97 citations as recorded by crossref.

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2. Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set S. Walther et al. 10.5194/bg-19-2805-2022
3. Tracking forest biophysical properties with automated digital repeat photography: A fisheye perspective using digital hemispherical photography from below the canopy L. Brown et al. 10.1016/j.agrformet.2020.107944
4. DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology G. Liu et al. 10.5194/gmd-17-6683-2024
5. Spatial and temporal variability of carbon dioxide fluxes in the Alpine Critical Zone: The case of the Nivolet Plain, Gran Paradiso National Park, Italy S. Lenzi et al. 10.1371/journal.pone.0286268
6. Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset B. Seyednasrollah et al. 10.1038/s41597-019-0229-9
7. 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
8. Extracting Deciduous Forests Spring Phenology From Sentinel-1 Cross Ratio Index H. Yu et al. 10.1109/JSTARS.2023.3247833
9. A Study of Forest Phenology Prediction Based on GRU Models P. Guan et al. 10.3390/app13084898
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11. Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series K. Kowalski et al. 10.1016/j.rse.2021.112781
12. Tree phenological ranks repeat from year to year and correlate with growth in temperate deciduous forests N. Delpierre et al. 10.1016/j.agrformet.2016.12.008
13. 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
14. Spatial-aware SAR-optical time-series deep integration for crop phenology tracking W. Zhao et al. 10.1016/j.rse.2022.113046
15. A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests K. Soudani et al. 10.5194/bg-18-3391-2021
16. 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
17. On the relationship between continuous measures of canopy greenness derived using near-surface remote sensing and satellite-derived vegetation products L. Brown et al. 10.1016/j.agrformet.2017.08.012
18. Phenology Across Scales: An Intercontinental Analysis of Leaf‐Out Dates in Temperate Deciduous Tree Communities N. Delpierre et al. 10.1111/geb.13910
19. Real-time and short-term predictions of spring phenology in North America from VIIRS data L. Liu et al. 10.1016/j.rse.2017.03.009
20. Detecting the onset of autumn leaf senescence in deciduous forest trees of the temperate zone B. Mariën et al. 10.1111/nph.15991
21. 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
22. 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
23. Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8 X. Ma et al. 10.3390/rs12081339
24. Leaf and male cone phenophases of Chinese pine (Pinus tabulaeformis Carr.) along a rural-urban gradient in Beijing, China Y. Su et al. 10.1016/j.ufug.2019.05.009
25. Precipitation versus temperature as phenology controls in drylands C. Currier & O. Sala 10.1002/ecy.3793
26. Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest R. Cheng et al. 10.5194/bg-17-4523-2020
27. Development and evaluation of a new algorithm for detecting 30 m land surface phenology from VIIRS and HLS time series X. Zhang et al. 10.1016/j.isprsjprs.2020.01.012
28. Earth system data cubes unravel global multivariate dynamics M. Mahecha et al. 10.5194/esd-11-201-2020
29. Utilizing machine learning for detecting flowering in mid-range digital repeat photography T. Kim et al. 10.1016/j.ecolmodel.2020.109419
30. Detection of year-to-year spring and autumn bio-meteorological variations in siberian ecosystems S. Nagai et al. 10.1016/j.polar.2020.100534
31. Can upward-facing digital camera images be used for remote monitoring of forest phenology? M. Wilkinson et al. 10.1093/forestry/cpx057
32. Evaluating MODIS vegetation products using digital images for quantifying local peatland CO2 gas fluxes N. Gatis et al. 10.1002/rse2.45
33. Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery X. Zhang et al. 10.1016/j.agrformet.2018.03.003
34. Measuring Vegetation Phenology with Near-Surface Remote Sensing in a Temperate Deciduous Forest: Effects of Sensor Type and Deployment F. Liu et al. 10.3390/rs11091063
