250 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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15. Comparative Quality and Trend of Remotely Sensed Phenology and Productivity Metrics across the Western United States E. Berman et al. 10.3390/rs12162538
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17. 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
18. 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
19. 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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24. 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
25. Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada S. Khare et al. 10.3390/rs14010100
26. 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
27. 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
28. Regional evaluation of satellite-based methods for identifying leaf unfolding date R. Shen et al. 10.1016/j.isprsjprs.2021.02.021
29. 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
30. Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing K. Noumonvi et al. 10.3390/rs13153015
31. 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
32. An Earlier Spring Phenology Reduces Vegetation Growth Rate during the Green-Up Period in Temperate Forests B. Wang et al. 10.3390/f14101984
33. 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
34. 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
35. Diverse Responses of Multiple Satellite‐Derived Vegetation Greenup Onsets to Dry Periods in the Amazon X. Zhang et al. 10.1029/2022GL098662
36. 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
37. Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand? A. Paschalis et al. 10.1111/gcb.15024
38. 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
39. Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series K. Kowalski et al. 10.1016/j.jag.2020.102172
40. 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
41. 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
42. Grassland Phenology’s Sensitivity to Extreme Climate Indices in the Sichuan Province, Western China B. Adu et al. 10.3390/atmos12121650
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44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence C. Livensperger et al. 10.1002/ecs2.2677
51. 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
52. Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series N. Vavlas et al. 10.3390/rs12152385
53. 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
54. 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
55. Tracking the seasonal dynamics of Himalayan birch using a time-lapse camera R. Sharma et al. 10.1007/s12224-021-09394-8
56. 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
57. Seasonal differences in relationships between changes in spring phenology and dynamics of carbon cycle in grasslands Z. Xie et al. 10.1002/ecs2.2733
58. 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
59. A Comparison of the Signal from Diverse Optical Sensors for Monitoring Alpine Grassland Dynamics M. Rossi et al. 10.3390/rs11030296
60. Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems J. Fang et al. 10.1038/s41597-023-02224-w
61. 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
62. Combining shape and crop models to detect soybean growth stages Z. Lou et al. 10.1016/j.rse.2023.113827
63. 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
64. Remotely sensed phenological heterogeneity of restored wetlands: linking vegetation structure and function I. Dronova et al. 10.1016/j.agrformet.2020.108215
65. Phenological Changes of Mongolian Oak Depending on the Micro-Climate Changes Due to Urbanization A. Kim et al. 10.3390/rs13101890
66. Earlier snowmelt predominates advanced spring vegetation greenup in Alaska J. Zheng et al. 10.1016/j.agrformet.2022.108828
67. 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
68. 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
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78. 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
79. 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
80. Assessing Landsat Images Availability and Its Effects on Phenological Metrics J. Mas & F. Soares de Araújo 10.3390/f12050574
81. 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
82. Differential phenological responses to temperature among various stages of spring vegetation green-up N. Jiang et al. 10.1093/jpe/rtae063
83. Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data M. Li et al. 10.3390/rs14164027
84. 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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92. “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
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108. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery A. Richardson et al. 10.1038/sdata.2018.28
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132. 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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