浏览全部资源
扫码关注微信
Published： 07 August 2021 ，
扫 描 看 全 文
刘萌，唐荣林，李召良，高懋芳，姚云军.2021.数据驱动的蒸散发遥感反演方法及产品研究进展.遥感学报，25（8）： 1517-1537
Liu M，Tang R L，Li Z L，Gao M F and Yao Y J. 2021. Progress of data-driven remotely sensed retrieval methods and products on land surface evapotranspiration. National Remote Sensing Bulletin， 25（8）：1517-1537
蒸散发是水圈、大气圈和生物圈中水分循环和能量交换的纽带。在全球尺度上，蒸散发约占陆地降水总量的60%；作为其能量表达形式，潜热通量约占地表净辐射的80%。随着通量观测技术的发展，全球长期持续的观测数据得以获取和共享，近年来基于数据驱动的蒸散发遥感反演方法取得了较好的研究进展。本文针对数据驱动的蒸散发遥感反演方法和产品，从经验回归、机器学习和数据融合3个方面展开，对现有的研究进展进行了梳理、归纳和总结，并从驱动数据、反演方法、已有产品等方面指出目前仍存在的问题和不足。未来仍需开展数据驱动的高时空分辨率的蒸散发遥感反演方法的研究，有效考虑地表温度和土壤水分等可以指示地表蒸散发短期变化的重要信息，同时加强基于过程驱动的物理模型与数据驱动的模型的结合，使两类模型能互为补充、各自发挥所长，共同推动蒸散发遥感反演研究水平的进步。
Evapotranspiration (ET) links the water cycle and energy exchange in hydrosphere
atmosphere
and biosphere. From a global perspective
ET accounts for approximately 60% of the total land precipitation
and the latent heat accompanying ET accounts for approximately 80% of the total surface net radiation energy. With the development of eddy covariance technology
global long-term and continuous observed meteorological and flux data are publicly available online. In last decade
data-driven remotely sensed ET retrieval methods have achieved rapid development. In terms of data-driven remotely sensed retrieval ET
this paper reviews and summarizes the existing researches on empirical regression methods
machine learning methods
data fusion methods and their corresponding products
then points out the existing problems and deficiencies on the driven data
retrieval methods
and available products. To be specific
these problems include: (1) There are few data-driven remotely sensed ET products with high precision and high spatiotemporal resolution; (2) The spatial scale mismatch between satellite pixel and in situ measurements makes the data-driven remotely sensed ET estimates challenging; (3) The lack of physical mechanisms for the data-driven remotely sensed retrieval ET methods and the insufficient regional representativeness for observed data from hundreds of sites
the spatial application of the ET model is limited; (4) Several important driving factors of ET
such as land surface temperature and soil moisture
were not sufficiently considered in previous studies; (5) The energy balance at flux measurement sites that based on eddy covariance method is not closed with about 0.8 unclosed rate globally
whether carry out energy balance closure correction before modeling is still a controversy; (6) The partitioning between soil evaporation and vegetation transpiration is of great significance
but the data-driven remotely sensed models that could estimate soil evaporation and vegetation transpiration respectively were not well studied. In the era of big data
as a double-edged sword
data-driven approaches are not only opportunities but also challenges
and several suggestions for future studies are proposed at the end. To begin with
the data-driven remotely sensed retrieval ET methods with high spatiotemporal resolution should be proposed. The observed source area should be introduced into the model constructing to solve the mismatch between satellite pixel and the measurements so as to improve the estimated ET accuracy. In addition
some important information
such as land surface temperature and soil moisture
which has an important effect on ET process should be taken into consideration effectively. Although vegetation index could indicate the long-term change of ET
land surface temperature could better indicate its short-term change. At the same time
soil moisture deficit would produce water stress on ET. Effective consideration of land surface temperature and soil moisture may improve the estimation accuracy of ET. Last but not least
it's important to emphasize that data-driven empirical approaches will not replace process-driven physical models
but strongly supplement and enrich the ET estimation methods. The combination of process-driven physical models and data-driven empirical approaches should be strengthened in order to obtain more reliable and accurate ET estimation by remote sensing. One suggestion is that
in future studies
data-driven approaches should be used to estimate important variables that closely related to ET but unavailable directly from remote sensing satellite at present
then physical models could be used for ET estimation. So as to the two kinds of models can fully play their roles respectively
jointly promote the research level of remotely sensed retrieval ET.
蒸散发数据驱动地表温度经验方法机器学习数据融合遥感产品
land surface evapotranspirationdata-drivenland surface temperatureempirical methodmachine learningdata fusionremote sensing products
Abdullah S S, Malek M A, Abdullah N S, Kisi O and Yap K S. 2015. Extreme Learning Machines: a new approach for prediction of reference evapotranspiration. Journal of Hydrology, 527: 184-195 [DOI: 10.1016/j.jhydrol.2015.04.073http://dx.doi.org/10.1016/j.jhydrol.2015.04.073]
Allen R G, Tasumi M and Trezza R. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model. Journal of Irrigation and Drainage Engineering, 133(4): 380-394 [DOI: 10.1061/(ASCE)0733-9437(2007)133:4(380http://dx.doi.org/10.1061/(ASCE)0733-9437(2007)133:4(380)]
Anderson M C, Kustas W P, Norman J M, Hain C R, Mecikalski J R, Schultz L, González-Dugo M P, Cammalleri C, d'Urso G, Pimstein A and Gao F. 2011. Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrology and Earth System Sciences, 15(1): 223-239 [DOI: 10.5194/hess-15-223-2011http://dx.doi.org/10.5194/hess-15-223-2011]
