Performance of sea surface salinity and soil moisture retrieval algorithms with different auxiliary datasets in 2-D L-band aperture synthesis interferometric radiometers

Camps, A.; Vall-llossera, M.; Duffo, N.; Torres, F.; Corbella, I.

doi:10.1109/tgrs.2004.842096

Cited by 39 publications

(55 citation statements)

References 19 publications

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“…Brightness temperature is taken every 1.2 s by hexagon-like, two-dimensional snapshots, which have a spatial dimension of about 1200 km across (Kerr et al, 2001). The geometric distribution of incidence angles and radiometric accuracy within the alias-free areas of a snapshot (Camps et al, 2005) is shown in Fig. 1.…”

Section: L1c Datamentioning

confidence: 99%

“…The intensity is the average of the horizontally and vertically polarized brightness temperatures, equal to 0.5 (TB h +TB v ). The intensity is independent of both geometric and Faraday rotations, and robust to instrumental and geophysical errors (Camps et al, 2005). We can avoid additional uncertainties caused by the transformation from the antenna reference frame to the Earth reference frame by using the intensity.…”

Section: Brightness Temperature Intensitymentioning

confidence: 99%

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SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Kaleschke²,

et al. 2014

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Abstract. Following the launch of ESA's Soil Moisture andOcean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).

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Section: L1c Datamentioning

confidence: 99%

Section: Brightness Temperature Intensitymentioning

confidence: 99%

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Kaleschke²,

et al. 2014

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“…As in the case when the SSS retrieval is formulated in terms of U 10 or the SWH, the SSS retrieval can now be formulated in the antenna (T xx and T yy ) or in the Earth reference frames (T hh and T vv ) [31], provided the appropriate corrections are applied (atmosphere, ionosphere, and sky downwelling radiation scattered over the Earth's surface). Both formulations require a precise knowledge of the observation geometry (Ψ) and the Faraday rotation (Ψ Faraday ) angles.…”

Section: E Sss Retrieval In Terms Of the Mss At The Points Of Speculmentioning

confidence: 99%

“…1) For each overpass, the error ε (variance) between the model and the measured data at all incidence angles θ must be minimized to obtain a set of estimated parameters (ˆ P = [SŜS,SŜT, mŝs]) [31].…”

Section: E Sss Retrieval In Terms Of the Mss At The Points Of Speculmentioning

confidence: 99%

Potential Synergetic Use of GNSS-R Signals to Improve the Sea-State Correction in the Sea Surface Salinity Estimation: Application to the SMOS Mission

Sabia

Caparrini²,

Ruffini³

2007

IEEE Trans. Geosci. Remote Sensing

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Abstract-It is accepted that the best way to monitor sea surface salinity (SSS) on a global basis is by means of L-band radiometry. However, the measured sea surface brightness temperature (T B ) depends not only on the SSS but also on the sea surface temperature (SST) and, more importantly, on the sea state, which is usually parameterized in terms of the 10-m-height wind speed (U 10 ) or the significant wave height. It has been recently proposed that the mean-square slope (mss) derived from global navigation satellite system (GNSS) signals reflected by the sea surface could be a potentially appropriate sea-state descriptor and could be used to make the necessary sea state T B corrections to improve the SSS estimates. This paper presents a preliminary error analysis of the use of reflected GNSS signals for the sea roughness correction and was performed to support the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission; the orbit and parameters for the SMOS instrument were assumed. The accuracy requirement for the retrieved SSS is 0.1 practical salinity units after monthly averaging over 2• × 2 • boxes. In this paper, potential improvements in salinity estimation are hampered mainly by the coarse sampling and by the requirements of the retrieval algorithm, particularly the need for a semiempirical model that relates T B and mss.Index Terms-Auxiliary data, global navigation satellite system-reflections (GNSS-R) signals, mean-square slope (mss), microwave radiometry, sea salinity retrieval.

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“…This new capability predicts better results than those obtained with other existing radiometers that scan the surface at a single incidence angle. However, as in any inversion problem in geophysical sciences and engineering, the retrieval of soil moisture relies on the accuracy of both the forward emission models (vegetation description and numerical emission model) used to develop the inversion procedure over the range of observed incidence angles, and on the quality of the auxiliary data [3]. To improve these models, a number of field experiments have been conducted both over land and over sea [4].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of the Full-Polarimetric Emissivity of Vines and Comparison with Experimental Data

Martinez-Vazquez¹,

Camps²,

López-Sánchez³

et al. 2009

Remote Sensing

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Surface soil moisture is a key variable needed to understand and predict the climate. L-band microwave radiometry seems to be the best technique to remotely measure the soil moisture content, since the influence of other effects such as surface roughness and vegetation is comparatively small. This work describes a numerical model developed to efficiently compute the four elements of the Stokes emission vector (T h , T v , T U and T V ) of vegetation-covered soils at low microwave frequencies, as well as the single-scattering albedo and the extinction coefficient of the vegetation layer over a wide range of incidence angles. A comparison with L-band (1.400-1.427 MHz) experimental radiometric data gathered during the SMOS REFLEX 2003 field experiment over vines is presented and discussed. The measured and simulated emissivities at vertical polarization agree very well. However, at horizontal polarization there is some disagreement introduced by the soil emission model. Important radiometric parameters, such as the albedo, the attenuation and the transmissivity are computed and analyzed in terms of their values and trends, as well as their dependence on the observation and scene parameters. It is found that the vegetation attenuation is mainly driven by the presence of branches and leaves, while the albedo is

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Performance of sea surface salinity and soil moisture retrieval algorithms with different auxiliary datasets in 2-D L-band aperture synthesis interferometric radiometers

Cited by 39 publications

References 19 publications

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Potential Synergetic Use of GNSS-R Signals to Improve the Sea-State Correction in the Sea Surface Salinity Estimation: Application to the SMOS Mission

Numerical Simulation of the Full-Polarimetric Emissivity of Vines and Comparison with Experimental Data

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