244 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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12. Comparative Quality and Trend of Remotely Sensed Phenology and Productivity Metrics across the Western United States E. Berman et al. 10.3390/rs12162538
13. Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data K. Bórnez et al. 10.3390/rs12183077
14. 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
15. 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
16. Comparing phenocam color indices with phenological observations of black spruce in the boreal forest X. Li et al. 10.1016/j.ecoinf.2023.102149
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20. 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
21. 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
22. Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada S. Khare et al. 10.3390/rs14010100
23. Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation X. Li et al. 10.3390/rs13245018
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25. 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
26. Regional evaluation of satellite-based methods for identifying leaf unfolding date R. Shen et al. 10.1016/j.isprsjprs.2021.02.021
27. Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing K. Noumonvi et al. 10.3390/rs13153015
28. 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
29. An Earlier Spring Phenology Reduces Vegetation Growth Rate during the Green-Up Period in Temperate Forests B. Wang et al. 10.3390/f14101984
30. Asymmetric Behavior of Vegetation Seasonal Growth and the Climatic Cause: Evidence from Long-Term NDVI Dataset in Northeast China Y. Zhou 10.3390/rs11182107
31. 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
32. 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
33. Diverse Responses of Multiple Satellite‐Derived Vegetation Greenup Onsets to Dry Periods in the Amazon X. Zhang et al. 10.1029/2022GL098662
34. 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
35. Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand? A. Paschalis et al. 10.1111/gcb.15024
36. 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
37. Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series K. Kowalski et al. 10.1016/j.jag.2020.102172
38. 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
39. 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
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42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence C. Livensperger et al. 10.1002/ecs2.2677
49. 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
50. Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series N. Vavlas et al. 10.3390/rs12152385
51. 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
52. 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
53. Tracking the seasonal dynamics of Himalayan birch using a time-lapse camera R. Sharma et al. 10.1007/s12224-021-09394-8
54. 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
55. Seasonal differences in relationships between changes in spring phenology and dynamics of carbon cycle in grasslands Z. Xie et al. 10.1002/ecs2.2733
56. 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
57. A Comparison of the Signal from Diverse Optical Sensors for Monitoring Alpine Grassland Dynamics M. Rossi et al. 10.3390/rs11030296
58. Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems J. Fang et al. 10.1038/s41597-023-02224-w
59. 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
60. Combining shape and crop models to detect soybean growth stages Z. Lou et al. 10.1016/j.rse.2023.113827
61. 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
62. Remotely sensed phenological heterogeneity of restored wetlands: linking vegetation structure and function I. Dronova et al. 10.1016/j.agrformet.2020.108215
63. Phenological Changes of Mongolian Oak Depending on the Micro-Climate Changes Due to Urbanization A. Kim et al. 10.3390/rs13101890
64. Earlier snowmelt predominates advanced spring vegetation greenup in Alaska J. Zheng et al. 10.1016/j.agrformet.2022.108828
65. 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
66. 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
67. Ecosystem fluxes of hydrogen in a mid‐latitude forest driven by soil microorganisms and plants L. Meredith et al. 10.1111/gcb.13463
68. 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
69. Monitoring mangrove phenology using camera images Q. Xiang et al. 10.1088/1755-1315/432/1/012001
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72. 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
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77. Assessing Landsat Images Availability and Its Effects on Phenological Metrics J. Mas & F. Soares de Araújo 10.3390/f12050574
78. 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
79. Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data M. Li et al. 10.3390/rs14164027
80. 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
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82. Leaf phenology as an indicator of ecological integrity L. Spafford et al. 10.1002/ecs2.4487
83. Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation H. Chen et al. 10.3390/s18113676
84. Climate‐driven shifts in leaf senescence are greater for boreal species than temperate species in the Acadian Forest region in contrast to leaf emergence shifts L. Spafford et al. 10.1002/ece3.10362
85. Satellite chlorophyll fluorescence measurements reveal large‐scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests S. Walther et al. 10.1111/gcb.13200
86. Characterizing Spring Phenological Changes of the Land Surface across the Conterminous United States from 2001 to 2021 W. Wu & Q. Xin 10.3390/rs15030737
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88. “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
89. Agricultural Research Using Social Media Data S. Zipper 10.2134/agronj2017.08.0495
90. Calibrating PhenoCam Data with Phenological Observations of a Black Spruce Stand S. Zhang et al. 10.1080/07038992.2020.1761251
91. Cross-scale phenological monitoring in forest ecosystems: a content-analysis-based review E. Reyes-González et al. 10.1007/s00484-021-02173-2
92. The impact of urban green space landscape on PM2.5 in the central urban area of Nanchang city, China S. Luo et al. 10.1016/j.apr.2023.101903
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104. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery A. Richardson et al. 10.1038/sdata.2018.28
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