Assessing water surface temperature from Landsat imagery and its relationship with a nuclear power plant (original) (raw)
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2002
The 1800 megawatt Daya Bay Nuclear Power Station (DBNPS), China's first nuclear power station, is located on the coast of the South China Sea. DBNPS discharges 29 million m3 y -1 of warm water from its cooling system into Daya Bay, which could have ecological consequences. This study examines satellite sea surface temperature data and shipboard water column measure ments
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Remote Sensing of Environment, 2001
The newest in the Landsat series of satellites was launched April 15, 1999. The imagery collected by Landsat is used for a myriad of applications, from coral reef studies to land management. In order to take advantage of Landsat 7 data, the Enhanced Thematic Mapper+ (ETM+) instrument must be calibrated. This study focuses on the immediate postlaunch calibration verification of the Landsat 7 thermal band (Band 6), specifically so that it can be useful in water resource studies. Two year's worth of thermal calibration results using a combination of underflight data and ground truth show the ETM+ to be extremely stable, though the prelaunch calibration produces an offset of 0.261 W/m 2 sr mm. This paper focuses on the details of the calibration process, including problems faced with ground truth instrumentation. While the technical emphasis in this paper is the calibration of Landsat thermal data, it is presented in the context of the water resource studies for which calibrated thermal data are required. At certain times in the year, water quality in large lakes, particularly the spatial structure of water quality, is driven by temperature of lake waters. During the spring warming, a phenomena called the thermal bar drives the current and sedimentation of large water bodies. A long-term goal of this study is to use thermally driven hydrodynamic models of lake processes to better understand and monitor water quality in large lakes. This paper presents the hydrodynamic model and the relationship between temperature and water quality in the Great Lakes as one example of why high-resolution, well-calibrated data are critical to earth observing. D 2001 Elsevier Science Inc. All rights reserved.
Journal of Applied Remote Sensing, 2019
The importance of lake water surface temperature has long been highlighted for ecological and hydrological studies as well as for water quality management. In the absence of regular field observations, satellite remote sensing has been recognized as a cost-effective way to monitor water surface temperature on large spatial and temporal scales. The thermal infrared sensors (TIRS) onboard of Landsat satellites (since 1984) are adequate tools for monitoring surface temperature of small to medium sized lakes with a biweekly frequency, as well as for performing retrospective analysis. Nonetheless, the satellite data have to deal with effects due to the atmosphere so that several approaches to correct for atmospheric contributions have been proposed. Among these are: (i) the radiative transfer equation (RTE); (ii) a single-channel algorithm that depends on water vapor content and emissivity (SC1); (iii) its improved version including air temperature (SC2); and (iv) a monowindow (MW) algorithm that requires emissivity, atmospheric transmissivity, and effective mean atmospheric temperature. We aim to evaluate these four approaches in a river dammed reservoir with a size of 12 km 2 using data gathered from the band 10 of the TIRS onboard of Landsat 8. Satellite-derived temperatures were then compared to in situ data acquired from thermistors at the time of Landsat 8 overpasses. All approaches showed a good performance, with the SC1 algorithm yielding the lowest root mean square error (0.73 K), followed by the SC2 method (0.89 K), the RTE (0.94 K), and then the MW algorithm (1.23 K). Based on the validation results, we then applied the SC1 algorithm to Landsat 4, 5, and 8 thermal data (1984 to 2018) to extend data series to past years. These data do not reveal any warming trend of the reservoir surface temperature. The results of this study also confirm how the 100-m spatial resolution of TIRS is valuable as an additional source of data to field-based monitoring.
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This paper presents a comparative analysis of thermal images from Landsat satellites with in situ measurements of water temperature based on the example of three analysed lakes in Poland (Łebsko, Gardno, and Jamno). The coefficient of determination R 2 in the first two lakes reached a value of 0.95, and in the third case 0.87. The obtained results suggest high coherence of both of the sources. Satellite data obtained with such coherence with in situ measurements can be considered to be of high quality. The fact opens a new chapter concerning continuous monitoring of surface temperature of lakes in Poland, which can be considerably expanded in comparison to the current state (the measurement network is currently constituted by several tens of relatively large lakes). The issue addressed in the paper refers to a dynamic development trend in research based on teledetection information. So far, however, such methodology has not been used for detailed research on lakes in Poland. The availability of information on thermal conditions in reference to a possibly high number of lakes is of key importance in the context of the observed climate changes and the resulting transformation of water ecosystems. Continuous monitoring offers a basis for the development of applicative solutions, potentially reducing the effects of global warming.