Bai J, Jia L, Liu S M, Xu Z W, Hu G C, Zhu M J and Song L S. 2015. Characterizing the footprint of eddy covariance system and large aperture scintillometer measurements to validate satellite-based surface fluxes. IEEE Geoscience and Remote Sensing Letters, 12(5): 943-947 [DOI: 10.1109/LGRS.2014.2368580http://dx.doi.org/10.1109/LGRS.2014.2368580]
Bai Y, Zhang S, Bhattarai N, Mallick K, Liu Q, Tang L L, Im J, Guo L and Zhang J H. 2021. On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient. Agricultural and Forest Meteorology, 298-299: 108308 [DOI: 10.1016/j.agrformet.2020.108308http://dx.doi.org/10.1016/j.agrformet.2020.108308]
Baldocchi D. 2008. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Australian Journal of Botany, 56(1): 1-26 [DOI: 10.1071/BT07151http://dx.doi.org/10.1071/BT07151]
Barman R, Jain A K and Liang M L. 2014. Climate-driven uncertainties in modeling terrestrial energy and water fluxes: a site-level to global-scale analysis. Global Change Biology, 20(6): 1885-1900 [DOI: 10.1111/gcb.12473http://dx.doi.org/10.1111/gcb.12473]
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]
Bodesheim P, Jung M, Gans F, Mahecha M D and Reichstein M. 2018. Upscaled diurnal cycles of land-atmosphere fluxes: a new global half-hourly data product. Earth System Science Data, 10(3): 1327-1365 [DOI: 10.5194/essd-10-1327-2018http://dx.doi.org/10.5194/essd-10-1327-2018]
Carlson T N, Capehart W J and Gillies R R. 1995. A new look at the simplified method for remote sensing of daily evapotranspiration. Remote Sensing of Environment, 54(2): 161-167 [DOI: 10.1016/0034-4257(95)00139-Rhttp://dx.doi.org/10.1016/0034-4257(95)00139-R]
Carter C and Liang S L. 2018. Comprehensive evaluation of empirical algorithms for estimating land surface evapotranspiration. Agricultural and Forest Meteorology, 256-257: 334-345 [DOI: 10.1016/j.agrformet.2018.03.027http://dx.doi.org/10.1016/j.agrformet.2018.03.027]
Carter C and Liang S L. 2019. Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing. International Journal of Applied Earth Observation and Geoinformation, 78: 86-92 [DOI: 10.1016/j.jag.2019.01.020http://dx.doi.org/10.1016/j.jag.2019.01.020]
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 Y, Xia J Z, Liang S L, Feng J M, Fisher J B, Li X, Li X L, Liu S G, Ma Z G, Miyata A, Mu Q Z, Sun L, Tang J W, Wang K C, Wen J, Xue Y J, Yu G R, Zha T G, Zhang L, Zhang Q, Zhao T B, Zhao L and Yuan W P. 2014. Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China. Remote Sensing of Environment, 140: 279-293 [DOI: 10.1016/j.rse.2013.08.045http://dx.doi.org/10.1016/j.rse.2013.08.045]
Chen Y, Yuan W P, Xia J Z, Fisher J B, Dong W J, Zhang X T, Liang S L, Ye A Z, Cai W W and Feng J M. 2015. Using Bayesian model averaging to estimate terrestrial evapotranspiration in China. Journal of Hydrology, 528: 537-549 [DOI: 10.1016/j.jhydrol.2015.06.059http://dx.doi.org/10.1016/j.jhydrol.2015.06.059]
Choudhury B J. 1991. Passive microwave remote sensing contribution to hydrological variables. Surveys in Geophysics, 12(1): 63-84 [DOI: 10.1007/BF01903412http://dx.doi.org/10.1007/BF01903412]
Cleugh H A, Leuning R, Mu Q Z and Running S W. 2007. Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment, 106(3): 285-304 [DOI: 10.1016/j.rse.2006.07.007http://dx.doi.org/10.1016/j.rse.2006.07.007]
Crow W T, Kustas W P and Prueger J H. 2008. Monitoring root-zone soil moisture through the assimilation of a thermal remote sensing-based soil moisture proxy into a water balance model. Remote Sensing of Environment, 112(4): 1268-1281 [DOI: 10.1016/j.rse.2006.11.033http://dx.doi.org/10.1016/j.rse.2006.11.033]
Dente L, Satalino G, Mattia F and Rinaldi M. 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote Sensing of Environment, 112(4): 1395-1407 [DOI: 10.1016/j.rse.2007.05.023http://dx.doi.org/10.1016/j.rse.2007.05.023]
Deo R C and Şahin M. 2015. Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia. Atmospheric Research, 161-162: 65-81 [DOI: 10.1016/j.atmosres.2015.03.018http://dx.doi.org/10.1016/j.atmosres.2015.03.018]
Deo R C and Samui P. 2017. Forecasting evaporative loss by least-square support-vector regression and evaluation with genetic programming, Gaussian process, and minimax probability machine regression: case study of Brisbane city. Journal of Hydrologic Engineering, 22(6): 05017003 [DOI: 10.1061/(ASCE)HE.1943-5584.0001506http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0001506]
Dirmeyer P A, Gao X, Zhao M, Guo Z C, Oki T and Hanasaki N. 2006. GSWP-2: multimodel analysis and implications for our perception of the land surface. Bulletin of the American Meteorological Society, 87(10): 1381-1398 [DOI: 10.1175/BAMS-87-10-1381http://dx.doi.org/10.1175/BAMS-87-10-1381]
Draper C S, Reichle R H and Koster R D. 2018. Assessment of MERRA-2 land surface energy flux estimates. Journal of Climate, 31(2): 671-691 [DOI: 10.1175/JCLI-D-17-0121.1http://dx.doi.org/10.1175/JCLI-D-17-0121.1]
Elnashar A, Wang L J, Wu B F, Zhu W W and Zeng H W. 2021. Synthesis of global actual evapotranspiration from 1982 to 2019. Earth System Science Data, 13: 447-480 [DOI: 10.5194/essd-13-447-2021http://dx.doi.org/10.5194/essd-13-447-2021]
Fan J L, Yue W J, Wu L F, Zhang F C, Cai H J, Wang X K, Lu X H and Xiang Y Z. 2018. Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agricultural and Forest Meteorology, 263: 225-241 [DOI: 10.1016/j.agrformet.2018.08.019http://dx.doi.org/10.1016/j.agrformet.2018.08.019]
Fan J L, Zheng J, Wu L F and Zhang F C. 2021. Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models. Agricultural Water Management, 245: 106547 [DOI: 10.1016/j.agwat.2020.106547http://dx.doi.org/10.1016/j.agwat.2020.106547]
Feng F, Chen J Q, Li X L, Yao Y J, Liang S L, Liu M, Zhang N N, Guo Y, Yu J and Sun M M. 2015. Validity of five satellite-based latent heat flux algorithms for semi-arid ecosystems. Remote Sensing, 7(12): 16733-16755 [DOI: 10.3390/rs71215853http://dx.doi.org/10.3390/rs71215853]
