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

Abstract

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.

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

“…Using Table 1, the manager can find the research developed by [87]. However, the manager can also find the research of [88] in Table 2 (pressures section), which can provide information about the type of crop, as well as provide some information about the quantity of fertilizer and nutrient ratios shown by the research of [89]. Furthermore, the manager can also assess the distribution of harmful algal blooms as a state change in Table 3, and follow the techniques used by [90].…”

“…The findings show which approach could be followed to assess a specific pressure found in one of the environmental indicators, descriptors, targets, or/and goals. For instance, an environmental engineer working on nutrient enrichment assessments could follow the application made by [89] and related frameworks (see Table 2).…”

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

“…Taking R 2 as the comparison standard of retrieval accuracy, this research surveyed most existing publications regarding the retrieval of TP, TN and COD from 2000 to present and compared the performance of the proposed method in this research with previous research works, Figure 12 shows the results of comparison. Since the average R 2 of TP in the existing publications [21,22,[24][25][26][27]29] was 0.653, the performance of TP retrieval was improved by about 44% over the average of the past research works. For TN, the average R 2 in the existing publications [21, [23][24][25][26][27] was 0.613, and the performance of TN retrieval in this research was improved by 43% over the average of the past research works.…”

“…Since the average R 2 of TP in the existing publications [21,22,[24][25][26][27]29] was 0.653, the performance of TP retrieval was improved by about 44% over the average of the past research works. For TN, the average R 2 in the existing publications [21, [23][24][25][26][27] was 0.613, and the performance of TN retrieval in this research was improved by 43% over the average of the past research works. For COD, the average R 2 in the past research works [17][18][19][20] was 0.722, and the performance of retrieval COD in this research was slightly higher than the upper quartile of the past research works.…”

“…Over the past several decades, scholars have carried out extensive research on water quality monitoring by remote sensing, and have achieved good results in estimating optically active parameters, such as Chlorophyll-a (Chl-a), suspended solids (SS), colored dissolved organic matter (CDOM), turbidity and transparency [11][12][13][14][15][16]. There are however few studies on non-optically active parameters, such as chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN) [17,18,[27][28][29][19][20][21][22][23][24][25][26]. Estimating non-optically active parameters directly from spectral characteristics is difficult.…”