58 citations as recorded by crossref.

1. Sap velocity, transpiration and water use efficiency of drip-irrigated cotton in response to chemical topping and row spacing Y. Chen et al. https://doi.org/10.1016/j.agwat.2022.107611
2. Modeling and assessing water and nitrogen use and crop growth of peanut in semi-arid areas of Northeast China Z. Huang et al. https://doi.org/10.1016/j.agwat.2022.107621
3. Feasibility analysis of using inverse modeling for estimating field-scale evapotranspiration in maize and soybean fields from soil water content monitoring networks F. Foolad et al. https://doi.org/10.5194/hess-21-1263-2017
4. Progress in water and energy flux studies in Asia: A review focused on eddy covariance measurements . Minseok KANG & . Sungsik CHO https://doi.org/10.2480/agrmet.D-20-00036
5. Transpiration of a Tropical Dry Deciduous Forest in Yucatan, Mexico E. Salas-Acosta et al. https://doi.org/10.3390/atmos13020271
6. Root growth, water uptake, and sap flow of winter wheat in response to different soil water conditions G. Cai et al. https://doi.org/10.5194/hess-22-2449-2018
7. Spatial Allocation Method from Coarse Evapotranspiration Data to Agricultural Fields by Quantifying Variations in Crop Cover and Soil Moisture Z. Ma et al. https://doi.org/10.3390/rs13030343
8. Impacts of Water Stress Severity and Duration on Potato Photosynthetic Activity and Yields M. Jacques et al. https://doi.org/10.3389/fagro.2020.590312
9. Small inaccuracies in estimating narrow sapwood depth produce large error in sap velocity corrections J. Niu et al. https://doi.org/10.1002/eco.2409
10. Determination of crop water use and coefficient in drip-irrigated cotton fields in arid regions S. Han et al. https://doi.org/10.1016/j.fcr.2019.03.008
11. Estimating Evapotranspiration of Maize under Different Management Practices in a Semiarid Tropical Area Using the Soil Water Balance Method K. Nyabwisho & F. Kahimba https://doi.org/10.4236/gep.2022.108013
12. Irrigation-Induced Changes in Evapotranspiration Demand of Awati Irrigation District, Northwest China: Weakening the Effects of Water Saving? S. Han et al. https://doi.org/10.3390/su9091531
13. Optimizing bent branch numbers improves transpiration and crop water productivity of cut rose (Rosa hybrida) in greenhouse X. Yin et al. https://doi.org/10.1016/j.agwat.2024.108795
14. Integrating the Maximum Entropy Production model and airborne imagery for understorey evapotranspiration mapping W. Liu et al. https://doi.org/10.1016/j.jhydrol.2025.133076
15. Evaluation of evapotranspiration and deep percolation under mulched drip irrigation in an oasis of Tarim basin, China X. Li et al. https://doi.org/10.1016/j.jhydrol.2016.04.045
16. Status of accuracy in remotely sensed and in-situ agricultural water productivity estimates: A review M. Blatchford et al. https://doi.org/10.1016/j.rse.2019.111413
17. Dynamics and Determinants of Maize Sap Flow Under Soil Compaction in the Black Soil Region of Northeast China X. Zhu et al. https://doi.org/10.3390/agriculture15181911
18. Spatial patterns of evaporation in a small catchment P. Hogan et al. https://doi.org/10.2478/johh-2024-0027
19. Observed near-surface atmospheric moisture content changes affected by irrigation development in Xinjiang, Northwest China S. Han et al. https://doi.org/10.1007/s00704-016-1899-2
20. Estimate canopy transpiration in larch plantations via the interactions among reference evapotranspiration, leaf area index, and soil moisture L. Wang et al. https://doi.org/10.1016/j.foreco.2020.118749
21. Effects of Plastic Mulch on Soil Heat Flux and Energy Balance in a Cotton Field in Northwest China N. Li et al. https://doi.org/10.3390/atmos7080107
22. Energy balance and canopy conductance for a cotton field under film mulched drip irrigation in an arid region of northwestern China F. Tian et al. https://doi.org/10.1016/j.agwat.2016.06.029
23. Estimating Fine‐Scale Transpiration From UAV‐Derived Thermal Imagery and Atmospheric Profiles B. Morgan & K. Caylor https://doi.org/10.1029/2023WR035251
24. The soil–water flow system beneath a cotton field in arid north-west China, serviced by mulched drip irrigation using brackish water X. Li et al. https://doi.org/10.1007/s10040-014-1210-5
25. Efficient Hyperparameter Optimization for Reference Evapotranspiration Estimation with Limited Parameters: A Comparison of Optuna and Grid Search in the Doukkala Region, Morocco Z. Belarbi et al. https://doi.org/10.1051/e3sconf/202568000076
26. Modeling two-dimensional soil water–heat–salt transport and cotton growth under strip-film mulched drip irrigation D. Lin et al. https://doi.org/10.1016/j.agsy.2025.104479
27. A Proposal for a Forest Digital Twin Framework and Its Perspectives L. Buonocore et al. https://doi.org/10.3390/f13040498
28. Influence of drought stress on growth, biochemical changes and leaf gas exchange of strawberry (<i>Fragaria</i> × <i>ananassa</i> Duch.) in Indonesia . Yenni et al. https://doi.org/10.3934/agrfood.2022003
29. Yields, growth and water use under chemical topping in relations to row configuration and plant density in drip-irrigated cotton X. Wang et al. https://doi.org/10.1186/s42397-024-00173-2
