{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T15:10:25Z","timestamp":1771686625807,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Fund of China","award":["42192581"],"award-info":[{"award-number":["42192581"]}]},{"name":"Natural Science Fund of China","award":["42171310"],"award-info":[{"award-number":["42171310"]}]},{"name":"Natural Science Fund of China","award":["CBAS2022ORP01"],"award-info":[{"award-number":["CBAS2022ORP01"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["42192581"],"award-info":[{"award-number":["42192581"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["42171310"],"award-info":[{"award-number":["42171310"]}]},{"name":"Open Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2022ORP01"],"award-info":[{"award-number":["CBAS2022ORP01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>It is a difficult undertaking to reliably estimate global terrestrial evapotranspiration (ET) using the Visible Infrared Imaging Radiometer Suite (VIIRS) at high spatial and temporal scales. We employ deep neural networks (DNN) to enhance the estimation of terrestrial ET on a global scale using satellite data. We accomplish this by merging five algorithms that are process-based and that make use of VIIRS data. These include the Shuttleworth\u2013Wallace dual-source ET method (SW), the Priestley\u2013Taylor-based ET algorithm (PT-JPL), the MOD16 ET product algorithm (MOD16), the modified satellite-based Priestley\u2013Taylor ET algorithm (MS-PT), and the simple hybrid ET algorithm (SIM). We used 278 eddy covariance (EC) tower sites from 2012 to 2022 to validate the DNN approach, comparing it to Bayesian model averaging (BMA), gradient boosting regression tree (GBRT) and random forest (RF). The validation results demonstrate that the DNN significantly improves the accuracy of daily ET estimates when compared to three other merging methods, resulting in the highest average determination coefficients (R2, 0.71), RMSE (21.9 W\/m2) and Kling\u2013Gupta efficiency (KGE, 0.83). Utilizing the DNN, we generated a VIIRS ET product with a 500 m spatial resolution for the years 2012\u20132020. The DNN method serves as a foundational approach in the development of a sustained and comprehensive global terrestrial ET dataset. The basis for characterizing and analyzing global hydrological dynamics and carbon cycling is provided by this dataset.<\/jats:p>","DOI":"10.3390\/rs16010044","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T08:16:02Z","timestamp":1703146562000},"page":"44","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Global Terrestrial Evapotranspiration Estimation from Visible Infrared Imaging Radiometer Suite (VIIRS) Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Zijing","family":"Xie","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3803-8170","authenticated-orcid":false,"given":"Yunjun","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5451-0546","authenticated-orcid":false,"given":"Qingxin","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Geography and Environment, Liaocheng University, Liaocheng 252000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions, CMA, Yinchuan 750002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5413-0247","authenticated-orcid":false,"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Infrastructure Engineering, Faculty of Engineering & IT, University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5000-0779","authenticated-orcid":false,"given":"Ruiyang","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Ning","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahui","family":"Fan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luna","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, K.C., and Dickinson, R.E. (2012). A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys., 50.","DOI":"10.1029\/2011RG000373"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.agrformet.2013.09.003","article-title":"A review of approaches for evapotranspiration partitioning","volume":"184","author":"Kool","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1175\/1520-0477(2001)082<2415:FANTTS>2.3.CO;2","article-title":"Fluxnet: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities","volume":"82","author":"Baldocchi","year":"2001","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112277","DOI":"10.1016\/j.rse.2020.112277","article-title":"Using smap level-4 soil moisture to constrain mod16 evapotranspiration over the contiguous USA","volume":"255","author":"Brust","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.3390\/rs70303056","article-title":"Monitoring of evapotranspiration in a semi-arid inland river basin by combining microwave and optical remote sensing observations","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.5194\/gmd-10-1903-2017","article-title":"Gleam v3: Satellite-based land evaporation and root-zone soil moisture","volume":"10","author":"Martens","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.rse.2018.12.031","article-title":"Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002\u20132017","volume":"222","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"19124","DOI":"10.1038\/srep19124","article-title":"Multi-decadal trends in global terrestrial evapotranspiration and its