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郑超磊，贾立，胡光成.2023.高分一号卫星遥感数据驱动ETMonitor模型估算16 m分辨率蒸散发及验证.遥感学报，27（3）： 758-768
Zheng C L，Jia L and Hu G C. 2023. Evapotranspiration Estimation at 16 m resolution in China based on GF-1 Satellite Remote Sensing Datasets. National Remote Sensing Bulletin， 27（3）：758-768
蒸散发（Evapotranspiration，简称ET）是研究地表水循环过程变化和开展田间水分管理的核心变量之一。遥感是获取大范围动态蒸散发的一个主要手段，但现有蒸散发产品分辨率主要以中低分辨率（1—25 km）为主，难以满足田间水分管理的应用需求。国产高分一号宽幅相机具有高时空分辨率特点，空间分辨率为16 m，能够为高时空分辨率蒸散发产品生产提供数据支撑。本文利用国产高分一号卫星遥感数据反演的地表参数（包括叶面积指数、植被覆盖度和反照率等）作为驱动，评估了采用ETMonitor蒸散发遥感模型获取高分辨率蒸散发的能力和精度。研究中收集黑河流域、闪电河流域、河北怀来、西藏纳木错等16个站点的涡动相关仪地面观测数据开展蒸散发估算的验证，下垫面类型主要涵盖农作物、草地、森林和裸地等。验证结果表明：高分一号卫星遥感估算日蒸散发均方根误差为0.85 mm d
-1
，相关系数为0.79，偏差为0.16 mm d
-1
，估算精度较高。与中低空间分辨率（例如1 km）蒸散发数据相比，基于高分一号卫星遥感估算的16 m分辨率蒸散发在地表异质性较强的区域更具优势，能较好地反映田块尺度的蒸散发空间分布差异。整体来说，基于国产高分卫星遥感结合ETMonitor模型能够获取精度较高的高时空分辨率蒸散发产品，有望满足日益精细化的农业水资源利用及管理的应用需求。
Evapotranspiration (ET) is one of the core variables for studying the surface water cycle and management of the field-scale water resources. Satellite remote sensing are widely adopted to obtain the variation of evapotranspiration at large spatial scale
but the resolution of existing ET products is mainly limited at low or medium resolution (1—25 km)
which cannot satisfy the field-scale water management application. The Chinese GF-1 Wide Field of View (WFV) camera has the characteristics of high spatial and temporal resolution
with a spatial resolution of 16m
and it can support to generate ET products with high spatial and temporal resolution
which has not yet been well presented. The objective of this study is to present the capacity of using the remote sensing data from the GF-1 satellite as the driving force to produce high resolution (16 m) ET. The ETMonitor model was adopted to estimate ET at 16 m resolution in this study. ETMonitor is a combined model with multi-process parameterizations
and it has been proven to be able to generate accurate regional and global ET estimation at relative coarse resolution (e.g.
1 km) mainly using the biophysical and hydrological parameters/variables retrieved from satellite observations. During the ET estimation procedure
the adopted GF-1 remote sensing datasets include the Leaf Area Index (LAI)
Fraction of Vegetation Cover (FVC)
Albedo
and NDVI datasets
which are retrieved from previous studies. Ground observation data from 16 sites in China was collected to validate the estimated ET
including 6 grassland sites
4 cropland sites
1 mixed forest site
2 shrubland sites
and 3 desert or Gobi sites. The validation results show that the overall Root Mean Square Error (RMSE) of estimated daily evapotranspiration based on GF-1 satellite remote sensing datasets is 0.85 mm d
-1
the correlation coefficient (R) is 0.79
and the Bias is 0.16 mm d
-1
which can demonstrate the high accuracy of estimated ET. The GF-1 based ET at 16 m resolution also presented better performance in terms of spatial variation of ET comparing with the low-resolution (e.g.
