Using a semi-analytical model to retrieve Secchi depth in coastal and estuarine waters

Secchi depth(SD,m)is a direct and intuitive measure of water's transparency,which is also an indicator of water quality.In 2015,a semi-analytical model was developed to derive SD from remote sensing reflectance,thus able to provide maps of water's transparency in satellite images.Here an in-situ dataset(338 stations)is used to evaluate its potential ability to monitor water quality in the coastal and estuarine waters,with measurements covering the Zhujiang(Pearl)River Estuary,the Yellow Sea and the East China Sea where measured SD values span a range of 0.2–21.0 m.As a preliminary validation result,according to the whole dataset,the unbiased percent difference(UPD)between estimated and measured SD is 23.3%(N=338,R^2=0.89),with about 60%of stations in the dataset having relative difference(RD)≤20%,over 80%of stations having RD≤40%.Furthermore,by excluding the field data which with relatively larger uncertainties,the semi-analytical model yielded the UPD of 17.7%(N=132,R^2=0.92)with SD range of 0.2–11.0 m.In addition,the semi-analytical model was applied to Landsat-8 images in the Zhujiang River Estuary,and retrieved high-quality mapping and reliable spatial-temporal patterns of water clarity.Taking into account the uncertainties associated with both field measurements and satellite data processing,and that there were no tuning of the semi-analytical model for these regions,these findings indicate highly robust retrieval of SD from spectral techniques for such turbid coastal and estuarine waters.The results suggest it is now possible to routinely monitor coastal water transparency or visibility at high-spatial resolutions from measurements,like Landsat-8 and Sentinel-2 and newly launched Gaofen-5.

Application of IGM(0,N)Model to Simulate Trends in Estuarine Water Quality

The water quality of rivers flowing into lakes is a reflection of the regional environment and significantly contributes to the nutrient levels of the lake.Based on multi-year monitoring data of the estuarine water quality of the Zi River which flows into the Dongting Lake,single factor analysis was performed for the dissolved oxygen,nitrogen ammonia(NH3-N),chemical oxygen demand,total phosphorus,permanganate index,and biochemical oxygen demand.The analysis indicated that the estuarine water quality class was dependent on the NH3-N level.The NH3-N trend of the Zi River was then simulated using an improved grey model IGM(0,N)based on a grey correlation analysis.The IGM(0,N)model produced a smaller average relative error and a more accurate prediction compared with a multiple regression model,thus validating the use of the IGM(0,N)model as a suitable method to simulate the estuarine water quality with a complex multi-factor system.

Spatial assessment of water quality using chemometrics in the Pearl River Estuary, China

A cruise was commissioned in the summer of 2009 to evaluate water quality in the Pearl River Estuary （PRE）. Chemometrics such as Principal Component Analysis （PCA）, Cluster analysis （CA） and Self-Organiz- ing Map （SOM） were employed to identify anthropogenic and natural influences on estuary water quality. The scores of stations in the surface layer in the first principal component （PC1） were related to NH4-N, PO4-P, NO2-N, NO3-N, TP, and Chlorophyll a while salinity, turbidity, and SiO3-Si in the second principal component （PC2）. Similarly, the scores of stations in the bottom layers in PC1 were related to PO4-P, NO2-N, NO3-N, and TP, while salinity, Chlorophyll a, NH4-N, and SiO3-Si in PC2. Results of the PCA identified the spatial distribution of the surface and bottom water quality, namely the Guangzhou urban reach, Middle reach, and Lower reach of the estuary. Both cluster analysis and PCA produced the similar results. Self-organizing map delineated the Guangzhou urban reach of the Pearl River that was mainly influenced by human activities. The middle and lower reaches of the PRE were mainly influenced by the waters in the South China Sea. The information extracted by PCA, CA, and SOM would be very useful to regional agencies in developing a strategy to carry out scientific plans for resource use based on marine system functions.