Feng F, Li X L, Yao Y J, Liang S L, Chen J Q, Zhao X, Jia K, Pintér K and McCaughey J H. 2016a. An empirical orthogonal function-based algorithm for estimating terrestrial latent heat flux from eddy covariance, meteorological and satellite observations. PLoS One, 11(7): e0160150 [DOI: 10.1371/journal.pone.0160150http://dx.doi.org/10.1371/journal.pone.0160150]
Feng Y, Cui N B, Gong D Z, Zhang Q W and Zhao L. 2017a. Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agricultural Water Management, 193: 163-173 [DOI: 10.1016/j.agwat.2017.08.003http://dx.doi.org/10.1016/j.agwat.2017.08.003]
Feng Y, Cui N B, Zhao L, Hu X T and Gong D Z. 2016b. Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. Journal of Hydrology, 536: 376-383 [DOI: 10.1016/j.jhydrol.2016.02.053http://dx.doi.org/10.1016/j.jhydrol.2016.02.053]
Feng Y, Peng Y, Cui N B, Gong D Z and Zhang K D. 2017b. Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture, 136: 71-78 [DOI: 10.1016/j.compag.2017.01.027http://dx.doi.org/10.1016/j.compag.2017.01.027]
Fisher J B, Tu K P and Baldocchi D D. 2008. Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment, 112(3): 901-919 [DOI: 10.1016/j.rse.2007.06.025http://dx.doi.org/10.1016/j.rse.2007.06.025]
Good S P, Noone D and Bowen G. 2015. Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science, 349(6244): 175-177 [DOI: 10.1126/science.aaa5931http://dx.doi.org/10.1126/science.aaa5931]
Goyal M K, Bharti B, Quilty J, Adamowski J and Pandey A. 2014. Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Systems with Applications, 41(11): 5267-5276 [DOI: 10.1016/j.eswa.2014.02.047http://dx.doi.org/10.1016/j.eswa.2014.02.047]
Guzinski R and Nieto H. 2019. Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations. Remote Sensing of Environment, 221: 157-172 [DOI: 10.1016/j.rse.2018.11.019http://dx.doi.org/10.1016/j.rse.2018.11.019]
Han X J, Duan S B, Leng P, Wang W and Li Z L. 2017. Estimation of annual daily averaged evapotranspiration across China during 1996-2015 using passive microwave observations//2017 Progress in Electromagnetics Research Symposium - Spring (PIERS). St. Petersburg, Russia: IEEE: 2150-2154 [DOI: 10.1109/PIERS.2017.8262107http://dx.doi.org/10.1109/PIERS.2017.8262107]
Helman D, Givati A and Lensky I M. 2015. Annual evapotranspiration retrieved from satellite vegetation indices for the eastern Mediterranean at 250 m spatial resolution. Atmospheric Chemistry and Physics, 15(21): 12567-12579 [DOI: 10.5194/acp-15-12567-2015http://dx.doi.org/10.5194/acp-15-12567-2015]
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]
Hu G C, Jia L and Menenti M. 2015. Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe for 2011. Remote Sensing of Environment, 156: 510-526 [DOI: 10.1016/j.rse.2014.10.017http://dx.doi.org/10.1016/j.rse.2014.10.017]
Huang C L, Li X, Wang J M and Gu J. 2008. Assimilation of remote sensing data products into common land model for evapotranspiration forecasting//Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. Shanghai: [s.n.]: 234-241
Im J, Park S, Rhee J, Baik J and Choi M. 2016. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environmental Earth Sciences, 75(15): 1120 [DOI: 10.1007/s12665-016-5917-6http://dx.doi.org/10.1007/s12665-016-5917-6]
Jackson R D, Reginato R J and Idso S B. 1977. Wheat canopy temperature: a practical tool for evaluating water requirements. Water Resources Research, 13(3): 651-656 [DOI: 10.1029/WR013i003p00651http://dx.doi.org/10.1029/WR013i003p00651]
Jajarmizadeh M, Lafdani E K, Harun S and Ahmadi A. 2015. Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran. KSCE Journal of Civil Engineering, 19(1): 345-357 [DOI: 10.1007/s12205-014-0060-yhttp://dx.doi.org/10.1007/s12205-014-0060-y]
Jia Z Z, Liu S M, Xu Z W, Chen Y J and Zhu M J. 2012. Validation of remotely sensed evapotranspiration over the Hai River Basin, China. Journal of Geophysical Research: Atmospheres, 117: D13113 [DOI: 10.1029/2011JD017037http://dx.doi.org/10.1029/2011JD017037]
Jiang C Y and Ryu Y. 2016. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS). Remote Sensing of Environment, 186: 528-547 [DOI: 10.1016/j.rse.2016.08.030http://dx.doi.org/10.1016/j.rse.2016.08.030]
Jiang L and Islam S. 1999. A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations. Geophysical Research Letters, 26(17): 2773-2776 [DOI: 10.1029/1999GL006049http://dx.doi.org/10.1029/1999GL006049]
Jiménez C, Prigent C and Aires F. 2009. Toward an estimation of global land surface heat fluxes from multisatellite observations. Journal of Geophysical Research: Atmospheres, 114: D06305 [DOI: 10.1029/2008JD011392http://dx.doi.org/10.1029/2008JD011392]
Jiménez C, Prigent C, Mueller B, Seneviratne S I, McCabe M F, Wood E F, Rossow W B, Balsamo G, Betts A K, Dirmeyer P A, Fisher J B, Jung M, Kanamitsu M, Reichle R H, Reichstein M, Rodell M, Sheffield J, Tu K and Wang K. 2011. Global intercomparison of 12 land surface heat flux estimates. Journal of Geophysical Research: Atmospheres, 116: D02102 [DOI: 10.1029/2010JD014545http://dx.doi.org/10.1029/2010JD014545]
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: 74 [DOI: 10.1038/s41597-019-0076-8http://dx.doi.org/10.1038/s41597-019-0076-8]
Jung M, Reichstein M, Ciais P, Seneviratne S I, Sheffield J, Goulden M L, Bonan G, Cescatti A, Chen J Q, de Jeu R, Dolman A J, Eugster W, Gerten D, Gianelle D, Gobron N, Heinke J, Kimball J, Law B E, Montagnani L, Mu Q Z, Mueller B, Oleson K, Papale D, Richardson A D, Roupsard O, Running S, Tomelleri E, Viovy N, Weber U, Williams C, Wood E, Zaehle S and Zhang K. 2010. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467(7318): 951-954 [DOI: 10.1038/nature09396http://dx.doi.org/10.1038/nature09396]