35. Using canopy greenness index to identify leaf ecophysiological traits during the foliar senescence in an oak forest Z. Liu et al. 10.1002/ecs2.2337
36. Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology C. Diao & G. Li 10.3390/rs14091957
37. Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest H. Yang et al. 10.1038/s41598-017-01260-y
38. Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand V. Songsom et al. 10.3390/rs11080955
39. Detecting impacts of extreme events with ecological in situ monitoring networks M. Mahecha et al. 10.5194/bg-14-4255-2017
40. Evergreen broadleaf greenness and its relationship with leaf flushing, aging, and water fluxes Y. Luo et al. 10.1016/j.agrformet.2022.109060
41. 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
42. 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
43. 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
44. 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
45. Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography G. Piazzi et al. 10.3390/geosciences9030129
46. Comparison of traditional ground-based observations and digital remote sensing of phenological transitions in a floodplain forest O. Nezval et al. 10.1016/j.agrformet.2020.108079
47. Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography S. Klosterman et al. 10.1016/j.agrformet.2017.10.015
48. A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales F. Parmentier et al. 10.5194/essd-13-3593-2021
49. Photographic records of plant phenology and spring river flush timing in a river lowland ecosystem at the taiga–tundra boundary, northeastern Siberia T. Morozumi et al. 10.1111/1440-1703.12107
50. Using ground observations of a digital camera in the VIS-NIR range for quantifying the phenology of Mediterranean woody species G. Weil et al. 10.1016/j.jag.2017.05.016
51. Limitations to winter and spring photosynthesis of a Rocky Mountain subalpine forest D. Bowling et al. 10.1016/j.agrformet.2018.01.025
52. Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions A. Arslan et al. 10.3390/geosciences7030055
53. Timing leaf senescence: A generalized additive models for location, scale and shape approach B. Mariën et al. 10.1016/j.agrformet.2022.108823
54. Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery A. Richardson 10.1111/nph.15591
55. Tracking vegetation phenology of pristine northern boreal peatlands by combining digital photography with CO2 flux and remote sensing data M. Linkosalmi et al. 10.5194/bg-19-4747-2022
56. “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
57. Near surface camera informed agricultural land monitoring for climate smart agriculture L. Yu et al. 10.1016/j.csag.2024.100008
58. Testing Hopkins’ Bioclimatic Law with PhenoCam data A. Richardson et al. 10.1002/aps3.1228
59. Modelling Fagus sylvatica stem growth along a wide thermal gradient in Italy by incorporating dendroclimatic classification and land surface phenology metrics L. Di Fiore et al. 10.1007/s00484-022-02367-2
60. Leaf phenology as an indicator of ecological integrity L. Spafford et al. 10.1002/ecs2.4487
61. Evaluation of Vegetation Indexes and Green-Up Date Extraction Methods on the Tibetan Plateau J. Xu et al. 10.3390/rs14133160
62. How did the characteristics of the growing season change during the past 100 years at a steep river basin in Japan? N. Shin et al. 10.1371/journal.pone.0255078
63. Understanding the role of phenology and summer physiology in controlling net ecosystem production: a multiscale comparison of satellite, PhenoCam and eddy covariance data Y. Liu & C. Wu 10.1088/1748-9326/abb32f
64. Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection G. Weil et al. 10.3390/rs9111130
65. Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island A. Vrieling et al. 10.1016/j.rse.2018.03.014
66. Productivity of an Australian mountain grassland is limited by temperature and dryness despite long growing seasons R. Marchin et al. 10.1016/j.agrformet.2018.02.030
67. Peatland vegetation composition and phenology drive the seasonal trajectory of maximum gross primary production M. Peichl et al. 10.1038/s41598-018-26147-4
