Alexandridis T, Cherif I, Chemin Y, Silleos G, Stavrinos E, Zalidis G (2009) Integrated methodology for estimating water use in mediterranean agricultural areas. Remote Sens 1:445–465
Allen RG, Tasumi M, Trezza R (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. J Irrig Drain Eng 133:380–394
Anderson MC, Norman JM, Mecikalski JR, Otkin JA, Kustas WP (2007) A climatological study of evapotranspiration and moisture stress across the continental United States based on the thermal remote sensing: 1. Model formulation. J Geophys Res-Atmos 112:D10117
Anurag M, Yazid T, Nadhir AA, Shamsuddin ShH, Harkanwaljot S, Sekhon RKP, Priya Rai KP, Padam S, Ahmed E, Saad S (2021) Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Eng Appl Comput Fluid Mech 15(1):1075–1094
Aybar-Ruiz A, Jiménez-Fernández S, Cornejo-Bueno L, Casanova-Mateo C, Sanz-Justo J, Salvador-González P, Salcedo-Sanz S (2016) A novel grouping genetic algorithm – extreme learning machine approach for global solar radiation prediction from numerical weather models inputs. Sol Energy 132:129–142
Bagley JE, Kueppers LM, Billesbach DP, Williams IN, Biraud SC, Torn MS (2017) The influence of land cover on surface energy partitioning and evaporative fraction regimes in the US Southern Great Plains. J Geophys Res-Atmos 122(11):5793–5807
Baldocchi DD (2020) How eddy covariance flux measurements have contributed to our understanding of global change biology. Glob Change Biol 26(1):242–260
Ballabio C, Lugato E, Fernández-Ugalde O, Orgiazzi A, Jones A, Borrelli P, Panagos P (2019) Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression. Geoderma 355:113912
Barboza F, Kimura H, Altman E (2017) Machine learning models and bankruptcy prediction. Expert Syst Appl 83:405–417
Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM (1998) A remote sensing surface energy balance algorithm for land (SEBAL)—1. Formulation. J Hydrol 213:198–212
Bateni SM, Entekhabi D, Jeng DS (2013) Variational assimilation of land surface temperature and the estimation of surface energy balance components. J Hydrol 481:143–156
Borges L, Fernando F, da Cunha F (2020) New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning. Agric Water Manag 234:106113
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Carlson TN, Petropoulos GP (2019) A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle. Int J Remote Sens 40(20):7716–7729
Dash SS, Nayak SK, Mishra D (2021) A review on machine learning algorithms. Intell Cloud Comput 495–507.
de Tomás A, Nieto H, Guzinski R, Salas J, Sandholt I, Berliner P (2014) Validation and scale dependencies of the triangle method for the evaporative fraction estimation over heterogeneous areas. Rem Sens Environ 152:493–511
Dou X, Yang Y (2018) Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems. Comput Electron Agricult 148:95–106
Eugster W, Senn WA (1995) Cospectral correction model for measurement of turbulent NO2 flux. Bound-Layer Meteorol 74(4):321–340
Fang K, Shen C, Kifer D, Yang X (2017) Prolongation of SMAP to spatiotemporally seamless coverage of continental U.S. using a deep learning neural network. Geophys Res Lett 44:11030–11039
Foken T (2008) The energy balance closure problem—an overview. Ecol Appl 18:1351–1367
Fu T, Li X, Jia R, Feng L (2021) A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland. J Hydrol 603:126881
Gentine P, Entekhabi D, Chehbouni A, Boulet G, Duchemin B (2007) Analysis of evaporative fraction diurnal behaviour. Agric For Meteorol 143(1–2):13–29
Gentine P, Entekhabi D, Polcher J (2011) The diurnal behavior of evaporative fraction in the soil–vegetation–atmospheric boundary layer continuum. J Hydrometeorol 12(6):1530–1546
Göckede M, Rebmann C, Foken T (2004) A combination of quality assessment tools for eddy covariance measurements with footprint modelling for the characterization of complex sites. Agric For Meteorol 127:175–188
Guevara-Escobar A, González-Sosa E, Cervantes-Jiménez M, Suzán-Azpiri H, Queijeiro-Bolaños ME, Carrillo Ángeles I, Cambrón-Sandoval VH (2020) Eddy covariance carbon flux in a scrub in the Mexican highland.
