Please wait a minute...
img img
地球科学进展  2005, Vol. 20 Issue (1): 42-048    DOI: 10.11867/j.issn.1001-8166.2005.01.0042
1.中国科学院大气物理研究所中层大气和全球环境探测开放实验室,北京 100029;
2.北京大学物理学院大气科学系,北京 100871;
3.中国气象科学研究院,北京 100081  
Using Satellite Remotely Sensed Data to Retrieve Sensible and Latent Heat Fluxes: A Review
WANG Kaicun1,2,Zhou Xiuji3,LI Weiliang3,LIU Jingmiao3,WANG Pucai1
1.Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmosheric Physics,Chinese Academy of Sciences, Beijing 100029, China;
2.Department of Atmospheric Science, College of Physics, Peking University, Beijing  100871,China;
3.Chinese Academy of Meteorological Sciences, Beijing  100081,China)
 全文: PDF(164 KB)  


关键词: 卫星遥感反演热通量物理模式精度评价    

Regional sensible and latent heat fluxes are the key physical parameters of the meteorological, hydrological and ecological models. However it is very difficult to obtain these fluxes from conventional ground measurement. Using satellite remotely sensed data to retrieve these fluxes supplies one possible way to solve this problem. However, the gradient measurement is needed when calculating the fluxes are calculated, while the measurements from only one layer can be obtained from satellite. To solve this problem, many studies have been carried out. There are two main ways that can be seen: the physical model and empirical model. The physical models have two directions: the gradient model and the thermal inertia method. Gradient models combine the satellite remotely sensed data with the ground measurements, and use the difference of surface temperature and air temperature at reference height to calculate sensible heat flux. The latent heat flux is obtained as the residual. The thermal inertia method uses the response of soil to the absorption of solar radiation to calculate the sensible and latent heat flux. The empirical method calculates empirical regression between the measurements of the fluxes and the satellite remotely sensed data, and then extends this relationship to calculate the fluxes. Here the daily-average fluxes are often used. 
    Four gradient models are reviewed, including one one-source model and three two-source models. When use the onesource model is used for to partly vegetation-covered surface, the difference of the air dynamic temperature and the thermal radioactive temperature hinds it usage. Two-source models can solve this problem. However, all the gradient models are sensitive to the error of the difference between the satellite retrieved surface temperature and the measurements of the air temperature. Another shortcoming of the gradient model is that they need to interpolate ground measurements, such as the air temperature and wind speed. These interpolations always are of low quality with unacceptable errors. The thermal inertia method calculates sensible and latent heat fluxes only using the satellite remotely sensed data, which will have a wider usage in the near future. However, up to today, this method only succeeded in the bare soil surface. More attention should be paid to it in the future. 
    At last, the methods used to evaluate the accuracy of the retrieval of sensible and latent heat fluxes are reviewed.

Key words: Satellite remote sensing    Retrieve    Heat fluxes    Physical model    Accuracy evaluation
收稿日期: 2003-08-13 出版日期: 2005-01-25
:  P412.27   


通讯作者: 王开存     E-mail:
作者简介: 王开存(1977-),男,河南永城人,博士研究生,主要从事大气边界层和卫星遥感资料的应用研究.E-mail:
E-mail Alert


王开存;周秀骥;李维亮;刘晶淼;王普才. 利用卫星遥感资料反演感热和潜热通量的研究综述[J]. 地球科学进展, 2005, 20(1): 42-048.

WANG Kaicun,Zhou Xiuji,LI Weiliang,LIU Jingmiao,WANG Pucai. Using Satellite Remotely Sensed Data to Retrieve Sensible and Latent Heat Fluxes: A Review. Advances in Earth Science, 2005, 20(1): 42-048.