1 km) ET
especially in the regions with high surface heterogeneity. These highlight the ability of Chinese GF-1 satellite remote sensing dataset could produce accurate ET at high spatial variation
and it has potential to meet the application of field-scale agricultural water resources management
irrigation management
ecological environment monitoring and government decision-making in China. However
due to the impact of the revisit cycle of the GF-1 satellite and the impact of clouds
there are some gaps or missing values in GF-1 based LAI
FVC or Albedo
and these further cause gaps in the GF-1 ET data. In order to improve the availability of high-resolution ET products
it is necessary to produce spatially and temporally continuous high-resolution ET products
which will be the focus of follow-up research.
蒸散发ETMonitor遥感高分一号真实性检验16 m分辨率中国区域
evapotranspirationETMonitorremote sensingGF-1validation16 m resolutionChina
Allen R G, Morton C, Kamble B, Kilic A, Huntington J, Thau D, Gorelick N, Erickson T, Moore R, Trezza R, Ratcliffe I and Clarence R. 2015. EEFlux: a landsat-based evapotranspiration mapping tool on the Google earth engine//Proceedings of 2015 ASABE/IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation-A Tribute to the Career of Terry Howell, Sr. Conference Proceedings. St. Joseph: American Society of Agricultural and Biological Engineer [DOI: 10.13031/irrig.20152143511http://dx.doi.org/10.13031/irrig.20152143511]
Bastiaanssen W G M, Menenti M, Feddes R A and Holtslag A A M. 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212-213: 198-212 [DOI: 10.1016/S0022-1694(98)00253-4http://dx.doi.org/10.1016/S0022-1694(98)00253-4]
Bennour A, Jia L, Menenti M, Zheng C L, Zeng Y L, Barnieh B A and Jiang M. 2022. Calibration and validation of SWAT model by using hydrological remote sensing observables in the Lake Chad Basin. Remote Sensing, 14(6): 1511 [DOI: 10.3390/rs14061511http://dx.doi.org/10.3390/rs14061511]
Bisht G, Venturini V, Islam S and Jiang L. 2005. Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear sky days. Remote Sensing of Environment, 97(1): 52-67 [DOI: 10.1016/j.rse.2005.03.014http://dx.doi.org/10.1016/j.rse.2005.03.014]
Chen J M and Liu J. 2020. Evolution of evapotranspiration models using thermal and shortwave remote sensing data. Remote Sensing of Environment, 237: 111594 [DOI: 10.1016/j.rse.2019.111594http://dx.doi.org/10.1016/j.rse.2019.111594]
Chen Q T, Jia L, Menenti M, Hutjes R, Hu G C, Zheng C L and Wang K. 2019. A numerical analysis of aggregation error in evapotranspiration estimates due to heterogeneity of soil moisture and leaf area index. Agricultural and Forest Meteorology, 269-270: 335-350 [DOI: 10.1016/j.agrformet.2019.02.017http://dx.doi.org/10.1016/j.agrformet.2019.02.017]
Chen X L, Su Z B, Ma Y M, Trigo I and Gentine P. 2021. Remote sensing of global daily evapotranspiration based on a surface energy balance method and reanalysis data. Journal of Geophysical Research: Atmospheres, 126(16): e2020JD032873 [DOI: 10.1029/2020JD032873http://dx.doi.org/10.1029/2020JD032873]
