地球科学进展 ›› 2022, Vol. 37 ›› Issue (7): 756 -770. doi: 10.11867/j.issn.1001-8166.2022.039

全球变化研究 上一篇    

基于 Landsat 8 OLI/TIRS的合肥市人为热通量遥感估算研究
王耀杰 1( ), 仲雷 1 , 2 , 3( ), 陈明星 4, 袁仁民 1, 吴晓庆 5, 邱学兴 6, 葛楠 1, 程美琳 1, 栗培真 1   
  1. 1.中国科学技术大学地球和空间科学学院,安徽 合肥 230026
    2.中国科学院比较行星学卓越创新中心,安徽 合肥 230026
    3.江苏省气候变化协同创新中心,江苏 南京 210023
    4.中国科学院地理 科学与资源研究所,北京 100101
    5.中国科学院安徽光学精密机械研究所,中国科学院 大气光学重点实验室,安徽 合肥 230031
    6.安徽省气象台,安徽 合肥 230031
  • 收稿日期:2022-03-01 修回日期:2022-05-22 出版日期:2022-07-10
  • 通讯作者: 仲雷 E-mail:wangyaoj@mail.ustc.edu.cn;zhonglei@ustc.edu.cn
  • 基金资助:
    中国科学院基础前沿科学研究计划从0到1原始创新项目“全球城镇化对气候变化影响的检测归因与地理分异机制”(ZDBS-LY-DQC005-01);中国科学院A类战略性先导科技专项“西风—季风断面上陆气相互作用和水热变化及其对周边的影响”(XDA20060101)

Estimation of Anthropogenic Heat Flux in Hefei Using Landsat 8 OLI/TIRS Data

Yaojie WANG 1( ), Lei ZHONG 1 , 2 , 3( ), Mingxing CHEN 4, Renmin YUAN 1, Xiaoqing WU 5, Xuexing QIU 6, Nan GE 1, Meilin CHENG 1, Peizhen LI 1   

  1. 1.School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China
    2.CAS Center for Excellence in Comparative Planetology, Hefei 230026, China
    3.Jiangsu Collaborative Innovation Center for Climate Change, Nanjing 210023, China
    4.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    5.Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
    6.Anhui Meteorological Service, Hefei 230031, China
  • Received:2022-03-01 Revised:2022-05-22 Online:2022-07-10 Published:2022-07-21
  • Contact: Lei ZHONG E-mail:wangyaoj@mail.ustc.edu.cn;zhonglei@ustc.edu.cn
  • About author:WANG Yaojie (1997-), female, Weifang City, Shandong Province, Master student. Research area include urban remote sensing research. E-mail: wangyaoj@mail.ustc.edu.cn
  • Supported by:
    the Chinese Academy of Sciences Basic Frontier Science Research Program from 0 to 1 Original Innovation Project “Detection attribution and geographical differentiation mechanism of the impact of global urbanization on climate change”(ZDBS-LY-DQC005-01);The Strategic Priority Research Program of Chinese Academy of Sciences “Land air interaction and hydrothermal changes on the westerly monsoon section and their effects on the surrounding areas”(XDA20060101)

人为热通量是城市能量收支的重要组成部分,人为热通量的定量遥感估算和验证对城市区域气候和热岛效应的研究具有重要意义。为此,利用Landsat 8 OLI/TIRS卫星数据和ERA5再分析数据集,基于能量平衡方程法估算得到2013—2020年合肥市的人为热通量,并利用站点资料对地表特征参数和能量平衡方程各分量的估算结果进行了定量评估,在此基础上分析了合肥市人为热通量的时空分布特征。所取得的主要研究结论如下: 确立了适合城市下垫面的地表温度反演算法和下行长波辐射估算方案,地表温度和下行长波辐射均方根误差分别为2.33 K和9.26 W/m2 估算得到的净辐射通量、感热通量、潜热通量和人为热通量与站点实测数据具有良好的一致性,均方根误差分别为82.00 W/m2、69.51 W/m2、55.19 W/m2和75.47 W/m2 人为热通量空间分布比较集中,城区人为热通量较高,工业区是城区中最大的人为热排放区域,量值远高于自然下垫面;人为热通量有明显的季节变化,夏季最高,春季次之,秋冬季最小;不同下垫面人为热通量有明显差异,城区最大,农田次之,森林和水体较小,城区在春、夏、秋、冬四季的平均值分别为:280 W/m2、321 W/m2、203 W/m2 131 W/m2。研究结果对于评估大中型城市能源排放、发展状况、布局规划以及城市区域气候的研究具有重要的科学意义和应用价值。

Anthropogenic heat flux is an important term of urban energy budget. Estimation of the anthropogenic heat flux is critical for the study of urban climate and heat island effect. Therefore, the anthropogenic heat flux of Hefei from 2013 to 2020 was estimated based on the energy balance equation method using Landsat 8 satellite data and ERA5 reanalysis data. Land surface characteristic parameters and each component of the energy balance equation was quantitatively validated against in-situ measurements, then the spatiotemporal distribution of anthropogenic heat flux was analyzed. The results showed that: The retrieval algorithm of surface temperature and the estimation scheme of downwelling long-wave radiation suitable for urban are established. The root mean square errors of surface temperature and downwelling long-wave radiation are 2.33 K and 9.26 W/m2, respectively. The estimated net radiation flux, sensible heat flux, latent heat flux and anthropogenic