190 citations as recorded by crossref.

1. A comparative study of four approaches to assess phenology of Populus in a short-rotation coppice culture S. Vanbeveren et al. 10.3832/ifor1800-009
2. Trail camera networks provide insights into satellite-derived phenology for ecological studies N. Liu et al. 10.1016/j.jag.2020.102291
3. Onset of drying and dormancy in relation to water dynamics of semi-arid grasslands from MODIS NDWI C. Ding et al. 10.1016/j.agrformet.2016.12.006
4. Changes in Meadow Phenology in Response to Grazing Management at Multiple Scales of Measurement W. Richardson et al. 10.3390/rs13204028
5. Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States E. Melaas et al. 10.1111/gcb.13122
6. 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
7. Generation and evaluation of the VIIRS land surface phenology product X. Zhang et al. 10.1016/j.rse.2018.06.047
8. Evaluating MODIS vegetation products using digital images for quantifying local peatland CO 2 gas fluxes N. Gatis et al. 10.1002/rse2.45
9. 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
10. 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
11. Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams Y. Guo et al. 10.3390/rs14020244
12. Regional evaluation of satellite‐based methods for identifying end of vegetation growing season R. Shen et al. 10.1002/rse2.223
13. Validation of Regional-Scale Remote Sensing Products in China: From Site to Network S. Wang et al. 10.3390/rs8120980
14. An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms . Stanimirova et al. 10.3390/rs11192201
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. 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
17. 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
18. 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
19. Urbanization and climate change jointly shift land surface phenology in the northern mid-latitude large cities T. Qiu et al. 10.1016/j.rse.2019.111477
20. 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
21. 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
22. Comparative Quality and Trend of Remotely Sensed Phenology and Productivity Metrics across the Western United States E. Berman et al. 10.3390/rs12162538
23. 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
24. Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat E. Melaas et al. 10.1016/j.rse.2016.09.014
25. Observing Spring and Fall Phenology in a Deciduous Forest with Aerial Drone Imagery S. Klosterman & A. Richardson 10.3390/s17122852
26. Interpreting canopy development and physiology using a European phenology camera network at flux sites L. Wingate et al. 10.5194/bg-12-5995-2015
27. Evaluation of VEGETATION and PROBA-V Phenology Using PhenoCam and Eddy Covariance Data K. Bórnez et al. 10.3390/rs12183077
28. 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
29. 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
30. A new seasonal‐deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios M. Chen et al. 10.1111/gcb.13326
31. Phenology Response to Climatic Dynamic across China’s Grasslands from 1985 to 2010 J. Wang et al. 10.3390/ijgi7080290
32. Species‐specific spring and autumn leaf phenology captured by time‐lapse digital cameras Y. Xie et al. 10.1002/ecs2.2089
33. Estimación de la fenología de la vegetación a partir de imágenes de satélite: el caso de la península ibérica e islas Baleares (2001-2017) J. Caparros-Santiago & V. Rodríguez-Galiano 10.4995/raet.2020.13632
34. 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
35. Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery A. Richardson 10.1111/nph.15591
36. 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
37. Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada S. Khare et al. 10.3390/rs14010100
38. 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
39. Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation X. Li et al. 10.3390/rs13245018
40. Remote sensing of mangrove forest phenology and its environmental drivers J. Pastor-Guzman et al. 10.1016/j.rse.2017.11.009
41. 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
42. Regional evaluation of satellite-based methods for identifying leaf unfolding date R. Shen et al. 10.1016/j.isprsjprs.2021.02.021
43. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data L. Zeng et al. 10.1016/j.rse.2019.111511
44. Optimal Color Composition Method for Generating High-Quality Daily Photographic Time Series From PhenoCam Q. Li et al. 10.1109/JSTARS.2021.3087814
45. Phenological Changes in Alpine Grasslands and Their Influencing Factors in Seasonally Frozen Ground Regions Across the Three Parallel Rivers Region, Qinghai-Tibet Plateau C. Wang et al. 10.3389/feart.2021.797928
46. Sensitivity of Deciduous Forest Phenology to Environmental Drivers: Implications for Climate Change Impacts Across North America B. Seyednasrollah et al. 10.1029/2019GL086788
47. Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing K. Noumonvi et al. 10.3390/rs13153015
48. Potential of C-band Synthetic Aperture Radar Sentinel-1 time-series for the monitoring of phenological cycles in a deciduous forest K. Soudani et al. 10.1016/j.jag.2021.102505
