23.2.14 Evapotranspiration, Evaporation, Remote Sensing

Chapter Contents (Back)
Evapotranspiration. Remote Sensing. Specific locations:
See also Specific Site Evapotranspiration Analyusis. Efficiency issues:
See also Water Use Analysis, Water Stress.

Hankerson, B., Kjaersgaard, J., Hay, C.,
Estimation of Evapotranspiration from Fields with and without Cover Crops Using Remote Sensing and in situ Methods,
RS(4), No. 12, December 2012, pp. 3796-3812.
DOI Link 1211
BibRef

Nagler, P.L.[Pamela L.], Glenn, E.P.[Edward P.], Nguyen, U.[Uyen], Scott, R.L.[Russell L.], Doody, T.[Tanya],
Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index,
RS(5), No. 8, 2013, pp. 3849-3871.
DOI Link 1309
BibRef

Tian, J., Su, H., Sun, X., Chen, S., He, H., Zhao, L.,
Impact of the Spatial Domain Size on the Performance of the Ts-VI Triangle Method in Terrestrial Evapotranspiration Estimation,
RS(5), No. 4, April 2013, pp. 1998-2013.
DOI Link 1305
BibRef

Sun, Z.G.[Zhi-Gang], Gebremichael, M.[Mekonnen], Wang, Q.X.[Qin-Xue], Wang, J.M.[Jun-Ming], Sammis, T.W.[Ted W.], Nickless, A.[Alecia],
Evaluation of Clear-Sky Incoming Radiation Estimating Equations Typically Used in Remote Sensing Evapotranspiration Algorithms,
RS(5), No. 10, 2013, pp. 4735-4752.
DOI Link 1311
BibRef

Nouri, H.[Hamideh], Beecham, S.[Simon], Anderson, S.[Sharolyn], Nagler, P.[Pamela],
High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors,
RS(6), No. 1, 2014, pp. 580-602.
DOI Link 1402
BibRef

Yao, Y.J.[Yun-Jun], Liang, S.L.[Shun-Lin], Zhao, S.H.[Shao-Hua], Zhang, Y.[Yuhu], Qin, Q.M.[Qi-Ming], Cheng, J.[Jie], Jia, K.[Kun], Xie, X.H.[Xian-Hong], Zhang, N.N.[Nan-Nan], Liu, M.[Meng],
Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration,
RS(6), No. 1, 2014, pp. 880-904.
DOI Link 1402
BibRef

Amri, R.[Rim], Zribi, M.[Mehrez], Lili-Chabaane, Z.[Zohra], Szczypta, C.[Camille], Calvet, J.C.[Jean Christophe], Boulet, G.[Gilles],
FAO-56 Dual Model Combined with Multi-Sensor Remote Sensing for Regional Evapotranspiration Estimations,
RS(6), No. 6, 2014, pp. 5387-5406.
DOI Link 1407
BibRef

Lu, J.[Jing], Tang, R.L.[Rong-Lin], Tang, H.J.[Hua-Jun], Li, Z.L.[Zhao-Liang], Zhou, G.Q.[Guo-Qing], Shao, K.[Kun], Bi, Y.Y.[Yu-Yun], Labed, J.[Jelila],
Daily Evaporative Fraction Parameterization Scheme Driven by Day-Night Differences in Surface Parameters: Improvement and Validation,
RS(6), No. 5, 2014, pp. 4369-4390.
DOI Link 1407
BibRef

Peng, J.[Jian], Loew, A.[Alexander],
Evaluation of Daytime Evaporative Fraction from MODIS TOA Radiances Using FLUXNET Observations,
RS(6), No. 7, 2014, pp. 5959-5975.
DOI Link 1408
BibRef

Wang, X.G.[Xiao-Gang], Wang, W.[Wen], Huang, D.[Dui], Yong, B.[Bin], Chen, X.[Xi],
Modifying SEBAL Model Based on the Trapezoidal Relationship between Land Surface Temperature and Vegetation Index for Actual Evapotranspiration Estimation,
RS(6), No. 7, 2014, pp. 5909-5937.
DOI Link 1408
BibRef

Lu, H.Y.[Han-Yu], Liu, T.X.[Ting-Xi], Yang, Y.T.[Yu-Ting], Yao, D.D.[Dan-Dan],
A Hybrid Dual-Source Model of Estimating Evapotranspiration over Different Ecosystems and Implications for Satellite-Based Approaches,
RS(6), No. 9, 2014, pp. 8359-8386.
DOI Link 1410
BibRef

Romaguera, M.[Mireia], Salama, M.S.[Mhd. Suhyb], Krol, M.S.[Maarten S.], Hoekstra, A.Y.[Arjen Y.], Su, Z.B.[Zhong-Bo],
Towards the Improvement of Blue Water Evapotranspiration Estimates by Combining Remote Sensing and Model Simulation,
RS(6), No. 8, 2014, pp. 7026-7049.
DOI Link 1410
BibRef

Romaguera, M.[Mireia], Krol, M.S.[Maarten S.], Salama, M.S.[Mhd. Suhyb], Su, Z.B.[Zhong-Bo], Hoekstra, A.Y.[Arjen Y.],
Application of a Remote Sensing Method for Estimating Monthly Blue Water Evapotranspiration in Irrigated Agriculture,
RS(6), No. 10, 2014, pp. 10033-10050.
DOI Link 1411
BibRef

Ahrends, H.E.[Hella Ellen], Haseneder-Lind, R.[Rainer], Schween, J.H.[Jan H.], Crewell, S.[Susanne], Stadler, A.[Anja], Rascher, U.[Uwe],
Diurnal Dynamics of Wheat Evapotranspiration Derived from Ground-Based Thermal Imagery,
RS(6), No. 10, 2014, pp. 9775-9801.
DOI Link 1411
BibRef

Hu, G.C.[Guang-Cheng], Jia, L.[Li],
Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations,
RS(7), No. 3, 2015, pp. 3056-3087.
DOI Link 1504
BibRef

Xu, T.R.[Tong-Ren], Liu, S.M.[Shao-Min], Xu, L.[Lu], Chen, Y.J.[Yu-Jie], Jia, Z.Z.[Zhen-Zhen], Xu, Z.W.[Zi-Wei], Nielson, J.[Jeffrey],
Temporal Upscaling and Reconstruction of Thermal Remotely Sensed Instantaneous Evapotranspiration,
RS(7), No. 3, 2015, pp. 3400-3425.
DOI Link 1504
BibRef

Song, Y.[Yi], Ma, M.G.[Ming-Guo], Jin, L.[Long], Wang, X.F.[Xu-Feng],
A Revised Temporal Scaling Method to Yield Better ET Estimates at a Regional Scale,
RS(7), No. 5, 2015, pp. 6433-6453.
DOI Link 1506
estimate total daytime evapotranspiration BibRef

Schwerdtfeger, J.[Julia], da Silveira, S.W.G.[Sérgio Wagner Gripp], Zeilhofer, P.[Peter], Weiler, M.[Markus],
Coupled Ground- and Space-Based Assessment of Regional Inundation Dynamics to Assess Impact of Local and Upstream Changes on Evaporation in Tropical Wetlands,
RS(7), No. 8, 2015, pp. 9769.
DOI Link 1509
BibRef

