46 citations as recorded by crossref.

1. Modeling Monthly Evaporation in Different Climates Using ANN–COOT Hybrid Algorithm F. Alempour Rajabi et al. https://doi.org/10.1007/s40996-023-01338-w
2. Agroecosystem research with big data and a modified scientific method using machine learning concepts M. Moran et al. https://doi.org/10.1002/ecs2.1493
3. Using radiative signatures to diagnose the cause of warming during the 2013–2014 Californian drought S. Wolf et al. https://doi.org/10.1016/j.jhydrol.2017.07.015
4. Energy, water and carbon exchange over a perennial Kernza wheatgrass crop G. de Oliveira et al. https://doi.org/10.1016/j.agrformet.2017.11.022
5. Downscaling soil moisture to sub-km resolutions with simple machine learning ensembles J. Poehls et al. https://doi.org/10.1016/j.jhydrol.2024.132624
6. Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests F. Montané et al. https://doi.org/10.5194/gmd-10-3499-2017
7. Estimating gross primary productivity of a tropical forest ecosystem over north-east India using LAI and meteorological variables P. Deb Burman et al. https://doi.org/10.1007/s12040-017-0874-3
8. The NASA Carbon Airborne Flux Experiment (CARAFE): instrumentation and methodology G. Wolfe et al. https://doi.org/10.5194/amt-11-1757-2018
9. Development of a Hybrid Algorithm for Temporal Normalization of Polar-Orbiting Satellite-Derived Land Surface Temperature W. Du et al. https://doi.org/10.1109/TGRS.2023.3304470
10. Comparison of the Efficiencies of the Prognostic Generalized Complementary Functions on Evaporation Estimation L. Wang et al. https://doi.org/10.1029/2023JD038683
11. Model representation of the coupling between evapotranspiration and soil water content at different depths J. Qiu et al. https://doi.org/10.5194/hess-24-581-2020
12. Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations Z. Xing et al. https://doi.org/10.1016/j.isprsjprs.2021.05.017
13. Seamless terrestrial evapotranspiration estimation by machine learning models across the Contiguous United States Y. Zhao et al. https://doi.org/10.1016/j.ecolind.2024.112203
14. The SCALEX Campaign: Scale-Crossing Land Surface and Boundary Layer Processes in the TERENO-preAlpine Observatory B. Wolf et al. https://doi.org/10.1175/BAMS-D-15-00277.1
15. A framework of coupling split-window and machine learning (SW-ML) for land surface temperature retrieval from MODIS thermal infrared data Y. Guan et al. https://doi.org/10.1016/j.rse.2026.115412
16. Evaporation process modelling over northern Iran: application of an integrative data-intelligence model with the krill herd optimization algorithm A. Ashrafzadeh et al. https://doi.org/10.1080/02626667.2019.1676428
17. Improved evapotranspiration estimation using the Penman-Monteith equation with a deep learning (DNN) model over the dry southwestern US: Comparison with ECOSTRESS, MODIS, and OpenET M. Jawad et al. https://doi.org/10.1016/j.jhydrol.2025.133460
18. Evaluating the Performance of Sentinel-3A OLCI Land Products for Gross Primary Productivity Estimation Using AmeriFlux Data Z. Zhang et al. https://doi.org/10.3390/rs12121927
19. The Niwot Ridge Subalpine Forest US-NR1 AmeriFlux site – Part 1: Data acquisition and site record-keeping S. Burns et al. https://doi.org/10.5194/gi-5-451-2016
20. A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) B. Qu et al. https://doi.org/10.1088/1748-9326/ace376
21. MANNGA: A Robust Method for Gap Filling Meteorological Data T. Ventura et al. https://doi.org/10.1590/0102-77863340035
22. The Impact of Vertical Measurement Depth on the Information Content of Soil Moisture for Latent Heat Flux Estimation J. Qiu et al. https://doi.org/10.1175/JHM-D-16-0044.1
23. Accumulated soil moisture deficit better indicates the effect of soil water stress on light use efficiency of grasslands during drought years Z. Zhang et al. https://doi.org/10.1016/j.agrformet.2022.109276
