The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation

Xie, Zijing; Yao, Yunjun; Zhang, Xiaotong; Liang, Shunlin; Fisher, Joshua B.; Chen, Jiquan; Jia, Kun; Shang, Ke; Yang, Junming; Yu, Ruiyang; Guo, Xiaozheng; Liu, Lu; Ning, Jing; Zhang, Lilin

doi:10.1016/j.jhydrol.2022.127990

Title: The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation

Journal Article · Sat May 28 00:00:00 EDT 2022 · Journal of Hydrology

DOI:https://doi.org/10.1016/j.jhydrol.2022.127990· OSTI ID:1977307

Xie, Zijing ^[1]; Yao, Yunjun ^[1]; Zhang, Xiaotong ^[1]; Liang, Shunlin ^[2]; Fisher, Joshua B. ^[3]; Chen, Jiquan ^[4]; Jia, Kun ^[1]; Shang, Ke ^[1]; Yang, Junming ^[1]; Yu, Ruiyang ^[1]; Guo, Xiaozheng ^[1]; Liu, Lu ^[1]; Ning, Jing ^[1]; Zhang, Lilin ^[5]

Beijing Normal University, Beijing (China)
Univ. of Maryland, College Park, MD (United States)
Chapman Univ., Orange, CA (United States)
Michigan State Univ., East Lansing, MI (United States)
Univ. of Twente, Enschede (Netherlands)

An accurate estimation of spatially and temporally continuous global terrestrial evapotranspiration (ET) is essential in the assessment of surface energy, water and carbon cycles. The Global LAnd Surface Satellite (GLASS) ET product Version 4.0 (v4.0) based on the Bayesian model averaging (BMA) method was generated to estimate global terrestrial ET. However, certain uncertainty for the GLASS ET product v4.0 limits its application. In this study, we introduced the deep neural networks (DNN) merging framework to improve terrestrial ET estimation for GLASS ET product Version 5.0 (v5.0) generation by integrating five satellite-derived ET products [Moderate Resolution Imaging Spectroradiometer (MODIS) ET product (MOD16), Shuttleworth–Wallace dual-source ET product (SW), Priestley–Taylor-based ET product (PT-JPL), modified satellite-based Priestley–Taylor ET product (MS-PT) and simple hybrid ET product (SIM)]. We compared the performance of DNN method against other merging methods, including GLASS ET algorithm v4.0 (BMA), the gradient boosting regression tree (GBRT) method and the random forest (RF) method, based on 195 global eddy covariance (EC) flux towers covering observations from 2000 through 2015. Validations indicated that the DNN had the highest accuracy among four merging methods across different land cover types, yielding the highest average determination coefficients (R², 0.62), root-mean-squared-error (RMSE, 24.1 W/m²) and Kling–Gupta efficiency (KGE, 0.77) with a of 99% confidence interval. Compared with GLASS ET algorithm v4.0, the DNN improved on the R² by approximately 7% (p < 0.01) and the KGE by 10%. Based on the DNN, we then generated 8-day GLASS ET product v5.0 globally with a 1 km spatial resolution from 2001 to 2015 driven by GLASS vegetation and surface net radiation (R_n) datasets and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) datasets. Finally, this global terrestrial ET product provides a valuable dataset for monitoring regional and global water resources and environmental changes.

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Research Organization:: Oregon State Univ., Corvallis, OR (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: FG02-04ER63911; FG02-04ER63917

OSTI ID:: 1977307

Journal Information:: Journal of Hydrology, Vol. 610, Issue C; ISSN 0022-1694

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (97)

Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China Chen, Yang; Xia, Jiangzhou; Liang, Shunlin Remote Sensing of Environment, Vol. 140 https://doi.org/10.1016/j.rse.2013.08.045	journal	January 2014
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Improvements to a MODIS global terrestrial evapotranspiration algorithm Mu, Qiaozhen; Zhao, Maosheng; Running, Steven W. Remote Sensing of Environment, Vol. 115, Issue 8 https://doi.org/10.1016/j.rse.2011.02.019	journal	August 2011
Long-Time-Series Global Land Surface Satellite Leaf Area Index Product Derived From MODIS and AVHRR Surface Reflectance Xiao, Zhiqiang; Liang, Shunlin; Wang, Jindi IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, Issue 9 https://doi.org/10.1109/TGRS.2016.2560522	journal	September 2016
Earth's Global Energy Budget Trenberth, Kevin E.; Fasullo, John T.; Kiehl, Jeffrey Bulletin of the American Meteorological Society, Vol. 90, Issue 3 https://doi.org/10.1175/2008BAMS2634.1	journal	March 2009
GLEAM v3: satellite-based land evaporation and root-zone soil moisture Martens, Brecht; Miralles, Diego G.; Lievens, Hans Geoscientific Model Development, Vol. 10, Issue 5 https://doi.org/10.5194/gmd-10-1903-2017	journal	January 2017
Multi-model, multi-sensor estimates of global evapotranspiration: climatology, uncertainties and trends: MULTI-MODEL, MULTI-SENSOR ESTIMATES OF GLOBAL EVAPOTRANSPIRATION Vinukollu, Raghuveer K.; Meynadier, Remi; Sheffield, Justin Hydrological Processes, Vol. 25, Issue 26 https://doi.org/10.1002/hyp.8393	journal	December 2011
Development of a global evapotranspiration algorithm based on MODIS and global meteorology data Mu, Qiaozhen; Heinsch, Faith Ann; Zhao, Maosheng Remote Sensing of Environment, Vol. 111, Issue 4 https://doi.org/10.1016/j.rse.2007.04.015	journal	December 2007
MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley–Taylor algorithm Yao, Yunjun; Liang, Shunlin; Cheng, Jie Agricultural and Forest Meteorology, Vol. 171-172 https://doi.org/10.1016/j.agrformet.2012.11.016	journal	April 2013
Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites Fisher, Joshua B.; Tu, Kevin P.; Baldocchi, Dennis D. Remote Sensing of Environment, Vol. 112, Issue 3 https://doi.org/10.1016/j.rse.2007.06.025	journal	March 2008
Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017 Zhang, Yongqiang; Kong, Dongdong; Gan, Rong Remote Sensing of Environment, Vol. 222 https://doi.org/10.1016/j.rse.2018.12.031	journal	March 2019
Increasing contribution of peatlands to boreal evapotranspiration in a warming climate Helbig, Manuel; Waddington, James Michael; Alekseychik, Pavel Nature Climate Change, Vol. 10, Issue 6 https://doi.org/10.1038/s41558-020-0763-7	journal	May 2020
The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources: THE FUTURE OF EVAPOTRANSPIRATION Fisher, Joshua B.; Melton, Forrest; Middleton, Elizabeth Water Resources Research, Vol. 53, Issue 4 https://doi.org/10.1002/2016WR020175	journal	April 2017
The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data Pastorello, Gilberto; Trotta, Carlo; Canfora, Eleonora Scientific Data, Vol. 7, Issue 1 https://doi.org/10.1038/s41597-020-0534-3	journal	July 2020
Bayesian Model Averaging for Linear Regression Models Raftery, Adrian E.; Madigan, David; Hoeting, Jennifer A. Journal of the American Statistical Association, Vol. 92, Issue 437 https://doi.org/10.1080/01621459.1997.10473615	journal	March 1997
Spatial representativeness of tall tower eddy covariance measurements using remote sensing and footprint analysis Barcza, Z.; Kern, A.; Haszpra, L. Agricultural and Forest Meteorology, Vol. 149, Issue 5 https://doi.org/10.1016/j.agrformet.2008.10.021	journal	May 2009
Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method Yao, Yunjun; Liang, Shunlin; Li, Xianglan Journal of Hydrology, Vol. 553 https://doi.org/10.1016/j.jhydrol.2017.08.013	journal	October 2017
Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach Khan, Muhammad Sarfraz; Liaqat, Umar Waqas; Baik, Jongjin Agricultural and Forest Meteorology, Vol. 252 https://doi.org/10.1016/j.agrformet.2018.01.022	journal	April 2018
Hysteresis of tropical forests in the 21st century Staal, Arie; Fetzer, Ingo; Wang-Erlandsson, Lan Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-18728-7	journal	October 2020
Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations Yao, Yunjun; Liang, Shunlin; Li, Xianglan Journal of Geophysical Research: Atmospheres, Vol. 119, Issue 8 https://doi.org/10.1002/2013JD020864	journal	April 2014
Reducing the Dimensionality of Data with Neural Networks Hinton, G. E. Science, Vol. 313, Issue 5786 https://doi.org/10.1126/science.1127647	journal	July 2006
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling Gupta, Hoshin V.; Kling, Harald; Yilmaz, Koray K. Journal of Hydrology, Vol. 377, Issue 1-2 https://doi.org/10.1016/j.jhydrol.2009.08.003	journal	October 2009
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups Hinton, Geoffrey; Deng, Li; Yu, Dong IEEE Signal Processing Magazine, Vol. 29, Issue 6 https://doi.org/10.1109/MSP.2012.2205597	journal	November 2012
Combining Datasets of Satellite-Retrieved Products. Part I: Methodology and Water Budget Closure Aires, Filipe Journal of Hydrometeorology, Vol. 15, Issue 4 https://doi.org/10.1175/JHM-D-13-0148.1	journal	August 2014
An observationally based energy balance for the Earth since 1950 Murphy, D. M.; Solomon, S.; Portmann, R. W. Journal of Geophysical Research, Vol. 114, Issue D17 https://doi.org/10.1029/2009JD012105	journal	January 2009
The Energy Balance Closure Problem: an Overview Foken, Thomas Ecological Applications, Vol. 18, Issue 6 https://doi.org/10.1890/06-0922.1	journal	September 2008
Comparison of physical-based, data-driven and hybrid modeling approaches for evapotranspiration estimation Hu, Xiaolong; Shi, Liangsheng; Lin, Guang Journal of Hydrology, Vol. 601 https://doi.org/10.1016/j.jhydrol.2021.126592	journal	October 2021
A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006: GLOBAL RECORD OF LAND SURFACE EVAPOTRANSPIRATION Zhang, Ke; Kimball, John S.; Nemani, Ramakrishna R. Water Resources Research, Vol. 46, Issue 9 https://doi.org/10.1029/2009WR008800	journal	September 2010
Reconstruction of Long-Term Temporally Continuous NDVI and Surface Reflectance From AVHRR Data Xiao, Zhiqiang; Liang, Shunlin; Tian, Xiaodan IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, Issue 12 https://doi.org/10.1109/JSTARS.2017.2744979	journal	December 2017
Benchmarking large-scale evapotranspiration estimates: A perspective from a calibration-free complementary relationship approach and FLUXCOM Ma, Ning; Szilagyi, Jozsef; Jozsa, Janos Journal of Hydrology, Vol. 590 https://doi.org/10.1016/j.jhydrol.2020.125221	journal	November 2020
A working guide to boosted regression trees Elith, J.; Leathwick, J. R.; Hastie, T. Journal of Animal Ecology, Vol. 77, Issue 4 https://doi.org/10.1111/j.1365-2656.2008.01390.x	journal	July 2008
The CR of Evaporation: A Calibration‐Free Diagnostic and Benchmarking Tool for Large‐Scale Terrestrial Evapotranspiration Modeling Ma, Ning; Szilagyi, Jozsef Water Resources Research, Vol. 55, Issue 8 https://doi.org/10.1029/2019WR024867	journal	August 2019
The FLUXCOM ensemble of global land-atmosphere energy fluxes Jung, Martin; Koirala, Sujan; Weber, Ulrich Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0076-8	journal	May 2019
Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations Hu, Guangcheng; Jia, Li Remote Sensing, Vol. 7, Issue 3 https://doi.org/10.3390/rs70303056	journal	March 2015
Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland Wang, Yujie; Woodcock, Curtis E.; Buermann, Wolfgang Remote Sensing of Environment, Vol. 91, Issue 1 https://doi.org/10.1016/j.rse.2004.02.007	journal	May 2004
Using SMAP Level-4 soil moisture to constrain MOD16 evapotranspiration over the contiguous USA Brust, Colin; Kimball, John S.; Maneta, Marco P. Remote Sensing of Environment, Vol. 255 https://doi.org/10.1016/j.rse.2020.112277	journal	March 2021
Exploring the merging of the global land evaporation WACMOS-ET products based on local tower measurements Jiménez, Carlos; Martens, Brecht; Miralles, Diego M. Hydrology and Earth System Sciences, Vol. 22, Issue 8 https://doi.org/10.5194/hess-22-4513-2018	journal	January 2018
Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches Vinukollu, Raghuveer K.; Wood, Eric F.; Ferguson, Craig R. Remote Sensing of Environment, Vol. 115, Issue 3 https://doi.org/10.1016/j.rse.2010.11.006	journal	March 2011
Multi-site evaluation of terrestrial evaporation models using FLUXNET data Ershadi, A.; McCabe, M. F.; Evans, J. P. Agricultural and Forest Meteorology, Vol. 187 https://doi.org/10.1016/j.agrformet.2013.11.008	journal	April 2014
Impact of changing soil moisture distribution on net ecosystem productivity of a boreal aspen forest during and following drought Krishnan, Praveena; Black, T. Andrew; Grant, Nicholas J. Agricultural and Forest Meteorology, Vol. 139, Issue 3-4 https://doi.org/10.1016/j.agrformet.2006.07.002	journal	October 2006
Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data Kalma, Jetse D.; McVicar, Tim R.; McCabe, Matthew F. Surveys in Geophysics, Vol. 29, Issue 4-5 https://doi.org/10.1007/s10712-008-9037-z	journal	August 2008
Representation Learning: A Review and New Perspectives Bengio, Y.; Courville, A.; Vincent, P. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Issue 8 https://doi.org/10.1109/TPAMI.2013.50	journal	August 2013
Gap filling strategies for long term energy flux data sets Falge, Eva; Baldocchi, Dennis; Olson, Richard Agricultural and Forest Meteorology, Vol. 107, Issue 1 https://doi.org/10.1016/S0168-1923(00)00235-5	journal	March 2001
A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability: GLOBAL TERRESTRIAL EVAPOTRANSPIRATION Wang, Kaicun; Dickinson, Robert E. Reviews of Geophysics, Vol. 50, Issue 2 https://doi.org/10.1029/2011RG000373	journal	May 2012
Generation of spatio-temporally continuous evapotranspiration and its components by coupling a two-source energy balance model and a deep neural network over the Heihe River Basin Cui, Yaokui; Song, Lisheng; Fan, Wenjie Journal of Hydrology, Vol. 597 https://doi.org/10.1016/j.jhydrol.2021.126176	journal	June 2021
'Breathing' of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems Baldocchi, Dennis Australian Journal of Botany, Vol. 56, Issue 1 https://doi.org/10.1071/BT07151	journal	January 2008
What controls the error structure in evapotranspiration models? Polhamus, Aaron; Fisher, Joshua B.; Tu, Kevin P. Agricultural and Forest Meteorology, Vol. 169 https://doi.org/10.1016/j.agrformet.2012.10.002	journal	February 2013
Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance Xiao, Zhiqiang; Liang, Shunlin; Wang, Jindi IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, Issue 1 https://doi.org/10.1109/TGRS.2013.2237780	journal	January 2014
Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation: TOWARD REDUCTION OF MODEL UNCERTAINTY Parrish, Mark A.; Moradkhani, Hamid; DeChant, Caleb M. Water Resources Research, Vol. 48, Issue 3 https://doi.org/10.1029/2011WR011116	journal	March 2012
Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning Saggi, Mandeep Kaur; Jain, Sushma Computers and Electronics in Agriculture, Vol. 156 https://doi.org/10.1016/j.compag.2018.11.031	journal	January 2019
Multi-model ensemble hydrologic prediction using Bayesian model averaging Duan, Qingyun; Ajami, Newsha K.; Gao, Xiaogang Advances in Water Resources, Vol. 30, Issue 5 https://doi.org/10.1016/j.advwatres.2006.11.014	journal	May 2007
Energy balance closure at FLUXNET sites Wilson, Kell; Goldstein, Allen; Falge, Eva Agricultural and Forest Meteorology, Vol. 113, Issue 1-4 https://doi.org/10.1016/S0168-1923(02)00109-0	journal	December 2002
A 10 per cent increase in global land evapotranspiration from 2003 to 2019 Pascolini-Campbell, Madeleine; Reager, John T.; Chandanpurkar, Hrishikesh A. Nature, Vol. 593, Issue 7860 https://doi.org/10.1038/s41586-021-03503-5	journal	May 2021
Deep Forest: Towards An Alternative to Deep Neural Networks Zhou, Zhi-Hua; Feng, Ji Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence https://doi.org/10.24963/ijcai.2017/497	conference	August 2017
Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community Ball, John E.; Anderson, Derek T.; Chan, Chee Seng Journal of Applied Remote Sensing, Vol. 11, Issue 04 https://doi.org/10.1117/1.JRS.11.042609	journal	September 2017
Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment Elbeltagi, Ahmed; Deng, Jinsong; Wang, Ke Agricultural Water Management, Vol. 241 https://doi.org/10.1016/j.agwat.2020.106334	journal	November 2020
Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data Yuan, Wenping; Liu, Shuguang; Yu, Guirui Remote Sensing of Environment, Vol. 114, Issue 7 https://doi.org/10.1016/j.rse.2010.01.022	journal	July 2010
Calibration‐Free Complementary Relationship Estimates Terrestrial Evapotranspiration Globally Ma, Ning; Szilagyi, Jozsef; Zhang, Yongqiang Water Resources Research, Vol. 57, Issue 9 https://doi.org/10.1029/2021WR029691	journal	September 2021
DNN-MET: A deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information Shang, Ke; Yao, Yunjun; Liang, Shunlin Agricultural and Forest Meteorology, Vol. 308-309 https://doi.org/10.1016/j.agrformet.2021.108582	journal	October 2021
Increased plant growth in the northern high latitudes from 1981 to 1991 Myneni, R. B.; Keeling, C. D.; Tucker, C. J. Nature, Vol. 386, Issue 6626 https://doi.org/10.1038/386698a0	journal	April 1997
Deep learning in environmental remote sensing: Achievements and challenges Yuan, Qiangqiang; Shen, Huanfeng; Li, Tongwen Remote Sensing of Environment, Vol. 241 https://doi.org/10.1016/j.rse.2020.111716	journal	May 2020
Regional evaporation estimates from flux tower and MODIS satellite data Cleugh, Helen A.; Leuning, Ray; Mu, Qiaozhen Remote Sensing of Environment, Vol. 106, Issue 3 https://doi.org/10.1016/j.rse.2006.07.007	journal	February 2007
Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin Ruhoff, A. L.; Paz, A. R.; Aragao, L. E. O. C. Hydrological Sciences Journal, Vol. 58, Issue 8 https://doi.org/10.1080/02626667.2013.837578	journal	October 2013
A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature Wang, Kaicun; Wang, Pucai; Li, Zhanqing Journal of Geophysical Research, Vol. 112, Issue D15 https://doi.org/10.1029/2006JD008351	journal	January 2007
Decision trees: a recent overview Kotsiantis, S. B. Artificial Intelligence Review, Vol. 39, Issue 4 https://doi.org/10.1007/s10462-011-9272-4	journal	June 2011