49. 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
50. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013) Q. Zhang et al. 10.1016/j.agrformet.2017.10.026
51. Asymmetric Behavior of Vegetation Seasonal Growth and the Climatic Cause: Evidence from Long-Term NDVI Dataset in Northeast China Y. Zhou 10.3390/rs11182107
52. 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
53. Spring vegetation green-up dynamics in Central Europe based on 20-year long MODIS NDVI data A. Kern et al. 10.1016/j.agrformet.2020.107969
54. Dynamic Threshold of Carbon Phenology in Two Cold Temperate Grasslands in China L. Xu et al. 10.3390/rs13040574
55. Development of a global annual land surface phenology dataset for 1982–2018 from the AVHRR data by implementing multiple phenology retrieving methods W. Wu et al. 10.1016/j.jag.2021.102487
56. 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
57. Estimation of plant area index and phenological transition dates from digital repeat photography and radiometric approaches in a hardwood forest in the Northeastern United States M. Toda & A. Richardson 10.1016/j.agrformet.2017.09.004
58. 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
59. 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
60. Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand? A. Paschalis et al. 10.1111/gcb.15024
61. 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
62. Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island A. Vrieling et al. 10.1016/j.rse.2018.03.014
63. Comparison of Landsat and Land-Based Phenology Camera Normalized Difference Vegetation Index (NDVI) for Dominant Plant Communities in the Great Basin K. Snyder et al. 10.3390/s19051139
64. Multi-year monitoring land surface phenology in relation to climatic variables using MODIS-NDVI time-series in Mediterranean forest, Northeast Tunisia I. Touhami et al. 10.1016/j.actao.2021.103804
65. Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series K. Kowalski et al. 10.1016/j.jag.2020.102172
66. 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
67. 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
68. 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
69. Grassland Phenology’s Sensitivity to Extreme Climate Indices in the Sichuan Province, Western China B. Adu et al. 10.3390/atmos12121650
70. 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
71. 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
72. Linking Phenological Indices from Digital Cameras in Idaho and Montana to MODIS NDVI J. St. Peter et al. 10.3390/rs10101612
73. 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
74. Experimentally warmer and drier conditions in an Arctic plant community reveal microclimatic controls on senescence C. Livensperger et al. 10.1002/ecs2.2677
75. 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
76. Cryptic phenology in plants: Case studies, implications, and recommendations L. Albert et al. 10.1111/gcb.14759
77. Applicability of Smoothing Techniques in Generation of Phenological Metrics of Tectona grandis L. Using NDVI Time Series Data R. Malhi et al. 10.3390/rs13173343
78. Autumn phenology of a temperate deciduous forest: Validation of remote sensing approach with decadal leaf-litterfall measurements F. Liu et al. 10.1016/j.agrformet.2019.107758
79. Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series N. Vavlas et al. 10.3390/rs12152385
80. 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
81. 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
82. 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
83. Tracking the seasonal dynamics of Himalayan birch using a time-lapse camera R. Sharma et al. 10.1007/s12224-021-09394-8
84. Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography S. Klosterman et al. 10.1016/j.agrformet.2017.10.015
85. 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
86. Seasonal differences in relationships between changes in spring phenology and dynamics of carbon cycle in grasslands Z. Xie et al. 10.1002/ecs2.2733
87. 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
88. Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics A. Lausch et al. 10.3390/rs8121029
89. A Comparison of the Signal from Diverse Optical Sensors for Monitoring Alpine Grassland Dynamics M. Rossi et al. 10.3390/rs11030296
90. Satellite Observed Strong Relationship Between Nighttime Surface Temperature and Leaf Coloring Dates of Terrestrial Ecosystems in East China H. Yuan et al. 10.1109/JSTARS.2020.2971098
91. 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
92. Long-term continuity in land surface phenology measurements: A comparative assessment of the MODIS land cover dynamics and VIIRS land surface phenology products M. Moon et al. 10.1016/j.rse.2019.03.034
93. Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe F. Tian et al. 10.1016/j.rse.2021.112456
94. Mangrove Phenology and Water Influences Measured with Digital Repeat Photography V. Songsom et al. 10.3390/rs13020307
95. Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17 K. Wheeler & M. Dietze 10.5194/bg-18-1971-2021
96. 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