24. An interdisciplinary approach to better assess global change impacts and drought vulnerability on forest dynamics M. Abrams et al. https://doi.org/10.1093/treephys/tpw005
25. SAPFLUXNET: towards a global database of sap flow measurements R. Poyatos et al. https://doi.org/10.1093/treephys/tpw110
26. Partitioning Evapotranspiration into Green and Blue Water Sources in the Conterminous United States N. Velpuri & G. Senay https://doi.org/10.1038/s41598-017-06359-w
27. Model‐data assimilation of multiple phenological observations to constrain and predict leaf area index T. Viskari et al. https://doi.org/10.1890/14-0497.1
28. An Accurate Estimate of Monthly Mean Land Surface Temperatures from MODIS Clear-Sky Retrievals X. Chen et al. https://doi.org/10.1175/JHM-D-17-0009.1
29. Dormant-Season Fire Inhibits Sixweeks Fescue and Enhances Forage Production in Shortgrass Steppe N. Dufek et al. https://doi.org/10.4996/fireecology.140132048
30. Publishing Environmental Assessment and Management Science: Crossing the Hurdles R. Efroymson & M. Peterson https://doi.org/10.1093/biosci/biaa107
31. Evaluation and comparison of multiple evapotranspiration data models over the contiguous United States: Implications for the next phase of NLDAS (NLDAS-Testbed) development B. Zhang et al. https://doi.org/10.1016/j.agrformet.2019.107810
32. A framework for estimating actual evapotranspiration at weather stations without flux observations by combining data from MODIS and flux towers through a machine learning approach C. Zhang et al. https://doi.org/10.1016/j.jhydrol.2021.127047
33. Comprehensive evaluation of terrestrial evapotranspiration from different models under extreme condition over conterminous United States X. Yu et al. https://doi.org/10.1016/j.agwat.2023.108555
34. Short‐term favorable weather conditions are an important control of interannual variability in carbon and water fluxes J. Zscheischler et al. https://doi.org/10.1002/2016JG003503
35. Warm spring reduced carbon cycle impact of the 2012 US summer drought S. Wolf et al. https://doi.org/10.1073/pnas.1519620113
36. Opinion: The strength of long-term comprehensive observations to meet multiple grand challenges in different environments and in the atmosphere M. Kulmala et al. https://doi.org/10.5194/acp-23-14949-2023
37. A new paradigm of quantifying ecosystem stress through chemical signatures B. Kravitz et al. https://doi.org/10.1002/ecs2.1559
38. Contrasting Physiological and Environmental Controls of Evapotranspiration over Kernza Perennial Crop, Annual Crops, and C4 and Mixed C3/C4 Grasslands C. Sutherlin et al. https://doi.org/10.3390/su11061640
39. Impact of physical parameterizations and initial conditions on simulated atmospheric transport and CO2 mole fractions in the US Midwest L. Díaz-Isaac et al. https://doi.org/10.5194/acp-18-14813-2018
40. Extending the SCOPE model to combine optical reflectance and soil moisture observations for remote sensing of ecosystem functioning under water stress conditions B. Bayat et al. https://doi.org/10.1016/j.rse.2018.11.021
41. Temporal Error Correlations in a Terrestrial Carbon Cycle Model Derived by Comparison to Carbon Dioxide Eddy Covariance Flux Tower Measurements D. Wesloh et al. https://doi.org/10.1029/2023JG007526
42. Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics T. Uttal et al. https://doi.org/10.5194/gmd-17-5225-2024
43. 100 Years of Progress in Atmospheric Observing Systems J. Stith et al. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0006.1
44. VCPNET: A new dataset to benchmark vegetation carbon phenology metrics X. Tang et al. https://doi.org/10.1016/j.ecoinf.2024.102741
45. Thermal infrared imaging of conifer leaf temperatures: Comparison to thermocouple measurements and assessment of environmental influences Y. Kim et al. https://doi.org/10.1016/j.agrformet.2017.10.010
46. Spatiotemporal variations and environmental controls of carbon-water dynamics and coupling on the Tibetan Plateau R. Liu et al. https://doi.org/10.1016/j.gloplacha.2026.105726