Du D D, Zheng C L, Jia L, Chen Q T, Jiang M, Hu G C and Lu J. 2022. Estimation of global cropland gross primary production from satellite observations by integrating water availability variable in light-use-efficiency model. Remote Sensing, 14(7): 1722 [DOI: 10.3390/rs14071722http://dx.doi.org/10.3390/rs14071722]
Famiglietti C A, Fisher J B, Halverson G and Borbas E E. 2018. Global validation of MODIS near-surface air and dew point temperatures. Geophysical Research Letters, 45(15): 7772-7780 [DOI: 10.1029/2018GL077813http://dx.doi.org/10.1029/2018GL077813]
Fisher J B, Melton F, Middleton E, Hain C, Anderson M, Allen R, McCabe M F, Hook S, Baldocchi D, Townsend P A, Kilic A, Tu K, Miralles D D, Perret J, Lagouarde J P, Waliser D, Purdy A J, French A, Schimel D, Famiglietti J S, Stephens G and Wood E F. 2017. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resources Research, 53(4): 2618-2626 [DOI: 10.1002/2016WR020175http://dx.doi.org/10.1002/2016WR020175]
Guzinski R, Nieto H, Sandholt I and Karamitilios G. 2020. Modelling high-resolution actual evapotranspiration through sentinel-2 and sentinel-3 data fusion. Remote Sensing, 12(9): 1433 [DOI: 10.3390/rs12091433http://dx.doi.org/10.3390/rs12091433]
Hou A Y, Kakar R K, Neeck S, Azarbarzin A A, Kummerow C D, Kojima M, Oki R, Nakamura K and Iguchi T. 2014. The global precipitation measurement mission. Bulletin of the American Meteorological Society, 95(5): 701-722 [DOI: 10.1175/BAMS-D-13-00164.1http://dx.doi.org/10.1175/BAMS-D-13-00164.1]
Hu G C and Jia L. 2015. Monitoring of evapotranspiration in a semi-arid inland river basin by combining microwave and optical remote sensing observations. Remote Sensing, 7(3): 3056-3087 [DOI: 10.3390/rs70303056http://dx.doi.org/10.3390/rs70303056]
Jarvis P G. 1976. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philosophical Transactions of the Royal Society B: Biological Sciences, 273(927): 593-610 [DOI: 10.1098/rstb.1976.0035http://dx.doi.org/10.1098/rstb.1976.0035]
Jia L. 2004. Modeling Heat Exchanges at the Land-Atmosphere Interface Using Multi-Angular Thermal Infrared Measurements. Wageningen: Wageningen University: 49-68
Jia L, Xi G, Liu S, Huang C, Yan Y and Liu G. 2009. Regional estimation of daily to annual regional evapotranspiration with MODIS data in the Yellow River Delta wetland. Hydrology and Earth System Sciences, 13(10): 1775-1787 [DOI: 10.5194/hess-13-1775-2009http://dx.doi.org/10.5194/hess-13-1775-2009]
Jia L, Zheng C, Hu G C and Menenti M. 2018. 4.03-Evapotranspiration. Comprehensive Remote Sensing, 4: 25-50 [DOI: 10.1016/B978-0-12-409548-9.10353-7http://dx.doi.org/10.1016/B978-0-12-409548-9.10353-7]
Jia L, Hu G C, Chen Q T and Zheng C L. 2021. Crop water productivity assessment of a representative irrigation district in Morocco//Guo H D, ed. Big Earth Data in Support of the Sustainable Development Goals. Beijing: Science Press
贾立, 胡光成, 陈琪婷, 郑超磊. 2021. 摩洛哥作物水分生产力评估//郭华东. 地球大数据支撑可持续发展目标报告(2020): “一带一路”篇. 北京: 科学出版社
Jiang C Y, Guan K Y, Pan M, Ryu Y, Peng B and Wang S B. 2020. BESS-STAIR: a framework to estimate daily, 30 m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt. Hydrology and Earth System Sciences, 24(3): 1251-1273 [DOI: 10.5194/hess-24-1251-2020http://dx.doi.org/10.5194/hess-24-1251-2020]