[1]Shuttleworth W J, Wallace J S. Evaporation from sparse crops-An energy combination theory[J].Quarterly Journal of the Royal Meteorological Society,1985,111:839-855.
[2]Zhang Lu, Lemeur R, Goutorbe J P. A one-layer resistance model for estimating regional evaportranspiration using remote sensing data[J].Agricultural and Forest Meteorology,1995,77: 241-261[3]Kustas W P, Choudhury B, Reginato,et al. Determination of sensible heat flux over sparse canopy using thermal infrared data[J].Agricultural and Forest Meteorology,1989,44:197-216.
[4]Ma Yaoming, Tsukamoto O, Ishikawa H, et al. Determination of regional land surface heat flux densities over heterogeneous landscape of HEIFE integrating satellite remote sensing with field observations [J].Journal of Meteorology Society of Japan, 2002,80: 485-501.
[5]Jacobsen A, Hansen B U. Estimation of the soil heat flux/ net radiation ratio based on spectral vegetation indexes in high-latitude Arctic areas [J].International Journal of Remote Sensing, 1999, 20(2): 445-461.
[6]Clothier B E, Clawson K L, Pinter, et al. Estimation of soil heat flux from net radiation during the growth of lfalfa [J].Agricultural and Forest Meteorology,1986, 37: 319- 329.
[7]Kustas W P, Daughtry C S T. Estimation of the soil heat flux-net radiation from spectral data[J].Agricultural and Forest Meteorology,1990, 49: 205-223.
[8]Kustas W P, Daughtry C S T, Van Oevelen P J. Analytical treatment of the relationships between soil heat flux/net radiation ratio and vegetation indices [J].Remote Sensing of Environment,1993, 46: 319-330.
[9]Monin A S, Oblukhov A M. Basic laws of turbulent mixing in the atmosphere near the ground[J].Trudy Geofizicheskogo Instituta Akademiya Nuak SSSR,1954, 24(151): 163-187.
[10]Businger J A, Wyngaard J C, Lzumi Y,et al. Flux profile relations in the atmospheric surface layer[J].Journal of Atmospheric Science,1971, 28: 181-189.
[11]Dyer A J. A review of the flux-profile relations [J].Boundary Layer Meteorology,1972,1: 336-372.
[12]Mecikalski John R, Diak George R, Anderson Martha C. Estimating fluxes on continental Scales using remotely sensing data in an atmospheric-land exchange model[J].Journal of Applied Meteorology,1999,38: 1 353-1 369.
[13]PauPach M R.Simplified expression for vegetation roughness length and zero-plane displacement as function of canopy height and area index[J].Boundary Layer Meteorology,1994,71: 211-216.
[14]Chehbouni A, LoSeen D, Njoku,et al. Examination of the difference between radiative and aerodynamic surface temperature over sparsely vegetated surfaces [J].Remote Sensing of Environment,1995, 58: 177-186.
[15]Friedl M A. Forward and inverse modeling of land surface energy balance using surface temperature measurements [J].Remote Sensing of Environment,2002,79: 344-354.
[16]Su Z, Schmugge T, Kusts W P. An evaluation of two models for estimation of the roughness height for heat transfer between the land surface and atmosphere[J].Journal of Applied Meteorology,2001,40(11): 1 933-1 951.
[17]Bl mel K. A simple formula for estimation of the roughness length for heat transfer over Partly vegetation surfaces [J].Journal of Applied Meteorology,1999, 38: 814-829.
[18]Verhoef W, de Bruin H A R, van den hurk B J M. Some practical notes on the parameter KB-1 for spares vegetation [J].Journal of Applied Meteorology,1997, 36: 560-572.
[19]Jia Li, Wang Jiemin, Hu Zeyong. The characteristics of roughness length for heat and it influence on the determination of sensible heat flux on arid zone[J].Plateau Meteorology,2000,19(4): 485-503.[贾立,王介民,胡泽勇.干旱区热力粗糙度特征及对感热通量估算的影响[J].高原气象,2000,19(4):485-503.]
[20]Norman J M, Kustas W P, Humes K S. Source approach for estimating soil and vegetation energy flux in observations of directional radiometric surface temperature [J].Agriculture and Forest Meteorology,1995,77: 263-293.
[21]Wallace J S. Evaporation, radiation interception by neighboring plants [J].Quarterly Journal of the Royal Meteorological Society,1997, 123:1 885-1 905.
[22]Lhomme J P, Chehbouni A. Comments on the vegetation-atmosphere transfer models [J].Agriculture and Forest Meteorology,1999, 94: 269-273.
[23]Kustas W P, Norman J M. Reply to comments on the basic equation of dual-source vegetation-atmosphere models [J].Agriculture and Forest Meteorology,1999, 94: 275-278.
[24]Kustas W P, Norman J M. Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover [J].Agriculture and Forest Meteorology,1999, 94: 13-29.
