Institute of Geomechanics, Chinese Academy of Geological SciencesHost
Advanced Search
Previous Article Next Article
2021 Vol. 27, No. 1
Article Contents
Article navigation > Journal of Geomechanics > 2021 > 27(1): 40-51
Next Article Previous Article

XIN Lei, LIU Xinxing, ZHANG Bin. 2021. Land surface temperature retrieval and geothermal resources prediction by remote sensing image: A case study in the Shijiazhuang area, Hebei province. Journal of Geomechanics, 27(1): 40-51. doi: 10.12090/j.issn.1006-6616.2021.27.01.005
Citation: XIN Lei, LIU Xinxing, ZHANG Bin. 2021. Land surface temperature retrieval and geothermal resources prediction by remote sensing image: A case study in the Shijiazhuang area, Hebei province. Journal of Geomechanics, 27(1): 40-51. doi: 10.12090/j.issn.1006-6616.2021.27.01.005
shu

Land surface temperature retrieval and geothermal resources prediction by remote sensing image: A case study in the Shijiazhuang area, Hebei province

  • 1.

    Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China

  • 2.

    Key Laboratory of Paleomagnetism and Tectonic Reconstruction of Natural Resources, Beijing 100081, China

  • 3.

    China University of Geosciences, Beijing 100083, China

  • 4.

    Hebei Key Laboratory of Strategic Critical Mineral Resources, Shijiazhuang 050031, Hebei, China

  • 5.

    School of Earth Sciences, Hebei GEO University, Shijiazhuang 050031, Hebei, China

  • 6.

    Henan Aero Geophysical Survey and Remote Sensing Center, Zhengzhou 450053, Henan, China

More Information
Received Date:  09 November 2020
Revised Date:  10 January 2021
Publish Date:  28 February 2021
    Abstract

  • Thermal infrared remote sensing technology can retrieve land surface temperature information, which has played an important role in the prediction of geothermal resources. Based on the geothermal accumulation theory of the North China Plain, the mono-window algorithm was used to retrieve the land surface temperature of the Shijiazhuang area on March 6, 2015. Combined with the night thermal infrared images, remote sensing structure interpretation results and residual gravity anomaly data, through the comprehensive analysis and mutual demonstration, one upland convection-type and two sedimentary basin-type prospective areas were delineated, whose formation modes were convection and conduction respectively. In Sigou Village of Pingshan County, the land surface temperature is higher than that of the surrounding land features day and night, and the residual gravity anomaly data confirm the presence of NE faults in this area. Therefore, it is inferred that this area is a geothermal field formed by faults as water and heat conduction channels connecting the surface and the deep crustal heat source. The Gaocheng-Wuji area and Mayu-Huanmadian area are interpreted as deep uplift structures base on the residual gravity anomaly data, and there are hidden faults passing through, suggesting that this area is a geothermal field formed by the heat transfer and collection from the hidden faults to the uplift. This study is a geothermal resource prediction that based on the geothermal accumulation theory and the comprehensive analysis of remote sensing technology and geological and geophysical data. The prediction of target area is geologically interpretable and more in line with the understanding of geothermal accumulation.

    • FullText(HTML)
  • 加载中
    • References
  • DING F, XU H Q, 2008. Comparison of three algorithms for retrieving land surface temperature from Landsat TM thermal infrared band[J]. Journal of Fujian Normal University (Natural Science Edition), 24(1): 91-96. (in Chinese with English abstract)

    Google Scholar

    ENEVA M, COOLBAUGH M, 2009. Importance of elevation and temperature inversions for the interpretation of thermal infrared satellite images used in geothermal exploration[J]. GRC Transacions 33: 467-470.