Jung M, Reichstein M, Margolis H A, Cescatti A, Richardson A D, Arain M A, Arneth A, Bernhofer C, Bonal D, Chen J Q, Gianelle D, Gobron N, Kiely G, Kutsch W, Lasslop G, Law B E, Lindroth A, Merbold L, Montagnani L, Moors E J, Papale D, Sottocornola M, Vaccari F and Williams C. 2011. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. Journal of Geophysical Research: Biogeosciences, 116: G00J07 [DOI: 10.1029/2010JG001566http://dx.doi.org/10.1029/2010JG001566]
Kaheil Y H, Rosero E, Gill M K, McKee M and Bastidas L A. 2008. Downscaling and forecasting of evapotranspiration using a synthetic model of wavelets and support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 46(9): 2692-2707 [DOI: 10.1109/TGRS.2008.919819http://dx.doi.org/10.1109/TGRS.2008.919819]
Kalma J D, McVicar T R and McCabe M F. 2008. Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surveys in Geophysics, 29(4/5): 421-469 [DOI: 10.1007/s10712-008-9037-zhttp://dx.doi.org/10.1007/s10712-008-9037-z]
Ke Y H, Im J, Park S and Gong H L. 2016. Downscaling of MODIS one kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sensing, 8(3): 215 [DOI: 10.3390/rs8030215http://dx.doi.org/10.3390/rs8030215]
Ke Y H, Im J, Park S and Gong H L. 2017. Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 79-93 [DOI: 10.1016/j.isprsjprs.2017.02.006http://dx.doi.org/10.1016/j.isprsjprs.2017.02.006]
Kim H W, Hwang K, Mu Q Z, Lee S O and Choi M. 2012. Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia. KSCE Journal of Civil Engineering, 16(2): 229-238 [DOI: 10.1007/s12205-012-0006-1http://dx.doi.org/10.1007/s12205-012-0006-1]
Kisi O. 2015. Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology, 528: 312-320 [DOI: 10.1016/j.jhydrol.2015.06.052http://dx.doi.org/10.1016/j.jhydrol.2015.06.052]
Kisi O, Sanikhani H, Zounemat-Kermani M and Niazi F. 2015. Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Computers and Electronics in Agriculture, 115: 66-77 [DOI: 10.1016/j.compag.2015.04.015http://dx.doi.org/10.1016/j.compag.2015.04.015]
Kumar M, Raghuwanshi N S, Singh R, Wallender W W and Pruitt W O. 2002. Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering, 128(4): 224-233 [DOI: 10.1061/(ASCE)0733-9437(2002)128:4(224http://dx.doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224)]
Kustas W P and Norman J M. 1996. Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrological Sciences Journal, 41(4): 495-516 [DOI: 10.1080/02626669609491522http://dx.doi.org/10.1080/02626669609491522]
Kustas W P, Schmugge T J, Humes K S, Jackson T J, Parry R, Weltz M A and Moran M S. 1993. Relationships between evaporative fraction and remotely sensed vegetation index and microwave brightness temperature for semiarid rangelands. Journal of Applied Meteorology and Climatology, 32(12): 1781-1790 [DOI: 10.1175/1520-0450(1993)032<1781:RBEFAR>2.0.CO;2http://dx.doi.org/10.1175/1520-0450(1993)032<1781:RBEFAR>2.0.CO;2]
LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature, 521(7553): 436-444 [DOI: 10.1038/nature14539http://dx.doi.org/10.1038/nature14539]
Li J, Xin X Z, Peng Z Q and Li X J. 2021. Remote sensing products of terrestrial evapotranspiration: comparison and outlook. Remote Sensing Technology and Application, 36(1): 103-120
李佳, 辛晓洲, 彭志晴, 李小军. 2021. 地表蒸散发遥感产品比较与分析. 遥感技术与应用, 36(1): 103-120 [DOI: 10.11873/j.issn.1004-0323.2021.1.0103http://dx.doi.org/10.11873/j.issn.1004-0323.2021.1.0103]
Li X, Liu S M, Li H X, Ma Y F, Wang J H, Zhang Y, Xu Z W, Xu T R, Song L S, Yang X F, Lu Z, Wang Z Y and Guo Z X. 2018. Intercomparison of six upscaling evapotranspiration methods: from site to the satellite pixel. Journal of Geophysical Research: Atmospheres, 123(13): 6777-6803 [DOI: 10.1029/2018JD028422http://dx.doi.org/10.1029/2018JD028422]
Li Z L, Tang R L, Wan Z M, Bi Y Y, Zhou C H, Tang B H, Yan G J and Zhang X Y. 2009. A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors, 9(5): 3801-3853 [DOI: 10.3390/s90503801http://dx.doi.org/10.3390/s90503801]
Liang S L, Cheng J, Jia K, Jiang B, Liu Q, Xiao Z Q, Yao Y J, Yuan W P, Zhang X T, Zhao X and Zhou J. 2021. The Global LAnd Surface Satellite (GLASS) product suite. Bulletin of the American Meteorological Society, 102(2): E323-E337 [DOI: 10.1175/BAMS-D-18-0341.1http://dx.doi.org/10.1175/BAMS-D-18-0341.1]
Liang S L, Li X W and Wang J D. 2013. Quantitative Remote Sensing: Concepts and Algorithms. Beijing: Science Publishing Press
梁顺林, 李小文, 王锦地. 2013. 定量遥感: 理念与算法. 北京: 科学出版社
Liu M, Tang R L, Li Z L, Mao H R, Zhou F C and Yan G J. 2018a. Estimation of annual averaged evapotranspiration by using passive microwave observations//2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: IEEE: 791-794 [DOI: 10.1109/IGARSS.2018.8518728http://dx.doi.org/10.1109/IGARSS.2018.8518728]
Liu M, Tang R L, Li Z L and Yan G J. 2019. Integration of two semi-physical models of terrestrial evapotranspiration using the China meteorological forcing dataset. International Journal of Remote Sensing, 40(5/6): 1966-1980 [DOI: 10.1080/01431161.2018.1482026http://dx.doi.org/10.1080/01431161.2018.1482026]
Liu M, Tang R L, Li Z L, Yao Y J and Yan G J. 2018b. Global land surface evapotranspiration estimation from meteorological and satellite data using the support vector machine and semiempirical algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2): 513-521 [DOI: 10.1109/JSTARS.2017.2788462http://dx.doi.org/10.1109/JSTARS.2017.2788462]
Liu S M, Xu Z W, Song L S, Zhao Q Y, Ge Y, Xu T R, Ma Y F, Zhu Z L, Jia Z Z and Zhang F. 2016. Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces. Agricultural and Forest Meteorology, 230-231: 97-113 [DOI: 10.1016/j.agrformet.2016.04.008http://dx.doi.org/10.1016/j.agrformet.2016.04.008]
