期刊
REMOTE SENSING
卷 10, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/rs10121896
关键词
satellite remote sensing; nutrients; East China Sea; remote-sensing reflectance; sea surface salinity; artificial neural network
类别
资金
- National Key R&D Program of China [2017YFC1405300]
- National Basic Research Programme (973 Programme) of China [2015CB954002]
- key research and development plan of Zhejiang Province [2017C03037]
- National Natural Science Foundation of China [41476157, L1624046, 41676170, 41676172, 41621064]
- Special Program of the Second Institute of Oceanography [JT1404]
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 (R-rs) were used as inputs in our model. Compared with models that only use R-rs or SSS, the newly proposed model performs much better in the study area, with determination coefficients (R-2) 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 R-rs 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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