DEVELOPMENT OF A BLUE TIDE ESTIMATION METHOD BASED ON OPTICAL PROPERTIES OF SULFUR BY GEOSTATIONARY OCEAN COLOR SATELLITE

2019 ◽  
Vol 75 (2) ◽  
pp. I_1057-I_1062
Author(s):  
Hiroto HIGA ◽  
Wataru NAKAMURA ◽  
Shogo SUGAHARA ◽  
Yoshiyuki NAKAMURA ◽  
Takayuki SUZUKI
Keyword(s):  
Optical Properties ◽  
Ocean Color ◽  
Estimation Method

scholarly journals Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations

2019 ◽  
Vol 231 ◽  
pp. 111249 ◽  
Author(s):  
Jaime Pitarch ◽  
Hendrik J. van der Woerd ◽  
Robert J.W. Brewin ◽  
Oliver Zielinski
Keyword(s):  
Optical Properties ◽  
Ocean Color ◽  
Water Types ◽  
Satellite Ocean Color

scholarly journals GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during DRAGON-NE Asia 2012 campaign

2015 ◽  
Vol 8 (9) ◽  
pp. 9565-9609 ◽  
Author(s):  
M. Choi ◽  
J. Kim ◽  
J. Lee ◽  
M. Kim ◽  
Y. Je Park ◽  
...  
Keyword(s):  
Optical Properties ◽  
Land Surface ◽  
Pearson Correlation ◽  
Ocean Color ◽  
Single Scattering ◽  
Surface Reflectance ◽  
Robotic Networks ◽  
Deep Blue ◽  
Aerosol Retrieval ◽  
Highly Correlated

Abstract. The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorology Satellites (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm over ocean and land together with validation results during the DRAGON-NE Asia 2012 campaign. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single scattering albedo (SSA) at 440 nm, Angstrom exponent (AE) between 440 and 860 nm, and aerosol type from selected aerosol models in calculating AOD. Assumed aerosol models are compiled from global Aerosol Robotic Networks (AERONET) inversion data, and categorized according to AOD, FMF, and SSA. Nonsphericity is considered, and unified aerosol models are used over land and ocean. Different assumptions for surface reflectance are applied over ocean and land. Surface reflectance over the ocean varies with geometry and wind speed, while surface reflectance over land is obtained from the 1–3 % darkest pixels in a 6 km × 6 km area during 30 days. In the East China Sea and Yellow Sea, significant area is covered persistently by turbid waters, for which the land algorithm is used for aerosol retrieval. To detect turbid water pixels, TOA reflectance difference at 660 nm is used. GOCI YAER products are validated using other aerosol products from AERONET and the MODIS Collection 6 aerosol data from "Dark Target (DT)" and "Deep Blue (DB)" algorithms during the DRAGON-NE Asia 2012 campaign from March to May 2012. Comparison of AOD from GOCI and AERONET gives a Pearson correlation coefficient of 0.885 and a linear regression equation with GOCI AOD =1.086 × AERONET AOD – 0.041. GOCI and MODIS AODs are more highly correlated over ocean than land. Over land, especially, GOCI AOD shows better agreement with MODIS DB than MODIS DT because of the choice of surface reflectance assumptions. Other GOCI YAER products show lower correlation with AERONET than AOD, but are still qualitatively useful.

scholarly journals Spatio-Temporal Variability in Bio-Optical Properties of the Southern Caspian Sea: A Historic Analysis of Ocean Color Data

2020 ◽  
Vol 12 (23) ◽  
pp. 3975
Author(s):  
Bonyad Ahmadi ◽  
Mehdi Gholamalifard ◽  
Tiit Kutser ◽  
Stefano Vignudelli ◽  
Andrey Kostianoy
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Attenuation Coefficient ◽  
Temporal Variability ◽  
Caspian Sea ◽  
Ocean Color ◽  
Time Lags ◽  
Chl A ◽  
Remote Sensing Reflectance ◽  
Southern Caspian Sea