[25]Kondo J,  Ishida S. Sensible heat flux from surface under natural convective conditions [J].Journal of Science,1997, 54: 498-509. 
[26]Kustas W P, Prueger J H, Hipps L E. Impact of different time-averaged inputs for estimating sensible heat flux of riparian vegetation using radiometric surface temperature [J].Journal of Applied Meteorology,2002, 41: 319-332. 
[27]Anderson M C, Norman J M, Dick G R,et al. A two-source time-integrated model for estimating surface flux using thermal infrared remote sensing [J].Remote Sensing of Environment,1997, 60: 195-216. 
[28]Mercikalski J R, Dick G R, Anderson M C. Estimating flux on continental scales using remotely sensed data in an atmospheric-land exchange model [J].Journal of Applied Meteorology,1999, 38: 1 353-1 369.
[29]Norman J M, Kustas W P, Prueger J H. Surface flux estimation using radiometric temperature, a dual temperature  difference method to minimize measurement errors [J].Water Resource Research,2000, 36(8): 2 263-2 274. 
[30]Kustas W P, Goodrich D C, Moran M S,et al. An interdisciplinary field study of the energy and water fluxes in the atmosphere- biosphere system over semiarid rangeland, description and some preliminary results[J].Bulletin of America Meteorological Societiy,1991, 72: 1 683-1 705.
[31]Sellers P J, Asrar F G, Strebel G,et al. An overview of the first international satellite land surface climatology project (ISLSCP) field experiment (FIFE) [J].Journal of Geophysics Research,1992, 97(D17): 18 345-18 371.
[32]Zhang Renhua, Sun Xiaomin, Zhu Zhilin,et al. A remote sensing model for monitoring soil evaporation based on thermal inertia and its validation [J].Science in China (D),2003, 46(4): 342-355.[33]Ma Ainai. Remote Sensing Information Model [M].Beijing: Peking University Press, 1997. 41-51.[马蔼乃.遥感信息模型[M].北京:北京大学出版社,1997:41-51.]
[34]Price J C.Estimation of regional scale evapotranspiration through abbalist’s satellite thermal infrared data [J].IEEE Transaction on Geosciences and Remote Sensing,1982, 20: 286-292.
[35]Zhang Renhua. Inertia model of soil moisture and its application [J].Chinese Science Bulletin,1991, 36(12): 924-927.
[36]Goward Samuel N, Xue Yongkang, Czajkowski Kevin P. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model [J].Remote Sensing of Environment,2002, 79: 225-242.
[37]Rabin Robert M. Relating remotely sensed vegetation and soil indices to surface energy fluxes in vicinity of an atmosphere dryline [J].Remote Sensing Reviews,2000, 18: 53-58.
[38]Jiang L, Islam S. A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations [J].Geophysical Research Letters,1999, 26(17): 2 773-2 776.
[39]Jiang L,Islam S.Estimation of surface evaporation map over southern Great Plains using remote sensing data [J].Water Resources Research,2001,37(2): 329-340
[40]Boegh E, Soegaard H, Hanan N,et al.A remote sensing study of the NDVI Ts relationship and the transpiration from sparse vegetation in the Sahel based on high resolution satellite data [J].Remote Sensing of Environment,1998, 69 (3): 224-240.
[41]Gillies R R, Carlson T N, Gui J,et al.A verification of the triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature [J].International Journal of Remote Sensing,1997,18 (15): 3 145-3 166.
[42]Sandholta Inge, Rasmussena Kjeld, Andersen Jens. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status [J].Remote Sensing of Environment,2002, 79: 213-224.
[43]Smith R C G, Choudhury B J. Analysis of normalized difference and surface temperature observations over southeastern Australia [J].International Journal of Remote Sensing,1991, 12 (10): 2 021-2 044.
[44]Bella C M DI, Rebella C M, Paruelo J M. Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina [J].International Journal of Remote sensing,2000, 21(4): 791-797.
[45]Kustas W P, Norman J. Evaluating the effects of sub-pixel heterogeneity on pixel averaged fluxes [J].Remote Sensing of Environment,2000, 74(3): 327-343.
[46]Wang J, Ma Y, Menenti Mi,et al.The scaling-up process in the heterogeneous landscape of HEIFE with the aid of satellite remote sensing [J].Journal of Meteorology Society of Japan,1995, 73(6): 1 235-1 244.
[47]Liu Jingmiao, Ding Yuguo, Zhou Xiuji,et al.The influences of the surface heterogeneity on the parameterization of region averaged moisture flux [J].Acta Meteorologica Sinica,2003, 61(6): 712-717.[刘晶淼,丁裕国,周秀骥,等.地表非均匀性对区域平均水分通量参数化的影响[J].气象学报,2003, 61(6): 712-717.]

