45 citations as recorded by crossref.

1. Modeling Monthly Evaporation in Different Climates Using ANN–COOT Hybrid Algorithm F. Alempour Rajabi et al.
2. Agroecosystem research with big data and a modified scientific method using machine learning concepts M. Moran et al.
3. Using radiative signatures to diagnose the cause of warming during the 2013–2014 Californian drought S. Wolf et al.
4. Energy, water and carbon exchange over a perennial Kernza wheatgrass crop G. de Oliveira et al.
5. Downscaling soil moisture to sub-km resolutions with simple machine learning ensembles J. Poehls et al.
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.
7. Estimating gross primary productivity of a tropical forest ecosystem over north-east India using LAI and meteorological variables P. Deb Burman et al.
8. The NASA Carbon Airborne Flux Experiment (CARAFE): instrumentation and methodology G. Wolfe et al.
9. Development of a Hybrid Algorithm for Temporal Normalization of Polar-Orbiting Satellite-Derived Land Surface Temperature W. Du et al.
10. Comparison of the Efficiencies of the Prognostic Generalized Complementary Functions on Evaporation Estimation L. Wang et al.
11. Model representation of the coupling between evapotranspiration and soil water content at different depths J. Qiu et al.
12. Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations Z. Xing et al.
13. Seamless terrestrial evapotranspiration estimation by machine learning models across the Contiguous United States Y. Zhao et al.
14. The SCALEX Campaign: Scale-Crossing Land Surface and Boundary Layer Processes in the TERENO-preAlpine Observatory B. Wolf et al.
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.
16. Evaporation process modelling over northern Iran: application of an integrative data-intelligence model with the krill herd optimization algorithm A. Ashrafzadeh et al.
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.
18. Evaluating the Performance of Sentinel-3A OLCI Land Products for Gross Primary Productivity Estimation Using AmeriFlux Data Z. Zhang et al.
19. The Niwot Ridge Subalpine Forest US-NR1 AmeriFlux site – Part 1: Data acquisition and site record-keeping S. Burns et al.
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.
21. MANNGA: A Robust Method for Gap Filling Meteorological Data T. Ventura et al.
22. The Impact of Vertical Measurement Depth on the Information Content of Soil Moisture for Latent Heat Flux Estimation J. Qiu et al.
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.
24. An interdisciplinary approach to better assess global change impacts and drought vulnerability on forest dynamics M. Abrams et al.
25. SAPFLUXNET: towards a global database of sap flow measurements R. Poyatos et al.
26. Partitioning Evapotranspiration into Green and Blue Water Sources in the Conterminous United States N. Velpuri & G. Senay
27. Model‐data assimilation of multiple phenological observations to constrain and predict leaf area index T. Viskari et al.
28. An Accurate Estimate of Monthly Mean Land Surface Temperatures from MODIS Clear-Sky Retrievals X. Chen et al.
29. Dormant-Season Fire Inhibits Sixweeks Fescue and Enhances Forage Production in Shortgrass Steppe N. Dufek et al.
30. Publishing Environmental Assessment and Management Science: Crossing the Hurdles R. Efroymson & M. Peterson
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.
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.
33. Comprehensive evaluation of terrestrial evapotranspiration from different models under extreme condition over conterminous United States X. Yu et al.
34. Short‐term favorable weather conditions are an important control of interannual variability in carbon and water fluxes J. Zscheischler et al.
35. Warm spring reduced carbon cycle impact of the 2012 US summer drought S. Wolf et al.
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.
37. A new paradigm of quantifying ecosystem stress through chemical signatures B. Kravitz et al.
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.
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.
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.
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.
42. Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics T. Uttal et al.
43. 100 Years of Progress in Atmospheric Observing Systems J. Stith et al.
44. VCPNET: A new dataset to benchmark vegetation carbon phenology metrics X. Tang et al.
45. Thermal infrared imaging of conifer leaf temperatures: Comparison to thermocouple measurements and assessment of environmental influences Y. Kim et al.