    Google Scholar

    FANG F, 2020. Interpretation of Hebei fault system and seismogeological structure based on gravity and magnetic data[J]. Geophysical and Geochemical Exploration, 44(3): 489-498. (in Chinese with English abstract)

    Google Scholar

    GAN H N, WANG G L, LIN W J, et al., 2020. Research on the status quo of environmental geology impact of enhanced geothermal system and countermeasures[J]. Journal of Geomechanics, 26(2): 211-220. (in Chinese with English abstract)

    Google Scholar

    HAN Y B, ZHANG C S, 2020. Characteristics of high precision aeromagnetic anomalies in Geothermal field in Wentang town, Pingshan County, Hebei province[J]. Hebei Geology, (2): 2-8. (in Chinese)

    Google Scholar

    HU J C, GUO C Q, 2007. Geothermal geological conditions and development prospect in Shijiazhuang, Hebei province[J]. Ground Water, 29(2): 110-114. (in Chinese)

    Google Scholar

    HUANG M F, XING X F, WANG P J, et al., 2006. Comparison between three different methods of retrieving surface temperature from Landsat TM thermal infrared band[J]. Arid Land Geography, 29(1): 132-137. (in Chinese)

    Google Scholar

    JIMENEZ-MUNOZ J C, CRISTOBAL J, SOBRINO J A, et al., 2009. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data[J]. IEEE Transactions on Geoscience and Remote Sensing, 47(1): 339-349. doi: 10.1109/TGRS.2008.2007125

    CrossRef Google Scholar

    LIAN Y Z, 2007. Research on the exploring methodology of thermal remote sensing of terrestrial heat resource[D]. Taiyuan: Taiyuan University of Technology. (in Chinese with English abstract)

    Google Scholar

    LUO W X, SUN G Q, ZHOU Y, et al., 2020. Discussion on the mechanism of deep geothermal energy transmission[J]. Earth Science Frontiers, 27(1): 10-16. (in Chinese with English abstract)

    Google Scholar

    MIA M, FUJIMITSU Y, NISHIJIMA J, 2018. Monitoring thermal activity of the Beppu geothermal area in Japan using multisource satellite thermal infrared data[J]. Geosciences, 8(8): 306. doi: 10.3390/geosciences8080306

    CrossRef Google Scholar

    NIU S Y, SUN A Q, LI H Y, 2001. Geothermal characteristics and genetic mechanism in North China[J]. Earth Science Frontiers, 8(1): 112. (in Chinese)

    Google Scholar

    QIN Z H, ZHANG M H, KARNIELI A, et al., 2001. Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data[J]. Acta Geographica Sinica, 56(4): 456-466. (in Chinese with English abstract)

    Google Scholar

    QIN Z H, LI W J, ZHANG M H, et al., 2003. Estimating of the essential atmospheric parameters of mono-window algorithm for land surface temperature retrieval from Landsat TM6[J]. Remote Sensing for Land & Resources(2): 37-43. (in Chinese with English abstract)

    Google Scholar

    RUAN C X, FENG S Y, SHEN J, et al., 2017. A study on the recycling utilization of geothermal resources in Binhai new area, Tianjin: study and demonstration of thermal storage and reinjection technology of Guantao reservoir[J]. Journal of Geomechanics, 23(3): 498-506. (in Chinese with English abstract)

    Google Scholar

    SUN H L, 2019. The Application of gravity data in fault structure identification and geologic body inference in Abaga[D]. Beijing: China University of Geosciences, Beijing. (in Chinese with English abstract)

    Google Scholar

    TANNOCK L, WANG Y, LI J F, et al., 2019. A preliminary study on the mechanics and tectonic relationship to the geothermal field of the Heyuan fault zone in Guangdong province[J]. Journal of Geomechanics, 25(3): 400-411. (in Chinese with English abstract)

    Google Scholar

    USGS, 2013. Landsat 8[EB/OL]. https://landsat.usgs.gov/.

    Google Scholar

    USGS, 2014. January 29, 2014-Landsat 8 Reprocessing to Begin February 3, 2014[EB/OL]. http://landsat.usgs.gov/.

    Google Scholar

    USGS, 2016. April 12, 2016-upcoming landsat 8 TIRS reprocessing information[EB/OL]. http://landsat.usgs.gov/.

    Google Scholar

    WANG G L, ZHANG W, LIN W J, et al., 2017. Research on formation mode and development potential of geothermal resources in Beijing-Tianjin-Hebei region[J]. Geology in China, 44(6): 1074-1085. (in Chinese with English abstract).

