131 citations as recorded by crossref.

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2. Responses of vegetation spring phenology to climatic factors in Xinjiang, China C. Li et al. 10.1016/j.ecolind.2020.107286
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5. An integrated phenology modelling framework in r K. Hufkens et al. 10.1111/2041-210X.12970
6. Flowering phenological changes in relation to climate change in Hungary B. Szabó et al. 10.1007/s00484-015-1128-1
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9. Evaluating Remotely Sensed Phenological Metrics in a Dynamic Ecosystem Model H. Xu et al. 10.3390/rs6064660
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11. Temperature and geographic attribution of change in the Taraxacum mongolicum growing season from 1990 to 2009 in eastern China’s temperate zone X. Chen et al. 10.1007/s00484-015-0955-4
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14. With warming, spring streamflow peaks are more coupled with vegetation green‐up than snowmelt in the northeastern United States M. Khodaee et al. 10.1002/hyp.14621
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21. Modeling and predicting spring land surface phenology of the deciduous broadleaf forest in northern China X. Luo et al. 10.1016/j.agrformet.2014.07.011
22. Photoperiod controls vegetation phenology across Africa T. Adole et al. 10.1038/s42003-019-0636-7
23. Using the red chromatic coordinate to characterize the phenology of forest canopy photosynthesis Y. Liu et al. 10.1016/j.agrformet.2020.107910
24. Climate warming may accelerate apple phenology but lead to divergent dynamics in late-spring frost and poor pollination risks in main apple production regions of China X. Ru et al. 10.1016/j.eja.2023.126945
25. Warming‐Induced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment J. Kim et al. 10.1029/2018JG004438
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27. Steeper declines in forest photosynthesis than respiration explain age-driven decreases in forest growth J. Tang et al. 10.1073/pnas.1320761111
28. Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers Y. Yuan et al. 10.1007/s00484-021-02110-3
29. Artificial Light at Night Advances Spring Phenology in the United States Q. Zheng et al. 10.3390/rs13030399
30. Photosynthesis-dependent isoprene emission from leaf to planet in a global carbon-chemistry-climate model N. Unger et al. 10.5194/acp-13-10243-2013
31. Artificial light at night: an underappreciated effect on phenology of deciduous woody plants L. Meng et al. 10.1093/pnasnexus/pgac046
32. Nonlinear responses of temperature sensitivities of community phenophases to warming and cooling events are mirroring plant functional diversity F. Meng et al. 10.1016/j.agrformet.2018.01.034
33. Disentangling the Relative Drivers of Seasonal Evapotranspiration Across a Continental‐Scale Aridity Gradient A. Young et al. 10.1029/2022JG006916
34. Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations S. Wu et al. 10.1016/j.isprsjprs.2020.10.017
35. Predictive simulation of spring development of Cydalima perspectalis Walker in the South of Russia S. Korsakova et al. 10.1088/1755-1315/949/1/012004
36. Exploring Short-Term Climate Change Effects on Rangelands and Broad-Leaved Forests by Free Satellite Data in Aosta Valley (Northwest Italy) T. Orusa & E. Borgogno Mondino 10.3390/cli9030047
37. Optimal representation of spring phenology on photosynthetic productivity across the northern hemisphere forests J. Fang et al. 10.1016/j.agrformet.2024.109975
38. Modeling vegetation green-up dates across the Tibetan Plateau by including both seasonal and daily temperature and precipitation R. Cao et al. 10.1016/j.agrformet.2017.11.032
39. Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography C. Moore et al. 10.5194/bg-13-5085-2016
40. Trends and controls of terrestrial gross primary productivity of China during 2000–2016 J. Ma et al. 10.1088/1748-9326/ab31e4
41. Performance of the ecosystem demography model (EDv2.2) in simulating gross primary production capacity and activity in a dryland study area H. Dashti et al. 10.1016/j.agrformet.2020.108270
42. Unexpected role of winter precipitation in determining heat requirement for spring vegetation green‐up at northern middle and high latitudes Y. Fu et al. 10.1111/gcb.12610
43. Representing explicit budburst and senescence processes for evergreen conifers in global models M. Peaucelle et al. 10.1016/j.agrformet.2018.12.008