Biogeosci Discuss 2020:1-16
Hollinger DY, Richardson AD (2005) Uncertainty in eddy covariance measurements and its application to physiological models. Tree Physiol 25:873–885
Horst TW, Lenschow DH (2009) Attenuation of scalar fluxes measured with spatially displaced sensors. Bound-Layer Meteorol 130:275–300
Hu X, Shi L, Lin L, Zhang B, Zha Y (2018) Optical-based and thermal-based surface conductance and actual evapotranspiration estimation, an evaluation study in the North China Plain. Agric For Meteorol 263:449–464
Hu X, Shi L, Lin L, Zha Y (2019) Nonlinear boundaries of land surface temperature–vegetation index space to estimate water deficit index and evaporation fraction. Agric For Meteorol 279:107736
Jo I, Lee S, Oh S (2019) Improved measures of redundancy and relevance for mRMR feature selection. Computers 8(2):42
Kang J, Schwartz R, Flickinger J, Beriwal S (2015) Machine learning approaches for predicting radiation therapy outcomes: a clinician’s perspective. Int J Radiat Oncol Biol Phys 93:1127–1135
Kolle O, Rebmann C (2007) Eddysoft-documentation of a software package to acquire and process eddy covariance data. Tech Rep Max Planck Inst Biogeochem 10:88
Kong L, Zhang Y, Ye ZQ, Liu XQ, Zhao SQ, Wei L, Gao G (2007) Assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res 35:345–349
Lhomme JP, Elguero E (1999) Examination of evaporative fraction diurnal behaviour using a soil-vegetation model coupled with a mixed-layer model. Hydrol Earth Syst Sci 3:259–270
Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: A review. Sensors 18(8):2674
Liu L, Liao J, Chen X, Zhou G, Su Y, Xiang Z, Wang Z, Liu X, Li Y, Wu J, Xiong X, Shao H (2017) The microwave temperature vegetation drought index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010). Remote Sens Environ 199:302–320
Liu Q, Wang T, Han Q, Sun S, Liu CQ, Chen X (2019) Diagnosing environmental controls on actual evapotranspiration and evaporative fraction in a water-limited region from northwest China. J Hydrol 578:124045
Liu X, Xu J, Zhou X, Wang W, Yang S (2020) Evaporative fraction and its application in estimating daily evapotranspiration of water-saving irrigated rice field. J Hydrol 584:124317
López-Cortés XA, Nachtigall FM, Olate VR, Araya M, Oyanedel S, Diaz V, Jakob E, Ríos-Momberg M, Santos LS (2017) Fast detection of pathogens in salmon farming industry. Aquaculture 470:17–24
Lu J, Li ZL, Tang R, Tang BH, Wu H, Yang F, Zhou G (2013a) Evaluating the SEBS-estimated evaporative fraction from MODIS data for a complex underlying surface. Hydrol Process 27(22):3139–3149
Lu J, Tang R, Tang H, Li ZL (2013b) Derivation of daily evaporative fraction based on temporal variations in surface temperature, air temperature, and net radiation. Remote Sens 5(10):5369–5396
Mosre J, Suárez F (2021) Actual evapotranspiration estimates in arid cold regions using machine learning algorithms with in situ and remote sensing data. Water 13(6):870
Milano M, Ruelland D, Fernandez S, Dezetter A, Fabre J, Servat E, Fritsch JM, Ardoin-Bardin S, Thivet G (2013) Current state of Mediterranean water resources and future trends under climatic and anthropogenic changes. Hydrol Sci J 58:498–518
Moncrieff JB, Clement R, Finnigan J, Meyers T (2004) Averaging, detrending and filtering of eddy covariance time series. In: Lee X, Massman WJ, Law BE (eds) Handbook of micrometeorology: a guide for surface flux measurement and analysis. Kluwer Academic Publisher, Dordrecht, pp 7–32
Mu Q, Heinsch F, Zhao M, Running S (2007) Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens Environ 111:519–536