An Improved Method for Estimating Global Evapotranspiration Based on Satellite Determination of Surface Net Radiation, Vegetation Index, Temperature, and Soil Moisture Wang, Kaicun; Liang, Shunlin Journal of Hydrometeorology, Vol. 9, Issue 4 https://doi.org/10.1175/2007JHM911.1	journal	August 2008
Quantifying uncertainty in a remote sensing-based estimate of evapotranspiration over continental USA Ferguson, Craig R.; Sheffield, Justin; Wood, Eric F. International Journal of Remote Sensing, Vol. 31, Issue 14 https://doi.org/10.1080/01431161.2010.483490	journal	August 2010
On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters Priestley, C. H. B.; Taylor, R. J. Monthly Weather Review, Vol. 100, Issue 2 https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2	journal	February 1972
A satellite-based hybrid algorithm to determine the Priestley–Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes Yao, Yunjun; Liang, Shunlin; Li, Xianglan Remote Sensing of Environment, Vol. 165 https://doi.org/10.1016/j.rse.2015.05.013	journal	August 2015
Learning representations by back-propagating errors Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. Nature, Vol. 323, Issue 6088 https://doi.org/10.1038/323533a0	journal	October 1986
On Uncertainty in Global Terrestrial Evapotranspiration Estimates from Choice of Input Forcing Datasets Badgley, Grayson; Fisher, Joshua B.; Jiménez, Carlos Journal of Hydrometeorology, Vol. 16, Issue 4 https://doi.org/10.1175/JHM-D-14-0040.1	journal	August 2015
Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance Yebra, Marta; Van Dijk, Albert; Leuning, Ray Remote Sensing of Environment, Vol. 129 https://doi.org/10.1016/j.rse.2012.11.004	journal	February 2013
Correcting eddy-covariance flux underestimates over a grassland Twine, T. E.; Kustas, W. P.; Norman, J. M. Agricultural and Forest Meteorology, Vol. 103, Issue 3 https://doi.org/10.1016/S0168-1923(00)00123-4	journal	June 2000
Empirical estimation of daytime net radiation from shortwave radiation and ancillary information Jiang, Bo; Zhang, Yi; Liang, Shunlin Agricultural and Forest Meteorology, Vol. 211-212 https://doi.org/10.1016/j.agrformet.2015.05.003	journal	October 2015
Global Hydrological Cycles and World Water Resources Oki, T. Science, Vol. 313, Issue 5790 https://doi.org/10.1126/science.1128845	journal	August 2006
Using Bayesian model averaging to estimate terrestrial evapotranspiration in China Chen, Yang; Yuan, Wenping; Xia, Jiangzhou Journal of Hydrology, Vol. 528 https://doi.org/10.1016/j.jhydrol.2015.06.059	journal	September 2015
Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis Mueller, B.; Hirschi, M.; Jimenez, C. Hydrology and Earth System Sciences, Vol. 17, Issue 10 https://doi.org/10.5194/hess-17-3707-2013	journal	January 2013
On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm Reichstein, Markus; Falge, Eva; Baldocchi, Dennis Global Change Biology, Vol. 11, Issue 9 https://doi.org/10.1111/j.1365-2486.2005.001002.x	journal	September 2005
Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS) Jiang, Chongya; Ryu, Youngryel Remote Sensing of Environment, Vol. 186 https://doi.org/10.1016/j.rse.2016.08.030	journal	December 2016
Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale Song, Lisheng; Liu, Shaomin; Kustas, William P. Remote Sensing of Environment, Vol. 219 https://doi.org/10.1016/j.rse.2018.10.002	journal	December 2018
ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station Fisher, Joshua B.; Lee, Brian; Purdy, Adam J. Water Resources Research, Vol. 56, Issue 4 https://doi.org/10.1029/2019WR026058	journal	April 2020
Multi-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging Zhu, Gaofeng; Li, Xin; Zhang, Kun Hydrological Processes, Vol. 30, Issue 16 https://doi.org/10.1002/hyp.10832	journal	April 2016
