Retrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications

2009 ◽  
Vol 114 (C10) ◽  
Author(s):  
Menghua Wang ◽  
SeungHyun Son ◽  
Lawrence W. Harding
Keyword(s):  
Chesapeake Bay ◽  
Attenuation Coefficient ◽  
Ocean Color ◽  
Diffuse Attenuation Coefficient ◽  
Satellite Ocean Color

scholarly journals Global land mask for satellite ocean color remote sensing

2021 ◽  
Vol 257 ◽  
pp. 112356
Author(s):  
Karlis Mikelsons ◽  
Menghua Wang ◽  
Xiao-Long Wang ◽  
Lide Jiang
Keyword(s):  
Remote Sensing ◽  
Ocean Color ◽  
Ocean Color Remote Sensing ◽  
Global Land ◽  
Satellite Ocean Color

scholarly journals A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration

2021 ◽  
Vol 13 (10) ◽  
pp. 2003
Author(s):  
Daeyong Jin ◽  
Eojin Lee ◽  
Kyonghwan Kwon ◽  
Taeyun Kim
Keyword(s):  
Deep Learning ◽  
Chlorophyll A ◽  
Hydrodynamic Model ◽  
Ocean Color ◽  
Learning Model ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Temporal And Spatial Distribution ◽  
Deep Learning Model ◽  
Satellite Ocean Color

In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a.

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
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