Long D and Singh V P. 2012. A two-source trapezoid model for evapotranspiration (TTME) from satellite imagery. Remote Sensing of Environment, 121: 370-388 [DOI: 10.1016/j.rse.2012.02.015http://dx.doi.org/10.1016/j.rse.2012.02.015]
Lu X H, Ju Y, Wu L F, Fan J L, Zhang F C and Li Z J. 2018. Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models. Journal of Hydrology, 566: 668-684 [DOI: 10.1016/j.jhydrol.2018.09.055http://dx.doi.org/10.1016/j.jhydrol.2018.09.055]
Mao Y, Wang K C, Liu X M and Liu C M. 2016. Water storage in reservoirs built from 1997 to 2014 significantly altered the calculated evapotranspiration trends over China. Journal of Geophysical Research: Atmospheres, 121(17): 10097-10112 [DOI: 10.1002/2016JD025447http://dx.doi.org/10.1002/2016JD025447]
Martens B, Miralles D G, Lievens H, van der Schalie R, de Jeu R A M, Fernández-Prieto D, Beck H E, Dorigo W A and Verhoest N E C. 2017. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10(5): 1903-1925 [DOI: 10.5194/gmd-10-1903-2017http://dx.doi.org/10.5194/gmd-10-1903-2017]
Mehdizadeh S, Behmanesh J and Khalili K. 2017. Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Computers and Electronics in Agriculture, 139: 103-114 [DOI: 10.1016/j.compag.2017.05.002http://dx.doi.org/10.1016/j.compag.2017.05.002]
Min Q L and Lin B. 2006. Remote sensing of evapotranspiration and carbon uptake at Harvard Forest. Remote Sensing of Environment, 100(3): 379-387 [DOI: 10.1016/j.rse.2005.10.020http://dx.doi.org/10.1016/j.rse.2005.10.020]
Monteith J L. 1965. Evaporation and environment. Symposia of the Society for Experimental Biology, 19: 205-234
Moran M S, Clarke T R, Inoue Y and Vidal A. 1994. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment, 49(3): 246-263 [DOI: 10.1016/0034-4257(94)90020-5http://dx.doi.org/10.1016/0034-4257(94)90020-5]
Mu Q Z, Heinsch F A, Zhao M S and Running S W. 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111(4): 519-536 [DOI: 10.1016/j.rse.2007.04.015http://dx.doi.org/10.1016/j.rse.2007.04.015]
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]
Mueller B, Seneviratne S I, Jimenez C, Corti T, Hirschi M, Balsamo G, Ciais P, Dirmeyer P, Fisher J B, Guo Z, Jung M, Maignan F, McCabe M F, Reichle R, Reichstein M, Rodell M, Sheffield J, Teuling A J, Wang K, Wood E F and Zhang Y. 2011. Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophysical Research Letters, 38: L06402 [DOI: 10.1029/2010GL046230http://dx.doi.org/10.1029/2010GL046230]
Nagler P L, Cleverly J, Glenn E, Lampkin D, Huete A and Wan Z M. 2005a. Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data. Remote Sensing of Environment, 94(1): 17-30 [DOI: 10.1016/j.rse.2004.08.009http://dx.doi.org/10.1016/j.rse.2004.08.009]
Nagler P L, Scott R L, Westenburg C, Cleverly J R, Glenn E P and Huete A R. 2005b. Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers. Remote Sensing of Environment, 97(3): 337-351 [DOI: 10.1016/j.rse.2005.05.011http://dx.doi.org/10.1016/j.rse.2005.05.011]
Norman J M, Kustas W P and Humes K S. 1995. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology, 77(3/4): 263-293 [DOI: 10.1016/0168-1923(95)02265-Yhttp://dx.doi.org/10.1016/0168-1923(95)02265-Y]
Oki T and Kanae S. 2006. Global hydrological cycles and world water resources. Science, 313(5790): 1068-1072 [DOI: 10.1126/science.1128845http://dx.doi.org/10.1126/science.1128845]
Penman H L. 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 193(1032): 120-145 [DOI: 10.1098/rspa.1948.0037http://dx.doi.org/10.1098/rspa.1948.0037]
Priestley C H B and Taylor R J. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100(2): 81-92 [DOI: 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2http://dx.doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2]
Qiu G Y, Li C and Yan C H. 2015. Characteristics of soil evaporation, plant transpiration and water budget of Nitraria dune in the arid Northwest China. Agricultural and Forest Meteorology, 203: 107-117 [DOI: 10.1016/j.agrformet.2015.01.006http://dx.doi.org/10.1016/j.agrformet.2015.01.006]
Raghavendra N S and Deka P C. 2014. Support vector machine applications in the field of hydrology: a review. Applied Soft Computing, 19: 372-386 [DOI: 10.1016/j.asoc.2014.02.002http://dx.doi.org/10.1016/j.asoc.2014.02.002]
Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N and Prabhat. 2019. Deep learning and process understanding for data-driven earth system science. Nature, 566(7743): 195-204 [DOI: 10.1038/s41586-019-0912-1http://dx.doi.org/10.1038/s41586-019-0912-1]
Schlesinger W H and Jasechko S. 2014. Transpiration in the global water cycle. Agricultural and Forest Meteorology, 189-190: 115-117 [DOI: 10.1016/j.agrformet.2014.01.011http://dx.doi.org/10.1016/j.agrformet.2014.01.011]
Seguin B, Baelz S, Monget J M and Petit V. 1982. Utilisation de la thermographie IR pour l'estimation de l'évaporation régionale II. - Résultats obtenus à partir des données de satellite. Agronomie, 2(2): 113-118 [DOI: 10.1051/agro:19820202http://dx.doi.org/10.1051/agro:19820202]
Seguin B, Courault D and Guérif M. 1994. Surface temperature and evapotranspiration: application of local scale methods to regional scales using satellite data. Remote Sensing of Environment, 49(3): 287-295 [DOI: 10.1016/0034-4257(94)90023-Xhttp://dx.doi.org/10.1016/0034-4257(94)90023-X]
Seguin B and Itier B. 1983. Using midday surface temperature to estimate daily evaporation from satellite thermal IR data. International Journal of Remote Sensing, 4(2): 371-383 [DOI:10.1080/01431168308948554http://dx.doi.org/10.1080/01431168308948554]
