Please wait a minute...
Remote Sensing for Land & Resources    2018, Vol. 30 Issue (3) : 83-88     DOI: 10.6046/gtzyyg.2018.03.12
Estimating latent heat flux over farmland from Landsat images using the improved METRIC model
Jian YU1, Yunjun YAO1(), Shaohua ZHAO2, Kun JIA1, Xiaotong ZHANG1, Xiang ZHAO1, Liang SUN3
1. State Key Laboratory of Remote Sensing Science, College of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China
2. Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094, China
3. USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville MD20705, USA
Download: PDF(3325 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

Estimation of latent heat flux based on thermal infrared remote sensing is of great significance in agricultural drought and water resources management. This paper examined the applicability of using METRIC model to estimate latent heat flux over farmland from Landsat images. Land surface temperature (Ts) required for estimation of the flux was computed from Landsat thermal infrared data by the mono-window algorithm. Meanwhile, an improved METRIC algorithm based on surface roughness was proposed to estimate the latent heat flux of farmland by improving the surface roughness parameters. The result of the algorithm was verified by the flux observation data from two observation stations of Huailai and Miyun in the Haihe River basin. The results show that the square of correlation coefficient (R 2) between simulated and observed values is 0.97, which is better than the conventional METRIC model (R 2 = 0.89). The improved algorithm has higher estimation accuracy of latent heat flux. In addition, the spatial distribution of latent heat flux also shows that the spatial pattern of the improved model is more reasonable. However, due to the limitation of data acquisition, only two stations in Beijing have been used to validate the algorithm, and hence further verification in other areas is needed.

Keywords farmland latent heat flux      thermal infrared remote sensing      METRIC      land surface temperature     
:  TP751.1  
Corresponding Authors: Yunjun YAO     E-mail:
Issue Date: 10 September 2018
E-mail this article
E-mail Alert
Articles by authors
Jian YU
Yunjun YAO
Shaohua ZHAO
Xiaotong ZHANG
Xiang ZHAO
Liang SUN
Cite this article:   
Jian YU,Yunjun YAO,Shaohua ZHAO, et al. Estimating latent heat flux over farmland from Landsat images using the improved METRIC model[J]. Remote Sensing for Land & Resources, 2018, 30(3): 83-88.
URL:     OR
Fig.1  Location of 2 flux tower sites throughout the study area
Fig.2  Scatter-plot of retrieved versus observed Ts and estimated versus observed Rn
Fig.3  Scatter-plot of estimated and ground-measured values
Fig.4  Spatial distribution of LE using improved METRIC, traditional METRIC and their differences
[1] 姚云军, 程洁, 赵少华 , 等. 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.
doi: 10.11867/j.issn.1001-8166.2012.12.1308 url:
[1] Yao Y J, Cheng J, Zhao S H , et al. Estimation of farmland evapotranspiration:A review of methods using thermal infrared remote sensing data[J]. Advances in Earth Science, 2012,27(12):1308-1318.
[2] 周倜, 彭志晴, 辛晓洲 , 等. 非均匀地表蒸散遥感研究综述[J]. 遥感学报, 2016,20(2):257-277.
doi: 10.11834/jrs.20165030 url:
[2] Zhou T, Peng Z Q, Xin X Z , et al. Remote sensing research of evapotranspiration over heterogeneous surfaces:A review[J]. Journal of Remote Sensing, 2016,20(2):257-277.
[3] Brown K W, Rosenberg N J . A resistance model to predict evapotranspiration and its application to a sugar beet field[J]. Agronomy Journal, 1973,65(3):341-347.
doi: 10.2134/agronj1973.00021962006500030001x url:
[4] Seguin B, Itier B . Using midday surface temperature to estimate daily evaporation from satellite thermal IR data[J]. International Journal of Remote Sensing, 1983,4(2):371-383.
doi: 10.1080/01431168308948554 url:
[5] Shuttleworth W J, Wallace J S . Evaporation from sparse crops-an energy combination theory[J]. Quarterly Journal of the Royal Meteorological Society, 1985,111(469):839-855.
doi: 10.1002/qj.49711146910 url:
[6] Shuttleworth W J . Evaporation Models in Hydrology[M]. New York:Springer, 1991: 93-120.