Stefan, V.G.[Vivien Georgiana], Merlin, O.[Olivier], Er-Raki, S.[Salah], Escorihuela, M.J.[Maria-José], Khabba, S.[Said],
Consistency between In Situ, Model-Derived and High-Resolution-Image-Based Soil Temperature Endmembers: Towards a Robust Data-Based Model for Multi-Resolution Monitoring of Crop Evapotranspiration,
RS(7), No. 8, 2015, pp. 10444.
DOI Link 1509
BibRef

Mi, S.J.[Su-Juan], Su, H.B.[Hong-Bo], Zhang, R.H.[Ren-Hua], Tian, J.[Jing],
Using Simplified Thermal Inertia to Determine the Theoretical Dry Line in Feature Space for Evapotranspiration Retrieval,
RS(7), No. 8, 2015, pp. 10856.
DOI Link 1509
BibRef

Huang, C.L.[Chun-Lin], Li, Y.[Yan], Gu, J.[Juan], Lu, L.[Ling], Li, X.[Xin],
Improving Estimation of Evapotranspiration under Water-Limited Conditions Based on SEBS and MODIS Data in Arid Regions,
RS(7), No. 12, 2015, pp. 15854.
DOI Link 1601
BibRef

Sun, H.[Hao],
A Two-Source Model for Estimating Evaporative Fraction (TMEF) Coupling Priestley-Taylor Formula and Two-Stage Trapezoid,
RS(8), No. 3, 2016, pp. 248.
DOI Link 1604
BibRef

Ke, Y.H.[Ying-Hai], Im, J.[Jungho], Park, S.[Seonyoung], Gong, H.[Huili],
Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches,
RS(8), No. 3, 2016, pp. 215.
DOI Link 1604
BibRef

Nouri, H.[Hamideh], Glenn, E.P.[Edward P.], Beecham, S.[Simon], Boroujeni, S.C.[Sattar Chavoshi], Sutton, P.[Paul], Alaghmand, S.[Sina], Noori, B.[Behnaz], Nagler, P.[Pamela],
Comparing Three Approaches of Evapotranspiration Estimation in Mixed Urban Vegetation: Field-Based, Remote Sensing-Based and Observational-Based Methods,
RS(8), No. 6, 2016, pp. 492.
DOI Link 1608
BibRef

Zhang, T., Jin, S.,
Evapotranspiration Variations in the Mississippi River Basin Estimated From GPS Observations,
GeoRS(54), No. 8, August 2016, pp. 4694-4701.
IEEE DOI 1608
Global Positioning System BibRef

Zhang, H.[Hua], Gorelick, S.M.[Steven M.], Avisse, N.[Nicolas], Tilmant, A.[Amaury], Rajsekhar, D.[Deepthi], Yoon, J.[Jim],
A New Temperature-Vegetation Triangle Algorithm with Variable Edges (TAVE) for Satellite-Based Actual Evapotranspiration Estimation,
RS(8), No. 9, 2016, pp. 735.
DOI Link 1610
BibRef

Hu, Z.M.[Zhong-Min], Wu, G.[Genan], Zhang, L.X.[Liang-Xia], Li, S.G.[Sheng-Gong], Zhu, X.J.[Xian-Jin], Zheng, H.[Han], Zhang, L.M.[Lei-Ming], Sun, X.M.[Xiao-Min], Yu, G.[Guirui],
Modeling and Partitioning of Regional Evapotranspiration Using a Satellite-Driven Water-Carbon Coupling Model,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Zhu, W.B.[Wen-Bin], Lv, A.[Aifeng], Jia, S.F.[Shao-Feng], Yan, J.[Jiabao],
A New Contextual Parameterization of Evaporative Fraction to Reduce the Reliance of the Ts-VI Triangle Method on the Dry Edge,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Knipper, K.[Kyle], Hogue, T.[Terri], Scott, R.L.[Russell L.], Franz, K.[Kristie],
Evapotranspiration Estimates Derived Using Multi-Platform Remote Sensing in a Semiarid Region,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Ke, Y.H.[Ying-Hai], Im, J.[Jungho], Park, S.[Seonyoung], Gong, H.[Huili],
Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration,
PandRS(126), No. 1, 2017, pp. 79-93.
Elsevier DOI 1704
Evapotranspiration BibRef

Mahour, M.[Milad], Tolpekin, V.[Valentyn], Stein, A.[Alfred], Sharifi, A.[Ali],
A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration,
PandRS(126), No. 1, 2017, pp. 56-67.
Elsevier DOI 1704
Downscaling cokriging BibRef

Qiu, G.Y.[Guo-Yu], Tan, S.L.[Sheng-Lin], Wang, Y.[Yue], Yu, X.H.[Xiao-Hui], Yan, C.H.[Chun-Hua],
Characteristics of Evapotranspiration of Urban Lawns in a Sub-Tropical Megacity and Its Measurement by the 'Three Temperature Model + Infrared Remote Sensing' Method,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

He, R.[Ruyan], Jin, Y.F.[Yu-Fang], Kandelous, M.M.[Maziar M.], Zaccaria, D.[Daniele], Sanden, B.L.[Blake L.], Snyder, R.L.[Richard L.], Jiang, J.B.[Jin-Bao], Hopmans, J.W.[Jan W.],
Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Wagle, P.[Pradeep], Bhattarai, N.[Nishan], Gowda, P.H.[Prasanna H.], Kakani, V.G.[Vijaya G.],
Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum,
PandRS(128), No. 1, 2017, pp. 192-203.
Elsevier DOI 1706
Eddy, covariance BibRef

Pérez, J.Á.M.[José Ángel Martínez], García-Galiano, S.G.[Sandra G.], Martin-Gorriz, B.[Bernardo], Baille, A.[Alain],
Satellite-Based Method for Estimating the Spatial Distribution of Crop Evapotranspiration: Sensitivity to the Priestley-Taylor Coefficient,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Najmaddin, P.M.[Peshawa M.], Whelan, M.J.[Mick J.], Balzter, H.[Heiko],
Estimating Daily Reference Evapotranspiration in a Semi-Arid Region Using Remote Sensing Data,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Tang, R., Li, Z.L.,
An End-Member-Based Two-Source Approach for Estimating Land Surface Evapotranspiration From Remote Sensing Data,
GeoRS(55), No. 10, October 2017, pp. 5818-5832.
IEEE DOI 1710
atmospheric radiation, evaporation, longwave radiation transmission, meteorology. BibRef

Bhattarai, N.[Nishan], Wagle, P.[Pradeep], Gowda, P.H.[Prasanna H.], Kakani, V.G.[Vijaya G.],
Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions,
PandRS(133), No. Supplement C, 2017, pp. 128-141.
Elsevier DOI 1711
Crop water stress index, Eddy covariance, Evapotranspiration, Single-source SEB models, Regression, model BibRef

