scholarly journals Spatiotemporal Variability of Lake Water Quality in the Context of Remote Sensing Models

2017 ◽  
Vol 9 (5) ◽  
pp. 409 ◽  
Author(s):  
Carly Hansen ◽  
Steven Burian ◽  
Philip Dennison ◽  
Gustavious Williams
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Lake Water ◽  
Spatiotemporal Variability ◽  
Lake Water Quality

A semi-operative approach to lake water quality retrieval from remote sensing data

2001 ◽  
Vol 268 (1-3) ◽  
pp. 79-93 ◽  
Author(s):  
Jouni Pulliainen ◽  
Kari Kallio ◽  
Karri Eloheimo ◽  
Sampsa Koponen ◽  
Henri Servomaa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Lake Water ◽  
Remote Sensing Data ◽  
Operative Approach ◽  
Lake Water Quality ◽  
Sensing Data

scholarly journals Numerical Simulation of Donghu Lake Hydrodynamics and Water Quality Based on Remote Sensing and MIKE 21

2020 ◽  
Vol 9 (2) ◽  
pp. 94 ◽  
Author(s):  
Xiaojuan Li ◽  
Mutao Huang ◽  
Ronghui Wang
Keyword(s):  
Remote Sensing ◽  
Numerical Simulation ◽  
Water Quality ◽  
Lake Water ◽  
Quality Data ◽  
Lake Water Quality ◽  
Water Quality Simulation ◽  
Donghu Lake ◽  
Mean Square Errors

Numerical simulation is an important method used in studying the evolution mechanisms of lake water quality. At the same time, lake water quality inversion technology using the characteristics of spatial optical continuity data from remote sensing satellites is constantly improving. It is, however, a research hotspot to combine the spatial and temporal advantages of both methods, in order to develop accurate simulation and prediction technology for lake water quality. This paper takes Donghu Lake in Wuhan as its research area. The spatial data from remote sensing and water quality monitoring information was used to construct a multi-source nonlinear regression fitting model (genetic algorithm (GA)-back propagation (BP) model) to invert the water quality of the lake. Based on the meteorological and hydrological data, as well as basic water quality data, a hydrodynamic model was established by using the MIKE21 model to simulate the evolution rules of water quality in Donghu Lake. Combining the advantages of the two, the best inversion results were used to provide a data supplement for optimization of the water quality simulation process, improving the accuracy and quality of the simulation. The statistical results were compared with water quality simulation results based on the data measured. The results show that the water quality simulation of chlorophyll a and nitrate nitrogen mean square errors fell to 17% and 24%, from 19% and 31% respectively, after optimization using remote sensing spatial information. The model precision was thus improved, and this is consistent with the actual pollution situation of Donghu Lake.

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