heat flux are in good agreement with the in-situ observations, and the root mean square errors are 82.00 W/m2, 69.51 W/m2, 55.19 W/m2 and 75.47 W/m2, respectively. The spatial distribution of anthropogenic heat flux is relatively concentrated, and anthropogenic heat flux in urban areas is much higher than that over the natural underlying surface; the industrial area in the main urban area is the largest anthropogenic heat flux emission area throughout the year; the anthropogenic heat flux has distinct seasonal variation, with the largest in summer, the second in spring and the smallest in autumn and winter; there are obvious differences in anthropogenic heat flux on different surface cover types, the urban area is the largest, followed by farmland, and the forest and water body are small, the seasonal average of the urban area in spring, summer, autumn and winter are as follows: 280 W/m2, 321 W/m2, 203 W/m2 and 131 W/m2, respectively. The research results have important scientific significance and application value for evaluating the energy emissions, development status, layout planning and urban regional climate of large and medium-sized cities.

中图分类号: 

图1 合肥市气象站点(a)和工业区分布图(b 33
(a)矩形框为主城区;(b)白色实线内为工业区
Fig. 1 The locations of the Hefei City and meteorological stationsaand the distribution of industrial areasb 33
(a) The main urban area is in the black rectangular frame; (b) The industrial area is located inside the white solid line
表1 Landsat 8波段列表
Table 1 Band list of Landsat 8
表2 质量评估波段( QA)不同位数的含义
Table 2 Meaning of different digits of Quality Assessment bandQA
表3 ERA5 再分析数据气象参数信息
Table 3 Meteorological parameters of ERA5 Reanalysis Data
表4 合肥市气象站点数据概况
Table 4 Overview of in situ measurements in Hefei Ctiy
图2 观测场环境
(a) 中科大站;(b) 科学岛站
Fig. 2 Environment of in situ measurements
(a) Station of University of Science and Technology of China;(b) Station of Hefei Institutes of Physical Science, Chinese Academy of Sciences
表5 土地利用分类
Table 5 Land use classification
表6 用于计算 T s 的分裂窗系数
Table 6 Split window algorithm coefficients used to calculate ? T s
表7 用于计算不同下垫面冠层储热通量 ? S 的系数 C g
Table 7 Coefficient C g used to calculate storage heat flux ? S for different land cover types
图3 ERA5各气象要素与实测站点数据的验证结果
(a) 气温;(b) 气压;(c) 相对湿度;(d) 风速
Fig. 3 Validation of ERA5 meteorological elements against in situ measurements
(a) Air temperature; (b) Pressure; (c) Relative humidity; (d) Wind speed
图4 卫星反演结果与站点实测的验证
(a) 地表宽带反照率;(b) 地表温度
Fig. 4 Validation of results retrieved from satellite images against in situ measurements
(a) Land surface broadband albedo; (b) Land surface temperature
图5 地表温度多种反演算法反演结果与站点实测数据的验证结果
Fig. 5 Verification results between the estimated values of various retrieval algorithms for land surface temperature and in situ measurements
图6 下行长波辐射多种估算模型估算值与站点实测数据的验证结果
Fig. 6 Downwelling long-wave radiation verification results between the estimated values from different models and in situ measurements
图7 地表能量平衡各分量的验证结果
(a) 净辐射通量;(b) 感热通量;(c) 潜热通量;(d) 人为热通量
Fig. 7 Validation of land surface energy balance components against in situ measurements
(a) Net radiation flux; (b) Sensible heat flux; (c) Latent heat flux; (d) Anthropogenic heat flux
图8 人为热通量时空分布特征
(a) 2016年7月25日;(b) 2018年10月3日;(c) 2019年1月23日;(d) 2019年3月12日
Fig. 8 Spatiotemporal distribution of anthropogenic heat flux
(a) July 25, 2016; (b) October 3, 2018; (c) January 23, 2019; (d) March 12, 2019
表8 主要下垫面类型的人为热通量平均值 ( W/m 2)
Table 8 Averages of AHF for main surface cover types
1 KONG Xiangli, ZHOU Xiaofeng. Effect of the regional differences of urbanization on the consumption structure of rural citizens[J]. Journal of Northwest University (Philosophy and Social Sciences Edition), 2021, 51(3): 54-68.