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
Remote sensing community is making enormous efforts to implement early warning systems capable for following spatio-temporal patterns of water quality and climate change risk indicators, being Horizon 2030 EOXPOSURE project one of them. This work presents first results of surface temperature Landsat 8 Level 2 Collection 2 products analysis for a reservoir and compare them with field data measurements. A Root Mean Square Error (RMSE) of 1.7 o C and a Mean Absolute Percentage Error (MAPE) of 7% were obtained for these products but validation curve resulted not confident at a 95% level. A semiempirical linear model with 94% accuracy, RMSE of 1.1 o C and a MAPE of 5% is presented. It was successfully validated with a control group data set obtaining 94% accuracy. A Water Surface Temperature temporal series is shown for the 2013-2020 period and spatio temporal patterns are analyzed and discussed. Water surface temperature behavior in zones with algal bloom occurrence present greater significant values, up to 3 o C, than those with clearer water, indicating that water emissitiviy must be revised for these cases.
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Background: The hierarchical use of remotely-sensed imagery from satellites, and then proximally-sensed imagery from helicopter sand drones, can provide a range of spatial and temporal coverage that supports water quality monitoring of complex pollution scenarios. Methods: The study used hierarchical satellite-, helicopter-, and drone-acquired thermal imagery of coastal plumes ranging from 3 to 300 m, near Naples, Italy, and captured temporally-and spatially-overlapping in situ samples to correlate thermal and water quality parameters in each plume and the seawater. Results: In situ sampling determined that between-plume salinity varied by 37%, chlorophyll-a varied by 356%, dissolved oxygen varied by 81%, and turbidity varied by 232%. The radiometric temperature, T rad , for the plume area of interest had a correlation of 0.81 with salinity, 0.74 with chlorophyll-a, 0.98 with dissolved oxygen, and −0.61 with turbidity. Conclusion: This study established hierarchical use of remote and proximal thermal imagery can provide monitoring of complex coastal areas.
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Most applications using remote sensing tend to assess fresh water quality via regression models between in situ data and spectral bands. Suspended Sediment (Turbidity), Dissolved Oxygen and Temperature are common parameters derived from RS and recurrently used in WQI and/or TDML indicators. In this study a series of ETM+ Landsat images, thermal band, in combination with in situ measurement over 7 years, 2001 to 2007, were used over the northern part of Lake Nasser (Egypt) to develop a regression model linking thermal band to water surface temperature. Relationship between Water Surface Temperature and Dissolved Oxygen was then extracted statistically at surface and at 80% depth of water column. A second series of eighteen Landsat ETM+ thermal band images was tested then to produce temporal and spatial pattern changes in the above mentioned parameters over various months of 2001 to 2003 and proposed to be implemented as shown in four dates of 2012/2013. The results showed a good resp...
Remote Sensing of Environment, 2003
The 1800 MW Daya Bay Nuclear Power Station (DNPS), China's first nuclear power station, is located on the coast of the South China Sea. DNPS discharges 29 10 Â 10 5 m 3 year À 1 of warm water from its cooling system into Daya Bay, which could have ecological consequences. This study examines satellite sea surface temperature data and shipboard water column measurements from Daya Bay. Field observations of water temperature, salinity, and chlorophyll a data were conducted four times per year at 12 sampling stations in Daya Bay during January 1997 to January 1999. Sea surface temperatures were derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites during November 1997 to February 1999. A total of 2905 images with 1.1 Â 1.1 km resolution were examined; among those images, 342 have sufficient quality for quantitative analysis. The results show a seasonal pattern of thermal plumes in Daya Bay. During the winter months (December to March), the thermal plume is localized to an area within a few km of the power plant, and the temperature difference between the plume and non-plume areas is about 1.5 jC. During the summer and fall months (May to November), there is a larger thermal plume extending 8 -10 km south along the coast from DNPS, and the temperature change is about 1.0 jC. Monthly variation of SST in the thermal plume is analyzed. AVHRR SST is higher in daytime than in nighttime in the bay during the whole year. The strong seasonal difference in the thermal plume is related to vertical mixing of the water column in winter and to stratification in summer. Further investigations are needed to determine any other ecological effects of the Daya Bay thermal plume. D 2002 Published by Elsevier Science B.V.