Please wait a minute...
 
Remote Sensing for Natural Resources    2024, Vol. 36 Issue (1) : 146-153     DOI: 10.6046/zrzyyg.2022492
|
Validation of Hi-GLASS products for latent heat flux based on Ameriflux observation data
FAN Jiahui1(), YAO Yunjun1(), YANG Junming1, YU Ruiyang1, LIU Lu1, ZHANG Xueyi1,2, XIE Zijing1, NING Jing1
1. State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China
2. Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions,CMA,Yinchuan 750002,China
Download: PDF(13413 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The validation and analysis of latent heat flux products are critical for research on climate change and energy circulation. High-resolution global land surface satellite evapotranspiration (Hi-GLASS ET) products, which integrate five traditional evapotranspiration algorithms, can produce high-precision products for land surface latent heat flux. However, these products are yet to be validated. This study obtained multiple sets of valid validation data by comparing the latent heat flux observed values from Ameriflux flux observation sites with the corresponding estimated values of Hi-GLASS land surface latent heat flux products. The validation results yielded a squared correlation coefficient (R2) of 0.6, a root mean square error (RMSE) of 34.4 W/m2, an average bias of -13.4 W/m2, and Kling-Gupta efficiency (KGE) of 0.49. These suggest that Hi-GLASS latent heat flux products boast high precision and that their algorithms enjoy satisfactory fitting results. In addition, spatial distributions imply that Hi-GLASS latent heat flux products conform to normal natural laws. Due to data acquisition limitations, the validation of this study was conducted based on data from only 18 sites in the U.S., and further validation using data from other areas is required.
Keywords land surface latent heat flux      Ameriflux sites      Hi-GLASS products      precision validation     
ZTFLH:  TP79  
Issue Date: 13 March 2024
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Jiahui FAN
Yunjun YAO
Junming YANG
Ruiyang YU
Lu LIU
Xueyi ZHANG
Zijing XIE
Jing NING
Cite this article:   
Jiahui FAN,Yunjun YAO,Junming YANG, et al. Validation of Hi-GLASS products for latent heat flux based on Ameriflux observation data[J]. Remote Sensing for Natural Resources, 2024, 36(1): 146-153.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022492     OR     https://www.gtzyyg.com/EN/Y2024/V36/I1/146
Fig.1  Flow chart of Hi-GLASS ET product production
站点名称 纬度/(°) 经度/(°) 植被覆盖类型
US-CRT 41.628 5 -83.347 1 CRO
US-Tw2 38.096 9 -121.636 5 CRO
US-Tw3 38.115 2 -121.646 9 CRO
US-Twt 38.108 7 -121.6531 CRO
US-GLE 41.366 5 -106.239 9 ENF
US-Me2 44.452 6 -121.558 9 ENF
US-NR1 40.032 9 -105.546 4 ENF
US-Prr 65.123 7 -147.487 6 ENF
US-MMS 39.323 2 -86.413 1 DBF
US-WCr 45.805 9 -90.079 9 DBF
US-Oho 41.554 5 -83.843 8 DBF
US-SRG 31.789 4 -110.827 7 GRA
US-Var 38.413 3 -120.950 8 GRA
US-Wkg 31.736 5 -109.941 9 GRA
US-SRC 31.908 3 -110.839 5 OSH
US-Whs 31.743 8 -110.052 2 OSH
US-SRM 31.821 4 -110.866 1 WSA
US-Ton 38.430 9 -120.966 0 WSA
Tab.1  Spatial distribution information of flux sites
Fig.2  Accuracy verification scatter plot of each site
Fig.3  Overall accuracy validation scatter plot
Fig.4  Comparison of Hi-GLASS land surface latent heat flux products in 2014
Fig.5  Spatial distribution map of Hi-GLASS latent heat flux products in the United States in 2014
[1] Monteith J L. Evaporation and environment[J]. Symposia of the Society for Experimental Biology, 1965, 19:205-234.
pmid: 5321565
[2] Ppriestley C H B, Taylor R J. On the assessment of surface heat flux and evaporation using large-scale parameters[J]. Monthly Weather Review, 1972, 100(2):81-92.
doi: 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2 url: http://journals.ametsoc.org/doi/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2
[3] Kalma J D, McVicar T R, McCabe M F. Estimating land surface evaporation:A review of methods using remotely sensed surface temperature data[J]. Surveys in Geophysics, 2008, 29(4):421-469.
doi: 10.1007/s10712-008-9037-z url: http://link.springer.com/10.1007/s10712-008-9037-z
[4] Liang S. Review on estimation of land surface radiation and energy budgets from ground measurement,remote sensing and model simulations[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 3(3):225-240.
doi: 10.1109/JSTARS.4609443 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4609443
[5] Wang K C, Dickinson R E. A review of global terrestrial evapotranspiration:Observation,modeling,climatology,and climatic variability[J]. Reviews of Geophysics, 2012, 50(2):1-54
[6] 李小文, 王锦地, 胡宝新, 等. 先验知识在遥感反演中的作用[J]. 中国科学(地球科学), 1998, 28(1):67-72.
[6] Li X W, Wang J D, Hu B X, et al. The role of prior knowledge in remote sensing inversion[J]. Scientia Sinica(Terrae), 1998, 28(1):67-72.
[7] 徐冠华. 论热红外遥感中的基础研究[J]. 中国科学(技术科学), 2000, 30(s1):1-5.
[7] Xu G H. On the basic research in thermal infrared remote sensing[J]. Sciertia Sinica Technologic, 2000, 30(s1):1-5.
