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Open AccessArticle

Monitoring of Fine-Scale Warm Drain-Off Water from Nuclear Power Stations in the Daya Bay Based on Landsat 8 Data

College of Oceanography, Hohai University, Nanjing 210098, China
Beijing PIESAT Information Technology Co., Ltd., Beijing 100195, China
Key Laboratory of Coastal Disaster and Defence (Hohai University), Ministry of Education, Nanjing 210098, China
South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, China
Beijing National Day School, Beijing 10039, China
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 627;
Received: 31 December 2019 / Revised: 8 February 2020 / Accepted: 12 February 2020 / Published: 13 February 2020
monitoring the drain-off water from nuclear power stations by high-resolution remote sensing satellites is of great significance for ensuring the safe operation of nuclear power stations and monitoring environmental changes. In order to select the optimal algorithm for Landsat 8 Thermal Infrared Sensor (TIRS) data to monitor warm drain-off water from the Daya Bay Nuclear Power Station (DNPS) and the Ling Ao Nuclear Power Station (LNPS) located on the southern coast of China, this study applies the edge detection method to remove stripes and produces estimates of four Sea Surface Temperature (SST) inversion methods, the Radiation Transfer Equation Method (RTM), the Single Channel algorithm (SC), the Mono Window algorithm (MW) and the Split Window algorithm (SW), using the buoy and Minimum Orbit Intersection Distances (MOIDS) SST data. Among the four algorithms, the SST from the SW algorithm is the most consistent with the buoy, the MODIS SST, the ERA-Interim and the Optimum Interpolation Sea Surface Temperature (OISST). Based on the SST retrieved from the SW algorithm, the tidal currents calculated by the Finite-Volume Coastal Ocean Model (FVCOM) and winds from ERA-Interim, the distribution of the warm drain-off from the two nuclear power stations is analyzed. First, warm drain-off water is mainly distributed in a fan-shaped area from the two nuclear power stations to the center of the Daya Bay. The SST of the warm drain-off is about 1–4 ℃ higher than the surrounding water and exceeds 6 ℃ at the drain-off outfall. Second, the tide determines the shape and distribution characteristics of the warm drain-off area. The warm drain-off water flows to the northeast during the flood tide. During the ebb tide, the warm drain-off water flows toward the southwest direction as the tide flows toward the bay mouth, forming a fan-shaped area. Moreover, the temperature increase intensity in the combined discharge channel during the flood tide is lower than that during the ebb tide, and the low temperature rising area during the flood tide is smaller than that during the ebb tide.
Keywords: Landsat 8; destriping; warm drain-off; split window algorithm; tide Landsat 8; destriping; warm drain-off; split window algorithm; tide
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MDPI and ACS Style

Liu, M.; Yin, X.; Xu, Q.; Chen, Y.; Wang, B. Monitoring of Fine-Scale Warm Drain-Off Water from Nuclear Power Stations in the Daya Bay Based on Landsat 8 Data. Remote Sens. 2020, 12, 627.

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