Land surface temperature retrieval and geothermal resources prediction by remote sensing image: A case study in the Shijiazhuang area, Hebei province
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摘要: 热红外遥感技术可以反演地表温度信息，可在地热资源预测方面发挥重要作用。研究基于京津冀地区地热成藏模式，利用单窗算法反演出石家庄地区2015年3月6日地表温度，结合夜间热红外影像、遥感构造解译结果和剩余重力异常数据，综合分析，相互论证，圈定1处山地对流型地热远景区和2处沉积盆地型地热远景区。其中，平山县寺沟村昼夜地温值均高于周边地物，且剩余重力异常数据印证该区存在北东向断裂，进而预测该区是断裂作为导水导热通道连接地表与地壳深部热源所形成的地热田；藁城—无极一带和马于—换马店一带剩余重力异常解译为深部隆起构造，且有隐伏断裂穿过，预测该区是由隐伏断裂将热量传递、汇集至隆起构造位置所形成的地热田。以地热成藏理论为依据，利用遥感技术和地质、地球物理资料综合分析进行地热资源预测，所圈定的地热预测靶区更具备地质可解释性。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.
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图 1 研究区位置图
a—河北地理位置图；b—京津冀地区地热资源潜力分区图(王贵玲等，2017)；c—研究区大地构造单元分区图(Ⅱ22燕山台褶带；Ⅲ25军都山岩浆岩带；Ⅳ219狼牙山凹陷断束；Ⅱ22山西断隆；Ⅳ221阜平穹褶束；Ⅲ210沁源台陷；Ⅲ211太行山拱断束；Ⅳ232赞皇穹断束；Ⅱ24为华北断拗；Ⅲ212冀中台陷；Ⅳ240保定断凹；Ⅳ241高阳台凸；Ⅳ242饶阳断凹；Ⅲ216临清台陷；Ⅳ264晋县断凹；Ⅳ265宁晋断凸；Ⅳ266束鹿断凹；Ⅳ267新河断凸；Ⅳ268南宫断凹)
Figure 1. Location map of the study area
图 2 京津冀地区地热资源成藏模式(据王贵玲等，2017修改)
a—隆起山地对流型地热成藏模式图；b—沉积盆地传导型地热成藏模式图
Figure 2. Accumulation model of the geothermal resources in the Beijing-Tianjin-Hebei region (modified after Wang et al., 2017)
图 4 构造解译结果(754彩色合成)
Figure 4. Structural interpretation results and previous research results(color composite of bands 7, 5, and 4)
图 6 平山县温塘镇温泉异常特征
a—卫星影像图；b—地表反演温度图(白天)；c—剩余重力异常图(详见图 7)；d—热红外影像(夜间)
Figure 6. Anomaly characteristics of the hot springs in Wentang Town, Pingshan County
图 7 平山县寺沟村异常特征
a—卫星影像图；b—地表反演温度图(白天)；c—剩余重力异常图(详见图 7)；d—热红外影像(夜间)
Figure 7. Anomaly characteristics of Sigou Village, Pingshan County
图 8 研究区构造解译与远景预测图(据张亚东等，2011；方菲，2020修改)
剩余重力异常主要反映深度约7 km处结晶基底以上的不同地层、地质体及构造单元之间的密度差异特征，即相邻重力高、低代表基底隆起与凹陷
Figure 8. Structural interpretation results and geothermal resource potential of the study area (modified after Zhang et al., 2011; Fang, 2020)
表 1 TIRS(热红外传感器)
Table 1. TIRS(Thermal Infrared Sensor)
波段名称 中心波长/μm 最小光谱响应范围/μm 最大光谱响应范围/μm 空间分辨率/m Band 10 TIRS 1 10.9 10.6 11.2 100 Band 11 TIRS 2 12.0 11.5 12.5 100 
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表 2 OLI(陆地成像仪)
Table 2. OLI(Operational Land Imager)
波段名称 波段范围/μm 空间分辨率/m Band 1 Coastal 0.433~0.453 30 Band 2 Blue 0.450~0.515 30 Band 3 Green 0.525~0.600 30 Band 4 Red 0.630~0.680 30 Band 5 NIR 0.845~0.885 30 Band 6 SWIR 1 1.560~1.660 30 Band 7 SWIR 2 2.100~2.300 30 Band 8 Pan 0.500~0.680 15 Band 9 Cirrus 1.360~1.390 30
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表 3 影像1
Table 3. Image 1
参数名称 参数 参数名称 参数 数据标识 LC81240332015065LGN01 卫星名称 LANDSAT 8 数据类型 OLI_TIRS 传感器 OLI_TIRS 接收站 LGN 白天晚上 DAY 太阳高度角 40.37572591 太阳方位角 149.04166674 开始时间(GTM) 2015-03-06 02∶59∶32 结束时间(GTM) 2015-03-06 03∶00∶04 平均云量 1.62 中心经度 114°41′55.75″E 中心纬度 38°54′16.24″N 注：Greenwich Mean Time(GTM)
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表 4 影像2
Table 4. Image 2
参数名称 参数 参数名称 参数 数据标识 LC81240342015065LGN01 卫星名称 LANDSAT 8 数据类型 OLI_TIRS 传感器 OLI_TIRS 接收站 LGN 白天晚上 DAY 太阳高度角 41.45845377 太阳方位角 147.99421452 开始时间(GTM) 2015-03-06 02∶59∶56 结束时间(GTM) 2015-03-06 03∶00∶28 平均云量 1.11 中心经度 114°15′56.59″E 中心纬度 37°28′28.42″N
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表 5 2015年3月6日石家庄正定气象数据
Table 5. Meteorological data of Zhengding on March 6, 2015
参数名称 参数 日期(yyyy-mm-dd) 2015-03-06 最高温度/℃ 13 最低温度/℃ 1 3∶00(GTM)石家庄气温/℃ 10 相对湿度/% 35 气压传感器海拔高度/m 72.1 气压/mb 1008.27
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表 6 大气透射率估算模型
Table 6. Atmospheric transmittance estimation model
参数名称 参数 数据时间(yyyy-mm-dd) 2015-03-06 纬度/经度 38°/114° 格林尼治标准时间(GTM) 3∶00 Landsat 8 TIRS Band 10光谱响应曲线 √ 中纬度冬季标准大气 √ 地表高程/km 0.072 地表大气压/mb 1008.27 地表温度/℃ 10 地表相对湿度/% 35 波段平均大气透射率 0.96 有效上行辐射/(W·m-2·Sr-1·μm-1) 0.27 有效下行辐射/(W·m-2·Sr-1·μm-1) 0.47
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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) 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. http://www.researchgate.net/publication/289614351_Importance_of_elevation_and_temperature_inversions_for_the_interpretation_of_thermal_infrared_satellite_images_used_in_geothermal_exploration 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) 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) 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) 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) 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) http://en.cnki.com.cn/Article_en/CJFDTOTAL-GHDL200601021.htm 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 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) 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) 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 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) 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) http://en.cnki.com.cn/Article_en/CJFDTOTAL-DLXB200104008.htm 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) http://en.cnki.com.cn/Article_en/CJFDTOTAL-GTYG200302010.htm 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) 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) 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) http://www.researchgate.net/publication/335396396_A_Preliminary_Study_on_the_Mechanics_and_Tectonic_Relationship_to_the_Geothermal_Field_of_the_Heyuan_Fault_Zone_in_the_Guangdong_Province USGS, 2013. Landsat 8[EB/OL]. https://landsat.usgs.gov/. USGS, 2014. January 29, 2014-Landsat 8 Reprocessing to Begin February 3, 2014[EB/OL]. http://landsat.usgs.gov/. USGS, 2016. April 12, 2016-upcoming landsat 8 TIRS reprocessing information[EB/OL]. http://landsat.usgs.gov/. 