44. Cryptic phenology in plants: Case studies, implications, and recommendations L. Albert et al. 10.1111/gcb.14759
45. Warmest extreme year in U.S. history alters thermal requirements for tree phenology J. Carter et al. 10.1007/s00442-017-3838-z
46. Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks B. Kraft et al. 10.3389/fdata.2019.00031
47. Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems Y. Luo et al. 10.3390/rs10081293
48. Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple Biosphere Model (SiB4) K. Haynes et al. 10.1029/2018MS001540
49. PhenoCam: An evolving, open-source tool to study the temporal and spatial variability of ecosystem-scale phenology A. Richardson 10.1016/j.agrformet.2023.109751
50. An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms . Stanimirova et al. 10.3390/rs11192201
51. Spatial and temporal variations in ecosystem response to monsoon precipitation variability in southwestern North America G. Forzieri et al. 10.1002/2014JG002710
52. Observed changes in false springs over the contiguous United States A. Peterson & J. Abatzoglou 10.1002/2014GL059266
53. Changes in the structure and function of northern Alaskan ecosystems when considering variable leaf‐out times across groupings of species in a dynamic vegetation model E. Euskirchen et al. 10.1111/gcb.12392
54. Process‐based models not always better than empirical models for simulating budburst of Norway spruce and birch in Europe C. Olsson & A. Jönsson 10.1111/gcb.12593
55. Crown Structure Explains the Discrepancy in Leaf Phenology Metrics Derived from Ground- and UAV-Based Observations in a Japanese Cool Temperate Deciduous Forest N. Budianti et al. 10.3390/f12040425
56. Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea C. Lim et al. 10.3390/rs12203282
57. Using time series of MODIS land surface phenology to model temperature and photoperiod controls on spring greenup in North American deciduous forests M. Moon et al. 10.1016/j.rse.2021.112466
58. Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance A. Norton et al. 10.5194/bg-20-2455-2023
59. Ecosystem warming extends vegetation activity but heightens vulnerability to cold temperatures A. Richardson et al. 10.1038/s41586-018-0399-1
60. Using satellite data to improve the leaf phenology of a global terrestrial biosphere model N. MacBean et al. 10.5194/bg-12-7185-2015
61. NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types G. Filippa et al. 10.1016/j.agrformet.2017.11.003
62. Codominant water control on global interannual variability and trends in land surface phenology and greenness M. Forkel et al. 10.1111/gcb.12950
63. Earlier-Season Vegetation Has Greater Temperature Sensitivity of Spring Phenology in Northern Hemisphere M. Shen et al. 10.1371/journal.pone.0088178
64. Budburst model performance: The effect of the spatial resolution of temperature data sets C. Olsson & A. Jönsson 10.1016/j.agrformet.2014.10.003
65. Floating in the air: forecasting allergenic pollen concentration for managing urban public health X. Zhu et al. 10.1080/17538947.2024.2306894
66. Климатогенные изменения и прогноз сроков пыления Junipenis deltoides (Cupressaceae), "Наука Юга России" С. Корсакова et al. 10.7868/S25000640200305
67. Diverging models introduce large uncertainty in future climate warming impact on spring phenology of temperate deciduous trees H. Zhao et al. 10.1016/j.scitotenv.2020.143903
68. Evaluating late spring frost risks of apple in the Loess Plateau of China under future climate change with phenological modeling approach X. Ru et al. 10.1016/j.scienta.2022.111604
69. Predicting canopy biophysical properties and sensitivity of plant carbon uptake to water limitations with a coupled eco-hydrological framework L. Lowman & A. Barros 10.1016/j.ecolmodel.2018.01.011
70. Performance of tree phenology models along a bioclimatic gradient in Sweden C. Olsson et al. 10.1016/j.ecolmodel.2013.06.026
71. Models to predict the start of the airborne pollen season C. Siniscalco et al. 10.1007/s00484-014-0901-x
72. Analysis and classification of data sets for calibration and validation of agro-ecosystem models K. Kersebaum et al. 10.1016/j.envsoft.2015.05.009
73. Herbarium records are reliable sources of phenological change driven by climate and provide novel insights into species' phenological cueing mechanisms C. Davis et al. 10.3732/ajb.1500237
74. Heat wave hinders green wave: The impact of climate extreme on the phenology of a mountain grassland E. Cremonese et al. 10.1016/j.agrformet.2017.08.016
75. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe D. Basler 10.1016/j.agrformet.2015.11.007
76. Sensitivity analysis of tree phenology models reveals increasing sensitivity of their predictions to winter chilling temperature and photoperiod with warming climate J. Gauzere et al. 10.1016/j.ecolmodel.2019.108805
77. Modelling of Climate Change’s Impact on Prunus armeniaca L.’s Flowering Time S. Korsakova et al. 10.3390/inventions8030065
78. Scale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model G. Song et al. 10.1016/j.rse.2024.114027
79. Effects of seasonal variation of photosynthetic capacity on the carbon fluxes of a temperate deciduous forest D. Medvigy et al. 10.1002/2013JG002421
80. Characterizing Spring Phenological Changes of the Land Surface across the Conterminous United States from 2001 to 2021 W. Wu & Q. Xin 10.3390/rs15030737
81. Determining past leaf‐out times of New England's deciduous forests from herbarium specimens P. Everill et al. 10.3732/ajb.1400045
82. Trends and uncertainties in budburst projections of Norway spruce in Northern Europe C. Olsson et al. 10.1002/ece3.3476
83. Humidity does not appear to trigger leaf out in woody plants L. Zipf & R. Primack 10.1007/s00484-017-1428-8
84. New insights on plant phenological response to temperature revealed from long‐term widespread observations in China H. Zhang et al. 10.1111/gcb.14002
85. Can a multi-model ensemble improve phenology predictions for climate change studies? K. Yun et al. 10.1016/j.ecolmodel.2017.08.003
86. Possible Increase of Vegetation Exposure to Spring Frost under Climate Change in Switzerland O. Lhotka & S. Brönnimann 10.3390/atmos11040391
87. Probing the past 30-year phenology trend of US deciduous forests X. Yue et al. 10.5194/bg-12-4693-2015
88. Using FLUXNET data to improve models of springtime vegetation activity onset in forest ecosystems E. Melaas et al. 10.1016/j.agrformet.2012.11.018
89. Abnormal shoot growth in Korean red pine as a response to microclimate changes due to urbanization in Korea S. Jung et al. 10.1007/s00484-019-01843-6
90. Hyperspectral remote sensing to quantify the flowering phenology of winter wheat Z. Zhang et al. 10.1080/00387010.2019.1649701
91. Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States E. Melaas et al. 10.1111/gcb.13122
92. Simulation of forest tree species’ bud burst dates for different climate scenarios: chilling requirements and photo-period may limit bud burst advancement M. Lange et al. 10.1007/s00484-016-1161-8
93. A prognostic vegetation phenology model to predict seasonal maximum and time series of global leaf area index using climate variables X. Zhou et al. 10.1016/j.agrformet.2023.109739
94. Allometric models for estimating aboveground biomass in the tropical woodlands of Ghana, West Africa R. Aabeyir et al. 10.1186/s40663-020-00250-3
95. The influence of spectral composition on spring and autumn phenology in trees C. Brelsford et al. 10.1093/treephys/tpz026
96. USA National Phenology Network’s volunteer-contributed observations yield predictive models of phenological transitions T. Crimmins et al. 10.1371/journal.pone.0182919
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99. The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models T. Keenan & A. Richardson 10.1111/gcb.12890
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101. Dynamically downscaling predictions for deciduous tree leaf emergence in California under current and future climate D. Medvigy et al. 10.1007/s00484-015-1086-7
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109. Is the yield change due to warming affected by photoperiod sensitivity? Effects of the soybean E4 locus E. Kumagai et al. 10.1002/fes3.186
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125. Assessing Phenological Shifts of Deciduous Forests in Turkey under Climate Change: An Assessment for Fagus orientalis with Daily MODIS Data for 19 Years T. Şenel et al. 10.3390/f14020413
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128. Predicting Climate Change Impacts on the Amount and Duration of Autumn Colors in a New England Forest M. Archetti et al. 10.1371/journal.pone.0057373
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131. Later springs green-up faster: the relation between onset and completion of green-up in deciduous forests of North America S. Klosterman et al. 10.1007/s00484-018-1564-9