Thermal Discharge Temperature Retrieval and Monitoring of NPPs Based on SDGSAT-1 Images

Huang, Wenwen; Jiao, Jingjie; Zhao, Lixing; Hu, Zhuoyue; Peng, Xiaohong; Lan, Yang; Li, Xiaoyan; Chen, Fansheng

doi:10.3390/rs15092298

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…The launch of the Sustainable Development Science Satellite 1 (SDGSAT-1) offered a new approach for identifying industrial heat sources. Among the thermal infrared data types, SDGSAT-1 thermal infrared spectrometer (TIS) data exhibit the highest spatial resolution in civilian use and have been widely applied in tasks such as target detection and monitoring [40][41][42]. Therefore, in this study, SDGSAT-1 TIS data were first introduced to identify industrial heat sources and production areas, aiming to address the issues of the insufficient spatial precision in identifying industrial heat sources and the insensitivity to low-heat emission sources.…”

Section: Introductionmentioning

confidence: 99%

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

Xie,

Ma,

Zhao

et al. 2024

Remote Sensing

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It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

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Section: Introductionmentioning

confidence: 99%

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

Xie,

Ma,

Zhao

et al. 2024

Remote Sensing

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“…Notably, the thermal infrared sensor possesses imaging capabilities with a 300 km swath and a spatial resolution of 30 m, addressing the low spatial resolution drawback in the thermal infrared spectral range of multispectral sensors. Scholars have used its data to conduct studies related to thermal emissions, but no studies have been found for lithology extraction [ 20 , 21 ]. In response to the current research gap, this paper proposes a method for extracting granite based on SDGSAT-1 TIS data, whereby a granite index based on SDGSAT-1 TIS data is first introduced using band ratio to enhance granite and suppress non-target information.…”

Section: Introductionmentioning

confidence: 99%

Granite Extraction Based on the SDGSAT-1 Satellite Thermal Infrared Spectrometer Imagery

Yuan,

Wang,

Yang

et al. 2024

Sensors

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Earth observation by remote sensing plays a crucial role in granite extraction, and many current studies use thermal infrared data from sensors such as ASTER. The challenge lies in the low spatial resolution of these satellites, hindering precise rock type identification. A breakthrough emerges with the Thermal Infrared Spectrometer (TIS) on the Sustainable Development Science Satellite 1 (SDGSAT-1) launched by the Chinese Academy of Sciences. With an exceptional 30 m spatial resolution, SDGSAT-1 TIS opens avenues for accurate granite extraction using remote sensing. This study, exemplified in Xinjiang’s Karamay region, introduces the BR-ISauvola method, leveraging SDGSAT-1 TIS data. The approach combines band ratio with adaptive k-value selection using local grayscale statistical features for Sauvola thresholding. Focused on large-scale granite extraction, results show F1 scores above 70% for Otsu, Sauvola, and BR-ISauvola. Notably, BR-ISauvola achieves the highest accuracy at 82.11%, surpassing Otsu and Sauvola by 9.62% and 0.34%, respectively. This underscores the potential of SDGSAT-1 TIS data as a valuable resource for granite extraction. The proposed method efficiently utilizes spectral information, presenting a novel approach for rapid granite extraction using remote sensing TIS imagery, even in scenarios with low spectral resolution and a single data source.

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“…Sustainable Development Science Satellite 1 (SDGSAT-1) launched on 5 November 2021, and its thermal infrared sensor (TIS) with a 30 m spatial resolution is a significant addition of data for Earth observation because of its highest spatial resolution in the civilian field and three-band advantage [28]. It has been widely used for target detection and monitoring, such as ship detection [29,30], sea surface temperature retrieval, monitoring near nuclear power plants [31], and arctic sea ice lead monitoring [32]. Therefore, nighttime SDGSAT-1 TIS data were first introduced to identify IHSPAs and improve the recognition accuracy and granularity of IHSs.…”

Section: Introductionmentioning

confidence: 99%

Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager

Wang,

Xie,

et al. 2024

Applied Sciences

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Industrial heat sources (IHSs) are key contributors to anthropogenic heat, air pollution, and carbon emissions. Accurately and automatically detecting their production areas (IHSPAs) on a large scale is vital for environmental monitoring and decision making, yet this is challenged by the lack of high-resolution thermal data. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. In this study, an IHSPA identification model using multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between IHSPAs and the background. Then, 10 features combined with three indices from Landsat OLI images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Some conclusions were obtained from different comparisons in Wuhai, China: (1) IHSPA identification based on SVM using thermal and optical features can detect IHSPAs and obtain the best results compared with different features and identification models. (2) The importance of using thermal features from the SDGSAT-1 TIS to detect IHSPAs was demonstrated by different importance analysis methods. (3) Our proposed method can detect more IHSs, with greater spatial coverage and smaller areas, compared with the methods of Ma and Liu. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing.

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Thermal Discharge Temperature Retrieval and Monitoring of NPPs Based on SDGSAT-1 Images

Cited by 11 publications

References 38 publications

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

Granite Extraction Based on the SDGSAT-1 Satellite Thermal Infrared Spectrometer Imagery

Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager

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