68. Using Digital Photography to Track Understory Phenology in Mediterranean Cork Oak Woodlands C. Jorge et al. 10.3390/rs13040776
69. Below-canopy contributions to ecosystem CO 2 fluxes in a temperate mixed forest in Switzerland E. Paul-Limoges et al. 10.1016/j.agrformet.2017.08.011
70. A new generation of sensors and monitoring tools to support climate-smart forestry practices C. Torresan et al. 10.1139/cjfr-2020-0295
71. COSMOS-UK: national soil moisture and hydrometeorology data for environmental science research H. Cooper et al. 10.5194/essd-13-1737-2021
72. Webcam network and image database for studies of phenological changes of vegetation and snow cover in Finland, image time series from 2014 to 2016 M. Peltoniemi et al. 10.5194/essd-10-173-2018
73. Mapping the scientific research on natural landscape change with rephotography J. Chen et al. 10.1016/j.ecoinf.2021.101387
74. A System for Acquisition, Processing and Visualization of Image Time Series from Multiple Camera Networks C. Tanis et al. 10.3390/data3030023
75. Tree–grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna C. Moore et al. 10.5194/bg-14-111-2017
76. Summarizing the state of the terrestrial biosphere in few dimensions G. Kraemer et al. 10.5194/bg-17-2397-2020
77. Monitoring Forest Phenology in a Changing World R. Gray & R. Ewers 10.3390/f12030297
78. Canopy and climate controls of gross primary production of Mediterranean-type deciduous and evergreen oak savannas J. Wang et al. 10.1016/j.agrformet.2016.05.020
79. Networked web-cameras monitor congruent seasonal development of birches with phenological field observations M. Peltoniemi et al. 10.1016/j.agrformet.2017.10.008
80. A simple visible and near-infrared (V-NIR) camera system for monitoring the leaf area index and growth stage of Italian ryegrass X. Fan et al. 10.1016/j.compag.2017.11.025
81. Drone-Sensed and Sap Flux-Derived Leaf Phenology in a Cool Temperate Deciduous Forest: A Tree-Level Comparison of 17 Species N. Budianti et al. 10.3390/rs14102505
82. Review: Monitoring of land cover changes and plant phenology by remote‐sensing in East Asia N. Shin et al. 10.1111/1440-1703.12371
83. Research on Forest Phenology Prediction Based on LSTM and GRU Model G. Peng & Z. Yili 10.5814/j.issn.1674-764x.2023.01.003
84. Enhancing global change experiments through integration of remote‐sensing techniques A. Shiklomanov et al. 10.1002/fee.2031
85. Impacts of nitrogen enrichment on vegetation growth dynamics are regulated by grassland degradation status X. Xu et al. 10.1002/ldr.3899
86. Digital photography for assessing the link between vegetation phenology and CO<sub>2</sub> exchange in two contrasting northern ecosystems M. Linkosalmi et al. 10.5194/gi-5-417-2016
87. Time series sUAV data reveal moderate accuracy and large uncertainties in spring phenology metric of deciduous broadleaf forest as estimated by vegetation index-based phenological models L. Pan et al. 10.1016/j.isprsjprs.2024.09.023
88. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery A. Richardson et al. 10.1038/sdata.2018.28
89. Mangrove Phenology and Water Influences Measured with Digital Repeat Photography V. Songsom et al. 10.3390/rs13020307
90. Using PhenoCams to track crop phenology and explain the effects of different cropping systems on yield Y. Liu et al. 10.1016/j.agsy.2021.103306
91. Phenology of fine root and shoot using high frequency temporal resolution images in a temperate larch forest A. Tamura et al. 10.1016/j.rhisph.2022.100541
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93. Seasonal and interannual variations in carbon fluxes in East Asia semi-arid grasslands H. Zhao et al. 10.1016/j.scitotenv.2019.02.378
94. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data L. Zeng et al. 10.1016/j.rse.2019.111511
95. Relationship between gross primary production and canopy colour indices from digital camera images in a rubber (Hevea brasiliensis) plantation, Southwest China R. Zhou et al. 10.1016/j.foreco.2019.01.019
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97. Emerging opportunities and challenges in phenology: a review J. Tang et al. 10.1002/ecs2.1436