Currently, satellite ocean color imageries play an important role in monitoring of water properties in various oceanic, coastal, and inland ecosystems. Although there is a long-time and global archive of such valuable data, no study has comprehensively used these data to assess the changes in the Caspian Sea. Hence, this study assessed the variability of bio-optical properties of the upper-water column in the Southern Caspian Sea (SCS) using the archive of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The images acquired from SeaWiFS (January 1998 to December 2002) and MODIS Aqua (January 2003 to December 2015) satellites were used to investigate the spatial–temporal variability of bio-optical properties including Chlorophyll-a (Chl-a), attenuation coefficient, and remote sensing reflectance, and environmental parameters such as sea surface temperature (SST), wind stress and the El Nino-southern oscillation (ENSO) phenomena at different time lags in the study area. The trend analysis demonstrated an overall increase of 0.3358 mg m−3 in the Chl-a concentration during 1998–2015 (annual increase rate of 0.018 mg m−3 year−1) and four algal blooms and in turn an abnormal increase in Chl-a concentration were occurred in August 2001, September 2005, 2009, and August 2010. The linear model revealed that Chl-a in the northern and middle part of the study area had been influenced by the attenuation coefficient after a one-month lag time. The analysis revealed a sharp decline in Chl-a concentration during 2011–2015 and showed a high correlation with the turbidity and attenuation coefficient in the southern region, while Kd_490nm and remote sensing reflectance did a low one. Generally, Chl-a concentration exhibited a positive correlation with the attenuation coefficient (r = 0.63) and with remote sensing reflectance at the 555 nm (r = 0.111). This study can be used as the basis for predictive modeling to evaluate the changes of water quality and bio-optical indices in the Southern Caspian Sea (SCS).

scholarly journals An Algorithm to Estimate Suspended Particulate Matter Concentrations and Associated Uncertainties from Remote Sensing Reflectance in Coastal Environments

2020 ◽  
Vol 12 (13) ◽  
pp. 2172 ◽  
Author(s):  
Juliana Tavora ◽  
Emmanuel Boss ◽  
David Doxaran ◽  
Paul Hill
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Particulate Matter ◽  
Suspended Particulate Matter ◽  
Ocean Color ◽  
Major Constituent ◽  
Coastal Environments ◽  
Suspended Particulate ◽  
Spm Concentration

Suspended Particulate Matter (SPM) is a major constituent in coastal waters, involved in processes such as light attenuation, pollutant propagation, and waterways blockage. The spatial distribution of SPM is an indicator of deposition and erosion patterns in estuaries and coastal zones and a necessary input to estimate the material fluxes from the land through rivers to the sea. In-situ methods to estimate SPM provide limited spatial data in comparison to the coverage that can be obtained remotely. Ocean color remote sensing complements field measurements by providing estimates of the spatial distributions of surface SPM concentration in natural waters, with high spatial and temporal resolution. Existing methods to obtain SPM from remote sensing vary between purely empirical ones to those that are based on radiative transfer theory together with empirical inputs regarding the optical properties of SPM. Most algorithms use a single satellite band that is switched to other bands for different ranges of turbidity. The necessity to switch bands is due to the saturation of reflectance as SPM concentration increases. Here we propose a multi-band approach for SPM retrievals that also provides an estimate of uncertainty, where the latter is based on both uncertainties in reflectance and in the assumed optical properties of SPM. The approach proposed is general and can be applied to any ocean color sensor or in-situ radiometer system with red and near-infra-red bands. We apply it to six globally distributed in-situ datasets of spectral water reflectance and SPM measurements over a wide range of SPM concentrations collected in estuaries and coastal environments (the focus regions of our study). Results show good performance for SPM retrieval at all ranges of concentration. As with all algorithms, better performance may be achieved by constraining empirical assumptions to specific environments. To demonstrate the flexibility of the algorithm we apply it to a remote sensing scene from an environment with highly variable sediment concentrations.

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