strongly polarized. Generally, the sea ice derived with a microwave imager has lower concentration than the actual sea ice (Walsh & Chapman, 2001).
A large contrast in emissivity exists between sea ice and water measured at microwave frequency. Polarization is used as a sensitive indicator of sea ice concentration (IC). However, complications arise because of other geophysical factors that influence the radiometric brightness of both the sea surface and sea ice, due to the wind-roughened seas, cloud droplets, and atmospheric water vapor. These create errors of < 10% in estimates of IC and extent (Steffen & Schweiger, 1991).
Surface roughness is known to significantly impact the microwave emission of the sea ice/snow surface. In general, surface roughness has been modeled by the geometric optics for large-scale roughness and the physical optics for small-scale roughness relative to the wavelength (Ulaby and Elachi, 1990). Large-scale roughness gives geometric specular reflection, and its effects are not modeled except for the ocean surface (English & Hewison, 1998). Meanwhile, small-scale roughness is important in determining the surface reflectivity. In this study, a semiempirical model based on the incoherent approach is used for small-scale surface roughness, which corresponds to identifying the height probability density function with a Gaussian distribution of zero mean and variance as follows (Wu and Fung, 1972, Choudhury et al., 1979):where R is the reflectivity, σ is the small-scale roughness height (Ulaby et al., 1982), λ is the wavelength, θ is the incidence angle. Subscripts R and S mean ‘rough’ and ‘specular’, respectively.
In general, the effect of roughness on emissivity is determined by sensitivity tests using forward models, by modeling the brightness temperature TB as a function of incidence angle, or by polarization ratios. Published results of surface roughness measurements of sea ice are rare (Manninen, 1997). Surface roughening of first-year ice has been suggested as a source for the increased backscatter which has been noted but not tabulated-again due to the limited data available (Carsey, 1989). Onstott and Gogineni (1985) observed a separate, distinct, and sharp change in surface roughness due to melt processes. The exact surface roughness changes over the winter and spring have not been observed or modeled.
In addition, the refractive index is crucial to determine the characteristics of objects and discriminate between them (Kramer, 2002). Many surface forward models require a priori information on the refractive index. However, this is problematic due to the heterogeneity and variation of surface in the field of view.
The scientific objective of this study is to determine the characteristics and distributions of the small-scale surface roughness and the refractive index of the sea ice and snow surface in the Polar Regions from the passive microwave satellite data. In addition, this study also validates the findings, and seeks a systematic procedure for estimating the roughness and refractive index.
Section snippets
Data and rough surface reflectivity
In this study, the Advanced Microwave Scanning Radiometer-Earth (AMSR-E) daily level-3 (L3) 25-km data are used for TB. The AMSR-E sensor measures vertically (V) and horizontally (H) polarized radiances at 6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz. The dates are 1 February and 1 August, 2007.
Basically, the ice temperature Ts is determined at AMSR-E 6.925 GHz as follows (Comiso et al., 2003):
In this investigation, the V and H reflectivities are assumed to be estimated
Polarization and specular surface reflectivity
Fig. 1 shows one result of the validation of the Hong approximation for ice, dry snow, and water at microwave frequencies. The refractive indexes of ice, snow, and water at 0 °C (Sadiku, 1985) are assumed to be 1.782 + 3.334 × 10− 3i, 1.016 + 6.339 × 10− 5i, and 8.227 + 2.341i at 6 GHz, 1.78 + 2.4 × 10− 3i, 1.016 + 7.066 × 10− 5i, and 5.234 + 2.933i at 20 GHz, respectively. The bias of RS,H from the Hong approximation is negligible for ice and snow. The bias for water is within 0.005. However, the relative bias is very
Summary and discussion
The small-scale roughness and refractive index of sea ice in the Arctic and Antarctic are retrieved using AMSR-E observations. A unique technique based on dielectric properties, polarization ratios, and surface emissivity is used. The effective rough emissivity and reflectivity at AMSR-E 6.9 GHz are estimated for V and H polarizations. Next, the specular reflectivity for the V polarization is estimated using the Hong approximation and the angular property of the roughness effect on each
Acknowledgements
The authors thank anonymous reviewers for constructive comments on the manuscript. This work is supported by the National Meteorological Satellite Center (Project No. 153-3100-3137-302-210-13).
References (34)
Detection of Asian dust (Hwangsa) over the Yellow sea by decomposition of unpolarized infrared reflectivity
Atmospheric Environment
(2009)- et al.
The behavior of snow and snow-free surface reflectance in boreal forests: Implications to the performance of snow covered area monitoring
Remote Sensing of Environment
(2009) - et al.
Controls on the activation and strength of a high-latitude convective cloud feedback
Journal of the Atmospheric Sciences
(2009) - et al.
Restrictions on the inversion of the Fresnel reflectance equations
Applied Optics
(1972) Review and status of remote sensing of sea ice
IEEE Journal of Oceanic Engineering
(1989)- et al.
Effect of surface roughness on the microwave emission from soils
Journal of Geophysical Research
(1979) - et al.
Sea ice concentration, ice temperature, and snow depth using AMSR-E data
IEEE Transactions on Geoscience and Remote Sensing
(2003) - et al.
- et al.
The influence of cloud and surface properties on the Arctic Ocean shortwave radiation budget in coupled models
Journal of Climate
(2008) - et al.
Influence of Arctic wetlands on Arctic atmospheric circulation
Journal of Climate
(2007)
Airborne measurements of forest and agricultural land surface emissivity at millimetre wavelengths
IEEE Transactions on Geoscience and Remote Sensing
Retrieval of refractive index over specular surfaces for remote sensing applications
Journal of Applied Remote Sensing
On the impact of ice emissivity on sea ice temperature retrieval using passive microwave radiance data
IEEE Geoscience and Remote Sensing Letters
Observation of the Earth and its environment
Surface roughness of Baltic sea ice
Journal of Geophysical Research
Cited by (31)
Various remote sensing approaches to understanding roughness in the marginal ice zone
2015, Physics and Chemistry of the EarthCitation Excerpt :Sea ice roughness affects passive microwave emission differently depending on microwave frequency, polarization, and sensor-surface geometry. There is still a perplexing ambiguity in deciphering dielectric and surface roughness contributions from the MIZ to the passive microwave emissions detected at the satellite sensor due to insufficient in situ data suitable for such work (Stroeve et al., 2006; Hong, 2010). Helicopter-based laser profiling and LiDAR (Light Detection And Ranging) imaging of rough sea ice further aid these investigations (Rivas et al., 2006; Haas et al., 2009).
Mapping global surface roughness using AMSR-E passive microwave remote sensing
2014, GeodermaCitation Excerpt :The small-scale roughness was retrieved within the reasonable range of previous works. Hong (2010b) expanded the model and applied for retrieving the small-scale roughness over sea ices using AMSR-E Tb. Results also showed reasonable agreement with the known observations, ranging from 0.2 cm to 0.6 cm for the sea ice in the Antarctic and Arctic regions.
A physically-based inversion algorithm for retrieving soil moisture in passive microwave remote sensing
2011, Journal of HydrologyPassive microwave algorithms for refractive index of Arctic sea ice: a comparison of two approaches and interpretations
2021, International Journal of Remote Sensing