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). http://www.researchgate.net/publication/324014114_Research_on_formation_mode_and_development_potential_of_geothermal_resources_in_Beijing-Tianjin-Hebei_region 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) 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 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) http://en.cnki.com.cn/Article_en/CJFDTOTAL-DZXE198303009.htm 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) 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) http://en.cnki.com.cn/Article_en/CJFDTOTAL-CDLG201601012.htm 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) http://www.cnki.com.cn/Article/CJFDTotal-GTYG200801014.htm 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) http://www.researchgate.net/publication/322919909_Potential_Geothermal_Fields_Remote_Sensing_Identification_in_Changbai_Mountain_Basalt_Area 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) 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) http://en.cnki.com.cn/Article_en/CJFDTOTAL-YGXX200602013.htm 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) 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) 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) 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) http://en.cnki.com.cn/Article_en/CJFDTOTAL-GTYG804.005.htm 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. 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. https://www.cnki.com.cn/Article/CJFDTOTAL-WTYH202003004.htm 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. https://journal.geomech.ac.cn/ch/reader/view_abstract.aspx?flag=1&file_no=20200206&journal_id=dzlxxb 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. 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. https://www.cnki.com.cn/Article/CJFDTOTAL-DXSU200702036.htm 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. https://www.cnki.com.cn/Article/CJFDTOTAL-GHDL200601021.htm 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. 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. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY202001003.htm 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. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY200101022.htm 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. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXB200104008.htm 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. https://www.cnki.com.cn/Article/CJFDTOTAL-GTYG200302010.htm 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. https://journal.geomech.ac.cn/ch/reader/view_abstract.aspx?flag=1&file_no=20170317&journal_id=dzlxxb 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). 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. https://journal.geomech.ac.cn/ch/reader/view_abstract.aspx?flag=1&file_no=20190310&journal_id=dzlxxb 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. https://www.cnki.com.cn/Article/CJFDTOTAL-DIZI201706004.htm 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. https://www.cnki.com.cn/Article/CJFDTOTAL-DXQY202001002.htm 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. https://www.cnki.com.cn/Article/CJFDTOTAL-DZXE198303009.htm 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. 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. https://www.cnki.com.cn/Article/CJFDTOTAL-CDLG201601012.htm 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. https://www.cnki.com.cn/Article/CJFDTOTAL-GTYG200801014.htm 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. https://www.cnki.com.cn/Article/CJFDTOTAL-CCDZ201706019.htm 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. 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. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXX200602013.htm 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. https://www.cnki.com.cn/Article/CJFDTOTAL-WTYH201102002.htm 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. 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. 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. https://www.cnki.com.cn/Article/CJFDTOTAL-GTYG804.005.htm -
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