Surface-roughness considerations for atmospheric correction of ocean color sensors 1: The Rayleigh-scattering component

1992 ◽  
Vol 31 (21) ◽  
pp. 4247 ◽  
Author(s):  
Howard R. Gordon ◽  
Menghua Wang
Keyword(s):  
Surface Roughness ◽  
Rayleigh Scattering ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Color Sensors

Preliminary Evaluation of the Atmospheric Correction Look-Up-Tables (LUTs) of the COCTS-HY1C

2020 ◽  
Author(s):  
Keping Du ◽  
Shuguo Chen ◽  
Jing Ding ◽  
Zhongping Lee
Keyword(s):  
Rayleigh Scattering ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Ultra Violet ◽  
Preliminary Evaluation ◽  
The Novel ◽  
Aerosol Scattering ◽  
Processing Step ◽  
Color Sensors ◽  
The Look

<p>The Chinese Ocean Colour and Temperature Scanner (COCTS), Coastal Zone Imager (CZI) and the novel Ultra-Violet Imager (UVI) which on-board the Chinese Ocean Satellite  HY-1C was launched in September 2018. The atmospheric correction of ocean color sensors was a critical step for accurate retrieval of the remote sensing reflectance, and the look-up-tables (LUTs), for the Rayleigh scattering, the aerosol scattering, and the diffuse transmittance, which were built bases on a Successive Order Scattering Vector Radiative Transfer Solver, played an important role in the processing step. Preliminary evaluation has been performed using the SeaWiFS LUTs and the MODIS data, it showed that COCTS can provide accurate ocean color products.</p>

Influence of oceanic whitecaps on atmospheric correction of ocean-color sensors

1994 ◽  
Vol 33 (33) ◽  
pp. 7754 ◽  
Author(s):  
Howard R. Gordon ◽  
Menghua Wang
Keyword(s):  
Ocean Color ◽  
Atmospheric Correction ◽  
Color Sensors

Atmospheric correction of ocean-color sensors: effects of the Earth’s curvature

1994 ◽  
Vol 33 (30) ◽  
pp. 7096 ◽  
Author(s):  
Kuiyuan Ding ◽  
Howard R. Gordon
Keyword(s):  
Ocean Color ◽  
Atmospheric Correction ◽  
Color Sensors

scholarly journals Atmospheric Correction of Satellite Ocean Color Remote Sensing in the Presence of High Aerosol Loads

2019 ◽  
Vol 12 (1) ◽  
pp. 31
Author(s):  
Zhihua Mao ◽  
Bangyi Tao ◽  
Peng Chen ◽  
Jianyu Chen ◽  
Zengzhou Hao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rayleigh Scattering ◽  
Layer Structure ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Wide Field ◽  
Systematic Bias ◽  
Multiple Reflections ◽  
Process Data ◽  
Scattering Layer

The coverage of valid pixels of remote-sensing reflectance (Rrs) from ocean color imagery is relatively low due to the presence of clouds. In fact, it is also related to the presence of high aerosol optical depth (AOD) and other factors. In order to increase the valid coverage of satellite-retrieved products, a layer removal scheme for atmospheric correction (LRSAC) has been developed to process the ocean color data. The LRSAC used a five-layer structure including atmospheric absorption layer, Rayleigh scattering layer, aerosol scattering layer, sea surface reflection layer, and water-leaving reflectance layer to deal with the relationship of the components of the atmospheric correction. A nonlinear approach was used to solve the multiple reflections of the interface between two adjoining layers and a step-by-step procedure was used to remove effects of each layer. The LRSAC was used to process data from the sea-viewing wide field-of-view sensor (SeaWiFS) and the results were compared with standard products. The average of valid pixels of the global daily Rrs images of the standard products from 1997 to 2010 is only 11.5%, while it reaches up to 30.5% for the LRSAC. This indicates that the LRSAC recovers approximately 1.65 times of invalid pixels as compared with the standard products. Eight-day standard composite images exhibit many large areas with invalid values due to the presence of high AOD, whereas these areas are filled with valid pixels wusing the LRSAC. The ratio image of the mean valid pixel of the LRSAC to that of the standard products indicates that the number of valid pixels of the LRSAC increases with an increase of AOD. The LRSAC can increase the number of valid pixels by more than two times in about 33.8% of ocean areas with high AOD values. The accuracy of Rrs from the LRSAC was validated using the following two in situ datasets: the Marine Optical BuoY (MOBY) and the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Most matchup pairs are distributed around the 1:1 line indicating that the systematic bias of the LRSAC is relatively small. The global mean relative error (MRE) of Rrs is 7.9% and the root mean square error (RMSE) is 0.00099 sr−1 for the MOBY matchups. Similarly, the MRE and RMSE are 2.1% and 0.0025 sr−1 for the NOMAD matchups, respectively. The accuracy of LRSAC was also evaluated by different groups of matchups according to the increase of AOD values, indicating that the errors of Rrs were little affected by the presence of high AOD values. Therefore, the LRSAC can significantly improve the coverage of valid pixels of Rrs with a similar accuracy in the presence of high AOD.

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