components","volume":"6","author":"Zhang","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.rse.2007.04.015","article-title":"Development of a global evapotranspiration algorithm based on modis and global meteorology data","volume":"111","author":"Mu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1016\/j.rse.2011.02.019","article-title":"Improvements to a modis global terrestrial evapotranspiration algorithm","volume":"115","author":"Mu","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.rse.2016.08.030","article-title":"Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from breathing earth system simulator (bess)","volume":"186","author":"Jiang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1038\/s41597-019-0076-8","article-title":"The fluxcom ensemble of global land-atmosphere energy fluxes","volume":"6","author":"Jung","year":"2019","journal-title":"Sci. Data"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"127990","DOI":"10.1016\/j.jhydrol.2022.127990","article-title":"The global land surface satellite (glass) evapotranspiration product version 5.0: Algorithm development and preliminary validation","volume":"610","author":"Xie","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108582","DOI":"10.1016\/j.agrformet.2021.108582","article-title":"Dnn-met: A deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information","volume":"308","author":"Shang","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.5194\/hess-17-3707-2013","article-title":"Benchmark products for land evapotranspiration: Landflux-eval multi-data set synthesis","volume":"17","author":"Mueller","year":"2013","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4513","DOI":"10.5194\/hess-22-4513-2018","article-title":"Exploring the merging of the global land evaporation wacmos-et products based on local tower measurements","volume":"22","author":"Martens","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"125221","DOI":"10.1016\/j.jhydrol.2020.125221","article-title":"Benchmarking large-scale evapotranspiration estimates: A perspective from a calibration-free complementary relationship approach and fluxcom","volume":"590","author":"Ma","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.agrformet.2017.04.011","article-title":"Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms","volume":"242","author":"Yao","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shang, K., Yao, Y.J., Li, Y.F., Yang, J.M., Jia, K., Zhang, X.T., Chen, X.W., Bei, X.Y., and Guo, X.Z. (2020). Fusion of five satellite-derived products using extremely randomized trees to estimate terrestrial latent heat flux over europe. Remote Sens., 12.","DOI":"10.3390\/rs12040687"},{"key":"ref_20","unstructured":"Chollet, F. (2017). Deep Learning with Python, Manning Publications."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2019.09.016","article-title":"Combining sentinel-1 and sentinel-2 satellite image time series for land cover mapping via a multi-source deep learning architecture","volume":"158","author":"Ienco","year":"2019","journal-title":"Isprs. J. Photogramm."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"127533","DOI":"10.1016\/j.jhydrol.2022.127533","article-title":"Variation in actual evapotranspiration and its ties to climate change and vegetation dynamics in northwest China","volume":"607","author":"Yang","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2018.10.002","article-title":"Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale","volume":"219","author":"Song","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.rse.2015.08.005","article-title":"A satellite-based hybrid algorithm to determine the priestley-taylor parameter for global terrestrial latent heat flux estimation across multiple biomes (vol 165, pg 216, 2015)","volume":"169","author":"Yao","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0168-1923(00)00225-2","article-title":"Gap filling strategies for defensible annual sums of net ecosystem exchange","volume":"107","author":"Falge","year":"2001","journal-title":"Agric. For. Meteorol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1890\/06-0922.1","article-title":"The energy balance closure problem: An overview","volume":"18","author":"Foken","year":"2008","journal-title":"Ecol. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/S0168-1923(00)00123-4","article-title":"Correcting eddy-covariance flux underestimates over a grassland","volume":"103","author":"Twine","year":"2000","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jung, M., Reichstein, M., Margolis, H.A., Cescatti, A., Richardson, A.D., Arain, M.A., Arneth, A., Bernhofer, C., Bonal, D., and Chen, J.Q. (2011). Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res.-Biogeosci., 116.","DOI":"10.1029\/2010JG001566"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.agrformet.2015.05.003","article-title":"Empirical estimation of daytime net radiation from shortwave radiation and ancillary information","volume":"211","author":"Jiang","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.agrformet.2012.11.016","article-title":"Modis-driven estimation of terrestrial latent heat flux in China based on a modified priestley-taylor algorithm","volume":"171","author":"Yao","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1002\/qj.49711146910","article-title":"Evaporation from sparse crops\u2014An energy combination theory","volume":"111","author":"Shuttleworth","year":"1985","journal-title":"Q. J. Roy. Meteor. Soc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1175\/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2","article-title":"On the assessment of surface heat flux and evaporation using large scale parameters","volume":"100","author":"Priestley","year":"1972","journal-title":"Mon. Weather. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.rse.2007.06.025","article-title":"Global estimates of the land-atmosphere water flux based on monthly avhrr and islscp-ii data, validated at 16 fluxnet sites","volume":"112","author":"Fisher","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1175\/2007JHM911.1","article-title":"An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture","volume":"9","author":"Wang","year":"2008","journal-title":"J. Hydrometeorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"D15","DOI":"10.1029\/2006JD008351","article-title":"A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature","volume":"112","author":"Wang","year":"2007","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.solener.2018.11.008","article-title":"Estimation of surface downward shortwave radiation over China from avhrr data based on four machine learning methods","volume":"177","author":"Wei","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/01621459.1997.10473615","article-title":"Bayesian model averaging for linear regression models","volume":"92","author":"Raftery","year":"1997","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.jhydrol.2015.06.059","article-title":"Using bayesian model averaging to estimate terrestrial evapotranspiration in China","volume":"528","author":"Chen","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","article-title":"Decomposition of the mean squared error and nse performance criteria: Implications for improving hydrological modelling","volume":"377","author":"Gupta","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.agrformet.2013.11.008","article-title":"Multi-site evaluation of terrestrial evaporation models using fluxnet data","volume":"187","author":"Ershadi","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.rse.2012.11.004","article-title":"Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance","volume":"129","author":"Yebra","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1016\/j.advwatres.2006.11.014","article-title":"Multi-model ensemble hydrologic prediction using bayesian model averaging","volume":"30","author":"Duan","year":"2007","journal-title":"Adv. Water Resour."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e2019WR026058","DOI":"10.1029\/2019WR026058","article-title":"Ecostress: Nasa\u2019s next generation mission to measure evapotranspiration from the international space station","volume":"56","author":"Fisher","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.agrformet.2010.01.015","article-title":"Computing turbulent fluxes near the surface: Needed improvements","volume":"150","author":"Mahrt","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhao, M., Running, S.W., and Nemani, R.R. (2006). Sensitivity of moderate resolution imaging spectroradiometer (modis) terrestrial primary production to the accuracy of meteorological reanalyses. J. Geophys. Res.-Biogeosci., 111.","DOI":"10.1029\/2004JG000004"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3624","DOI":"10.1175\/JCLI-D-11-00015.1","article-title":"Merra: Nasa\u2019s modern-era retrospective analysis for research and applications","volume":"24","author":"Rienecker","year":"2011","journal-title":"J. Clim."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s10712-008-9037-z","article-title":"Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data","volume":"29","author":"Kalma","year":"2008","journal-title":"Surv. Geophys."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"D22","DOI":"10.1029\/2008JD011590","article-title":"Watershed allied telemetry experimental research","volume":"114","author":"Li","year":"2009","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1029\/2009WR008800","article-title":"A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006","volume":"46","author":"Zhang","year":"2010","journal-title":"Water Resour. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. Acm."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep neural networks for acoustic modeling in speech recognition","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Proc. Mag."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhou, Z.H., and Feng, J. (2017, January 19\u201325). Deep forest: Towards an alternative to deep neural networks. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/497"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"126592","DOI":"10.1016\/j.jhydrol.2021.126592","article-title":"Comparison of physical-based, data-driven and hybrid modeling approaches for evapotranspiration estimation","volume":"601","author":"Hu","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_58","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/44\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:39:44Z","timestamp":1760132384000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,21]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010044"],"URL":"https:\/\/doi.org\/10.3390\/rs16010044","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,21]]}}}