scholarly journals Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

2018 ◽  
Vol 10 (2) ◽  
pp. 304 ◽  
Author(s):  
François Jonard ◽  
Simone Bircher ◽  
François Demontoux ◽  
Lutz Weihermüller ◽  
Stephen Razafindratsima ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Surface ◽  
Microwave Remote Sensing ◽  
Organic Soil ◽  
Surface Layers ◽  
L Band

scholarly journals L-Band Relative Permittivity of Organic Soil Surface Layers—A New Dataset of Resonant Cavity Measurements and Model Evaluation

2016 ◽  
Vol 8 (12) ◽  
pp. 1024 ◽  
Author(s):  
Simone Bircher ◽  
François Demontoux ◽  
Stephen Razafindratsima ◽  
Elena Zakharova ◽  
Matthias Drusch ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Relative Permittivity ◽  
Resonant Cavity ◽  
Soil Surface ◽  
Organic Soil ◽  
Surface Layers ◽  
L Band

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.

Experimental Study of C Band Passive Microwave Remote Sensing of Soil Moisture

2013 ◽  
Vol 477-478 ◽  
pp. 624-627
Author(s):  
Xiao Liu Gao ◽  
Hui Hui Zhang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Emission Rate ◽  
Soil Surface ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Passive Microwave Remote Sensing ◽  
Soil Surface Roughness ◽  
Relationship Of

Passive microwave remote sensing is one of the most effective methods for inversing soil moisture. Under the condition of laboratory, firstly, C band microwave radiation was used to achieve the trial of ground-based remote sensing soil moisture, and then regression analysis was carried out according to the data measured, finally, got the C band experience regression model of soil moisture inversion. The results showed that: in the level-off state of soil surface, soil humidity and soil microwave emission rate is linear negative correlation, in the other words, soil microwave emission rate decreased while the soil moisture increased. Besides, with the increasing of soil surface roughness, both the value of microwave polarization index (MPDI) and microwave emission rate polarization difference Δe have the same trend of quick drop, stabilization and slow raise, and it presented the relationship of quadratic curve with the change of roughness.

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