Nguyen TPL, Mula L, Cortignani R, Seddaiu G, Dono G, Virdis SG, Roggero PP (2016) Perceptions of present and future climate change impacts on water availability for agricultural systems in the western Mediterranean region. Water 8(11):523
Nishida K, Nemani RR, Running SW, Glassy JM (2003) An operational remote sensing algorithm of land surface evaporation. J Geophys Res-Atmos 108(D9):4270
Norman JM, Kustas WP, Humes KS (1995) A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperature. Agric For Meteorol 77:263–293
Nutini F, Boschetti M, Candiani G, Bocchi S, Brivio PA (2014) Evaporative fraction as an indicator of moisture condition and water stress status in semi-arid rangeland ecosystems. Remote Sens 6(7):6300–6323
Op de Beeck M, Sabbatini S, Papale D (2017) ICOS ecosystem instructions for soil meteorological measurements (TS, SWC, G) (Version 20180615). ICOS Ecosystem Thematic Centre. https://doi.org/10.18160/1a28-gex6
Pan S, Pan N, Tian H, Friedlingstein P, Sitch S, Shi H, Running SW (2020) Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling. Hydrol Earth Syst Sci 24(3):1485–1509
Pastorello G, Trotta C, Canfora E et al (2020) The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7:225. https://doi.org/10.1038/s41597-020-0534-3
Peng J, Loew A (2014) Evaluation of daytime evaporative fraction from MODIS TOA radiances using FLUXNET observations. Remote Sens 6(7):5959–5975
Peng J, Borsche M, Liu Y, Loew A (2013) How representative are instantaneous evaporative fraction measurements for daytime fluxes? Hydrol Earth Syst Sci 17:3913–3919
Puma MJ, Koster RD, Cook BI (2013) Phenological versus meteorological controls on land-atmosphere water and carbon fluxes. J Geophys Res-Biogeosci 118:14–29. https://doi.org/10.1029/2012JG002088
Rahimzadeh-Bajgiran P, Omasa K, Shimizu Y (2012) Comparative evaluation of the Vegetation Dryness Index (DVI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran. ISPRS J Photogramm Remote Sens 68:1–12
Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N (2019) Deep learning and process understanding for data-driven Earth system science. Nature 566(7743):195–204
Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Valentini R (2005) On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob change Biol 11(9):1424–1439
Richardson AD, Hollinger DY, Burba GG et al (2006) A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes. Agric For Meteorol 136:1–18
Richardson A, Signor BM, Lidbury BA, Badrick T (2016) Clinical chemistry in higher dimensions: machine-learning and enhanced prediction from routine clinical chemistry data. Clin Biochem 49:1213–1220
Rodríguez JD, Pérez A, Lozano JA (2010) Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans Pattern Anal Mach Intell 32:569–575. https://doi.org/10.1109/TPAMI.2009.187
Schuepp PH, Leclerc MY, MacPherson JI, Desjardins RL (1990) Footprint prediction of scalar fluxes from analytical solutions of the diffusion equation. Bound-Layer Meteorol 50(1):355–373
Schwalm CR, Williams CA, Schaefer K, Arneth A, Bonal D, Buchmann N, Reichstein M (2010) Assimilation exceeds respiration sensitivity to drought: a FLUXNET synthesis. Glob Change Biol 16(2):657–670
Sen PC, Hajra M, Ghosh M (2020) Supervised classification algorithms in machine learning: a survey and review. In: Mandal J, Bhattacharya D (eds) Emerging technology in modelling and graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore
Seneviratne SI, Luthi D, Litschi M, Schar C (2006) Land–atmosphere coupling and climate change in Europe. Nature 443(7108):205–209
Stein ML (1999) Interpolation of spatial data: some theory for kriging. Springer Science & Business Media.
