Proceedings Article | 31 January 2023
KEYWORDS: Data modeling, Infrared radiation, Thermography, Windows, Temperature metrology, Remote sensing, Nuclear power plants, Thermal modeling, Spatial resolution, Large Synoptic Survey Telescope
The thermal drainage of coastal power plants have adversely affect the ecological environment in the nearby sea area. Thermal infrared remote sensing data has the characteristics of macroscopic and periodic revisiting, and has certain advantages in monitoring the sea surface temperature and the thermal drainage of coastal power plants.FY-3D MERSI-2 thermal infrared channel data has 250m spatial resolution and once a day revisit cycle, it has potential application value in the sea surface temperature monitoring. In the construction of sea surface temperature retrieval model, it is necessary to deal with all kinds of data needed for the construction of the experiment, including atmospheric profile data set, emissivity data, spectral response information, ocean station data, MODTRAN atmospheric radiation transmission simulation model data, etc., to meet the needs of the experimental process. A high-precision model temperature retrieval model based on split window algorithm is constructed by using FY-3D MERSI-2 thermal infrared channel data. It is used to retrieve the sea surface temperature in the waters near Fuqing nuclear power plant and analyze the environmental application problems such as the diffusion trend, temperature change trend, the form of thermal drainage and the distribution of temperature rise zone. Firstly, the FY-3D MERSI-2 data is subjected to geometric correction with the Geographic Lookup Table (GLT), and radiation correction to obtain the image brightness temperature data. Then, the sea surface temperature is retrieved according to the established model, and it is matched with the thermal infrared data of Infrared Multi-spectral Imager of Gaofen-5 (GF-5 VIMS), with a spatial resolution of 40m in spectrum and geometry. Finally, the retrieval results of the two images on the same day are compared. The retrieval results are verified by the measured data, and compared with the retrieval results of the traditional split window algorithm retrieval models. The results show that: Based on the split window algorithm, the sea surface temperature retrieval model established by adding two thermal infrared channel temperature difference terms is better than 1.7K in accuracy. Because of its high frequency of time revisiting, the MERSI-2 data can monitor the distribution of temperature and drainage in different seasons and tidal levels. According to the statistics of base temperature, temperature rise area, tide and temperature rise diagram of FY-3D MERSI-2, it can realize ideal spatial distribution monitoring of warm water and drainage. According to the form of warm drainage, the monitoring results of GF-5 VIMS are relatively fine. After being discharged through the water outlet of the sewage pipe, due to the influence of the instantaneous sea surface wind direction and wind speed, it will be discharged to the southeast, and its diffuse shape is obvious. For small-scale power plants, it is suggested to combine the thermal infrared remote sensing data with high spatial resolution to improve the monitoring accuracy of time frequency and diffusion details distribution of thermal drainage in power plants. In the future research, if there are conditions to measure or collect more relevant environmental data, such as sea surface wind direction record, sea area circulation record, etc., there will be more beneficial to further analyze the distribution and change of temperature and drainage in the study area.