Jung M, Koirala S, Weber U, Ichii K, Gans F, Camps-Valls G, Papale D, Schwalm C, Tramontana G and Reichstein M. 2019. The FLUXCOM ensemble of global land-atmosphere energy fluxes. Scientific Data, 6(1): 74 [DOI: 10.1038/s41597-019-0076-8http://dx.doi.org/10.1038/s41597-019-0076-8]
King M D, Menzel W P, Kaufman Y J, Tanre D, Gao B C, Platnick S, Ackerman S A, Remer L A, Pincus R and Hubanks P A. 2003. Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 442-458 [DOI: 10.1109/TGRS.2002.808226http://dx.doi.org/10.1109/TGRS.2002.808226]
Kljun N, Calanca P, Rotach M W and Schmid H P. 2015. A simple two-dimensional parameterisation for flux footprint prediction (FFP). Geoscientific Model Development, 8(11): 3695-3713 [DOI: 10.5194/gmd-8-3695-2015http://dx.doi.org/10.5194/gmd-8-3695-2015]
Koppa A, Rains D, Hulsman P, Poyatos R and Miralles D G. 2022. A deep learning-based hybrid model of global terrestrial evaporation. Nature Communications, 13(1): 1912 [DOI: 10.1038/s41467-022-29543-7http://dx.doi.org/10.1038/s41467-022-29543-7]
Letu H, Shi J C, Li M, Wang T X, Shang H Z, Lei Y H, Ji D B, Wen J G, Yang K and Chen L F. 2020. A review of the estimation of downward surface shortwave radiation based on satellite data: methods, progress and problems. Science China Earth Sciences, 63(6): 774-789 [DOI: 10.1007/s11430-019-9589-0http://dx.doi.org/10.1007/s11430-019-9589-0]
Li Z L, Tang B H, Wu H, Ren H Z, Yan G J, Wan Z M, Trigo I F, Sobrino J A. 2013. Satellite-derived land surface temperature: current status and perspectives Remote Sensing of Environment, 131: 14-37 [DOI: 10.1016/j.rse.2012.12.008http://dx.doi.org/10.1016/j.rse.2012.12.008]
Liu Q H, Wen J G, Zhou X, Zhao J, Li Z Y, Li X, Ma M G, Wang W Z, Liao X H, Liu S M, Fan W J, Xiao Q, Zhong B, Li J, Xin X Z, Li L, Jia L, Gao Z H, Jin J D, Liang S, Xin J, Liao C J and Wu Y R. 2023. Technique system of remote sensing product generation and validation of GF common products. National Remote Sensing Bulletin， 27(3): 544-562
柳钦火, 闻建光, 周翔, 赵坚, 李增元, 李新, 马明国, 王维真, 廖小罕, 刘绍民, 范闻捷, 肖青, 仲波, 李静, 辛晓洲, 李丽, 贾立, 高志海, 金家栋, 梁师, 邢进, 廖楚江, 吴一戎. 2023. 高分遥感共性产品生成和真实性检验技术体系. 遥感学报, 27(3): 544-562 [DOI： 10.11834/jrs.20235022http://dx.doi.org/10.11834/jrs.20235022]
Liu S M, Xu Z W, Wang W Z, Jia Z Z, Zhu M J, Bai J and Wang J M. 2011. A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem. Hydrology and Earth System Sciences, 15(4): 1291-1306 [DOI: 10.5194/hess-15-1291-2011http://dx.doi.org/10.5194/hess-15-1291-2011]
Liu S M, Li X, Xu Z W, Che T, Xiao Q, Ma M G, Liu Q H, Jin R, Guo J W, Wang L X, Wang W Z, Qi Y, Li H Y, Xu T R, Ran Y H, Hu X L, Shi S J, Zhu Z L, Tan J L, Zhang Y and Ren Z G. 2018. The Heihe integrated observatory network: a basin-scale land surface processes observatory in China. Vadose Zone Journal, 17(1): 1-21 [DOI: 10.2136/vzj2018.04.0072http://dx.doi.org/10.2136/vzj2018.04.0072]