    Google Scholar

    WANG G L, LIU Y G, ZHU X, et al., 2020. The status and development trend of geothermal resources in China[J]. Earth Science Frontiers, 27(1): 1-9. (in Chinese with English abstract)

    Google Scholar

    WANG K, JIANG Q G, YU D H, et al., 2019. Detecting daytime and nighttime land surface temperature anomalies using thermal infrared remote sensing in Dandong geothermal prospect[J]. International Journal of Applied Earth Observation and Geoinformation, 80: 196-205. doi: 10.1016/j.jag.2019.03.016

    CrossRef Google Scholar

    WANG J, HUANG S Y, HUANG G S, et al., 1983. Geotemperature distribution and geothermal resources in the Meso-Cenozoic basins of North China[J]. Acta Geologica Sinica, (3): 304-316. (in Chinese with English abstract)

    Google Scholar

    WEI Y C, TANG G A, WANG M, et al., 2015. Remote sensing digital image processing tutorial[M]. 2nd ed. Beijing: Science Press. (in Chinese)

    Google Scholar

    XIONG Y Z, CHEN F, HUANG S P. 2016. Application of remote sensing technique to the identification of geothermal anomaly in Tengchong area, southwest China[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 43(1): 109-118. (in Chinese with English abstract)

    Google Scholar

    XU J Q, BAI C J, LIU J Y, 2008. Geothermal resource prognosis based on remote sensing technology in Changbaishan volcanic area[J]. Remote Sensing for Land & Resources(1): 68-71. (in Chinese with English abstract)

    Google Scholar

    YAN B Z, QIU S W, XIAO C L, et al., 2017. Potential geothermal fields remote sensing identification in Changbai mountain basalt area[J]. Journal of Jilin University(Earth Science Edition), 47(6): 1819-1828. (in Chinese with English abstract)

    Google Scholar

    ZHANG B, 2015. Study on dynamic change characteristics of hot water in geothermal field in Wentang town, Pingshan County, Hebei province[J]. Hebei Geology, (2): 36-29. (in Chinese)

    Google Scholar

    ZHANG P M, ZHANG J L, 2006. Identification of remote sensing information in geothermal abnormal area[J]. Remote Sensing Information(2): 42-45. (in Chinese)

    Google Scholar

    ZHANG Y D, DONG J, XIAO J P, 2011. Gravity deduction and interpretation of geological structure in Hebei province[J]. Geophysical and Geochemical Exploration, 35(2): 143-148. (in Chinese with English abstract)

    Google Scholar

    ZHANG Z Y, 2010. Temperature inversion of remote sensing data zone and investigation of geothermal anomaly in Yangbajing region of Tibet[D]. Chengdu: Chengdu University of Technology. (in Chinese with English abstract)

    Google Scholar

    ZHENG G Q, WANG H P, ZHANG W C, et al., 2006. Satellite image characteristics of geothermal resources in Shijiazhuang plain area, Hebei province[J]. Hebei Geology(3): 27-29. (in Chinese)

    Google Scholar

    ZHOU Y R, 1998. The application of thermal infrared remote sensing techniques in geothermal surveying[J]. Remote Sensing for Land & Resources(4): 24-28. (in Chinese with English abstract)

    Google Scholar

    丁凤, 徐涵秋, 2008. 基于Landsat TM的3种地表温度反演算法比较分析[J]. 福建师范大学学报(自然科学版), 24(1): 91-96.

    Google Scholar

    方菲, 2020. 根据重磁资料解释河北断裂体系与地震地质构造[J]. 物探与化探, 44(3): 489-498.

    Google Scholar

    甘浩男, 王贵玲, 蔺文静, 等, 2020. 增强型地热系统环境地质影响现状研究与对策建议[J]. 地质力学学报, 26(2): 211-220.

    Google Scholar

    韩亚彬, 张崇山, 2020. 平山县温塘地热田区域高精度航磁异常特征[J]. 河北地质, (2): 2-8.

    Google Scholar

    胡君春, 郭纯青, 2007. 石家庄地热地质条件及开发远景[J]. 地下水, 29(2): 110-114.

    Google Scholar

    黄妙芬, 邢旭峰, 王培娟, 等, 2006. 利用LANDSAT/TM热红外通道反演地表温度的三种方法比较[J]. 干旱区地理, 29(1): 132-137.

