Remote Sensing of Floating Macroalgae Blooms in the East China Sea Based on UNet Deep Learning Model

2021 ◽  
Vol 41 (2) ◽  
pp. 0201002
Author(s):  
李潇凡 Li Xiaofan ◽  
王胜强 Wang Shengqiang ◽  
翁轩 Weng Xuan ◽  
孙德勇 Sun Deyong ◽  
张海龙 Zhang Hailong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
East China Sea ◽  
Learning Model ◽  
East China ◽  
The East China Sea ◽  
China Sea ◽  
Deep Learning Model

scholarly journals REMOTE SENSING OF AEROSOL OPTICAL THICKNESS ON THE EAST CHINA SEA

2001 ◽  
Vol 9 ◽  
pp. 175-180
Author(s):  
Shuichi HASEGAWA ◽  
Sachio OHTA ◽  
Naoto MURAO ◽  
Sadamu YAMAGATA
Keyword(s):  
Remote Sensing ◽  
East China Sea ◽  
Optical Thickness ◽  
Aerosol Optical Thickness ◽  
East China ◽  
The East China Sea ◽  
China Sea

scholarly journals Estimating dissolved organic carbon inventories in the East China Sea using remote-sensing data

2014 ◽  
Vol 119 (10) ◽  
pp. 6557-6574 ◽  
Author(s):  
Qiong Liu ◽  
Delu Pan ◽  
Yan Bai ◽  
Kai Wu ◽  
Chen-Tung Authur Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Dissolved Organic Carbon ◽  
East China Sea ◽  
Remote Sensing Data ◽  
East China ◽  
The East China Sea ◽  
Sensing Data ◽  
China Sea

A regional remote sensing algorithm for total suspended matter in the East China Sea

2012 ◽  
Vol 124 ◽  
pp. 819-831 ◽  
Author(s):  
Zhihua Mao ◽  
Jianyu Chen ◽  
Delu Pan ◽  
Bangyi Tao ◽  
Qiankun Zhu
Keyword(s):  
Remote Sensing ◽  
East China Sea ◽  
Suspended Matter ◽  
Total Suspended Matter ◽  
East China ◽  
The East China Sea ◽  
China Sea ◽  
Remote Sensing Algorithm

scholarly journals Satellite Retrieval of Surface Water Nutrients in the Coastal Regions of the East China Sea

2018 ◽  
Vol 10 (12) ◽  
pp. 1896 ◽  
Author(s):  
Difeng Wang ◽  
Qiyuan Cui ◽  
Fang Gong ◽  
Lifang Wang ◽  
Xianqiang He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
East China Sea ◽  
Spatiotemporal Variability ◽  
East China ◽  
Changjiang River ◽  
The East China Sea ◽  
Coastal Regions ◽  
China Sea ◽  
Satellite Retrieval ◽  
Proposed Model

Due to the tremendous flux of terrestrial nutrients from the Changjiang River, the waters in the coastal regions of the East China Sea (ECS) are exposed to heavy eutrophication. Satellite remote sensing was proven to be an ideal way of monitoring the spatiotemporal variability of these nutrients. In this study, satellite retrieval models for nitrate and phosphate concentrations in the coastal regions of the ECS are proposed using the back-propagation neural network (BP-NN). Both the satellite-retrieved sea surface salinity (SSS) and remote-sensing reflectance (Rrs) were used as inputs in our model. Compared with models that only use Rrs or SSS, the newly proposed model performs much better in the study area, with determination coefficients (R2) of 0.98 and 0.83, and mean relative error (MRE) values of 18.2% and 17.2% for nitrate and phosphate concentrations, respectively. Based on the proposed model and satellite-retrieved Rrs and SSS datasets, monthly time-series maps of nitrate and phosphate concentrations in the coastal regions of the ECS for 2015–2017 were retrieved for the first time. The results show that the distribution of nutrients had a significant seasonal variation. Phosphate concentrations in the ECS were lower in spring and summer than those in autumn and winter, which was mainly due to phytoplankton uptake and utilization. However, nitrate still spread far out into the ocean in summer because the diluted Changjiang River water remained rich in nitrogen.

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