孔祥利, 周晓峰. 城镇化率区域差异对农村居民消费结构的影响[J]. 西北大学学报(哲学社会科学版), 2021, 51(3): 54-68.
2 SAILOR D J, LU L. A top-down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areas[J]. Atmospheric Environment, 2004, 38(17): 2 737-2 748.
3 PIGEON G, LEGAIN D, DURAND P, et al. Anthropogenic heat release in an old European agglomeration (Toulouse, France)[J]. International Journal of Climatology, 2007, 27(14): 1 969-1 981.
4 CHOW W T L, SALAMANCA F, GEORGESCU M, et al. A multi-method and multi-scale approach for estimating city-wide anthropogenic heat fluxes[J]. Atmospheric Environment, 2014, 99: 64-76.
5 MIRZAEI P A, HAGHIGHAT F. Approaches to study urban heat island-abilities and limitations[J]. Building and Environment, 2010, 45(10): 2 192-2 201.
6 BOHNENSTENGEL S I, HAMILTON I, DAVIES M, et al. Impact of anthropogenic heat emissions on London’s temperatures[J]. Quarterly Journal of the Royal Meteorological Society, 2014, 140(679): 687-698.
7 ZHAO Y, ZHONG L, MA Y M, et al. WRF/UCM simulations of the impacts of urban expansion and future climate change on atmospheric thermal environment in a Chinese megacity[J]. Climatic Change, 2021, 169(3/4): 1-17.
8 CHEN B, DONG L, LIU X, et al. Exploring the possible effect of anthropogenic heat release due to global energy consumption upon global climate: a climate model study[J]. International Journal of Climatology, 2016, 36(15): 4 790-4 796.
9 NIE W S, ZAITCHIK B F, NI G H, et al. Impacts of anthropogenic heat on summertime rainfall in Beijing[J]. Journal of Hydrometeorology, 2017, 18(3): 693-712.
10 ZHANG W, VILLARINI G, VECCHI G A, et al. Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston[J]. Nature, 2018, 563(7 731): 384-388.
11 CHEN Y, JIANG W M, ZHANG N, et al. Numerical simulation of the anthropogenic heat effect on urban boundary layer structure[J]. Theoretical and Applied Climatology, 2009, 97(1/2): 123-134.
12 MENG C, JIANG L, JIN H, et al. Impact of anthropogenic heat on surface balance of energy and water in Beijing[J]. Russian Meteorology and Hydrology, 2020, 45(6): 438-446.
13 ZHANG G J, CAI M, HU A. Energy consumption and the unexplained winter warming over northern Asia and North America[J]. Nature Climate Change, 2013, 3(5): 466-470.
14 FLANNER M G. Integrating anthropogenic heat flux with global climate models[J]. Geophysical Research Letters, 2009, 36(2): L02801.
15 LIU B, XIE Z H, QIN P H, et al. Increases in anthropogenic heat release from energy consumption lead to more frequent extreme heat events in urban cities[J]. Advances in Atmospheric Sciences, 2021, 38(3): 430-445.
16 PERKINS S E, LEWIS S C, KING A D, et al. Increased simulated risk of the hot Australian summer of 2012/13 due to anthropogenic activity as measured by heat wave frequency and intensity[J]. Bulletin of the American Meteorological Society, 2014, 95(9): S34-S37.
