Abstract
The combination of remote sensing technology and traditional field sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were verified with measured data (R2 = 0.803), and then the results were improved (R2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and fine resolution in space, which provide a stronger scientific basis for rapid diagnosis of eutrophication in inland lakes.
Highlights
Water quality monitoring is an important basis for water quality assessments and water pollution management
The objectives of this paper are to: (1) generate high-resolution images by using spatio-temporal fusion (STF) methods with Landsat, Sentinel-2, and GF-2 data, (2) design a chlorophyll a (Chl-a) inversion model based on a convolutional neural network (CNN), and (3) evaluate the Chl-a variations in BYD lake from July to September
We found found that that there there was a slight decrease in Chl-a concentration in BYD lake during July, and the regional differences gradually decreased
Summary
Water quality monitoring is an important basis for water quality assessments and water pollution management. Water quality conditions directly affect public health and the economic productivity of nations [1–3]. Large-scale, accurate and fast water quality monitoring is important [4,5]. On-site water quality measurements can obtain water quality parameters in detail, they are time-consuming, labor-intensive, costly, and are restricted by weather and hydrological conditions, making it difficult to conduct timely, large-scale monitoring [6]. Remote sensing technology has macroscopic qualities that are well suited to the spectral characteristics of water quality monitoring at large scales [7–9]. Various remote sensing data and continuously updated water quality inversion models make water quality inversion a convenient means of achieving real-time monitoring; this has facilitated the emergence of new applications [10–12]
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