Lunar Surface Temperature and Emissivity Retrieval From SDGSAT-1 Thermal Imager Spectrometer

Wang, Q.W.; Hu, Zhuoyue; Zou, Lu; Chen, Fansheng

doi:10.1109/lgrs.2023.3278768

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The 15 counties at the medium-concentration level largely occur in the southeastern part of Tangshan, the eastern part of Shijiazhuang, and the western part of Handan. The four counties at the medium-high-concentration level include Wuan (28) in Handan, Huanghua (33) in Cangzhou, Fengnan District (36) in Tangshan, and the Binhai New Area (42) in Tianjin. Fengrun District in Tangshan city is classified at the highconcentration level, with 64 industrial heat sources, accounting for 8.07% of the total number of heat sources in the study region.…”

Section: Spatial Distribution Characteristics Of Industrial Heat Sour...mentioning

confidence: 99%

“…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%

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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: Spatial Distribution Characteristics Of Industrial Heat Sour...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%