[1] 居为民, 方红亮, 田向军, 江飞, 占文凤, 刘洋, 王正兴, 何剑锋, 王绍强, 彭书时, 张永光, 周艳莲, 贾炳浩, 杨东旭, 符瑜, 李荣, 柳竟先, 王海鲲, 李贵才, 陈卓奇. 基于多源卫星遥感的高分辨率全球碳同化系统研究[J]. 地球科学进展, 2016, 31(11): 1105-1110.
[2] 于文涛, 李静, 柳钦火, 曾也鲁, 尹高飞, 赵静, 徐保东. 中国地表覆盖异质性参数提取与分析[J]. 地球科学进展, 2016, 31(10): 1067-1077.
[3] 郭瑞芳, 刘元波. 多传感器联合反演高分辨率降水方法综述[J]. 地球科学进展, 2015, 30(8): 891-903.
[4] 韩成鸣, 李耀东, 史小康. 云分析预报方法研究进展[J]. 地球科学进展, 2015, 30(4): 505-516.
[5] 尹剑, 占车生, 顾洪亮, 王飞宇. 基于水文模型的蒸散发数据同化实验研究[J]. 地球科学进展, 2014, 29(9): 1075-1084.
[6] 李大治, 晋锐, 车涛, 高莹, 耶楠, 王树果. 联合机载PLMR微波辐射计和MODIS产品反演黑河中游张掖绿洲土壤水分研究*[J]. 地球科学进展, 2014, 29(2): 295-305.
[7] 曲伟, 路京选, 宋文龙, 张婷婷, 谭亚男, 黄萍. TRMM遥感降水数据在伊洛瓦底江流域的精度检验和校正方法研究[J]. 地球科学进展, 2014, 29(11): 1262-1270.
[8] 王振宇, 杨勤勇, 李振春, 胡光辉, 尹力, 王杰. 近地表速度建模研究现状及发展趋势[J]. 地球科学进展, 2014, 29(10): 1138-1148.
[9] 刘泽栋, 万修全, 刘福凯. 海底地热通量对海洋深层温度和环流的长期影响[J]. 地球科学进展, 2014, 29(10): 1167-1174.
[10] 陈洪萍, 贾根锁, 冯锦明, 董燕生. 气候模式中关键陆面植被参量遥感估算的研究进展[J]. 地球科学进展, 2014, 29(1): 56-67.
[11] 徐自为,刘绍民,徐同仁,丁闯. 不同土壤热通量测算方法的比较及其对地表能量平衡闭合影响的研究[J]. 地球科学进展, 2013, 28(8): 875-889.
[12] 刘旸,蔡波,班显秀,袁健,耿树江,赵姝慧,李帅彬. AIRS红外高光谱资料反演大气水汽廓线研究进展[J]. 地球科学进展, 2013, 28(8): 890-896.
[13] 牟龙江,赵进平. 格陵兰海海冰外缘线变化特征分析[J]. 地球科学进展, 2013, 28(6): 709-717.
[14] 解国爱,王宗秀,张庆龙,吕赟珊,邹旭. 江西永平铜矿区古构造应力场与构造演化[J]. 地球科学进展, 2013, 28(5): 608-617.
[15] 刘佳,张廷军. 利用钻孔温度梯度重建过去气候变化进展[J]. 地球科学进展, 2013, 28(4): 429-446.