    Google Scholar

    连胤卓, 2007. 地热资源的热红外遥感技术探测方法研究[D]. 太原: 太原理工大学.

    Google Scholar

    罗文行, 孙国强, 周洋, 等, 2020. 试论地球深部地热能传输机理[J]. 地学前缘, 27(1): 10-16.

    Google Scholar

    牛树银, 孙爱群, 李红阳, 2001. 华北地区地热特征及其成因机制[J]. 地学前缘, 8(1): 112.

    Google Scholar

    覃志豪, ZHANG M H, KARNIELI A, 等, 2001. 用陆地卫星TM6数据演算地表温度的单窗算法[J]. 地理学报, 56(4): 456-466.

    Google Scholar

    覃志豪, LI W J, ZHANG M H, 等, 2003. 单窗算法的大气参数估计方法[J]. 国土资源遥感, (2): 37-43.

    Google Scholar

    阮传侠, 冯树友, 沈健, 等, 2017. 天津滨海新区地热资源循环利用研究: 馆陶组热储回灌技术研究与示范[J]. 地质力学学报, 23(3): 498-506.

    Google Scholar

    孙会玲, 2019. 重力数据在阿巴嘎旗地区断裂构造识别及推断地质体中的应用[D]. 北京: 中国地质大学(北京).

    Google Scholar

    TANNOCK L, 王亚, 李景富, 等, 2019. 广东河源断裂带地热成因及与构造关系初探[J]. 地质力学学报, 25(3): 400-411.

    Google Scholar

    王贵玲, 张薇, 蔺文静, 等, 2017. 京津冀地区地热资源成藏模式与潜力研究[J]. 中国地质, 44(6): 1074-1085.

    Google Scholar

    王贵玲, 刘彦广, 朱喜, 等, 2020. 中国地热资源现状及发展趋势[J]. 地学前缘, 27(1): 1-9.

    Google Scholar

    王钧, 黄尚瑶, 黄歌山, 等, 1983. 华北中、新生代沉积盆地的地温分布及地热资源[J]. 地质学报(3): 304-316.

    Google Scholar

    韦玉春, 汤国安, 汪闽, 等, 2015. 遥感数字图像处理教程[M]. 2版. 北京: 科学出版社.

    Google Scholar

    熊永柱, 陈峰, 黄少鹏, 2016. 基于遥感技术的腾冲地热异常区识别[J]. 成都理工大学学报(自然科学版), 43(1): 109-118.

    Google Scholar

    许军强, 白朝军, 刘嘉宜, 2008. 基于遥感技术的长白山火山区地热预测研究[J]. 国土资源遥(1): 68-71.

    Google Scholar

    闫佰忠, 邱淑伟, 肖长来, 等, 2017. 长白山玄武岩区地热异常区遥感识别[J]. 吉林大学学报(地球科学版), 47(6): 1819-1828.

    Google Scholar

    张博, 2015. 平山县温塘地热田地热水动态变化特征研究[J]. 河北地质, (2): 26-29.

    Google Scholar

    张佩民, 张金良, 2006. 地热异常区的遥感信息识别[J]. 遥感信息(2): 42-45.

    Google Scholar

    张亚东, 董杰, 肖金平, 2011. 河北省地质构造重力推断解释[J]. 物探与化探, 35(2): 143-148.

    Google Scholar

    张中言, 2010. 西藏羊八井地区遥感数据地温反演与地热异常探讨[D]. 成都: 成都理工大学.

    Google Scholar

    郑国庆, 王会平, 张维宸, 等, 2006. 石家庄平原地热资源卫星影像特征[J]. 河北地质(3): 27-29.

    Google Scholar

    周彦儒, 1998. 热红外遥感技术在地热资源调查中的应用与潜力[J]. 国土资源遥感(4): 24-28.

    Google Scholar

    • Relative Articles
  • Cited by
  • Supplements
    • Proportional views
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(8)

Tables(6)

Export Citation
PDF
XML

Article Metrics

Article views(2077) PDF downloads(67) Cited by(0)

Access History

Other Articles By Authors

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    版权所有：中国地质调查局发展研究中心 copyright @2020