[7] Bastiaanssen W G M, Pelgrum H, Wang J, et al.A remote sensing surface energy balance algorithm for land( SEBAL) .:Part 2:Validation[J].Journal of Hydrology, 1998, 212-213:213-229.
doi: 10.1016/S0022-1694(98)00254-6 url:
[8] Bastiaanssen W G M . SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin,Turkey[J]. Journal of Hydrology, 2000,229(1/2):87-100.
doi: 10.1016/S0022-1694(99)00202-4 url:
[9] Allen R G, Tasumi M, Trezza R . Satellite-based energy balance for mapping evapotranspiration with internalized calibration(METRIC)-model[J]. Journal of Irrigation and Drainage Engineering, 2007,133(4):380-394.
doi: 10.1061/(ASCE)0733-9437(2007)133:4(380) url:
[10] Allen R, Irmak A, Trezza R , et al. Satellite-based ET estimation in agriculture using SEBAL and METRIC[J]. Hydrological Processes, 2011,25(26):4011-4027.
doi: 10.1002/hyp.8408 url:
[11] 何磊, 王瑶, 别强 , 等. 基于SEBS-METRIC方法的黑河流域中游地区农田蒸散[J]. 兰州大学学报(自然科学版), 2013,49(4):504-510.
[11] He L, Wang Y, Bie Q , et al. Estimation of field evapotranspiration in the middle reaches of Heihe River basin based on SEBS-METRIC method[J]. Journal of Lanzhou University(Natural Sciences), 2013,49(4):504-510.
[12] 连晋姣, 黄明斌, 李杏鲜 , 等. 夏季黑河中游绿洲样带蒸散量遥感估算[J]. 农业工程学报, 2014,30(15):120-129.
doi: 10.3969/j.issn.1002-6819.2014.15.017 url:
[12] Lian J J, Huang M B, Li X X , et al. Evapotranspiration estimation for oasis transect in middle reach of Heihe river basin based on remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014,30(15):120-129.
[13] 曹永强, 张亭亭, 徐丹 , 等. 海河流域蒸散发时空演变规律分析[J]. 资源科学, 2014,36(7):1489-1500.
[13] Cao Y Q, Zhang T T, Xu D , et al. Analysis of evapotranspiration of temporal-space evolution in the Haihe basin[J]. Resources Science, 2014,36(7):1489-1500.
[14] 刘小莽, 郑红星, 刘昌明 , 等. 海河流域潜在蒸散发的气候敏感性分析[J]. 资源科学, 2009,31(9):1470-1476.
[14] Liu X M, Zheng H X, Liu C M , et al. Sensitivity of the potential evapotranspiration to key climatic variables in the Haihe River basin[J]. Resources Science, 2009,31(9):1470-1476.
[15] 覃志豪, Zhang M H, Karnieli A, 等. 用陆地卫星TM6数据演算地表温度的单窗算法[J]. 地理学报, 2001,56(4):456-466.
[15] Qin Z H, Zhang M H, Karnieli A , et al. Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data[J]. Acta Geographica Sinica, 2001,56(4):456-466.
[16] 覃志豪 , Li W J, Zhang M H, 等. 单窗算法的大气参数估计方法[J].国土资源遥感, 2003(2):37-43.doi: 10.6046/gtzyyg.2003.02.10.
doi: 10.3969/j.issn.1001-070X.2003.02.010 url:
[16] Qin Z H, Li W J, Zhang M H , et al. Estimating of the essential atmospheric parameters of mono-window algorithm for land surface temperature retrieval from landsat TM6[J].Remote Sensing For Land and Resources, 2003(2):37-43.doi: 10.6046/gtzyyg.2003.02.10.