Ma Y F, Liu S M, Song L S, Xu Z W, Liu Y L, Xu T R and Zhu Z L. 2018. Estimation of daily evapotranspiration and irrigation water efficiency at a Landsat-like scale for an arid irrigation area using multi-source remote sensing data. Remote Sensing of Environment, 216: 715-734 [DOI: 10.1016/j.rse.2018.07.019http://dx.doi.org/10.1016/j.rse.2018.07.019]
Miralles D G, Holmes T R H, De Jeu R A M, Gash J H, Meesters A G C A and Dolman A J. 2011. Global land-surface evaporation estimated from satellite-based observations. Hydrology and Earth System Sciences, 15(2): 453-469 [DOI: 10.5194/hess-15-453-2011http://dx.doi.org/10.5194/hess-15-453-2011]
Mo X G, Liu S X, Lin Z H and Zhao W M. 2004. Simulating temporal and spatial variation of evapotranspiration over the Lushi basin. Journal of Hydrology, 285(1/4): 125-142 [DOI: 10.1016/j.jhydrol.2003.08.013http://dx.doi.org/10.1016/j.jhydrol.2003.08.013]
Mu Q Z, Zhao M S and Running S W. 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8): 1781-1800 [DOI: 10.1016/j.rse.2011.02.019http://dx.doi.org/10.1016/j.rse.2011.02.019]
Shuttleworth W J and Wallace J S. 1985. Evaporation from sparse crops‐an energy combination theory. Quarterly Journal of the Royal Meteorological Society, 111(469): 839-855 [DOI: 10.1002/qj.49711146910http://dx.doi.org/10.1002/qj.49711146910]
Stewart J B. 1988. Modelling surface conductance of pine forest. Agricultural and Forest Meteorology, 43(1): 19-35 [DOI: 10.1016/0168-1923(88)90003-2http://dx.doi.org/10.1016/0168-1923(88)90003-2]
Su Z. 2002. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences, 6(1): 85-99 [DOI: 10.5194/hess-6-85-2002http://dx.doi.org/10.5194/hess-6-85-2002]
Susskind J, Barnet C D and Blaisdell J M. 2003. Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 390-409 [DOI: 10.1109/TGRS.2002.808236http://dx.doi.org/10.1109/TGRS.2002.808236]
Tang R L, Li Z L and Tang B H. 2010. An application of the Ts-VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: implementation and validation. Remote Sensing of Environment, 114(3): 540-551 [DOI: 10.1016/j.rse.2009.10.012http://dx.doi.org/10.1016/j.rse.2009.10.012]
Wen J G, Xiao Q, Zhong S Y, Tang Y, Chen X, Wei Q F, Wu X D, Lin X W, Ouyang X Y, You D Q and Liu Q H. 2023. Technology system for product validation and algorithm test of GF common products and an application example. Journal of Remote Sensing, 27(3): 780-789
闻建光, 肖青, 钟守熠, 唐勇, 陈曦, 魏秋方, 吴小丹, 林兴稳, 欧阳晓莹, 游冬琴, 柳钦火. 2023. 高分遥感共性产品算法测评与真实性检验技术体系及应用实例. 遥感学报, 27(3): 780-789 [DOI: 10.11834/jrs.20221716http://dx.doi.org/10.11834/jrs.20221716]
Xu Z W, Liu S M, Gong L J, Wang J M and Li X W. 2008. A study on the data processing and quality assessment of the eddy covariance system. Advances in Earth Science, 23(4): 357-370
徐自为, 刘绍民, 宫丽娟, 王介民, 李小文. 2008. 涡动相关仪观测数据的处理与质量评价研究. 地球科学进展, 23(4): 357-370 [DOI: 10.3321/j.issn:1001-8166.2008.04.005http://dx.doi.org/10.3321/j.issn:1001-8166.2008.04.005]
You D Q, Wen J G, Tang Y, Liu Q, Zhong S Y, Han Y, Gong B C, Zhong B, Wu S L and Liu Q H. 2023. The GaoFen land surface albedo product based on the high-spatial-and-temporal-resolution BRDF priori-knowledge and its preliminary validation. Journal of Remote Sensing, 27(3): 738-747
游冬琴, 闻建光, 唐勇, 刘强, 钟守熠, 韩源, 宫宝昌, 仲波, 吴善龙, 柳钦火. 2023. 基于高时空分辨率BRDF 先验知识的高分卫星地表反照率产品及其初步验证. 遥感学报, 27(3): 738-747 [DOI：10.11834/jrs.20235018http://dx.doi.org/10.11834/jrs.20235018]