Shen H F, Jiang M H, Li J, Yuan Q Q, Wei Y C and Zhang L P. 2019. Spatial-spectral fusion by combining deep learning and variational model. IEEE Transactions on Geoscience and Remote Sensing, 57(8): 6169-6181 [DOI: 10.1109/TGRS.2019.2904659http://dx.doi.org/10.1109/TGRS.2019.2904659]
Shiri J. 2018. Improving the performance of the mass transfer-based reference evapotranspiration estimation approaches through a coupled wavelet-random forest methodology. Journal of Hydrology, 561: 737-750 [DOI: 10.1016/j.jhydrol.2018.04.042http://dx.doi.org/10.1016/j.jhydrol.2018.04.042]
Shrestha N K and Shukla S. 2015. Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment. Agricultural and Forest Meteorology, 200: 172-184 [DOI: 10.1016/j.agrformet.2014.09.025http://dx.doi.org/10.1016/j.agrformet.2014.09.025]
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]
Song L S, Kustas W P, Liu S M, Colaizzi P D, Nieto H, Xu Z W, Ma Y F, Li M S, Xu T R, Agam N, Tolk J A and Evett S R. 2016. Applications of a thermal-based two-source energy balance model using Priestley-Taylor approach for surface temperature partitioning under advective conditions. Journal of Hydrology, 540: 574-587 [DOI: 10.1016/j.jhydrol.2016.06.034http://dx.doi.org/10.1016/j.jhydrol.2016.06.034]
Song L S, Liu S M, Xu T R, Xu Z W and Ma Y F. 2017. Soil evaporation and vegetation transpiration: remotely sensed estimation and validation. Journal of Remote Sensing, 21(6): 966-981
宋立生, 刘绍民, 徐同仁, 徐自为, 马燕飞. 2017. 土壤蒸发和植被蒸腾遥感估算与验证. 遥感学报, 21(6): 966-981 [DOI: 10.11834/jrs.20176391http://dx.doi.org/10.11834/jrs.20176391]
Springer A, Kusche J, Hartung K, Ohlwein C and Longuevergne L. 2014. New estimates of variations in water flux and storage over Europe based on regional (Re)analyses and multisensor observations. Journal of Hydrometeorology, 15(6): 2397-2417 [DOI: 10.1175/JHM-D-14-0050.1http://dx.doi.org/10.1175/JHM-D-14-0050.1]
Srivastava P K, Han D W, Ramirez M R and Islam T. 2013. Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resources Management, 27(8): 3127-3144 [DOI: 10.1007/s11269-013-0337-9http://dx.doi.org/10.1007/s11269-013-0337-9]
Stegehuis A I, Teuling A J, Ciais P, Vautard R and Jung M. 2013. Future European temperature change uncertainties reduced by using land heat flux observations. Geophysical Research Letters, 40(10): 2242-2245 [DOI: 10.1002/grl.50404http://dx.doi.org/10.1002/grl.50404]
Su Z. 2002. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrology and Earth System Sciences, 6(1): 85-100 [DOI: 10.5194/hess-6-85-2002http://dx.doi.org/10.5194/hess-6-85-2002]
Swenson S C and Lawrence D M. 2014. Assessing a dry surface layer-based soil resistance parameterization for the Community Land Model using GRACE and FLUXNET-MTE data. Journal of Geophysical Research: Atmospheres, 119(17): 10299-10312 [DOI: 10.1002/2014jd022314http://dx.doi.org/10.1002/2014jd022314]
Tang R L. 2011. Retrieval of Land Surface Evapotranspiration from Remotely Sensed Surface Temperature-Fractional Vegetation Cover Characteristic Space. Beijing: Chinese Academy of Sciences
唐荣林. 2011. 基于地表温度—植被覆盖度特征空间的地表蒸散发遥感反演方法研究. 北京: 中国科学院研究生院
Tang R L and Li Z L. 2017a. An end-member-based two-source approach for estimating land surface evapotranspiration from remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 55(10): 5818-5832 [DOI: 10.1109/TGRS.2017.2715361http://dx.doi.org/10.1109/TGRS.2017.2715361]
Tang R L and Li Z L. 2017b. An improved constant evaporative fraction method for estimating daily evapotranspiration from remotely sensed instantaneous observations. Geophysical Research Letters, 44(5): 2319-2326 [DOI: 10.1002/2017GL072621http://dx.doi.org/10.1002/2017GL072621]
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]
Torres A F, Walker W R and McKee M. 2011. Forecasting daily potential evapotranspiration using machine learning and limited climatic data. Agricultural Water Management, 98(4): 553-562 [DOI: 10.1016/j.agwat.2010.10.012http://dx.doi.org/10.1016/j.agwat.2010.10.012]
Tramontana G, Jung M, Schwalm C R, Ichii K, Camps-Valls G, Ráduly B, Reichstein M, Arain M A, Cescatti A, Kiely G, Merbold L, Serrano-Ortiz P, Sickert S, Wolf S and Papale D. 2016. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences, 13: 4291-4313 [DOI: 10.5194/bg-13-4291-2016http://dx.doi.org/10.5194/bg-13-4291-2016]
Trenberth K E, Fasullo J T and Kiehl J. 2009. Earth's global energy budget. Bulletin of the American Meteorological Society, 90(3): 311-324 [DOI: 10.1175/2008BAMS2634.1http://dx.doi.org/10.1175/2008BAMS2634.1]
Vinukollu R K, Wood E F, Ferguson C R and Fisher J B. 2011. Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: evaluation of three process-based approaches. Remote Sensing of Environment, 115(3): 801-823 [DOI: 10.1016/j.rse.2010.11.006http://dx.doi.org/10.1016/j.rse.2010.11.006]
Wang K C and Dickinson R E. 2012. A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability. Reviews of Geophysics, 50(2): (RG2005) [DOI: 10.1029/2011RG000373]
Wang K C, Dickinson R E, Wild M and Liang S L. 2010. Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development. Journal of Geophysical Research: Atmospheres, 115(D20): D20112 [DOI: 10.1029/2009 JD013671http://dx.doi.org/10.1029/2009JD013671]
Wang K C, Li Z Q and Cribb M. 2006. Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley-Taylor parameter. Remote Sensing of Environment, 102(3/4): 293-305 [DOI: 10.1016/j.rse.2006.02.007http://dx.doi.org/10.1016/j.rse.2006.02.007]
Wang K C and Liang S L. 2008. An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture. Journal of Hydrometeorology, 9(4): 712-727 [DOI: 10.1175/2007JHM911.1http://dx.doi.org/10.1175/2007JHM911.1]
Wang K C, Wang P C, Li Z Q, Cribb M and Sparrow M. 2007. A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature. Journal of Geophysical Research: Atmospheres, 112: D15107 [DOI: 10.1029/2006JD008351http://dx.doi.org/10.1029/2006JD008351]
