Retrieving Inherent Optical Properties of Coastal Waters From Remote Sensing Reflectance by a Split- Window Matrix Inversion Method

2006 ◽  
Author(s):  
C. Chang ◽  
S. Liew ◽  
W. Heng
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Coastal Waters ◽  
Matrix Inversion ◽  
Inversion Method ◽  
Inherent Optical Properties ◽  
Remote Sensing Reflectance ◽  
Matrix Inversion Method

Remote sensing reflectance and inherent optical properties of oceanic waters derived from above-water measurements

1997 ◽  
Author(s):  
Zhongping Lee ◽  
Kendall L. Carder ◽  
Robert G. Steward ◽  
Thomas G. Peacock ◽  
Curtiss O. Davis ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Inherent Optical Properties ◽  
Remote Sensing Reflectance

scholarly journals A Reflectance Model for Relatively Clear and Turbid Waters

2013 ◽  
Vol 3 (1) ◽  
pp. 325-337
Author(s):  
S. P. Tiwari ◽  
P. Shanmugam ◽  
Y. H. Ahn ◽  
J. H. Ryu
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Suspended Sediments ◽  
Optical Data ◽  
Spectral Curves ◽  
Inherent Optical Properties ◽  
Turbid Waters ◽  
Remote Sensing Reflectance ◽  
South Sea ◽  
Q Factors

Accurate modeling of spectral remote sensing reflectance (Rrs) is of great interest for ocean colour studies in highly turbid and relatively clear waters. In this work a semianalytical model that simulates the spectral curves of remote sensing reflectance of these waters is developed based on the inherent optical properties (IOPs) and f and Q factors. For accommodating differences in the optical properties of the water and accounting for their directional variations, IOPs and f and Q  factors are derived as a function of phytoplankton pigments, suspended sediments and solar zenith angle. Results of this model are compared with in-situ bio-optical data collected at 83 stations encompassing highly turbid/relatively cleared waters of the South Sea of Korea. Measured and modeled remote sensing reflectances agree favorably in both magnitude and spectral shape, with considerably low errors (mean relative error MRE -0.0327; root mean square error RMSE 0.205, bias -0.0727 and slope 1.15 and correlation coefficient R2 0.74). These results suggest that the new model has the ability to reproduce measured reflectance values and has potentially profound implications for remote sensing of complex waters in this region.

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