270 citations as recorded by crossref.

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2. Changes in Meadow Phenology in Response to Grazing Management at Multiple Scales of Measurement W. Richardson et al. 10.3390/rs13204028
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8. Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams Y. Guo et al. 10.3390/rs14020244
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10. EMET: An emergence-based thermal phenological framework for near real-time crop type mapping Z. Yang et al. 10.1016/j.isprsjprs.2024.07.007
11. Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites C. Wu et al. 10.1016/j.agrformet.2016.11.193
12. Consistent time allocation fraction to vegetation green-up versus senescence across northern ecosystems despite recent climate change F. Meng et al. 10.1126/sciadv.adn2487
13. GPP of a Chinese Savanna Ecosystem during Different Phenological Phases Simulated from Harmonized Landsat and Sentinel-2 Data X. Zhang et al. 10.3390/rs16183475
14. Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements Y. Hou et al. 10.3390/rs15245689
15. Comparative Quality and Trend of Remotely Sensed Phenology and Productivity Metrics across the Western United States E. Berman et al. 10.3390/rs12162538
16. Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data K. Bórnez et al. 10.3390/rs12183077
17. Cross-scalar analysis of multisensor land surface phenology X. Gao et al. 10.1016/j.rse.2025.114624
18. Estimation of Gross Primary Productivity (GPP) Phenology of a Short-Rotation Plantation Using Remotely Sensed Indices Derived from Sentinel-2 Images M. Maleki et al. 10.3390/rs12132104
19. Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data X. Li et al. 10.1016/j.isprsjprs.2021.01.018
20. Comparing phenocam color indices with phenological observations of black spruce in the boreal forest X. Li et al. 10.1016/j.ecoinf.2023.102149
21. Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology C. Diao & G. Li 10.3390/rs14091957
22. Precipitation versus temperature as phenology controls in drylands C. Currier & O. Sala 10.1002/ecy.3793
23. Floating in the air: forecasting allergenic pollen concentration for managing urban public health X. Zhu et al. 10.1080/17538947.2024.2306894
24. Monitoring succession after a non-cleared windthrow in a Norway spruce mountain forest using webcam, satellite vegetation indices and turbulent CO 2 exchange M. Matiu et al. 10.1016/j.agrformet.2017.05.020
25. Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales Y. Liu et al. 10.1016/j.agrformet.2017.02.026
26. Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada S. Khare et al. 10.3390/rs14010100
27. Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing A. Richardson et al. 10.1038/s41598-018-23804-6
28. Plugging the Gaps in the Global PhenoCam Monitoring of Forests—The Need for a PhenoCam Network across Indian Forests K. Jose et al. 10.3390/rs15245642
29. Regional evaluation of satellite-based methods for identifying leaf unfolding date R. Shen et al. 10.1016/j.isprsjprs.2021.02.021
30. A novel framework of smart monitoring to face the challenges of tree management in historic gardens E. Carrari et al. 10.1016/j.envres.2024.119790
31. Predicting end-of-season timing across diverse North American grasslands A. Post & A. Richardson 10.1007/s00442-025-05675-7
32. Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing K. Noumonvi et al. 10.3390/rs13153015
33. Evaluating a spatiotemporal shape-matching model for the generation of synthetic high spatiotemporal resolution time series of multiple satellite data X. Zhang et al. 10.1016/j.jag.2021.102545
34. An Earlier Spring Phenology Reduces Vegetation Growth Rate during the Green-Up Period in Temperate Forests B. Wang et al. 10.3390/f14101984
35. 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
36. Grazing alters the phenology of alpine steppe by changing the surface physical environment on the northeast Qinghai-Tibet Plateau, China G. Li et al. 10.1016/j.jenvman.2019.07.028
37. Diverse Responses of Multiple Satellite‐Derived Vegetation Greenup Onsets to Dry Periods in the Amazon X. Zhang et al. 10.1029/2022GL098662
38. Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America Y. Liu et al. 10.1016/j.rse.2016.01.021
39. Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand? A. Paschalis et al. 10.1111/gcb.15024
40. PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits H. Aasen et al. 10.3389/fpls.2020.00593
41. PhenoCam Guidelines for Phenological Measurement and Analysis in an Agricultural Cropping Environment: A Case Study of Soybean S. Sunoj et al. 10.3390/rs17040724
42. Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series K. Kowalski et al. 10.1016/j.jag.2020.102172
43. 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
44. LiDAR derived topography and forest stand characteristics largely explain the spatial variability observed in MODIS land surface phenology G. Misra et al. 10.1016/j.rse.2018.09.027
45. Grassland Phenology’s Sensitivity to Extreme Climate Indices in the Sichuan Province, Western China B. Adu et al. 10.3390/atmos12121650
46. Monitoring crop phenology with street-level imagery using computer vision R. d’Andrimont et al. 10.1016/j.compag.2022.106866
47. Tracking annual changes in the distribution and composition of saltmarsh vegetation on the Jiangsu coast of China using Landsat time series–based phenological parameters C. Sun et al. 10.1016/j.rse.2022.113370
48. 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
49. Observational evidence of legacy effects of the 2018 drought on a mixed deciduous forest in Germany F. Pohl et al. 10.1038/s41598-023-38087-9
50. 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
51. Combining high spatial resolution multi-temporal satellite data with leaf-on LiDAR to enhance tree species discrimination at the crown level F. Fang et al. 10.1080/01431161.2018.1504343
52. Urban heat island impacts on plant phenology: intra-urban variability and response to land cover S. Zipper et al. 10.1088/1748-9326/11/5/054023
53. Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence C. Livensperger et al. 10.1002/ecs2.2677
54. Greenness Index from Phenocams Performs Well in Linking Climatic Factors and Monitoring Grass Phenology in a Temperate Prairie Ecosystem Z. Yuke 10.5814/j.issn.1674-764x.2019.05.003
55. Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series N. Vavlas et al. 10.3390/rs12152385
56. Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands C. Ding et al. 10.3390/rs9090968
57. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling S. Shammi & Q. Meng 10.1016/j.ecolind.2020.107124
58. Tracking the seasonal dynamics of Himalayan birch using a time-lapse camera R. Sharma et al. 10.1007/s12224-021-09394-8
59. Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations E. Berra et al. 10.1016/j.rse.2019.01.010
60. Seasonal differences in relationships between changes in spring phenology and dynamics of carbon cycle in grasslands Z. Xie et al. 10.1002/ecs2.2733
61. Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing S. Thapa et al. 10.3390/rs13081597
62. A Comparison of the Signal from Diverse Optical Sensors for Monitoring Alpine Grassland Dynamics M. Rossi et al. 10.3390/rs11030296
63. Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems J. Fang et al. 10.1038/s41597-023-02224-w
64. Effects of temperature variability and extremes on spring phenology across the contiguous United States from 1982 to 2016 L. Liu & X. Zhang 10.1038/s41598-020-74804-4
65. Combining shape and crop models to detect soybean growth stages Z. Lou et al. 10.1016/j.rse.2023.113827
66. 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
67. Remotely sensed phenological heterogeneity of restored wetlands: linking vegetation structure and function I. Dronova et al. 10.1016/j.agrformet.2020.108215
68. Phenological Changes of Mongolian Oak Depending on the Micro-Climate Changes Due to Urbanization A. Kim et al. 10.3390/rs13101890
69. Earlier snowmelt predominates advanced spring vegetation greenup in Alaska J. Zheng et al. 10.1016/j.agrformet.2022.108828
70. 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
71. Interannual variation in the start of vegetation growing season and its response to climate change in the Qinghai–Tibet Plateau derived from MODIS data during 2001 to 2016 J. Xia et al. 10.1117/1.JRS.13.048506
72. Ecosystem fluxes of hydrogen in a mid‐latitude forest driven by soil microorganisms and plants L. Meredith et al. 10.1111/gcb.13463
73. A novel algorithm for the generation of gap-free time series by fusing harmonized Landsat 8 and Sentinel-2 observations with PhenoCam time series for detecting land surface phenology K. Tran et al. 10.1016/j.rse.2022.113275
74. A transformer-based model for detecting land surface phenology from the irregular harmonized Landsat and Sentinel-2 time series across the United States K. Tran et al. 10.1016/j.rse.2025.114656
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76. Monitoring spring phenology of individual tree crowns using drone‐acquired NDVI data D. Fawcett et al. 10.1002/rse2.184
77. Monitoring Forest Phenology in a Changing World R. Gray & R. Ewers 10.3390/f12030297
