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.

Effects of Earth curvature on atmospheric correction for ocean color remote sensing

2018 ◽  
Vol 209 ◽  
pp. 118-133 ◽  
Author(s):  
Xianqiang He ◽  
Knut Stamnes ◽  
Yan Bai ◽  
Wei Li ◽  
Difeng Wang
Keyword(s):  
Remote Sensing ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Ocean Color Remote Sensing

scholarly journals Performance evaluation of ocean color satellite models for deriving accurate chlorophyll estimates in the Gulf of Saint Lawrence

2014 ◽  
Vol 11 (6) ◽  
pp. 9299-9340
Author(s):  
M. Montes-Hugo ◽  
H. Bouakba ◽  
R. Arnone
Keyword(s):  
Chlorophyll Concentration ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Relative Bias ◽  
Wide Field ◽  
Absolute Bias ◽  
Analysis System ◽  
Data Analysis System ◽  
The Impact

Abstract. The understanding of phytoplankton dynamics in the Gulf of the Saint Lawrence (GSL) is critical for managing major fisheries off the Canadian East coast. In this study, the accuracy of two atmospheric correction techniques (NASA standard algorithm, SA, and Kuchinke's spectral optimization, KU) and three ocean color inversion models (Carder's empirical for SeaWiFS (Sea-viewing Wide Field-of-View Sensor), EC, Lee's quasi-analytical, QAA, and Garver- Siegel-Maritorena semi-empirical, GSM) for estimating the phytoplankton absorption coefficient at 443 nm (aph(443)) and the chlorophyll concentration (chl) in the GSL is examined. Each model was validated based on SeaWiFS images and shipboard measurements obtained during May of 2000 and April 2001. In general, aph(443) estimates derived from coupling KU and QAA models presented the smallest differences with respect to in situ determinations as measured by High Pressure liquid Chromatography measurements (median absolute bias per cruise up to 0.005, RMSE up to 0.013). A change on the inversion approach used for estimating aph(443) values produced up to 43.4% increase on prediction error as inferred from the median relative bias per cruise. Likewise, the impact of applying different atmospheric correction schemes was secondary and represented an additive error of up to 24.3%. By using SeaDAS (SeaWiFS Data Analysis System) default values for the optical cross section of phytoplankton (i.e., aph(443) = aph(443)/chl = 0.056 m2mg−1), the median relative bias of our chl estimates as derived from the most accurate spaceborne aph(443) retrievals and with respect to in situ determinations increased up to 29%.

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>

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