30. Carbon budget for a plastic-film mulched and drip-irrigated cotton field in an oasis of Northwest China G. Ming et al. https://doi.org/10.1016/j.agrformet.2021.108447
31. Using bromide data tracer and HYDRUS‐1D to estimate groundwater recharge and evapotranspiration under film‐mulched drip irrigation in an arid Inland Basin, Northwest China W. Chen et al. https://doi.org/10.1002/hyp.14290
32. Comparison of the partitioning of evapotranspiration – numerical modeling with different isotopic models using various kinetic fractionation coefficients Y. Zhang et al. https://doi.org/10.1007/s11104-018-3737-z
33. Estimation of ecosystem evapotranspiration in aRobinia pseudoacaciaL. plantation with the use of the eddy covariance technique and modeling approaches N. Markos & K. Radoglou https://doi.org/10.2166/ws.2021.142
34. Are calibrations of sap flow measurements based on thermal dissipation needed for each sample in Japanese cedar and cypress trees? Y. Shinohara et al. https://doi.org/10.1007/s00468-022-02283-3
35. Coupling water and carbon processes to estimate field-scale maize evapotranspiration with Sentinel-2 data Z. Ma et al. https://doi.org/10.1016/j.agrformet.2021.108421
36. Improving evapotranspiration estimation by integrating process-based biophysical variables into a deep learning approach M. Xie et al. https://doi.org/10.1016/j.ejrh.2026.103114
37. Heat and Mass Transfer in the Food, Energy, and Water Nexus—A Review M. Derby et al. https://doi.org/10.1115/1.4047089
38. An Assessment of Eddy-Covariance-Based Surface Fluxes Above an Evaporating Heated Surface Under Fair-Weather Daytime Conditions S. Kang https://doi.org/10.1007/s10546-018-0412-0
39. Extrapolating continuous vegetation water content to understand sub-daily backscatter variations P. Vermunt et al. https://doi.org/10.5194/hess-26-1223-2022
40. Comparing different methods for determining forest evapotranspiration and its components at multiple temporal scales Q. Tie et al. https://doi.org/10.1016/j.scitotenv.2018.03.082
41. Modeling soil water-salt dynamics and crop response under severely saline condition using WAVES: Searching for a target irrigation volume for saline water irrigation Q. Yu et al. https://doi.org/10.1016/j.agwat.2021.107100
42. Groundwater dynamics under water-saving irrigation and implications for sustainable water management in an oasis: Tarim River basin of western China Z. Zhang et al. https://doi.org/10.5194/hess-18-3951-2014
43. A new approach for predicting the water balance of hops T. Graf et al. https://doi.org/10.17660/ActaHortic.2019.1236.10
44. Insentek Sensor: An Alternative to Estimate Daily Crop Evapotranspiration for Maize Plants A. Qin et al. https://doi.org/10.3390/w11010025
45. Analysis of scale-dependent spatial correlations of actual evapotranspiration measured by lysimeters X. Lu et al. https://doi.org/10.1016/j.agrformet.2024.110288
46. A Simple Method for Estimating Field Crop Evapotranspiration from Pot Experiments Y. Lu et al. https://doi.org/10.3390/w10121823
47. In situ measurements of tritium evapotranspiration ( 3 H-ET) flux over grass and soil using the gradient and eddy covariance experimental methods and the FAO-56 model O. Connan et al. https://doi.org/10.1016/j.jenvrad.2015.06.004
48. A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning Z. Belarbi et al. https://doi.org/10.1051/e3sconf/202560100078
49. Crop coefficient for cotton under plastic mulch and drip irrigation based on eddy covariance observation in an arid area of northwestern China P. Yang et al. https://doi.org/10.1016/j.agwat.2016.03.007
50. Extensive investigation of the sap flow of maize plants in an oasis farmland in the middle reach of the Heihe River, Northwest China L. Zhao et al. https://doi.org/10.1007/s10265-016-0835-y
51. Partitioning of Cotton Field Evapotranspiration under Mulched Drip Irrigation Based on a Dual Crop Coefficient Model F. Tian et al. https://doi.org/10.3390/w8030072
52. Study on the Water and Heat Fluxes of a Very Humid Forest Ecosystem and Their Relationship with Environmental Factors in Jinyun Mountain, Chongqing K. Wang et al. https://doi.org/10.3390/atmos13050832
53. A Review of Methods for Data-Driven Irrigation in Modern Agricultural Systems M. Jenkins & D. Block https://doi.org/10.3390/agronomy14071355
54. Evaluation of remotely sensed actual evapotranspiration products from COMS and MODIS at two different flux tower sites in Korea J. Baik & M. Choi https://doi.org/10.1080/01431161.2014.998349
55. Improved Water Use of the Maize Soil–Root–Shoot System under the Integrated Effects of Organic Manure and Plant Density L. Wei et al. https://doi.org/10.3390/agronomy13041172
56. Spatio‐temporal variations of soil moisture and salinity and their effects on cotton growth in a mulched drip irrigation field* H. Hu et al. https://doi.org/10.1002/ird.2436
57. A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation D. Gómez-Candón et al. https://doi.org/10.3390/plants12223871
58. Assessment of irrigation efficiency for arid-zone spring wheat production under flood irrigation H. Gao et al. https://doi.org/10.1007/s00271-024-00946-2