Microwave remote sensing of multi-layered rough-surface snow-covered sea ice dielectric profile: Sensitivity analysis and inversion

2014 ◽  
Author(s):  
Nariman Firoozy ◽  
Puyan Mojabi ◽  
David Barber
Keyword(s):  
Remote Sensing ◽  
Sensitivity Analysis ◽  
Sea Ice ◽  
Rough Surface ◽  
Microwave Remote Sensing ◽  
Surface Snow ◽  
And Inversion

scholarly journals ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

2018 ◽  
Vol IV-5 ◽  
pp. 223-228 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Remote Sensing ◽  
Water Resource Management ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Physical Relationship ◽  
Sensing Data ◽  
Surface Snow ◽  
Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

Considerations for microwave remote sensing of thin sea ice

1992 ◽  
pp. 291-301 ◽  
Author(s):  
Thomas C. Grenfell ◽  
Donald J. Cavalieri ◽  
Josefino C. Comiso ◽  
Mark R. Drinkwater ◽  
Robert G. Onstott ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Microwave Remote Sensing

Sea-ice characterization measurements needed for testing of microwave remote sensing models

1989 ◽  
Vol 14 (2) ◽  
pp. 149-158 ◽  
Author(s):  
D.P. Winebrenner ◽  
L. Tsang ◽  
B. Wen ◽  
R. West
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Microwave Remote Sensing

scholarly journals On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

2018 ◽  
Vol 12 (3) ◽  
pp. 993-1012 ◽  
Author(s):  
Lu Zhou ◽  
Shiming Xu ◽  
Jiping Liu ◽  
Bin Wang
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Snow Depth ◽  
Microwave Remote Sensing ◽  
Ice Cover ◽  
Ice Thickness ◽  
Laser Altimetry ◽  
Sea Ice Thickness ◽  
Passive Remote Sensing ◽  
L Band

Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

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