Zhang H, Li J, Zhang Z X, Wu S L, Zhong B and Liu Q H. 2022. A dataset of 16m/10-day leaf area indices of MuSyQ GF-series (2018-2020, China, Version 01). China Scientific Data, 7(1): 211-220
张虎, 李静, 张召星, 吴善龙, 仲波, 柳钦火. 2022. MuSyQ高分16米分辨率10天合成的叶面积指数产品(2018-2020年中国01版). 中国科学数据, 7(1): 211-220 [DOI: 10.11922/csdata.2021.0029.zhhttp://dx.doi.org/10.11922/csdata.2021.0029.zh]
Zhang X, Liu L Y, Chen X D, Gao Y, Xie S and Mi J. 2021. GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth System Science Data, 13(6): 2753-2776 [DOI: 10.5194/essd-13-2753-2021http://dx.doi.org/10.5194/essd-13-2753-2021]
Zhang Y Q, Kong D D, Gan R, Chiew F H S, McVicar T R, Zhang Q and Yang Y T. 2019. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017. Remote Sensing of Environment, 222: 165-182 [DOI: 10.1016/j.rse.2018.12.031http://dx.doi.org/10.1016/j.rse.2018.12.031]
Zhao J, Li J, Zhang Z X, Wu S L, Zhong B and Liu Q H. 2022. A dataset of 16 m/10-day fractional vegetation cover of MuSyQ GF-series (2018-2020, China, Version 01). China Scientific Data, 7(1): 1-10
赵静, 李静, 张召星, 吴善龙, 仲波, 柳钦火. 2022. MuSyQ高分16米空间分辨率10天合成的植被覆盖度产品(2018-2020年中国01版). 中国科学数据, 7(1): 1-10 [DOI: 10.11922/11-6035.csd.2021.0037.zhhttp://dx.doi.org/10.11922/11-6035.csd.2021.0037.zh]
Zhao T J, Shi J C, Xu H X, Sun Y L, Chen D Q, Cui Q, Jia L, Huang S, Niu S D, Li X W, Yan G J, Chen L F, Liu Q H, Zhao K, Zheng X M, Zhao L M, Zheng C L, Ji D B, Xiong C, Wang T X, Li R, Pan J M, Wen J G, Mu X H, Yu C, Zheng Y M, Jiang L M, Chai L N, Lu H, Yao P P, Ma J W, Lyu H S, Wu J J, Zhao W, Yang N, Guo P, Li Y X, Hu L, Geng D Y, Zhang Z Q, Hu J F and Du A P. 2021. Comprehensive remote sensing experiment of water cycle and energy balance in the Shandian river basin. National Remote Sensing Bulletin, 25(4): 871-887
赵天杰, 施建成, 徐红新, 孙彦龙, 陈德清, 崔倩, 贾立, 黄硕, 牛升达, 李秀伟, 阎广建, 陈良富, 柳钦火, 赵凯, 郑兴明, 赵利民, 郑超磊, 姬大彬, 熊川, 王天星, 李睿, 潘金梅, 闻建光, 穆西晗, 余超, 郑姚闽, 蒋玲梅, 柴琳娜, 卢麾, 姚盼盼, 马建威, 吕海深, 武建军, 赵伟, 杨娜, 郭鹏, 李玉霞, 胡路, 耿德源, 张子谦, 胡建峰, 杜爱萍. 2021. 闪电河流域水循环和能量平衡遥感综合试验. 遥感学报, 25(4): 871-887 [DOI: 10.11834/jrs.20219401http://dx.doi.org/10.11834/jrs.20219401]
Zheng C L, Jia L, Hu G C and Lu J. 2019. Earth observations-based evapotranspiration in northeastern Thailand. Remote Sensing, 11(2): 138 [DOI: 10.3390/rs11020138http://dx.doi.org/10.3390/rs11020138]
Zheng C L, and Jia L. 2020. Global canopy rainfall interception loss derived from satellite earth observations. Ecohydrology, 13(2): e2186 [DOI: 10.1002/eco.2186http://dx.doi.org/10.1002/eco.2186]
Zheng C L, Hu G C, Chen Q T and Jia L. 2021. Impact of remote sensing soil moisture on the evapotranspiration estimation. National Remote Sensing Bulletin, 25(4): 990-999
郑超磊, 胡光成, 陈琪婷, 贾立. 2021. 遥感土壤水分对蒸散发估算的影响. 遥感学报, 25(4): 990-999 [DOI: 10.11834/jrs.20210038http://dx.doi.org/10.11834/jrs.20210038]
Zheng C L, Jia L and Hu G C. 2022. Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations. Journal of Hydrology, 613: 128444 [DOI: 10.1016/j.jhydrol.2022.128444http://dx.doi.org/10.1016/j.jhydrol.2022.128444]
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