17 CHEN Huopo, SUN Jianqi. Anthropogenic influence has increased climate extreme occurrence over China[J]. Science Bulletin, 2021, 66(8): 749-752.
陈活泼, 孙建奇. 人类活动加剧中国极端气候变化[J]. 科学通报, 2021, 66(8): 749-752.
18 SAILOR D J. A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment[J]. International Journal of Climatology, 2011, 31(2): 189-199.
19 CAO Z, WEN Y, SONG S, et al. Spatiotemporal variations and controls on anthropogenic heat fluxes in 12 selected cities in the eastern China[J]. Chinese Geographical Science, 2021, 31(3): 444-458.
20 CHEN Q, YANG X C, OUYANG Z T, et al. Estimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data[J]. Environmental Pollution, 2020, 266(Pt. 1): 115183.
21 LU Y, WANG Q G, ZHANG Y Y, et al. An estimate of anthropogenic heat emissions in China[J]. International Journal of Climatology, 2016, 36(3): 1 134-1 142.
22 LIN Z L, XU H Q. Anthropogenic heat flux estimation based on Luojia 1-01 new nighttime light data: a case study of Jiangsu Province, China[J]. Remote Sensing, 2020, 12(22): 3707.
23 NIE W S, SUN T, NI G H. Spatiotemporal characteristics of anthropogenic heat in an urban environment: a case study of Tsinghua campus[J]. Building and Environment, 2014, 82: 675-686.
24 ZHOU Y Y, WENG Q H, GURNEY K R, et al. Estimation of the relationship between remotely sensed anthropogenic heat discharge and building energy use[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67: 65-72.
25 ZHENG Y F, WENG Q H. High spatial- and temporal-resolution anthropogenic heat discharge estimation in Los Angeles County, California[J]. Journal of Environmental Management, 2018, 206: 1 274-1 286.
26 KATO S, YAMAGUCHI Y. Analysis of urban heat-island effect using ASTER and ETM+ data: separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux[J]. Remote Sensing of Environment, 2005, 99(1/2): 44-54.
27 OKE T R. The urban energy balance[J]. Progress in Physical Geography: Earth and Environment, 1988, 12(4): 471-508.
28 HU D Y, YANG L M, ZHOU J, et al. Estimation of urban energy heat flux and anthropogenic heat discharge using aster image and meteorological data: case study in Beijing metropolitan area[J]. Journal of Applied Remote Sensing, 2012, 6(1): 3559.
29 WONG M S, YANG J X, NICHOL J, et al. Modeling of anthropogenic heat flux using HJ-1B Chinese small satellite image: a study of heterogeneous urbanized areas in Hong Kong[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(7): 1 466-1 470.
30 LIU Jiahui, ZHAO Xiaofeng, LIN Jianyi. Analysis of anthropogenic heat discharge of urban functional regions based on surface energy balance in Xiamen Island[J]. Journal of Geo-Information Science, 2018, 20(7): 1 026-1 036.
刘嘉慧, 赵小锋, 林剑艺. 基于地表能量平衡的厦门岛城市功能区人为热排放分析[J]. 地球信息科学学报, 2018, 20(7): 1 026-1 036.
31 SIVAK M. Air conditioning versus heating: climate control is more energy demanding in Minneapolis than in Miami[J]. Environmental Research Letters, 2013, 8(1): 014050.
32 CHEN S S, HU D Y. Parameterizing anthropogenic heat flux with an energy-consumption inventory and multi-source remote sensing data[J]. Remote Sensing, 2017, 9(11): 1165.
33 GONG P, CHEN B, LI X C, et al. Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018[J]. Science Bulletin, 2020, 65(3): 182-187.
34 Hefei Bureau of Statistics. Hefei statistical bulletin on national economic and social development in 2021[EB/OL]. [2022-04-22]. .
合肥市统计局. 合肥市2021年国民经济和社会发展统计公报[EB/OL]. [2022-04-22]. .
35 Hefei Municipal People’s Government. Overview of Hefei [EB/OL]. [2021-04-22]. .
合肥市人民政府网. 合肥概况[EB/OL]. [2021-04-22]. .
36 LI Xiali. Study on the impact of urbanization on climate change in Hefei[D]. Hefei: Anhui Agricultural University, 2015.