Su Z (2002) The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol Earth Syst Sc 6:85–99
Takahashi K, Kim K, Ogata T, Sugano S (2017) Tool-body assimilation model considering grasping motion through deep learning. Rob Auton Syst 91:115–127
Tang R, Li ZL (2017a) An improved constant evaporative fraction method for estimating daily evapotranspiration from remotely sensed instantaneous observations. Geophys Res Lett 44:2319–2326. https://doi.org/10.1002/2017GL072621
Tang R, Li Z-L (2017b) Estimating daily evapotranspiration from remotely sensed instantaneous observations with simplified derivations of a theoretical model. J Geophys Res-Atmos 122:10177–10190. https://doi.org/10.1002/2017JD027094
Tramontana G, Jung M, Schwalm CR, Ichii K, Camps-Valls G, Ráduly B, Papale D (2016) Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13(14):4291–4313
Trenberth KE, Guillemot CJ (1996) Physical processes involved in the 1988 drought and 1993 floods in North America. J Clim 9:1288–1298
Vapnik V (1999) The nature of statistical learning theory. Springer Science & Business Media, Berlin
Vitale L, Di Tommasi P, Arena C, Fierro A, De Santo AV, Magliulo V (2007) Effects of water stress on gas exchange of field grown Zea mays L. in Southern Italy: an analysis at canopy and leaf level. Acta Physiol Plant 29(4):317–326
Vitale L, Di Tommasi P, D’Urso G, Magliulo V (2016) The response of ecosystem carbon fluxes to LAI and environmental drivers in a maize crop grown in two contrasting seasons. Int J Biometeorol 60(3):411–420
Wang D, Tan X (2017) Bayesian neighborhood component analysis. IEEE Trans Neural Netw Learn Syst 29(7):3140–3151
Wauters M, Vanhoucke M (2014) Support vector machine regression for project control forecasting. Autom Constr 47:92–106
Webb EK, Pearman G, Leuning R (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R Meteorol Soc 106:85–100
Wildenhain J, Spitzer M, Dolma S, Jarvik N, White R, Roy M, Griffiths E, Bellows DS, Wright GD, Tyers M (2015) Prediction of synergism from chemical–genetic interactions by machine learning. Cell Syst 1:383–395
Williams IN, Torn MS (2015) Vegetation controls on surface heat flux partitioning, and land–atmosphere coupling. Geophys Res Lett 42(21):9416–9424
Xu T, Bateni SM, Liang S, Entekhabi D, Mao K (2014) Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from geostationary operational environmental satellites. J Geophys Res-Atmos 119(18):10780–10798
Yang D, He W, Chen HE, Lei HM (2013) Analysis of the diurnal pattern of evaporative fraction and its controlling factors over croplands in the Northern China. J Integr Agric 12(8):1316–1329
Yang Y, Long D, Guan H, Liang W, Simmons C, Batelaan O (2015) Comparison of three dual-source remote sensing evapotranspiration models during the MUSOEXE-12 campaign: revisit of model physics. Water Resour Res 51:3145–3165
Yin L, Tao F, Chen Y, Liu F, Hu J (2021) Improving terrestrial evapotranspiration estimation across China during 2000–2018 with machine learning methods. J Hydrol 600:126538
Zenone T, Fischer M, Arriga N, Broeckx LS, Verlinden MS, Vanbeveren S, Zona D, Ceulemans R (2015) Biophysical drivers of the carbon dioxide, water vapor, and energy exchanges of a short-rotation poplar coppice. Agric For Meteorol 209:22–35
Zhao WL, Gentine P, Reichstein M, Zhang Y, Zhou S, Wen Y, Qiu GY (2019) Physics-constrained machine learning of evapotranspiration. Geophys Res Lett 46(24):14496–14507
Zhou C, Wang K (2016) Biological and environmental controls on evaporative fractions at AmeriFlux sites. J Appl Meteorol Climatol 55(1):145–161
Zhu W, Jia S, Lall U, Cheng Y, Gentine P (2020) An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas. Remote Sens Environ 247:111887
Zveryaev II, Allan RP (2010) Summertime precipitation variability over Europe and its links to atmospheric dynamics and evaporation. J Geophys Res: Atmos 115(D12).
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