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
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.
范佳慧, 姚云军, 杨军明, 于瑞阳, 刘露, 张学艺, 谢紫菁, 宁静. 基于Ameriflux通量观测数据的Hi-GLASS潜热通量产品验证[J]. 自然资源遥感, 2024, 36(1): 146-153.
FAN Jiahui, YAO Yunjun, YANG Junming, YU Ruiyang, LIU Lu, ZHANG Xueyi, XIE Zijing, NING Jing. Validation of Hi-GLASS products for latent heat flux based on Ameriflux observation data. Remote Sensing for Natural Resources, 2024, 36(1): 146-153.
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
[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
[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
[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
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.
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
[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
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
[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
[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
[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
[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
[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
[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
[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
[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
[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
2024, Vol. 36 
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