李侠丽. 城市化对合肥地区气候变化的影响研究[D]. 合肥:安徽农业大学, 2015.
37 YANG J, HUANG X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021, 13(8): 3 907-3 925.
38 LIANG S L. Narrowband to broadband conversions of land surface albedo I: algorithms[J]. Remote Sensing of Environment, 2001, 76(2): 213-238.
39 QIN Zhihao, LI Wenjuan, XU Bin, et al. The estimation of land surface emissivity for landsat TM6[J]. Remote Sensing for Land & Resources, 2004, 16(3): 28-32, 36, 41.
覃志豪, 李文娟, 徐斌, 等. 陆地卫星TM6波段范围内地表比辐射率的估计[J]. 国土资源遥感, 2004, 16(3): 28-32, 36, 41.
40 CARLSON T N, RIPLEY D A. On the relation between NDVI, fractional vegetation cover, and leaf area index[J]. Remote Sensing of Environment, 1997, 62(3): 241-252.
41 DU C, REN H Z, QIN Q M, et al. A practical split-window algorithm for estimating land surface temperature from Landsat 8 data[J]. Remote Sensing, 2015, 7(1): 647-665.
42 SONG Ting, DUAN Zheng, LIU Junzhi, et al. Comparison of four algorithms to retrieve land surface temperature using Landsat 8 satellite[J]. Journal of Remote Sensing, 2015, 19(3): 451-464.
宋挺, 段峥, 刘军志, 等. Landsat 8数据地表温度反演算法对比[J]. 遥感学报, 2015, 19(3): 451-464.
43 PRATA A J. A new long-wave formula for estimating downward clear-sky radiation at the surface[J]. Quarterly Journal of the Royal Meteorological Society, 1996, 122(533): 1 127-1 151.
44 ZHONG Lei, GE Nan, MA Yaoming, et al. Estimation of land surface latent heat flux over the Tibetan Plateau using geostationary satellite data[J]. Advances in Earth Science, 2021, 36(8): 773-784.
仲雷, 葛楠, 马耀明, 等. 利用静止卫星估算青藏高原全域地表潜热通量[J]. 地球科学进展, 2021, 36(8): 773-784.
45 PENG T, SUN C G, FENG S S, et al. Temporal and spatial variation of anthropogenic heat in the central urban area: a case study of Guangzhou, China[J]. ISPRS International Journal of Geo-Information, 2021, 10(3): 160.
46 SU Z. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes[J]. Hydrology and Earth System Sciences, 2002, 6(1): 85-99.
47 CHEN X L, SU Z B, MA Y M, et al. An improvement of roughness height parameterization of the Surface Energy Balance System (SEBS) over the Tibetan Plateau[J]. Journal of Applied Meteorology and Climatology, 2013, 52(3): 607-622.
48 MU Q Z, HEINSCH F A, ZHAO M S, et al. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data[J]. Remote Sensing of Environment, 2007, 111(4): 519-536.
49 MU Q Z, ZHAO M S, RUNNING S W. Improvements to a MODIS global terrestrial evapotranspiration algorithm[J]. Remote Sensing of Environment, 2011, 115(8): 1 781-1 800.
50 ZHANG Yu. Quantitative remote sensing estimation of urban surface evapotranspiration based on a modified Penman-Monteith model[D]. Xuzhou: China University of Mining and Technology, 2018.
张宇. 基于改进Penman-Monteith模型的城市地表蒸散发定量遥感估算研究[D]. 徐州:中国矿业大学, 2018.
51 FISHER J B, DEBIASE T A, QI Y, et al. Evapotranspiration models compared on a Sierra Nevada forest ecosystem[J]. Environmental Modelling & Software, 2005, 20(6): 783-796.
52 WANG F, QIN Z H, SONG C Y, et al. An improved mono-window algorithm for land surface temperature retrieval from landsat 8 thermal infrared sensor data[J]. Remote Sensing, 2015, 7(4): 4 268-4 289.
53 COLL C, GALVE J M, SANCHEZ J M, et al. Validation of landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(1): 547-555.
54 JIMÉNEZ-MUÑOZ J C, SOBRINO J A, SKOKOVIĆ D, et al. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10): 1 840-1 843.
55 YU X L, GUO X L, WU Z C. Land surface temperature retrieval from Landsat 8 TIRS—comparison between radiative transfer equation-based method, split window algorithm and single channel method[J]. Remote Sensing, 2014, 6(10): 9 829-9 852.
56 JIN M J, LI J M, WANG C L, et al. A practical split-window algorithm for retrieving land surface temperature from Landsat-8 data and a case study of an urban area in China[J]. Remote Sensing, 2015, 7(4): 4 371-4 390.
57 HAN C B, MA Y M, CHEN X L, et al. Estimates of land surface heat fluxes of the Mt. Everest region over the Tibetan Plateau utilizing ASTER data[J]. Atmospheric Research, 2016, 168: 180-190.
58 SWINBANK W C. Long-wave radiation from clear skies[J]. Quarterly Journal of the Royal Meteorological Society, 1963, 89(381): 339-348.
59 IDSO S B, JACKSON R D. Thermal radiation from the atmosphere[J]. Journal of Geophysical Research, 1969, 74(23): 5 397-5 403.
60 BRUTSAERT W. Derivable formula for long-wave radiation from clear skies[J]. Water Resources Research, 1975, 11(5): 742-744.
61 IDSO S B. A set of equations for full spectrum and 8- to 14-μm and 10.5- to 12.5-μm thermal radiation from cloudless skies[J]. Water Resources Research, 1981, 17(2): 295-304.
[1] 仲雷,葛楠,马耀明,傅云飞,马伟强,韩存博,王显,程美琳. 利用静止卫星估算青藏高原全域地表潜热通量[J]. 地球科学进展, 2021, 36(8): 773-784.
[2] 江笑薇, 白建军, 刘宪锋. 基于多源信息的综合干旱监测研究进展与展望[J]. 地球科学进展, 2019, 34(3): 275-287.
[3] 陈泽青,刘诚,胡启后,洪茜茜,刘浩然,邢成志,苏文静. 大气成分的遥感监测方法与应用[J]. 地球科学进展, 2019, 34(3): 255-264.
[4] 李青, 雷连发, 王振会, 魏鸣, 李东帅. 雷电流热效应的遥感观测研究进展[J]. 地球科学进展, 2017, 32(5): 481-487.
[5] 徐凯, 姚志刚, 韩志刚, 赵增亮, 方涵先. 临近空间重力波强扰动的卫星观测研究进展[J]. 地球科学进展, 2017, 32(1): 66-74.
[6] 张勇, 戎志国, 闵敏. 中国遥感卫星辐射校正场热红外通道在轨场地辐射定标方法精度评估[J]. 地球科学进展, 2016, 31(2): 171-179.
[7] 姚云军,程洁,赵少华,贾坤,谢先红,孙亮. 基于热红外遥感的农田蒸散估算方法研究综述[J]. 地球科学进展, 2012, 27(12): 1308-1318.
[8] 崔月菊,杜建国,陈志,李静,谢超,周晓成,刘雷. 2010年玉树Ms 7.1地震前后大气物理化学遥感信息[J]. 地球科学进展, 2011, 26(7): 787-794.
[9] 祝善友,张桂欣. 近地表气温遥感反演研究进展[J]. 地球科学进展, 2011, 26(7): 724-730.
[10] 郑有飞,董自鹏,吴荣军,李占清,江洪. MODIS气溶胶光学厚度在长江三角洲地区适用性分析[J]. 地球科学进展, 2011, 26(2): 224-234.
[11] 石广玉,戴铁,徐娜. 卫星遥感探测大气CO 2浓度研究最新进展[J]. 地球科学进展, 2010, 25(1): 7-13.
[12] 凌飞龙,李增元,陈尔学,何祺胜. 青海云杉林叶面积指数半球摄影测量方法研究[J]. 地球科学进展, 2009, 24(7): 803-809.
[13] 陈玲,阎广建,李静,余莹洁. 行播作物地面方向性测量的视场不确定性分析[J]. 地球科学进展, 2009, 24(7): 793-802.
[14] 康国婷,阎广建,任华忠,王颢星,钱永刚. 田块尺度作物辐射温度获取方法对比研究[J]. 地球科学进展, 2009, 24(7): 784-792.
[15] 徐春亮,陈彦,贾明权,刘增灿,卢海平,童玲. 典型地物后向散射特性的测量与分析[J]. 地球科学进展, 2009, 24(7): 810-816.
阅读次数
全文


摘要