Zhang, Y.[Yu], Li, L.[Long], Chen, L.Q.[Long-Qian], Liao, Z.H.[Zhi-Hong], Wang, Y.C.[Yu-Chen], Wang, B.Y.[Bing-Yi], Yang, X.Y.[Xiao-Yan],
A Modified Multi-Source Parallel Model for Estimating Urban Surface Evapotranspiration Based on ASTER Thermal Infrared Data,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Kalmus, P., Lebsock, M.,
Correcting Biased Evaporation in CloudSat Warm Rain,
GeoRS(55), No. 11, November 2017, pp. 6207-6217.
IEEE DOI 1711
Atmospheric modeling, Clouds, Radar, Rain, Sea measurements, Sea surface, Table lookup, Atmospheric modeling, clouds, error correction, radar remote sensing BibRef

Raoufi, R.[Roozbeh], Beighley, E.[Edward],
Estimating Daily Global Evapotranspiration Using Penman-Monteith Equation and Remotely Sensed Land Surface Temperature,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Bahir, M.[Malik], Boulet, G.[Gilles], Olioso, A.[Albert], Rivalland, V.[Vincent], Gallego-Elvira, B.[Belen], Mira, M.[Maria], Rodriguez, J.C.[Julio-Cesar], Jarlan, L.[Lionel], Merlin, O.[Olivier],
Evaluation and Aggregation Properties of Thermal Infra-Red-Based Evapotranspiration Algorithms from 100 m to the km Scale over a Semi-Arid Irrigated Agricultural Area,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Li, X.J.[Xiao-Jun], Xin, X.Z.[Xiao-Zhou], Peng, Z.Q.[Zhi-Qing], Zhang, H.L.[Hai-Long], Yi, C.X.[Chuan-Xiang], Li, B.[Bin],
Analysis of the Spatial Variability of Land Surface Variables for ET Estimation: Case Study in HiWATER Campaign,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Xu, J.[Jia_Ming], Wu, B.F.[Bing-Fang], Yan, N.[Nana], Tan, S.[Shen],
Regional Daily ET Estimates Based on the Gap-Filling Method of Surface Conductance,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Yang, Y.M.[Yong-Min], Qiu, J.X.[Jian-Xiu], Zhang, R.H.[Ren-Hua], Huang, S.F.[Shi-Feng], Chen, S.[Sheng], Wang, H.[Hui], Luo, J.[Jiashun], Fan, Y.[Yue],
Intercomparison of Three Two-Source Energy Balance Models for Partitioning Evaporation and Transpiration in Semiarid Climates,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Grosso, C.[Carla], Manoli, G.[Gabriele], Martello, M.[Marco], Chemin, Y.H.[Yann H.], Pons, D.H.[Diego H.], Teatini, P.[Pietro], Piccoli, I.[Ilaria], Morari, F.[Francesco],
Mapping Maize Evapotranspiration at Field Scale Using SEBAL: A Comparison with the FAO Method and Soil-Plant Model Simulations,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Tan, S.[Shen], Wu, B.F.[Bing-Fang], Yan, N.[Nana], Zeng, H.W.[Hong-Wei],
Satellite-Based Water Consumption Dynamics Monitoring in an Extremely Arid Area,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Talsma, C.J.[Carl J.], Good, S.P.[Stephen P.], Miralles, D.G.[Diego G.], Fisher, J.B.[Joshua B.], Martens, B.[Brecht], Jimenez, C.[Carlos], Purdy, A.J.[Adam J.],
Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Yi, Z.Y.[Zhen-Yan], Zhao, H.L.[Hong-Li], Jiang, Y.Z.[Yun-Zhong],
Continuous Daily Evapotranspiration Estimation at the Field-Scale over Heterogeneous Agricultural Areas by Fusing ASTER and MODIS Data,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Dhungel, S.[Sulochan], Barber, M.E.[Michael E.],
Estimating Calibration Variability in Evapotranspiration Derived from a Satellite-Based Energy Balance Model,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Martens, B.[Brecht], de Jeu, R.A.M.[Richard A. M.], Verhoest, N.E.C.[Niko E. C.], Schuurmans, H.[Hanneke], Kleijer, J.[Jonne], Miralles, D.G.[Diego G.],
Towards Estimating Land Evaporation at Field Scales Using GLEAM,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Poon, P.K.[Patrick K.], Kinoshita, A.M.[Alicia M.],
Estimating Evapotranspiration in a Post-Fire Environment Using Remote Sensing and Machine Learning,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Delogu, E.[Emilie], Boulet, G.[Gilles], Olioso, A.[Albert], Garrigues, S.[Sébastien], Brut, A.[Aurore], Tallec, T.[Tiphaine], Demarty, J.[Jérôme], Soudani, K.[Kamel], Lagouarde, J.P.[Jean-Pierre],
Evaluation of the SPARSE Dual-Source Model for Predicting Water Stress and Evapotranspiration from Thermal Infrared Data over Multiple Crops and Climates,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
BibRef

Small, E.E.[Eric E.], Badger, A.M.[Andrew M.], Abolafia-Rosenzweig, R.[Ronnie], Livneh, B.[Ben],
Estimating Soil Evaporation Using Drying Rates Determined from Satellite-Based Soil Moisture Records,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Sousa, D.[Daniel], Small, C.[Christopher],
Spectral Mixture Analysis as a Unified Framework for the Remote Sensing of Evapotranspiration,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Bellvert, J.[Joaquim], Adeline, K.[Karine], Baram, S.[Shahar], Pierce, L.[Lars], Sanden, B.L.[Blake L.], Smart, D.R.[David R.],
Monitoring Crop Evapotranspiration and Crop Coefficients over an Almond and Pistachio Orchard Throughout Remote Sensing,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Zou, Z.D.[Zhen-Dong], Yang, Y.J.[Ya-Jun], Qiu, G.Y.[Guo Yu],
Quantifying the Evapotranspiration Rate and Its Cooling Effects of Urban Hedges Based on Three-Temperature Model and Infrared Remote Sensing,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Pagán, B.R.[Brianna R.], Maes, W.H.[Wouter H.], Gentine, P.[Pierre], Martens, B.[Brecht], Miralles, D.G.[Diego G.],
Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Khand, K.[Kul], Taghvaeian, S.[Saleh], Gowda, P.[Prasanna], Paul, G.[George],
A Modeling Framework for Deriving Daily Time Series of Evapotranspiration Maps Using a Surface Energy Balance Model,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Guillevic, P.C.[Pierre C.], Olioso, A.[Albert], Hook, S.J.[Simon J.], Fisher, J.B.[Joshua B.], Lagouarde, J.P.[Jean-Pierre], Vermote, E.F.[Eric F.],
Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty: A Sensitivity Study Using AmeriFlux Data,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Cheng, J.[Jie], Kustas, W.P.[William P.],
Using Very High Resolution Thermal Infrared Imagery for More Accurate Determination of the Impact of Land Cover Differences on Evapotranspiration in an Irrigated Agricultural Area,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Wang, T.[Tong], Tang, R.L.[Rong-Lin], Li, Z.L.[Zhao-Liang], Jiang, Y.[Yazhen], Liu, M.[Meng], Niu, L.[Lu],
An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Jamshidi, S.[Sajad], Zand-Parsa, S.[Shahrokh], Jahromi, M.N.[Mojtaba Naghdyzadegan], Niyogi, D.[Dev],
Application of A Simple Landsat-MODIS Fusion Model to Estimate Evapotranspiration over A Heterogeneous Sparse Vegetation Region,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

McCabe, M.F.[Matthew F.], Miralles, D.G.[Diego G.], Holmes, T.R.H.[Thomas R.H.], Fisher, J.B.[Joshua B.],
Advances in the Remote Sensing of Terrestrial Evaporation,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Zhao, Y.[Yan], Lu, Z.X.[Zhi-Xiang], Wei, Y.P.[Yong-Ping],
An Assessment of Global Precipitation and Evapotranspiration Products for Regional Applications,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Mokhtari, A.[Ali], Noory, H.[Hamideh], Pourshakouri, F.[Farrokh], Haghighatmehr, P.[Parisa], Afrasiabian, Y.[Yasamin], Razavi, M.[Maryam], Fereydooni, F.[Fatemeh], Naeni, A.S.[Ali Sadeghi],
Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2,
PandRS(154), 2019, pp. 231-245.
Elsevier DOI 1907
Landsat 8, MSDF-ET method, Potential evapotranspiration, Sentinel-2, Single crop coefficient, TsHARP algorithm BibRef

Nocco, M.A.[Mallika A.], Zipper, S.C.[Samuel C.], Booth, E.G.[Eric G.], Cummings, C.R.[Cadan R.], Loheide, S.P.[Steven P.], Kucharik, C.J.[Christopher J.],
Combining Evapotranspiration and Soil Apparent Electrical Conductivity Mapping to Identify Potential Precision Irrigation Benefits,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Fang, L.[Li], Zhan, X.[Xiwu], Schull, M.[Mitchell], Kalluri, S.[Satya], Laszlo, I.[Istvan], Yu, P.[Peng], Carter, C.[Corinne], Hain, C.[Christopher], Anderson, M.[Martha],
Evapotranspiration Data Product from NESDIS GET-D System Upgraded for GOES-16 ABI Observations,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Du, T.[Tao], Wang, L.[Li], Yuan, G.F.[Guo-Fu], Sun, X.M.[Xiao-Min], Wang, S.[Shusen],
Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Aboutalebi, M.[Mahyar], Torres-Rua, A.F.[Alfonso F.], McKee, M.[Mac], Kustas, W.P.[William P.], Nieto, H.[Hector], Alsina, M.M.[Maria Mar], White, A.[Alex], Prueger, J.H.[John H.], McKee, L.[Lynn], Alfieri, J.[Joseph], Hipps, L.[Lawrence], Coopmans, C.[Calvin], Dokoozlian, N.[Nick],
Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
BibRef

Wu, B.F.[Bing-Fang], Zhu, W.W.[Wei-Wei], Yan, N.[Nana], Xing, Q.A.[Qi-Ang], Xu, J.M.[Jia-Ming], Ma, Z.H.[Zong-Han], Wang, L.J.[Lin-Jiang],
Regional Actual Evapotranspiration Estimation with Land and Meteorological Variables Derived from Multi-Source Satellite Data,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Jiang, L.[Lulu], Wu, H.[Huan], Tao, J.[Jing], Kimball, J.S.[John S.], Alfieri, L.[Lorenzo], Chen, X.W.[Xiu-Wan],
Satellite-Based Evapotranspiration in Hydrological Model Calibration,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

He, X.L.[Xin-Lei], Xu, T.R.[Tong-Ren], Xia, Y.L.[You-Long], Bateni, S.M.[Sayed M.], Guo, Z.X.[Zhi-Xia], Liu, S.M.[Shao-Min], Mao, K.[Kebiao], Zhang, Y.[Yuan], Feng, H.Z.[Huai-Ze], Zhao, J.X.[Jing-Xue],
A Bayesian Three-Cornered Hat (BTCH) Method: Improving the Terrestrial Evapotranspiration Estimation,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Yu, B.[Bing], Shang, S.H.[Song-Hao],
Estimating Growing Season Evapotranspiration and Transpiration of Major Crops over a Large Irrigation District from HJ-1A/1B Data Using a Remote Sensing-Based Dual Source Evapotranspiration Model,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Burchard-Levine, V.[Vicente], Nieto, H.[Héctor], Riaño, D.[David], Migliavacca, M.[Mirco], El-Madany, T.S.[Tarek S.], Perez-Priego, O.[Oscar], Carrara, A.[Arnaud], Martín, M.P.[M. Pilar],
Seasonal Adaptation of the Thermal-Based Two-Source Energy Balance Model for Estimating Evapotranspiration in a Semiarid Tree-Grass Ecosystem,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Sun, H.[Hao], Zhou, B.C.[Bai-Chi], Zhang, C.J.[Chuan-Jun], Liu, H.X.[Hong-Xing], Yang, B.[Bo],
DSCALE_mod16: A Model for Disaggregating Microwave Satellite Soil Moisture with Land Surface Evapotranspiration Products and Gridded Meteorological Data,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Guzinski, R.[Radoslaw], Nieto, H.[Hector], Sandholt, I.[Inge], Karamitilios, G.[Georgios],
Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Weksler, S.[Shahar], Rozenstein, O.[Offer], Haish, N.[Nadav], Moshelion, M.[Menachem], Walach, R.[Rony], Ben-Dor, E.[Eyal],
A Hyperspectral-Physiological Phenomics System: Measuring Diurnal Transpiration Rates and Diurnal Reflectance,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Barrios, J.M.[José Miguel], Arboleda, A.[Alirio], de Pue, J.[Jan], Chormanski, J.[Jaroslaw], Gellens-Meulenberghs, F.[Françoise],
Continuous Daily Evapotranspiration with Optical Spaceborne Observations at Sub-Kilometre Spatial Resolution,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Cha, M.X.[Ming-Xing], Li, M.M.[Meng-Meng], Wang, X.Q.[Xiao-Qin],
Estimation of Seasonal Evapotranspiration for Crops in Arid Regions Using Multisource Remote Sensing Images,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Chen, H.L.[Hui-Ling], Zhu, G.F.[Gao-Feng], Zhang, K.[Kun], Bi, J.[Jian], Jia, X.P.[Xiao-Peng], Ding, B.Y.[Bing-Yue], Zhang, Y.[Yang], Shang, S.S.[Sha-Sha], Zhao, N.[Nan], Qin, W.H.[Wen-Hua],
Evaluation of Evapotranspiration Models Using Different LAI and Meteorological Forcing Data from 1982 to 2017,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Sobejano-Paz, V.[Verónica], Mikkelsen, T.N.[Teis Nørgaard], Baum, A.[Andreas], Mo, X.G.[Xing-Guo], Liu, S.[Suxia], Köppl, C.J.[Christian Josef], Johnson, M.S.[Mark S.], Gulyas, L.[Lorant], García, M.[Mónica],
Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Kim, N.[Nari], Kim, K.[Kwangjin], Lee, S.[Soobong], Cho, J.[Jaeil], Lee, Y.[Yangwon],
Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Liu, R.[Rong], Wen, J.[Jun], Wang, X.[Xin], Wang, Z.L.[Zuo-Liang], Liu, Y.[Yu], Zhang, M.[Ming],
Estimates of Daily Evapotranspiration in the Source Region of the Yellow River Combining Visible/Near-Infrared and Microwave Remote Sensing,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Bajgain, R.[Rajen], Xiao, X.M.[Xiang-Ming], Wagle, P.[Pradeep], Kimball, J.S.[John S.], Brust, C.[Colin], Basara, J.B.[Jefferey B.], Gowda, P.[Prasanna], Starks, P.J.[Patrick J.], Neel, J.P.S.[James P. S.],
Comparing Evapotranspiration Products of Different Temporal and Spatial Scales in Native and Managed Prairie Pastures,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Wang, D.K.[Da-Kang], Yu, T.[Tao], Liu, Y.[Yan], Gu, X.F.[Xing-Fa], Mi, X.F.[Xiao-Fei], Shi, S.Y.[Shuai-Yi], Ma, M.H.[Mei-Hong], Chen, X.R.[Xin-Ran], Zhang, Y.[Yin], Liu, Q.X.[Qi-Xin], Mumtaz, F.[Faisal], Zhan, Y.L.[Yu-Lin],
Estimating Daily Actual Evapotranspiration at a Landsat-Like Scale Utilizing Simulated and Remote Sensing Surface Temperature,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Ma, Z.H.[Zong-Han], Wu, B.F.[Bing-Fang], Yan, N.[Nana], Zhu, W.W.[Wei-Wei], Zeng, H.W.[Hong-Wei], Xu, J.M.[Jia-Ming],
Spatial Allocation Method from Coarse Evapotranspiration Data to Agricultural Fields by Quantifying Variations in Crop Cover and Soil Moisture,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Soltani, M.[Mohsen], Koch, J.[Julian], Stisen, S.[Simon],
Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Yao, Y.J.[Yun-Jun], Di, Z.H.[Zhen-Hua], Xie, Z.J.[Zi-Jing], Xiao, Z.Q.[Zhi-Qiang], Jia, K.[Kun], Zhang, X.T.[Xiao-Tong], Shang, K.[Ke], Yang, J.M.[Jun-Ming], Bei, X.Y.[Xiang-Yi], Guo, X.Z.[Xiao-Zheng], Yu, R.Y.[Rui-Yang],
Simplified Priestley-Taylor Model to Estimate Land-Surface Latent Heat of Evapotranspiration from Incident Shortwave Radiation, Satellite Vegetation Index, and Air Relative Humidity,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Majozi, N.P.[Nobuhle P.], Mannaerts, C.M.[Chris M.], Ramoelo, A.[Abel], Mathieu, R.[Renaud], Verhoef, W.[Wouter],
Uncertainty and Sensitivity Analysis of a Remote-Sensing-Based Penman-Monteith Model to Meteorological and Land Surface Input Variables,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Delogu, E.[Emilie], Olioso, A.[Albert], Alliès, A.[Aubin], Demarty, J.[Jérôme], Boulet, G.[Gilles],
Evaluation of Multiple Methods for the Production of Continuous Evapotranspiration Estimates from TIR Remote Sensing,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Mobilia, M.[Mirka], Longobardi, A.[Antonia],
Prediction of Potential and Actual Evapotranspiration Fluxes Using Six Meteorological Data-Based Approaches for a Range of Climate and Land Cover Types,
IJGI(10), No. 3, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Li, C.G.[Chan-Glong], Li, Z.Y.[Zeng-Yuan], Gao, Z.H.[Zhi-Hai], Sun, B.[Bin],
Estimation of Evapotranspiration in Sparse Vegetation Areas by Applying an Optimized Two-Source Model,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Li, B.Y.[Bo-Yang], Cui, Y.K.[Yao-Kui], Geng, X.Z.[Xiao-Zhuang], Li, H.[Huan],
Improving the Evapotranspiration Estimation under Cloudy Condition by Extending the Ts-VI Triangle Model,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Le, M.S.[Mai Son], Liou, Y.A.[Yuei-An],
Spatio-Temporal Assessment of Surface Moisture and Evapotranspiration Variability Using Remote Sensing Techniques,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Silvestro, P.C.[Paolo Cosmo], Casa, R.[Raffaele], Hanuš, J.[Jan], Koetz, B.[Benjamin], Rascher, U.[Uwe], Schuettemeyer, D.[Dirk], Siegmann, B.[Bastian], Skokovic, D.[Drazen], Sobrino, J.[José], Tudoroiu, M.[Marin],
Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Mokhtari, A.[Ali], Ahmadi, A.[Arman], Daccache, A.[Andre], Drechsler, K.[Kelley],
Actual Evapotranspiration from UAV Images: A Multi-Sensor Data Fusion Approach,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Chao, L.J.[Li-Jun], Zhang, K.[Ke], Wang, J.F.[Jing-Feng], Feng, J.[Jin], Zhang, M.J.[Meng-Jie],
A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Laipelt, L.[Leonardo], Henrique Bloedow Kayser, R.[Rafael], Santos Fleischmann, A.[Ayan], Ruhoff, A.[Anderson], Bastiaanssen, W.[Wim], Erickson, T.A.[Tyler A.], Melton, F.[Forrest],
Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing,
PandRS(178), 2021, pp. 81-96.
Elsevier DOI 2108
Cloud computation, ERA5 land, geeSEBAL, Google earth engine, Landsat, Meteorological reanalysis BibRef

Nassar, A.[Ayman], Torres-Rua, A.[Alfonso], Kustas, W.[William], Alfieri, J.[Joseph], Hipps, L.[Lawrence], Prueger, J.[John], Nieto, H.[Héctor], Alsina, M.M.[Maria Mar], White, W.[William], McKee, L.[Lynn], Coopmans, C.[Calvin], Sanchez, L.[Luis], Dokoozlian, N.[Nick],
Assessing Daily Evapotranspiration Methodologies from One-Time-of-Day sUAS and EC Information in the GRAPEX Project,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Dimitriadou, S.[Stavroula], Nikolakopoulos, K.G.[Konstantinos G.],
Annual Actual Evapotranspiration Estimation via GIS Models of Three Empirical Methods Employing Remotely Sensed Data for the Peloponnese, Greece, and Comparison with Annual MODIS ET and Pan Evaporation Measurements,
IJGI(10), No. 8, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Xing, W.Q.[Wan-Qiu], Wang, W.G.[Wei-Guang], Shao, Q.X.[Quan-Xi], Song, L.Y.[Lin-Ye], Cao, M.Z.[Ming-Zhu],
Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley-Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Xue, J.[Jie], Anderson, M.C.[Martha C.], Gao, F.[Feng], Hain, C.[Christopher], Yang, Y.[Yun], Knipper, K.R.[Kyle R.], Kustas, W.P.[William P.], Yang, Y.[Yang],
Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Liu, Y.[Yuan], Yue, Q.M.[Qi-Meng], Wang, Q.Y.[Qian-Yang], Yu, J.S.[Jing-Shan], Zheng, Y.X.[Yue-Xin], Yao, X.L.[Xiao-Lei], Xu, S.[Shugao],
A Framework for Actual Evapotranspiration Assessment and Projection Based on Meteorological, Vegetation and Hydrological Remote Sensing Products,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Liu, Y.[Yan], Zhang, S.[Sha], Zhang, J.H.[Jia-Hua], Tang, L.L.[Li-Li], Bai, Y.[Yun],
Assessment and Comparison of Six Machine Learning Models in Estimating Evapotranspiration over Croplands Using Remote Sensing and Meteorological Factors,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
Evaluation, Evapotranspiration. BibRef

Kadam, S.I.A.[Sun-Il A.], Stöckle, C.O.[Claudio O.], Liu, M.L.[Ming-Liang], Gao, Z.M.[Zhong-Ming], Russell, E.S.[Eric S.],
Suitability of Earth Engine Evaporation Flux (EEFlux) Estimation of Evapotranspiration in Rainfed Crops,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Rojas, L.A.R.[Luis A. Reyes], Moletto-Lobos, I.[Italo], Corradini, F.[Fabio], Mattar, C.[Cristian], Fuster, R.[Rodrigo], Escobar-Avaria, C.[Cristián],
Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Li, X.[Xiang], Liu, S.M.[Shao-Min], Yang, X.F.[Xiao-Fan], Ma, Y.F.[Yan-Fei], He, X.L.[Xin-Lei], Xu, Z.W.[Zi-Wei], Xu, T.R.[Tong-Ren], Song, L.S.[Li-Sheng], Zhang, Y.[Yuan], Hu, X.[Xiao], Ju, Q.[Qian], Zhang, X.D.[Xiao-Dong],
Upscaling Evapotranspiration from a Single-Site to Satellite Pixel Scale,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Castelli, M.[Mariapina],
Evapotranspiration Changes over the European Alps: Consistency of Trends and Their Drivers between the MOD16 and SSEBop Algorithms,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Bhattarai, N.[Nishan], Wagle, P.[Pradeep],
Recent Advances in Remote Sensing of Evapotranspiration,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Zhao, Q.Z.[Qing-Zhi], Sun, T.T.[Ting-Ting], Zhang, T.X.[Teng-Xu], He, L.[Lin], Zhang, Z.[Zhiyi], Shen, Z.Y.[Zi-Yu], Xiong, S.[Si],
High-Precision Potential Evapotranspiration Model Using GNSS Observation,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Abbasi, N.[Neda], Nouri, H.[Hamideh], Didan, K.[Kamel], Barreto-Muñoz, A.[Armando], Borujeni, S.C.[Sattar Chavoshi], Salemi, H.[Hamidreza], Opp, C.[Christian], Siebert, S.[Stefan], Nagler, P.[Pamela],
Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Koch, J.[Julian], Demirel, M.C.[Mehmet Cüneyd], Stisen, S.[Simon],
Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Nassar, A.[Ayman], Torres-Rua, A.[Alfonso], Hipps, L.[Lawrence], Kustas, W.[William], McKee, M.[Mac], Stevens, D.[David], Nieto, H.[Héctor], Keller, D.[Daniel], Gowing, I.[Ian], Coopmans, C.[Calvin],
Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Long, X.J.[Xun-Jian], Cui, Y.K.[Yao-Kui],
Spatially Downscaling a Global Evapotranspiration Product for End User Using a Deep Neural Network: A Case Study with the GLEAM Product,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Guo, X.[Xiao], Wu, Z.Y.[Zhi-Yong], He, H.[Hai], Xu, Z.G.[Zheng-Guang],
Evaluating the Potential of Different Evapotranspiration Datasets for Distributed Hydrological Model Calibration,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, L.J.[Lin-Jiang], Wu, B.F.[Bing-Fang], Elnashar, A.[Abdelrazek], Zhu, W.W.[Wei-Wei], Yan, N.[Nana], Ma, Z.H.[Zong-Han], Liu, S.R.[Shi-Rong], Niu, X.D.[Xiao-Dong],
Incorporation of Net Radiation Model Considering Complex Terrain in Evapotranspiration Determination with Sentinel-2 Data,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Guo, D.[Danlu], Parehkar, A.[Arash], Ryu, D.[Dongryeol], Wang, Q.J.[Quan J.], Western, A.W.[Andrew W.],
Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Astuti, I.S.[Ike Sari], Wiwoho, B.S.[Bagus Setiabudi], Purwanto, P.[Purwanto], Wagistina, S.[Satti], Deffinika, I.[Ifan], Sucahyo, H.R.[Hetty Rahmawati], Herlambang, G.A.[Gilang Aulia], Alfarizi, I.A.G.[Imam Abdul Gani],
An Application of Improved MODIS-Based Potential Evapotranspiration Estimates in a Humid Tropic Brantas Watershed: Implications for Agricultural Water Management,
IJGI(11), No. 3, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Yang, Y.[Yun], Anderson, M.[Martha], Gao, F.[Feng], Xue, J.[Jie], Knipper, K.[Kyle], Hain, C.[Christopher],
Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Ebert, L.A.[Logan A.], Talib, A.[Ammara], Zipper, S.C.[Samuel C.], Desai, A.R.[Ankur R.], U, K.T.P.[Kyaw Tha Paw], Chisholm, A.J.[Alex J.], Prater, J.[Jacob], Nocco, M.A.[Mallika A.],
How High to Fly? Mapping Evapotranspiration from Remotely Piloted Aircrafts at Different Elevations,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Matta, E.[Erica], Amadori, M.[Marina], Free, G.[Gary], Giardino, C.[Claudia], Bresciani, M.[Mariano],
A Satellite-Based Tool for Mapping Evaporation in Inland Water Bodies: Formulation, Application, and Operational Aspects,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Garcia-Santos, V.[Vicente], Niclòs, R.[Raquel], Valor, E.[Enric],
Evapotranspiration Retrieval Using S-SEBI Model with Landsat-8 Split-Window Land Surface Temperature Products over Two European Agricultural Crops,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Luo, Z.L.[Ze-Lin], Guo, M.[Mengjing], Bai, P.[Peng], Li, J.[Jing],
Different Vegetation Information Inputs Significantly Affect the Evapotranspiration Simulations of the PT-JPL Model,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

García-Santos, V.[Vicente], Sánchez, J.M.[Juan Manuel], Cuxart, J.[Joan],
Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zhang, Y.[Yuan], Liu, S.M.[Shao-Min], Song, L.S.[Li-Sheng], Li, X.[Xiang], Jia, Z.Z.[Zhen-Zhen], Xu, T.R.[Tong-Ren], Xu, Z.W.[Zi-Wei], Ma, Y.F.[Yan-Fei], Zhou, J.[Ji], Yang, X.F.[Xiao-Fan], He, X.L.[Xin-Lei], Yao, Y.J.[Yun-Jun], Hu, G.C.[Guang-Cheng],
Integrated Validation of Coarse Remotely Sensed Evapotranspiration Products over Heterogeneous Land Surfaces,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Jiang, Y.[Yazhen], Wang, J.[Junrui], Wang, Y.[Yafei],
Daily Evapotranspiration Estimations by Direct Calculation and Temporal Upscaling Based on Field and MODIS Data,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Chai, X.Y.[Xing-Yu], Li, J.C.[Jin-Cai], Zhao, J.[Jun], Wang, W.X.[Wu-Xin], Zhao, X.F.[Xiao-Feng],
LGB-PHY: An Evaporation Duct Height Prediction Model Based on Physically Constrained LightGBM Algorithm,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Li, H.[Haobo], Jiang, C.[Chenhui], Choy, S.[Suelynn], Wang, X.M.[Xiao-Ming], Zhang, K.[Kefei], Zhu, D.J.[De-Jun],
A Comprehensive Study on Factors Affecting the Calibration of Potential Evapotranspiration Derived from the Thornthwaite Model,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Athira, K.V., Eswar, R., Boulet, G.[Gilles], Nigam, R.[Rahul], Bhattacharya, B.K.[Bimal K.],
Modeling Evapotranspiration at Larger Temporal Scales: Effects of Temporal Aggregation and Data Gaps,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Yang, C.[Cheng], Wang, J.[Jian], Shi, Y.[Yafei],
A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Che, X.H.[Xiang-Hong], Zhang, H.K.[Hankui K.], Sun, Q.[Qing], Ouyang, Z.[Zutao], Liu, J.P.[Ji-Ping],
MODIS Evapotranspiration Downscaling Using a Deep Neural Network Trained Using Landsat 8 Reflectance and Temperature Data,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Wang, L.J.[Li-Juan], Guo, N.[Ni], Yue, P.[Ping], Hu, D.[Die], Sha, S.[Sha], Wang, X.P.[Xiao-Ping],
Regulation of Evapotranspiration in Different Precipitation Zones and Its Application in High-Temperature and Drought Monitoring,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Saboori, M.[Mojtaba], Mousivand, Y.[Yousef], Cristóbal, J.[Jordi], Shah-Hosseini, R.[Reza], Mokhtari, A.[Ali],
An Automated and Improved Methodology to Retrieve Long-time Series of Evapotranspiration Based on Remote Sensing and Reanalysis Data,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Mueller, M.M.[Marlin M.], Dubois, C.[Clémence], Jagdhuber, T.[Thomas], Hellwig, F.M.[Florian M.], Pathe, C.[Carsten], Schmullius, C.[Christiane], Steele-Dunne, S.[Susan],
Sentinel-1 Backscatter Time Series for Characterization of Evapotranspiration Dynamics over Temperate Coniferous Forests,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Chang, Y.P.[Ya-Ping], Ding, Y.J.[Yong-Jian], Zhao, Q.D.[Qiu-Dong], Zhang, S.Q.[Shi-Qiang],
Attributing Evapotranspiration Changes with an Extended Budyko Framework Considering Glacier Changes in a Cryospheric-Dominated Watershed,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Abbasi, N.[Neda], Nouri, H.[Hamideh], Didan, K.[Kamel], Barreto-Muñoz, A.[Armando], Borujeni, S.C.[Sattar Chavoshi], Opp, C.[Christian], Nagler, P.[Pamela], Thenkabail, P.S.[Prasad S.], Siebert, S.[Stefan],
Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

He, Y.[Yan], Wang, C.[Chen], Hu, J.H.[Jing-Hao], Mao, H.H.[Hui-Hui], Duan, Z.[Zheng], Qu, C.[Cixiao], Li, R.[Runkui], Wang, M.Y.[Ming-Yu], Song, X.F.[Xian-Feng],
Discovering Optimal Triplets for Assessing the Uncertainties of Satellite-Derived Evapotranspiration Products,
RS(15), No. 13, 2023, pp. 3215.
DOI Link 2307
BibRef

Blagoveshchenskaya, N.F.[Nataly F.], Borisova, T.D.[Tatiana D.], Kalishin, A.S.[Alexey S.], Egorov, I.M.[Ivan M.],
Artificial Ducts Created via High-Power HF Radio Waves at EISCAT,
RS(15), No. 9, 2023, pp. 2300.
DOI Link
See also Comment on Blagoveshchenskaya et al. Artificial Ducts Created via High-Power HF Radio Waves at EISCAT. Remote Sens. 2023, 15, 2300. BibRef 2300

Huang, L.F.[Li-Feng], Liu, C.G.[Cheng-Guo], Wu, Z.P.[Zhi-Peng], Zhang, L.J.[Li-Jun], Wang, H.G.[Hong-Guang], Zhu, Q.L.[Qing-Lin], Han, J.[Jie], Sun, M.C.[Ming-Chen],
Comparative Analysis of Intelligent Optimization Algorithms for Atmospheric Duct Inversion Using Automatic Identification System Signals,
RS(15), No. 14, 2023, pp. 3577.
DOI Link 2307
BibRef

Wang, T.T.[Tian-Teng], Wang, X.P.[Xu-Ping], Jiang, Y.P.[Yi-Ping], Sun, Z.[Zilai], Liang, Y.[Yuhu], Hu, X.P.[Xiang-Pei], Li, H.[Hao], Shi, Y.[Yan], Xu, J.[Jingjun], Ruan, J.[Junhu],
Hybrid Machine Learning Approach for Evapotranspiration Estimation of Fruit Tree in Agricultural Cyber-Physical Systems,
Cyber(53), No. 9, September 2023, pp. 5677-5691.
IEEE DOI 2309
BibRef

Zhu, W.B.[Wen-Bin], Fan, L.[Li], Jia, S.F.[Shao-Feng],
Integration of microwave satellite soil moisture products in the contextual surface temperature-vegetation index models for spatially continuous evapotranspiration estimation,
PandRS(203), 2023, pp. 211-229.
Elsevier DOI 2310
Evapotranspiration, Soil moisture, Contextual TVX models, Downscaling, Satellite remote sensing BibRef

Yang, C.[Chao], Wang, Y.[Yulu], Zhang, A.[Aoxiang], Fan, H.[Hualei], Guo, L.X.[Li-Xin],
A Random Forest Algorithm Combined with Bayesian Optimization for Atmospheric Duct Estimation,
RS(15), No. 17, 2023, pp. 4296.
DOI Link 2310
BibRef

Rietveld, M.[Michael], Senior, A.[Andrew],
Comment on Blagoveshchenskaya et al. Artificial Ducts Created via High-Power HF Radio Waves at EISCAT. Remote Sens. 2023, 15, 2300,
RS(15), No. 17, 2023, pp. 4294.
DOI Link 2310

See also Artificial Ducts Created via High-Power HF Radio Waves at EISCAT. BibRef