Evapotranspiration depletes groundwater under warming over the contiguous United States Condon, Laura E.; Atchley, Adam L.; Maxwell, Reed M. Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-14688-0	journal	February 2020
The Global Land Surface Satellite (GLASS) Product Suite Liang, Shunlin; Cheng, Jie; Jia, Kun Bulletin of the American Meteorological Society, Vol. 102, Issue 2 https://doi.org/10.1175/BAMS-D-18-0341.1	journal	February 2021
Variation in actual evapotranspiration and its ties to climate change and vegetation dynamics in northwest China Yang, Linshan; Feng, Qi; Zhu, Meng Journal of Hydrology, Vol. 607 https://doi.org/10.1016/j.jhydrol.2022.127533	journal	April 2022
On Parameterizing Soil Evaporation in a Direct Remote Sensing Model of ET: PT‐JPL Marshall, Michael; Tu, Kevin; Andreo, Veronica Water Resources Research, Vol. 56, Issue 5 https://doi.org/10.1029/2019WR026290	journal	May 2020
A three-source satellite algorithm for retrieving all-sky evapotranspiration rate using combined optical and microwave vegetation index at twenty AsiaFlux sites Wang, Yipu; Li, Rui; Min, Qilong Remote Sensing of Environment, Vol. 235 https://doi.org/10.1016/j.rse.2019.111463	journal	December 2019
A cross-biome comparison of daily light use efficiency for gross primary production Turner, David P.; Urbanski, Shawn; Bremer, Dale Global Change Biology, Vol. 9, Issue 3 https://doi.org/10.1046/j.1365-2486.2003.00573.x	journal	March 2003
FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities Baldocchi, Dennis; Falge, Eva; Gu, Lianhong Bulletin of the American Meteorological Society, Vol. 82, Issue 11 https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2	journal	November 2001
Evaporation from sparse crops-an energy combination theory Shuttleworth, W. James; Wallace, J. S. Quarterly Journal of the Royal Meteorological Society, Vol. 111, Issue 469 https://doi.org/10.1002/qj.49711146910	journal	July 1985
Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources Salcedo-Sanz, S.; Ghamisi, P.; Piles, M. Information Fusion, Vol. 63 https://doi.org/10.1016/j.inffus.2020.07.004	journal	November 2020
Illuminating water cycle modifications and Earth system resilience in the Anthropocene Gleeson, Tom; Wang‐Erlandsson, Lan; Porkka, Miina Water Resources Research, Vol. 56, Issue 4 https://doi.org/10.1029/2019WR024957	journal	March 2020
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Generating global Leaf Area Index from Landsat: Algorithm formulation and demonstration Ganguly, Sangram; Nemani, Ramakrishna R.; Zhang, Gong Remote Sensing of Environment, Vol. 122 https://doi.org/10.1016/j.rse.2011.10.032	journal	July 2012
Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms Yao, Yunjun; Liang, Shunlin; Li, Xianglan Agricultural and Forest Meteorology, Vol. 242 https://doi.org/10.1016/j.agrformet.2017.04.011	journal	August 2017
On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient Bai, Yun; Zhang, Sha; Bhattarai, Nishan Agricultural and Forest Meteorology, Vol. 298-299 https://doi.org/10.1016/j.agrformet.2020.108308	journal	March 2021
Physics‐Constrained Machine Learning of Evapotranspiration Zhao, Wen Li; Gentine, Pierre; Reichstein, Markus Geophysical Research Letters, Vol. 46, Issue 24 https://doi.org/10.1029/2019GL085291	journal	December 2019

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54 ENVIRONMENTAL SCIENCES
Engineering
Geology
Water resources
GLASS
Evapotranspiration
Deep neural networks
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Bayesian model averaging

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