Wang T, Tang R L, Li Z L, Jiang Y Z, Liu M and Niu L. 2019. An improved spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping. Remote Sensing, 11(7): 761 [DOI: 10.3390/rs11070761http://dx.doi.org/10.3390/rs11070761]
Wang X Y, Yao Y J, Zhao S H, Jia K, Zhang X T, Zhang Y H, Zhang L L, Xu J and Chen X W. 2017. MODIS-based estimation of terrestrial latent heat flux over North America using three machine learning algorithms. Remote Sensing, 9(12): 1326 [DOI: 10.3390/rs9121326http://dx.doi.org/10.3390/rs9121326]
Wu B F, Xiong J and Yan S S. 2011. ETWatch: models and methods. Journal of Remote Sensing, 15(2): 224-239
吴炳方, 熊隽, 闫姗姗. 2011. ETWatch的模型与方法. 遥感学报, 15(2): 224-239 [DOI: 10.11834/jrs.20110297http://dx.doi.org/10.11834/jrs.20110297]
Wu B F, Yan N, Xiong J, Bastiaanssen W G M, Zhu W W and Stein A. 2012. Validation of ETWatch using field measurements at diverse landscapes: a case study in Hai Basin of China. Journal of Hydrology, 436-437: 67-80 [DOI: 10.1016/j.jhydrol.2012.02.043http://dx.doi.org/10.1016/j.jhydrol.2012.02.043]
Wu H and Ying W M. 2019. Benchmarking machine learning algorithms for instantaneous net surface shortwave radiation retrieval using remote sensing data. Remote Sensing, 11(21): 2520 [DOI: 10.3390/rs11212520http://dx.doi.org/10.3390/rs11212520]
Xin X and Liu Q. 2010. The Two-layer Surface Energy Balance Parameterization Scheme (TSEBPS) for estimation of land surface heat fluxes. Hydrology and Earth System Sciences, 14(3): 491-504 [DOI: 10.5194/hess-14-491-2010http://dx.doi.org/10.5194/hess-14-491-2010]
Xiong Y J, Feng F G, Fang Y Z, Qiu G Y, Zhao S H and Yao Y J. 2021. Critical problems when applying remotely sensed evapotranspiration products. Remote Sensing Technology and Application, 36(1): 121-131
熊育久, 冯房观, 方奕舟, 邱国玉, 赵少华, 姚云军. 2021. 蒸散发遥感反演产品应用关键问题浅议. 遥感技术与应用, 36(1): 121-131 [DOI: 10.11873/j.issn.1004-0323.2021.1.0121http://dx.doi.org/10.11873/j.issn.1004-0323.2021.1.0121]
Xiong Y J, Zhao S H, Tian F and Qiu G Y. 2015. An evapotranspiration product for arid regions based on the three-temperature model and thermal remote sensing. Journal of Hydrology, 530: 392-404 [DOI: 10.1016/j.jhydrol.2015.09.050http://dx.doi.org/10.1016/j.jhydrol.2015.09.050]
Xu J, Yao Y J, Liang S L, Liu S M, Fisher J B, Jia K, Zhang X T, Lin Y, Zhang L L and Chen X W. 2019a. Merging the MODIS and Landsat terrestrial latent heat flux products using the multiresolution tree method. IEEE Transactions on Geoscience and Remote Sensing, 57(5): 2811-2823 [DOI: 10.1109/TGRS.2018.2877807http://dx.doi.org/10.1109/TGRS.2018.2877807]
Xu T R, Guo Z X, Liu S M, He X L, Meng Y F Y, Xu Z W, Xia Y L, Xiao J F, Zhang Y, Ma Y F and Song L S. 2018. Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale. Journal of Geophysical Research: Atmospheres, 123(16): 8674-8690 [DOI: 10.1029/2018JD 028447http://dx.doi.org/10.1029/2018JD028447]
Xu T R, He X L, Bateni S M, Auligne T, Liu S M, Xu Z W, Zhou J and Mao K B. 2019b. Mapping regional turbulent heat fluxes via variational assimilation of land surface temperature data from polar orbiting satellites. Remote Sensing of Environment, 221: 444-461 [DOI: 10.1016/j.rse.2018.11.023http://dx.doi.org/10.1016/j.rse.2018.11.023]
Xu T R, Liang S L and Liu S M. 2011. Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble Kalman filter. Journal of Geophysical Research: Atmospheres, 116: D09109 [DOI: 10.1029/2010JD 015150http://dx.doi.org/10.1029/2010JD015150]
Xu Z W, Zhu Z L, Liu S M, Song L S, Wang X C, Zhou S, Yang X F and Xu T R. 2021. Evapotranspiration partitioning for multiple ecosystems within a dryland watershed: seasonal variations and controlling factors. Journal of Hydrology, 598: 126483 [DOI: 10.1016/j.jhydrol.2021.126483http://dx.doi.org/10.1016/j.jhydrol.2021.126483]
Yang F, White M A, Michaelis A R, Ichii K, Hashimoto H, Votava P, Zhu A X and Nemani R R. 2006. Prediction of continental-scale evapotranspiration by combining MODIS and AmeriFlux data through support vector machine. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3452-3461 [DOI: 10.1109/TGRS.2006.876297http://dx.doi.org/10.1109/TGRS.2006.876297]
Yang Y, Sun H W, Xue J, Liu Y, Liu L G, Yan D and Gui D W. 2021. Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms. Environmental Monitoring and Assessment, 193: 156 [DOI: 10.1007/s10661-021-08934-1http://dx.doi.org/10.1007/s10661-021-08934-1]
Yao Y J, Cheng J, Zhao S H, Jia K, Xie X H and Sun L. 2012. Estimation of farmland evapotranspiration: a review of methods using thermal infrared remote sensing data. Advances in Earth Sciences, 27(12): 1308-1318
姚云军, 程洁, 赵少华, 贾坤, 谢先红, 孙亮. 2012. 基于热红外遥感的农田蒸散估算方法研究综述. 地球科学进展, 27(12): 1308-1318 [DOI: 10.11867/j.issn.1001-8166.2012.12.1308http://dx.doi.org/10.11867/j.issn.1001-8166.2012.12.1308]
Yao Y J, Di Z H, Xie Z J, Xiao Z Q, Jia K, Zhang X T, Shang K, Yang J M, Bei X Y, Guo X Z and Yu R Y. 2021. Simplified Priestley-Taylor model to estimate land-surface latent heat of evapotranspiration from incident shortwave radiation, satellite vegetation index, and air relative humidity. Remote Sensing, 13(5): 902 [DOI: 10.3390/rs13050902http://dx.doi.org/10.3390/rs13050902]
Yao Y J, Liang S L, Cheng J, Liu S M, Fisher J B, Zhang X D, Jia K, Zhao X, Qing Q M, Zhao B, Han S J, Zhou G S, Zhou G Y, Li Y L and Zhao S H. 2013. MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm. Agricultural and Forest Meteorology, 171-172: 187-202 [DOI: 10.1016/j.agrformet.2012.11.016http://dx.doi.org/10.1016/j.agrformet.2012.11.016]