[8] 张仁华, 孙晓敏, 朱治林, 等. 以微分热惯量为基础的地表蒸发全遥感信息模型及在甘肃沙坡头地区的验证[J]. 中国科学(地球科学), 2002, 32(12):1041-1050.
[8] Zhang R H, Sun X M, Zhu Z L, et al. Remote sensing information model of surface evaporation based on differential thermal inertia and its verification in Shapotou area of Gansu Province[J]. Science in China, 2002, 32(12):1041-1050.
[9] 刘绍民, 孙睿, 孙中平, 等. 基于互补相关原理的区域蒸散量估算模型比较[J]. 地理学报, 2004, 59(3):331-340.
[9] Liu S M, Sun R, Sun Z P, et al. Comparison of different complementary relationship models for regional evapotranspiration estimation[J]. Acta Geographica Sinica, 2004, 59(3):331-340.
doi: 10.11821/xb200403002
[10] Mu Q Z, Heinsch A F, 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.
doi: 10.1016/j.rse.2007.04.015 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425707001903
[11] 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):1781-1800.
doi: 10.1016/j.rse.2011.02.019 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425711000691
[12] 范德芹, 赵学胜, 朱文泉, 等. 植物物候遥感监测精度影响因素研究综述[J]. 地理科学进展, 2016, 35(3):304-319.
doi: 10.18306/dlkxjz.2016.03.005
[12] Fan D Q, Zhao X S, Zhu W Q, et al. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data[J]. Progress in Geography, 2016, 35(3):304-319.
doi: 10.18306/dlkxjz.2016.03.005
[13] Klaes K D, Schmetz J. The EUMETSAT Polar System:Status and first results[J]. Atmospheric and Environmental Remote Sensing Data Processing and Utilization III:Readiness for GEOSS, 2007, 6684:84-90.
[14] Chen Y, Xia J Z, Liang S L, et al. Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China[J]. Remote Sensing of Environment, 2014, 140:279-293.
doi: 10.1016/j.rse.2013.08.045 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425713003040
[15] Hu G C, Jia L, Menenti M. Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe for 2011[J]. Remote Sensing of Environment, 2015, 156:510-526.
doi: 10.1016/j.rse.2014.10.017 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425714004271
[16] Kumar S V, Peters-Lidard C D, Tian Y, et al. Land information system:An interoperable framework for high resolution land surface modeling[J]. Environmental Modelling and Software, 2006, 21(10):1402-1415.
doi: 10.1016/j.envsoft.2005.07.004 url: https://linkinghub.elsevier.com/retrieve/pii/S1364815205001283
[17] Anderson M C, Norman J M, Diak G R, et al. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing[J]. Remote Sensing of Environment, 1997, 60(2):195-216.
doi: 10.1016/S0034-4257(96)00215-5 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425796002155
[18] Cleugh H A, Leuning R, Mu Q, et al. Regional evaporation estimates from flux tower and MODIS satellite data[J]. Remote Sensing of Environment, 2007, 106(3):285-304.
doi: 10.1016/j.rse.2006.07.007 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425706002677
[19] Wang K C, Liang S L. An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation,vegetation index,temperature,and soil moisture[C]// 2008 IEEE International Geoscience and Remote Sensing Symposium.IEEE, 2008:III-875-878.
[20] Martin J, Markus R, Philippe C, et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply[J]. Nature, 2010, 467(7318):951-954.
doi: 10.1038/nature09396
[21] Yao Y J, Liang S L, Cheng J, et al. MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm[J]. Agricultural and Forest Meteorology, 2013,171-172:187-202.
[22] Kim H W, Hwang K, Mu Q Z, et al. Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia[J]. KSCE Journal of Civil Engineering, 2012, 16(2):229-238.
doi: 10.1007/s12205-012-0006-1 url: http://link.springer.com/10.1007/s12205-012-0006-1
[23] Yao Y J, Liang S L, Li X L, et al. Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method[J]. Journal of Hydrology, 2017, 553:508-526.
doi: 10.1016/j.jhydrol.2017.08.013 url: https://linkinghub.elsevier.com/retrieve/pii/S0022169417305395
[24] Zhou H M, Liang S L, He Tao, et al. Evaluating the spatial representativeness of the MODerate resolution image spectroradiometer albedo product (MCD43) at AmeriFlux sites[J]. Remote Sensing, 2019, 11(5):547.
doi: 10.3390/rs11050547 url: https://www.mdpi.com/2072-4292/11/5/547
[25] Zhang Z J, Zhao L, Lin A W. Evaluating the performance of Sentinel-3A OLCI land products for gross primary productivity estimation using AmeriFlux data[J]. Remote Sensing, 2020, 12(12):1927.
doi: 10.3390/rs12121927 url: https://www.mdpi.com/2072-4292/12/12/1927
[26] Gupta H V, Kling H, Yilmaz K K, et al. Decomposition of the mean squared error and NSE performance criteria:Implications for improving hydrological modelling[J]. Journal of Hydrology, 2009, 377(1):80-91.
doi: 10.1016/j.jhydrol.2009.08.003 url: https://linkinghub.elsevier.com/retrieve/pii/S0022169409004843
[1] Kun LU, Qingyan MENG, Yunxiao SUN, Zhenhui SUN, Linlin ZHANG. Estimating leaf area index of wheat at the booting stage using GF-2 data: A case study of Langfang City,Hebei Province[J]. Remote Sensing for Land & Resources, 2018, 30(1): 196-202.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech