66 citations as recorded by crossref.

1. A cloud-based framework for the quantification of the spatially-explicit uncertainty of remotely sensed benthic habitats S. Christofilakos et al. https://doi.org/10.1016/j.jag.2025.104670
2. The methodology for upscaling surface albedo with consideration of surface heterogeneity Z. Wang et al. https://doi.org/10.1016/j.srs.2025.100340
3. A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components H. Chen et al. https://doi.org/10.1016/j.jhydrol.2026.134985
4. Blue and green water simulation in the river basin using remote sensing data fusion and dual-variable hydrological calibration Y. Lu et al. https://doi.org/10.1016/j.agwat.2025.110046
5. Satellite-based land surface temperature and soil moisture observed during the 2023–2024 drought–heatwave events in the Amazon Basin V. Miranda et al. https://doi.org/10.1088/2752-5295/ae2d89
6. Estimating actual evapotranspiration from widely available meteorological data with a hybrid CNN–LSTM D. Woo https://doi.org/10.1016/j.agwat.2025.110078
7. Extending our understanding on the retrievals of surface energy fluxes and surface soil moisture from the “triangle” technique G. Petropoulos https://doi.org/10.1016/j.envsoft.2024.106180
8. Sub-diurnal asymmetric warming has amplified atmospheric dryness since the 1980s Z. Zhong et al. https://doi.org/10.1038/s41467-025-63672-z
9. Spatiotemporal Variations and Driving Factors of Evapotranspiration in Subtropical China from 2001 to 2020 Y. Li et al. https://doi.org/10.3390/rs18111866
10. Influence of land use and cover on storm surge induced coastal flooding in the sundarbans during cyclone Amphan V. Yalla et al. https://doi.org/10.1007/s11069-025-07653-x
11. Sensitivity of thermal evapotranspiration models to surface and atmospheric drivers across ecosystems and aridity A. Jia et al. https://doi.org/10.1016/j.agrformet.2025.110930
12. Reducing uncertainty of evapotranspiration products across China under extreme climatic conditions using flux-tower observations and machine learning L. Qian et al. https://doi.org/10.1016/j.agwat.2026.110445
13. A novel remote sensing-based calibration and validation method for distributed hydrological modelling in ungauged basins D. Zheng et al. https://doi.org/10.1016/j.jhydrol.2025.133119
14. Investigating the meteorological causes of hydrological drought through the integration of spatiotemporal cubes and interpretable machine learning: A case study of the Yangtze River Basin H. Wang et al. https://doi.org/10.1016/j.ejrh.2025.102796
15. Advances in geospatial artificial intelligence for remote sensing applications E. Dritsas & M. Trigka https://doi.org/10.1016/j.cosrev.2026.100913
16. Large variability in satellite-based estimates of irrigation water use A. Amali et al. https://doi.org/10.1088/3033-4942/ae4feb
17. A Cloud-Based Framework for the Quantification of the Uncertainty of a Machine Learning Produced Satellite-Derived Bathymetry S. Christofilakos et al. https://doi.org/10.3390/rs17173060
18. A First Exploration of the Ts/VI “Analytical Triangle” Technique with UAV Imagery for Deriving Key Surface Energy Balance Parameters at Very High Spatial Resolution G. Petropoulos et al. https://doi.org/10.3390/drones8070290
19. Assimilating Farmers’ Behaviour in the Development of an ET-Based Irrigation Water-Accounting Model I. Portoghese et al. https://doi.org/10.1007/s11269-025-04316-1
20. High resolution (1 km-daily) potential evapotranspiration dataset over Europe and the Mediterranean region S. Bouabdelli et al. https://doi.org/10.1016/j.jhydrol.2025.133839
21. Modelling the diurnal cycle of evapotranspiration using remote sensing models – are we there yet? K. Athira et al. https://doi.org/10.1080/01431161.2025.2576598
22. Multi-scale analysis of six evapotranspiration products across China: Accuracy, uncertainty and spatiotemporal pattern L. Zuo et al. https://doi.org/10.1016/j.jhydrol.2024.132516
23. Spatiotemporal variation of actual evapotranspiration in a transboundary river basin based on MGWR downscaling B. Li et al. https://doi.org/10.1016/j.ejrh.2025.102803
24. Uncertainty Quantification of Satellite-Based Essential Climate Variables Derived from Deep Learning J. Gou et al. https://doi.org/10.1007/s10712-025-09919-2
25. Quantifying the Synergistic Effects of Environmental Drivers and Irrigation on Evapotranspiration in Shijin Irrigation District Using Projection Pursuit H. Duan et al. https://doi.org/10.3390/atmos17060540
26. High Spatial Resolution Building Characteristics for the Global South: Insights from the Google Open Buildings Temporal Dataset (2016-2023) R. Priyatikanto et al. https://doi.org/10.12688/gatesopenres.16386.1
27. Declining sensitivity of transpiration fraction to vegetation greening in China’s arid grasslands: Role of soil moisture and vapor pressure deficit T. Yang et al. https://doi.org/10.1016/j.ejrh.2025.102963
28. Estimation of Evaporation and Transpiration in the Southern Shores of the Caspian Sea Using a Remote Sensing Model S. Sayadi et al. https://doi.org/10.1134/S0097807825602778
29. Enhancing evapotranspiration estimates in orchards with the Surface Energy Balance for Partially Vegetated surfaces (SEB-PV) model through combined use of gridded soil moisture and temporal upscaling methods L. Cigarra-Guíñez et al. https://doi.org/10.1016/j.scitotenv.2025.180569
30. Assessing the groundwater recharge of a steep-sided catchment from thermal-derived evapotranspiration estimates: Usefulness of methods for correcting topographical effects B. Sebbar et al. https://doi.org/10.1016/j.jhydrol.2025.134457
31. Improving latent heat flux estimation under high evaporative demand in arid and semi-arid regions by restoring nonlinear Clausius-Clapeyron relationship: a case study at the Heihe River basin S. Liu et al. https://doi.org/10.1016/j.jhydrol.2026.135537
32. Integrating remote sensing assimilation and SCE-UA to construct a grid-by-grid spatialized crop model can dramatically improve winter wheat yield estimate accuracy Q. Li et al. https://doi.org/10.1016/j.compag.2024.109594
33. Incorporating plant access to groundwater in global root zone storage capacity estimate S. Du et al. https://doi.org/10.1016/j.agrformet.2025.110807
34. Data-driven attribution of evapotranspiration dynamics in the Heihe River Basin: Controlling factors from site measurements to regional satellite observations Z. Lin et al. https://doi.org/10.1016/j.agwat.2026.110324
35. Estimating global evapotranspiration and its components based on a modified Priestley–Taylor jet propulsion laboratory model S. Yang et al. https://doi.org/10.1080/15481603.2026.2686530
36. A Comparative Analysis of Different Algorithms for Estimating Evapotranspiration with Limited Observation Variables: A Case Study in Beijing, China D. Sun et al. https://doi.org/10.3390/rs17040636
37. Comparative Analysis of Multi-Source Evapotranspiration Products in Xinjiang, China J. Chen et al. https://doi.org/10.3390/rs17193297
38. Warming Reshapes Land-Atmosphere Coupling: The LST-SM-ET-GPP Framework R. Mi et al. https://doi.org/10.3390/atmos17040352
39. Evaluating reanalysis datasets as meteorological input for estimating reference evapotranspiration in Africa and Southwest Asia B. Tran et al. https://doi.org/10.1080/02626667.2025.2600682
40. Comparison and validation of spatial reference evapotranspiration datasets over Africa S. Dehati et al. https://doi.org/10.1080/02626667.2025.2600684
41. Efficacy of evapotranspiration products on improving streamflow prediction in ungauged catchments under various hydrological models and climates S. Wu et al. https://doi.org/10.1016/j.jhydrol.2026.135857
42. Climate-responsive crop forecasting: an EEMD-LSTM fusion approach for improved strategic crop yield simulation S. Asadollah et al. https://doi.org/10.1007/s11600-025-01764-6
43. Comprehensive analysis of methods for estimating actual paddy evapotranspiration—A review K. Behura et al. https://doi.org/10.3389/frwa.2025.1553732
44. Enhanced daily retrieval of Evapotranspiration from a Transformer-based deep learning model combined with satellite passive microwave and optical observations F. Bu et al. https://doi.org/10.1016/j.isprsjprs.2026.05.027
45. Evapotranspiration Disaggregation Using an Integrated Indicating Factor Based on Slope Units L. Wang et al. https://doi.org/10.3390/rs17071201
46. The impact of COVID-19 lockdowns on ethnic disparities in NO₂ exposure in England: A policy perspective on environmental injustice S. Wang et al. https://doi.org/10.1016/j.scs.2025.106198
47. Evaluating the performance of satellite-derived evapotranspiration products across varying bioclimates in South Africa L. Cogill et al. https://doi.org/10.1016/j.rsase.2025.101612
48. Advancing forest mapping: Pretraining strategies and deep-ensemble based uncertainty for predicting evergreen broad-leaved cover from Sentinel-2 time series B. Hiebl et al. https://doi.org/10.1016/j.jag.2025.104734
49. High resolution evapotranspiration from UAV multispectral thermal imagery: Validation and comparison with EC, Landsat, and fused S2-MODIS HSEB ET H. H. Jaafar & L. H. Sujud https://doi.org/10.1016/j.jag.2025.104359
50. Estimation of Leaf Area Index and Vegetation Fractional Cover in SBG-TIR Configuration Using SCOPE Simulated Data and Sentinel-2 Images L. Tuzzi et al. https://doi.org/10.3390/rs18121931
51. Epistemic and aleatoric uncertainty in optical vegetation trait retrieval: Concepts, Methods, and Outlook J. Verrelst et al. https://doi.org/10.1016/j.isprsjprs.2026.02.020
52. Multiscale evaluation of three remote sensing-based evapotranspiration models across the humid to arid tropics: a study over India K. Athira et al. https://doi.org/10.1080/02626667.2025.2541749
53. Land surface energy balance modeling over rotational croplands: The added value of remotely sensed latent heat-leaf area coupling W. Lv et al. https://doi.org/10.1016/j.rse.2026.115415
54. Improved estimation of crop coefficients and water requirements for agronomic crops using a remote sensing modeling approach T. Zhang et al. https://doi.org/10.1007/s00271-026-01112-6
55. Revealing the importance of urban water source areas in China by integrating supply and beneficiaries H. Liu et al. https://doi.org/10.1016/j.apgeog.2025.103869
56. Variation Characteristics of Evapotranspiration and Water Consumption Effectiveness Evaluation in the Aksu River Basin Based on Multi-Source Data Fusion M. Yang et al. https://doi.org/10.3390/atmos17030244
57. A flexible semi-continuous time series framework for hydrological water balance analysis using multi-source data M. Alijanian et al. https://doi.org/10.1080/02626667.2026.2625285
58. A macroscale evapotranspiration benchmark based on spatial and temporal enhancements to the Budyko framework L. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.133625
59. Impact of groundwater depth on crop coefficient: An improved evapotranspiration model Q. Ju et al. https://doi.org/10.1016/j.agwat.2025.110060
60. A GEE TSEB workflow for daily high-resolution fully remote sensing evapotranspiration: Validation over four crops in semi-arid conditions and comparison with the SSEBop experimental product I. El Hazdour et al. https://doi.org/10.1016/j.envsoft.2025.106365
61. Water scarcity indicator based on GRACE derived total water storage for fast water scarcity monitoring F. Wolkeba et al. https://doi.org/10.1016/j.jhydrol.2026.135280
62. Estimating the long-term daily evapotranspiration in the source region of the Yangtze River based on a universal trapezoid method Y. Li et al. https://doi.org/10.1016/j.ejrh.2026.103119
63. Monitoring changes in the landscape water balance: validation of satellite- and model-based evapotranspiration data in Lusatia, Germany J. Kröcher et al. https://doi.org/10.1007/s10661-026-15120-8
64. High spatial resolution thermal infrared data from airborne acquisition reveal inconsistencies in evapotranspiration and crop water stress retrieval from different models C. Corbari et al. https://doi.org/10.1016/j.rsase.2025.101563
65. A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints K. Khand et al. https://doi.org/10.1016/j.jhydrol.2025.133824
66. Discerning regional water balance over forests of northeast India using satellite-observed stable water isotopes R. Pradhan et al. https://doi.org/10.1080/02626667.2025.2461695