78. Comparing the performance of phenocam GCC, MODIS GCC, and MODIS EVI for retrieving vegetation phenology and estimating gross primary production J. Zhang et al. 10.1016/j.ecolind.2024.112251
79. Response and biophysical regulation of carbon dioxide fluxes to climate variability and anomaly in contrasting ecosystems in northwestern Ohio, USA H. Chu et al. 10.1016/j.agrformet.2016.01.008
80. Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset B. Seyednasrollah et al. 10.1038/s41597-019-0229-9
81. Evaluations and comparisons of rule-based and machine-learning-based methods to retrieve satellite-based vegetation phenology using MODIS and USA National Phenology Network data Q. Xin et al. 10.1016/j.jag.2020.102189
82. Effect of preseason diurnal temperature range on the start of vegetation growing season in the Northern Hemisphere Y. Huang et al. 10.1016/j.ecolind.2020.106161
83. Mapping and monitoring of mangrove species in Eastern Lagoon National Park, Abu Dhabi, using Landsat 8 OLI data I. Salem et al. 10.1007/s12517-024-11925-4
84. Assessing Landsat Images Availability and Its Effects on Phenological Metrics J. Mas & F. Soares de Araújo 10.3390/f12050574
85. Uncertainty of Remote Sensing Data in Monitoring Vegetation Phenology: A Comparison of MODIS C5 and C6 Vegetation Index Products on the Tibetan Plateau Z. Zheng & W. Zhu 10.3390/rs9121288
86. Differential phenological responses to temperature among various stages of spring vegetation green-up N. Jiang et al. 10.1093/jpe/rtae063
87. Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data M. Li et al. 10.3390/rs14164027
88. Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA B. Seo et al. 10.1016/j.fcr.2019.03.015
89. Monitoring Mangrove Phenology Based on Gap Filling and Spatiotemporal Fusion: An Optimized Mangrove Phenology Extraction Approach (OMPEA) Y. Hong et al. 10.3390/rs17030549
90. Leaf phenology paradox: Why warming matters most where it is already warm B. Seyednasrollah et al. 10.1016/j.rse.2018.02.059
91. Leaf phenology as an indicator of ecological integrity L. Spafford et al. 10.1002/ecs2.4487
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93. Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation H. Chen et al. 10.3390/s18113676
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97. Greater temperature sensitivity of vegetation greenup onset date in areas with weaker temperature seasonality across the Northern Hemisphere M. Shen et al. 10.1016/j.agrformet.2021.108759
98. “Green pointillism”: detecting the within-population variability of budburst in temperate deciduous trees with phenological cameras N. Delpierre et al. 10.1007/s00484-019-01855-2
99. Agricultural Research Using Social Media Data S. Zipper 10.2134/agronj2017.08.0495
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103. Five years of phenological monitoring in a mountain grassland: inter-annual patterns and evaluation of the sampling protocol G. Filippa et al. 10.1007/s00484-015-0999-5
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114. Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery D. Morresi et al. 10.1016/j.rse.2021.112800
115. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery A. Richardson et al. 10.1038/sdata.2018.28
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118. Mapping corn and soybean phenometrics at field scales over the United States Corn Belt by fusing time series of Landsat 8 and Sentinel-2 data with VIIRS data Y. Shen et al. 10.1016/j.isprsjprs.2022.01.023
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128. Satellite Remote Sensing for Monitoring Agriculture Growth and Agricultural Drought Vulnerability Using Long-Term (1982–2015) Climate Variability and Socio-economic Data set P. Bhavani et al. 10.1007/s40010-017-0445-7
129. Understanding the spring phenology of Arctic tundra using multiple satellite data products and ground observations J. Zheng et al. 10.1007/s11430-019-9644-8
130. Regional evaluation of satellite‐based methods for identifying end of vegetation growing season R. Shen et al. 10.1002/rse2.223
131. Multi-species, ecological and climate change temporalities: Opening a dialogue with phenology M. Bastian & R. Bayliss Hawitt 10.1177/25148486221111784
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142. Extracting Plant Phenology Metrics in a Great Basin Watershed: Methods and Considerations for Quantifying Phenophases in a Cold Desert K. Snyder et al. 10.3390/s16111948
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159. Plant phenological responses to the warm island effect in the lake group region of the Badain Jaran Desert, northwestern China X. Liang et al. 10.1016/j.ecoinf.2020.101066
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