Liu, Y.[Yi], Ortega-Farías, S.[Samuel], Fan, Y.F.[Yun-Fei], Hou, Y.[Yu], Wang, S.[Sufen], Yang, W.[Weicai], Li, S.[Sien], Tian, F.[Fei],
Comparison of Differences in Actual Cropland Evapotranspiration under Two Irrigation Methods Using Satellite-Based Model,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Hascoet, T.[Tristan], Pellet, V.[Victor], Aires, F.[Filipe], Takiguchi, T.[Tetsuya],
Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Xie, Z.J.[Zi-Jing], Yao, Y.J.[Yun-Jun], Tang, Q.X.[Qing-Xin], Zhang, X.Y.[Xue-Yi], Zhang, X.T.[Xiao-Tong], Jiang, B.[Bo], Xu, J.[Jia], Yu, R.Y.[Rui-Yang], Liu, L.[Lu], Ning, J.[Jing], Fan, J.[Jiahui], Zhang, L.[Luna],
Global Terrestrial Evapotranspiration Estimation from Visible Infrared Imaging Radiometer Suite (VIIRS) Data,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Cui, Z.L.[Zi-Long], Zhang, Y.[Yuan], Wang, A.[Anzhi], Wu, J.[Jiabing], Li, C.[Chunbo],
Uncertainty Analysis and Data Fusion of Multi-Source Land Evapotranspiration Products Based on the TCH Method,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Dai, Q.[Qin], Chen, H.[Hong], Cui, C.F.[Chen-Feng], Li, J.[Jie], Sun, J.[Jun], Ma, Y.X.[Yu-Xin], Peng, X.[Xuelian], Wang, Y.K.[Ya-Kun], Hu, X.T.[Xiao-Tao],
Spatiotemporal Characteristics of Actual Evapotranspiration Changes and Their Climatic Causes in China,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link 2401
BibRef

Wang, Z.J.[Zi-Jun], Liu, Y.Y.[Yang-Yang], Wang, Z.Q.[Zhen-Qian], Zhang, H.[Hong], Chen, X.[Xu], Wen, Z.M.[Zhong-Ming], Lin, Z.Q.[Zi-Qi], Han, P.[Peidong], Xue, T.[Tingyi],
Quantifying the Spatiotemporal Changes in Evapotranspiration and Its Components Driven by Vegetation Greening and Climate Change in the Northern Foot of Yinshan Mountain,
RS(16), No. 2, 2024, pp. 357.
DOI Link 2402
BibRef

Zhao, G.[Gengle], Song, L.S.[Li-Sheng], Zhao, L.[Long], Tao, S.[Sinuo],
A Comparison of Different Machine Learning Methods to Reconstruct Daily Evapotranspiration Time Series Estimated by Thermal-Infrared Remote Sensing,
RS(16), No. 3, 2024, pp. 509.
DOI Link 2402
BibRef

Rautiainen, L.[Laura], Tyynelä, J.[Jani], Lensu, M.[Mikko], Siiriä, S.[Simo], Vakkari, V.[Ville], O'Connor, E.[Ewan], Hämäläinen, K.[Karoliina], Lonka, H.[Harry], Stenbäck, K.[Ken], Koistinen, J.[Jarmo], Laakso, L.[Lauri],
Uto Observatory for Analysing Atmospheric Ducting Events over Baltic Coastal and Marine Waters,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Jin, T.[Taekyeong], Cho, J.[Jeongmin], Jang, D.[Doyoung], Choo, H.[Hosung],
Prediction of Atmospheric Duct Conditions from a Clutter Power Spectrum Using Deep Learning,
RS(16), No. 4, 2024, pp. 674.
DOI Link 2402
BibRef

Du, C.M.[Chang-Min], Jiang, S.Z.[Shou-Zheng], Chen, C.Q.[Chu-Qiang], Guo, Q.Y.[Qian-Yue], He, Q.Y.[Qing-Yan], Zhan, C.[Cun],
Machine Learning-Based Estimation of Daily Cropland Evapotranspiration in Diverse Climate Zones,
RS(16), No. 5, 2024, pp. 730.
DOI Link 2403
BibRef

Zheng, X.[Xin], Zhang, S.[Sha], Zhang, J.H.[Jia-Hua], Yang, S.S.[Shan-Shan], Huang, J.J.[Jiao-Jiao], Meng, X.[Xianye], Bai, Y.[Yun],
Prediction of Large-Scale Regional Evapotranspiration Based on Multi-Scale Feature Extraction and Multi-Headed Self-Attention,
RS(16), No. 7, 2024, pp. 1235.
DOI Link 2404
BibRef

Hiestand, M.P.[Mikael P.], Tollerud, H.J.[Heather J.], Funk, C.[Chris], Senay, G.B.[Gabriel B.], Fickas, K.C.[Kate C.], Friedrichs, M.O.[MacKenzie O.],
SSEBop Evapotranspiration Estimates Using Synthetically Derived Landsat Data from the Continuous Change Detection and Classification Algorithm,
RS(16), No. 7, 2024, pp. 1297.
DOI Link 2404
BibRef


Chen, X., Su, Z., Ma, Y.,
Remote Sensing of Global Monthly Evapotranspiration With An Energy Balance (EB) Model,
CHGCS19(1729-1733).
DOI Link 1912
BibRef

Liu, X., Tang, Q.,
Changes in Evapotranspiration and The Potential Drivers in Asian Arid Regions During 2003 to 2017,
SMPR19(679-682).
DOI Link 1912
BibRef

Abid, N., Mannaerts, C., Bargaoui, Z.,
Sensitivity of Actual Evapotranspiration Estimation Using The Sebs Model to Variation of Input Parameters (lst, Dssf, Aerodynamics Parameters, Lai, Fvc),
ISSDQ19(1193-1200).
DOI Link 1912
BibRef

Fonseca-Luengo, D.[David], Lillo-Saavedra, M.[Mario], Lagos, L.O., García-Pedrero, A.[Angel], Gonzalo-Martín, C.[Consuelo],
Use of Machine Learning to Improve the Robustness of Spatial Estimation of Evapotranspiration,
CIARP17(237-245).
Springer DOI 1802
BibRef

Martens, B., Miralles, D.G., Dorigo, W.A., Waegeman, W., Verhoest, N.E.C.,
Investigating the control of ocean-atmospheric oscillations over global terrestrial evaporation using a simple supervised learning method,
MultiTemp17(1-3)
IEEE DOI 1712
atmospheric movements, climatology, ecology, evaporation, climate impact, climatic variables, Terrestrial atmosphere BibRef

Barrios, J.M., Ghilain, N., Arboleda, A., Gellens-Meulenberghs, F.,
Evaluating an energy balance setting and random forest-based downscaling for the estimation of daily ET at sub-kilometer spatial resolution,
MultiTemp17(1-4)
IEEE DOI 1712
evaporation, remote sensing, transpiration, Biebrza river, North Eastern Poland, Random Forest, energy balance, Spatial resolution BibRef

Barrios, J.M., Ghilain, N., Arboleda, A., Gellens-Meulenberghs, F.,
Retrieving daily evapotranspiration from the combination of geostationary and polar-orbit satellite data,
MultiTemp15(1-4)
IEEE DOI 1511
ecology BibRef

Atasever, Ü.H., Kesikoglu, M.H., Özkan, C.,
Evamapper: A Novel Matlab Toolbox For Evapotranspiration Mapping,
SSG13(23-26).
DOI Link 1402
BibRef

Doklestic, D.[Dea], Smith, R.B.[Ronald B.],
Does evapotranspiration influence the strength of the North American monsoon? Multitemporal satellite analysis of evapotranspiration and its effects,
MultiTemp11(173-176).
IEEE DOI 1109
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Specific Site Evapotranspiration Analyusis .


Last update:May 6, 2024 at 15:50:14