Yao Y J, Liang S L, Li X L, Chen J Q, Liu S M, Jia K, Zhang X T, Xiao Z Q, Fisher J B, Mu Q Z, Pan M, Liu M, Cheng J, Jiang B, Xie X H, Grünwald T, Bernhofer C and Roupsard O. 2017b. Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. Agricultural and Forest Meteorology, 242: 55-74 [DOI: 10.1016/j.agrformet.2017.04.011http://dx.doi.org/10.1016/j.agrformet.2017.04.011]
Yao Y J, Liang S L, Li X L, Chen J Q, Wang K C, Jia K, Cheng J, Jiang B, Fisher J B, Mu Q Z, Grünwald T, Bernhofer C and Roupsard O. 2015. A satellite-based hybrid algorithm to determine the Priestley-Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes. Remote Sensing of Environment, 165: 216-233 [DOI: 10.1016/j.rse.2015.05.013http://dx.doi.org/10.1016/j.rse.2015.05.013]
Yao Y J, Liang S L, Li X L, Hong Y, Fisher J B, Zhang N N, Chen J Q, Cheng J, Zhao S H, Zhang X T, Jiang B, Sun L, Jia K, Wang K C, Chen Y, Mu Q Z and Feng F. 2014. Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations. Journal of Geophysical Research: Atmospheres, 119(8): 4521-4545 [DOI: 10.1002/2013 JD020864http://dx.doi.org/10.1002/2013JD020864]
Yao Y J, Liang S L, Li X L, Liu S M, Chen J Q, Zhang X T, Jia K, Jiang B, Xie X H, Munier S, Liu M, Yu J, Lindroth A, Varlagin A, Raschi A, Noormets A, Pio C, Wohlfahrt G, Sun G, Domec J C, Montagnani L, Lund M, Eddy M, Blanken P D, Grünwald T, Wolf S and Magliulo V. 2016. Assessment and simulation of global terrestrial latent heat flux by synthesis of CMIP5 climate models and surface eddy covariance observations. Agricultural and Forest Meteorology, 223: 151-167 [DOI: 10.1016/j.agrformet.2016.03.016http://dx.doi.org/10.1016/j.agrformet.2016.03.016]
Yao Y J, Liang S L, Li X L, Zhang Y H, Chen J Q, Jia K, Zhang X T, Fisher J B, Wang X Y, Zhang L L, Xu J, Shao C L, Posse G, Li Y N, Magliulo V, Varlagin A, Moors E J, Boike J, Macfarlane C, Kato T, Buchmann N, Billesbach D P, Beringer J, Wolf S, Papuga S A, Wohlfahrt G, Montagnani L, Ardö J, Paul-Limoges E, Emmel C, Hörtnagl L, Sachs T, Gruening C, Gioli B, López-Ballesteros A, Steinbrecher R and Gielen B. 2017c. Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method. Journal of Hydrology, 553: 508-526 [DOI: 10.1016/j.jhydrol.2017.08.013http://dx.doi.org/10.1016/j.jhydrol.2017.08.013]
Yao Y J, Liang S L, Qin Q M, Wang K C and Zhao S H. 2011. Monitoring global land surface drought based on a hybrid evapotranspiration model. International Journal of Applied Earth Observation and Geoinformation, 13(3): 447-457
DOI 10.1016/j.jag.2010.09.009
Yao Y J, Liang S L, Yu J, Chen J Q, Liu S M, Lin Y, Fisher J B, McVicar T R, Cheng J, Jia K, Zhang X T, Xie X H, Jiang B and Sun L. 2017a. A simple temperature domain two-source model for estimating agricultural field surface energy fluxes from Landsat images. Journal of Geophysical Research: Atmospheres, 122(10): 5211-5236 [DOI: 10.1002/2016JD026370http://dx.doi.org/10.1002/2016JD026370]
Yuan Q Q, Shen H F, Li T W, Li Z W, Li S W, Jiang Y, Xu H Z, Tan W W, Yang Q Q, Wang J W, Gao J H and Zhang L P. 2020a. Deep learning in environmental remote sensing: achievements and challenges. Remote Sensing of Environment, 241: 111716 [DOI: 10.1016/j.rse.2020.111716http://dx.doi.org/10.1016/j.rse.2020.111716]
Yuan Q Q, Xu H Z, Li T W, Shen H F and Zhang L P. 2020b. Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S. Journal of Hydrology, 580: 124351 [DOI: 10.1016/j.jhydrol.2019.124351http://dx.doi.org/10.1016/j.jhydrol.2019.124351]
Yuan W P, Liu S G, Yu G R, Bonnefond J M, Chen J Q, Davis K, Desai A R, Goldstein A H, Gianelle D, Rossi F, Suyker A E and Verma S B. 2010. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sensing of Environment, 114(7): 1416-1431 [DOI: 10.1016/j.rse.2010.01.022http://dx.doi.org/10.1016/j.rse.2010.01.022]
Zeng Z Z, Wang T, Zhou F, Ciais P, Mao J F, Shi X Y and Piao S L. 2014. A worldwide analysis of spatiotemporal changes in water balance-based evapotranspiration from 1982 to 2009. Journal of Geophysical Research: Atmospheres, 119(3): 1186-1202 [DOI: 10.1002/2013JD020941http://dx.doi.org/10.1002/2013JD020941]
Zhang K, Kimball J S, Nemani R R and Running S W. 2010. A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006. Water Resources Research, 46(9): W09522 [DOI: 10.1029/2009WR008800http://dx.doi.org/10.1029/2009WR008800]
Zhang K, Kimball J S and Running S W. 2016. A review of remote sensing based actual evapotranspiration estimation. Wiley Interdisciplinary Reviews: Water, 3(6): 834-853 [DOI: 10.1002/wat2.1168http://dx.doi.org/10.1002/wat2.1168]
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 W, Duan S B, Li A N and Yin G F. 2019. A practical method for reducing terrain effect on land surface temperature using random forest regression. Remote Sensing of Environment, 221: 635-649 [DOI: 10.1016/j.rse.2018.12.008http://dx.doi.org/10.1016/j.rse.2018.12.008]
Zheng C L, Hu G C, Chen Q T and Jia L. 2021. Impact of remote sensing soil moisture on the evapotranspiration estimation. Journal of Remote Sensing, 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 H. 2016. Research on the Spatial Variation in Actual Evapotranspiration of Terrestrial Ecosystems. Beijing: University of Chinese Academy of Sciences
郑涵. 2016. 陆地生态系统实际蒸散量的空间格局及其形成机制研究. 北京: 中国科学院大学
Zhou T, Peng Z Q, Xin X Z and Li F G. 2016. Remote sensing research of evapotranspiration over heterogeneous surfaces: a review. Journal of Remote Sensing, 20(2): 257-277
周倜, 彭志晴, 辛晓洲, 李福根. 2016. 非均匀地表蒸散遥感研究综述. 遥感学报, 20(2): 257-277 [DOI: 10.11834/jrs.20165030http://dx.doi.org/10.11834/jrs.20165030]
Zhu G F, Li X, Zhang K, Ding Z Y, Han T, Ma J Z, Huang C L, He J H and Ma T. 2016. Multi-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging. Hydrological Processes, 30(16): 2861-2879 [DOI: 10.1002/hyp.10832http://dx.doi.org/10.1002/hyp.10832]
相关文章
相关作者
相关机构
京公网安备11010802024621