Radiometric Approach for Estimating Relative Changes in Intraglacier Average Temperature

Jezek, Kenneth C.; Johnson, Joel T.; Drinkwater, Mark R.; Macelloni, Giovanni; Tsang, Leung; Aksoy, Mustafa; Durand, M. T.

doi:10.1109/tgrs.2014.2319265

Cited by 57 publications

(32 citation statements)

References 20 publications

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“…In contrast, recent studies Pablos, Piles, González-Gambau, Camps, & Vall-llossera, 2015) have pointed out that the T B variations are related to the thickness of the ice sheet. In particular in Jezek et al (2015), it was shown that, for the region near Lake Vostok (77°S-105°E), where variations in the surface temperature are weak, the SMOS T B variation (up to 8 K) over certain transects is clearly related to ice sheet thickness. Pablos et al (2015) extended the analysis to the entire Antarctic Plateau: using Aquarius data, they demonstrated that there is a fairly good agreement between T B and ice thickness variations over selected transects in East Antarctica, except for the case of spatially-rapid variations, which have not been resolved due to the coarse resolution (about 100 km) of Aquarius.…”

Section: Introductionmentioning

confidence: 99%

“…Again in Jezek et al (2015) and using a relatively simple electromagnetic first-order radiative transfer model, a first attempt was made to relate the T B variation observed by SMOS to the modification in the temperature profile of the ice sheet, which in turns depends on various glaciological parameters (i.e. thickness, surface temperature, geothermal flux, etc.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing and modeling the SMOS spatial variations in the East Antarctic Plateau

Macelloni

Leduc-Leballeur

Brogioni

et al. 2016

Remote Sensing of Environment

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing and modeling the SMOS spatial variations in the East Antarctic Plateau

Macelloni

Leduc-Leballeur

Brogioni

et al. 2016

Remote Sensing of Environment

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“…The e.m. model requires several inputs which are derived from glaciological models or experimental results. In particular the grain size a is estimated as a function of depth (z) using an equation from [7], a 3 = 0.0377 + 0.00472 z. The snow density is obtained as a function of depth z. Static temperature changes as a function of ice thickness and its profile are also obtained from [7]:…”

Section: The Electromagnetic Modelmentioning

confidence: 99%

“…Simple models to explain these spatial variations in Tb have been proposed [7]. The aim of the present work is to provide an improved interpretation of such Tb features by relating them to a more complete set of geophysical properties of the Antarctic ice cap and for assessing their impact on the satellite observations.…”

Section: Introductionmentioning

confidence: 99%

Understanding SMOS data in Antarctica

Macelloni¹,

Brogioni²,

Aksoy

et al. 2014

2014 IEEE Geoscience and Remote Sensing Symposium

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Since the SMOS satellite launch in 2009, its L-band radiometer data have been analyzed in depth by scientists worldwide and have resulted in significant steps forward in different disciplines. As primary objectives of the mission, the main research focus has been related to soil moisture and ocean salinity. However, the availability of a complete longterm, all-weather time-series of calibrated global brightness temperature data has enabled much broader research investigations on other topics such as the Cryosphere. SMOS data collected over central of Antarctica were also analyzed and whereas Tb is in general very stable in time it presents some intriguing spatial variations which are not yet fully explained. Using electromagnetic model simulations, and ancillary data for describing the physical parameters of the ice sheet, the observed variability and features in SMOS data are reproduced and explained.

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“…Interest has recently been increasing in predicting thermal emissions from layered media with rough interfaces that represent ice sheets [29][30][31]. In particular, the Ultra-Wideband Software-Defined Microwave Radiometer (UWBRAD) instrument is being developed to observe ice sheet emissions over 0.5-2 GHz, where understanding of the influence of layered roughness on UWBRAD measurements is important.…”

Section: Introductionmentioning

confidence: 99%

Scattering and Transmission of Waves in Multiple Random Rough Surfaces: Energy Conservation Studies With the Second Order Small Perturbation Method

Wang¹,

Tsang²,

Johnson³

et al. 2016

PIER

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Abstract-Energy conservation is an important consideration in wave scattering and transmission from random rough surfaces and is particularly important in passive microwave remote sensing. In this paper, we study energy conservation in scattering from layered random rough surfaces using the second order small perturbation method (SPM2). SPM2 includes both first order incoherent scattering and a second order correction to the coherent fields. They are combined to compute the total reflected and transmitted powers, as a sum of integrations over wavenumber k x , in which each integration includes the surface power spectra of a rough interface weighted by an emission kernel function (assuming the roughness of each interface is uncorrelated). We calculate the corresponding kernel functions which are the power spectral densities for one-dimensional (1D) surfaces in 2D scattering problems and examine numerical results for the cases of 2 rough interfaces and 51 rough interfaces. Because it is known that the SPM when evaluated to second order conserves energy, and it can be applied to second order for arbitrary surface power spectra, energy conservation can be shown to be satisfied for each value of k x in the kernel functions. The numerical examples show that energy conservation is obeyed for any dielectric contrast, any layer configuration and interface, and arbitrary roughness spectra. The values of reflected or transmitted powers predicted, however, are accurate only to second order in small surface roughness.

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Radiometric Approach for Estimating Relative Changes in Intraglacier Average Temperature

Cited by 57 publications

References 20 publications

Analyzing and modeling the SMOS spatial variations in the East Antarctic Plateau

Analyzing and modeling the SMOS spatial variations in the East Antarctic Plateau

Understanding SMOS data in Antarctica

Scattering and Transmission of Waves in Multiple Random Rough Surfaces: Energy Conservation Studies With the Second Order Small Perturbation Method

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