[17] Moran M S, Jackson R D . Assessing the spatial distribution of evapotranspiration using remotely sensed inputs[J]. Journal of Environmental Quality, 1991,20(4):725-737.
doi: 10.2134/jeq1991.00472425002000040003x url:
[1] BO Yingjie, ZENG Yelong, LI Guoqing, CAO Xingwen, YAO Qingxiu. Impacts of floating solar parks on spatial pattern of land surface temperature[J]. Remote Sensing for Natural Resources, 2022, 34(1): 158-168.
[2] HE Chenlinqiu, CHENG Bo, CHEN Jinfen, ZHANG Xiaoping. Information extraction methods of coastal wetland based on GF-3 fully polarimetric SAR data[J]. Remote Sensing for Natural Resources, 2021, 33(4): 105-110.
[3] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[4] ZHAO Xiaochen, WU Haonan, LI Linyi, MENG Lingkui. GPU-based parallel image processing algorithm for flood and drought monitoring[J]. Remote Sensing for Natural Resources, 2021, 33(3): 107-113.
[5] XIAO Dongsheng, LIAN Hong. Population spatialization based on geographically weighted regression model considering spatial stability of parameters[J]. Remote Sensing for Natural Resources, 2021, 33(3): 164-172.
[6] LING Xiao, LIU Jiamei, WANG Tao, ZHU Yueqin, YUAN Lingling, CHEN Yangyang. Application of information value model based on symmetrical factors classification method in landslide hazard assessment[J]. Remote Sensing for Land & Resources, 2021, 33(2): 172-181.
[7] YUAN Qianying, MA Caihong, WEN Qi, LI Xuemei. Vegetation cover change and its response to water and heat conditions in the growing season in Liupanshan poverty-stricken area[J]. Remote Sensing for Land & Resources, 2021, 33(2): 220-227.
[8] YE Wantong, CHEN Yihong, LU Yinhao, Wu Penghai. Spatio-temporal variation of land surface temperature and land cover responses in different seasons in Shengjin Lake wetland during 2000—2019 based on Google Earth Engine[J]. Remote Sensing for Land & Resources, 2021, 33(2): 228-236.
[9] FAN Jiazhi, LUO Yu, TAN Shiqi, MA Wen, ZHANG Honghao, LIU Fulai. Accuracy evaluation of the FY-3C/MWRI land surface temperature product in Hunan Province[J]. Remote Sensing for Land & Resources, 2021, 33(1): 249-255.
[10] ZHOU Fangcheng, TANG Shihao, HAN Xiuzhen, SONG Xiaoning, CAO Guangzhen. Research on reconstructing missing remotely sensed land surface temperature data in cloudy sky[J]. Remote Sensing for Land & Resources, 2021, 33(1): 78-85.
[11] WANG Xiaolong, YAN Haowen, ZHOU Liang, ZHANG Liming, DANG Xuewei. Using SVM classify Landsat image to analyze the spatial and temporal characteristics of main urban expansion analysis in Democratic People’s Republic of Korea[J]. Remote Sensing for Land & Resources, 2020, 32(4): 163-171.
[12] Pengyan HUANG, Lijing BU, Yongliang FAN. Integrating visual features in polarimetric SAR image classification[J]. Remote Sensing for Land & Resources, 2020, 32(2): 88-93.
[13] Bing ZHAO, Kebiao MAO, Yulin CAI, Xiangjin MENG. Study of the temporal and spatial evolution law of land surface temperature in China[J]. Remote Sensing for Land & Resources, 2020, 32(2): 233-240.
[14] Zhuhong ZHANG, Baoyun WANG, Yumei SUN, Caidong LI, Xianchen SUN, Lingli ZHANG. River extraction from GF-1 satellite images combining stroke width transform and a geometric feature set[J]. Remote Sensing for Land & Resources, 2020, 32(2): 54-62.
[15] Yachao HAN, Qi LI, Yongjun ZHANG, Zihong GAO, Dachang YANG, Jie CHEN. Geometric calibration method of airborne hyperspectral instrument and its demonstration application in coastal airborne remote sensing survey[J]. Remote Sensing for Land & Resources, 2020, 32(1): 60-65.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech