Polarimetric Passive Remote Sensing of Ocean Surface

1993 ◽  
Author(s):  
J. A. Kong
Keyword(s):  
Remote Sensing ◽  
Ocean Surface ◽  
Passive Remote Sensing

Whitecaps and the passive remote sensing of the ocean surface

1986 ◽  
Vol 7 (5) ◽  
pp. 627-642 ◽  
Author(s):  
EDWARD C. MONAHAN ◽  
IOGNAID G. O'MUIRCHEARTAIGH
Keyword(s):  
Remote Sensing ◽  
Ocean Surface ◽  
Passive Remote Sensing

scholarly journals Effects of ice particle size vertical inhomogeneity on the passive remote sensing of ice clouds

2010 ◽  
Vol 115 (D17) ◽  
Author(s):  
Zhibo Zhang ◽  
Steven Platnick ◽  
Ping Yang ◽  
Andrew K. Heidinger ◽  
Jennifer M. Comstock
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Ice Clouds ◽  
Passive Remote Sensing ◽  
Vertical Inhomogeneity

Remote sensing of the ocean surface refractive index via short-wave infrared polarimetry

2019 ◽  
Vol 221 ◽  
pp. 14-23 ◽  
Author(s):  
Matteo Ottaviani ◽  
Jacek Chowdhary ◽  
Brian Cairns
Keyword(s):  
Remote Sensing ◽  
Refractive Index ◽  
Ocean Surface ◽  
Short Wave ◽  
Short Wave Infrared

scholarly journals Using Combined Close-Range Active and Passive- Remote Sensing Methods to Detect Sinkholes

2018 ◽  
Vol 07 (01) ◽  
Author(s):  
Naftly Goldshleger ◽  
Uri Basson ◽  
Ilan Azaria ◽  
Shlomo Fastig
Keyword(s):  
Remote Sensing ◽  
Passive Remote Sensing ◽  
Close Range ◽  
Remote Sensing Methods

scholarly journals Aerodynamic roughness length estimation from very high-resolution imaging LIDAR observations over the Heihe basin in China

2010 ◽  
Vol 14 (12) ◽  
pp. 2661-2669 ◽  
Author(s):  
J. Colin ◽  
R. Faivre
Keyword(s):  
Remote Sensing ◽  
Surface Roughness ◽  
Land Cover ◽  
River Basin ◽  
Land Surface ◽  
Surface Density ◽  
Roughness Length ◽  
Aerodynamic Roughness Length ◽  
Passive Remote Sensing ◽  
Aerodynamic Roughness

Abstract. Roughness length of land surfaces is an essential variable for the parameterisation of momentum and heat exchanges. The growing interest in the estimation of the surface turbulent flux parameterisation from passive remote sensing leads to an increasing development of models, and the common use of simple semi-empirical formulations to estimate surface roughness. Over complex surface land cover, these approaches would benefit from the combined use of passive remote sensing and land surface structure measurements from Light Detection And Ranging (LIDAR) techniques. Following early studies based on LIDAR profile data, this paper explores the use of imaging LIDAR measurements for the estimation of the aerodynamic roughness length over a heterogeneous landscape of the Heihe river basin, a typical inland river basin in the northwest of China. The point cloud obtained from multiple flight passes over an irrigated farmland area were used to separate the land surface topography and the vegetation canopy into a Digital Elevation Model (DEM) and a Digital Surface Model (DSM) respectively. These two models were then incorporated in two approaches: (i) a strictly geometrical approach based on the calculation of the plan surface density and the frontal surface density to derive a geometrical surface roughness; (ii) a more aerodynamic approach where both the DEM and DSM are introduced in a Computational Fluid Dynamics model (CFD). The inversion of the resulting 3-D wind field leads to a fine representation of the aerodynamic surface roughness. Examples of the use of these three approaches are presented for various wind directions together with a cross-comparison of results on heterogeneous land cover and complex roughness element structures.

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