97. Remotely sensed phenological heterogeneity of restored wetlands: linking vegetation structure and function I. Dronova et al. 10.1016/j.agrformet.2020.108215
98. Phenological Changes of Mongolian Oak Depending on the Micro-Climate Changes Due to Urbanization A. Kim et al. 10.3390/rs13101890
99. 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
100. Earlier snowmelt predominates advanced spring vegetation greenup in Alaska J. Zheng et al. 10.1016/j.agrformet.2022.108828
101. 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
102. 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
103. Comparison of Grassland Phenology Derived from MODIS Satellite and PhenoCam Near-Surface Remote Sensing in North America T. Cui et al. 10.1080/07038992.2019.1674643
104. Ecosystem fluxes of hydrogen in a mid‐latitude forest driven by soil microorganisms and plants L. Meredith et al. 10.1111/gcb.13463
105. Monitoring mangrove phenology using camera images Q. Xiang et al. 10.1088/1755-1315/432/1/012001
106. Monitoring Grass Phenology and Hydrological Dynamics of an Oak–Grass Savanna Ecosystem Using Sentinel-2 and Terrestrial Photography P. Gómez-Giráldez et al. 10.3390/rs12040600
107. Monitoring spring phenology of individual tree crowns using drone‐acquired NDVI data D. Fawcett et al. 10.1002/rse2.184
108. 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
109. Influence of Landscape Heterogeneity and Spatial Resolution in Multi-Temporal In Situ and MODIS NDVI Data Proxies for Seasonal GPP Dynamics M. Balzarolo et al. 10.3390/rs11141656
110. Monitoring Forest Phenology in a Changing World R. Gray & R. Ewers 10.3390/f12030297
111. 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
112. Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset B. Seyednasrollah et al. 10.1038/s41597-019-0229-9
113. Detecting the onset of autumn leaf senescence in deciduous forest trees of the temperate zone B. Mariën et al. 10.1111/nph.15991
114. Representing explicit budburst and senescence processes for evergreen conifers in global models M. Peaucelle et al. 10.1016/j.agrformet.2018.12.008
115. 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
116. 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
117. Deep Learning in Plant Phenological Research: A Systematic Literature Review N. Katal et al. 10.3389/fpls.2022.805738
118. Assessing Landsat Images Availability and Its Effects on Phenological Metrics J. Mas & F. Soares de Araújo 10.3390/f12050574
119. 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
120. 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
121. Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics E. Berra & R. Gaulton 10.1016/j.foreco.2020.118663
122. 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
123. A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States N. Robinson et al. 10.3390/rs9080863
124. Leaf phenology paradox: Why warming matters most where it is already warm B. Seyednasrollah et al. 10.1016/j.rse.2018.02.059
125. No two are the same: Assessing variability in broad-leaved savanna tree phenology, with watering, from 2012 to 2014 at Nylsvley, South Africa M. Whitecross et al. 10.1016/j.sajb.2016.03.016
126. Using the red chromatic coordinate to characterize the phenology of forest canopy photosynthesis Y. Liu et al. 10.1016/j.agrformet.2020.107910
127. Savanna tree-grass interactions: A phenological investigation of green-up in relation to water availability over three seasons M. Whitecross et al. 10.1016/j.sajb.2016.09.003
128. Review: advances in in situ and satellite phenological observations in Japan S. Nagai et al. 10.1007/s00484-015-1053-3
129. Higher sample sizes and observer inter‐calibration are needed for reliable scoring of leaf phenology in trees G. Liu et al. 10.1111/1365-2745.13656
130. Integrating a PhenoCam-derived vegetation index into a light use efficiency model to estimate daily gross primary production in a semi-arid grassland H. Wang et al. 10.1016/j.agrformet.2020.107983
131. Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation H. Chen et al. 10.3390/s18113676
132. Fractional coverage rather than green chromatic coordinate is a robust indicator to track grassland phenology using smartphone photography Z. Liu et al. 10.1016/j.ecoinf.2021.101544
133. Spring green-up phenology products derived from MODIS NDVI and EVI: Intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations D. Peng et al. 10.1016/j.ecolind.2017.02.024
134. 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
135. Spatially detailed retrievals of spring phenology from single-season high-resolution image time series A. Vrieling et al. 10.1016/j.jag.2017.02.021
136. Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing M. Kosmala et al. 10.3390/rs8090726
137. Modeling spatiotemporal variations in leaf coloring date of three tree species across China Z. Tao et al. 10.1016/j.agrformet.2017.10.034
138. Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product Z. Wang et al. 10.1016/j.jag.2017.03.008
139. Testing Hopkins’ Bioclimatic Law with PhenoCam data A. Richardson et al. 10.1002/aps3.1228
140. 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
141. “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
142. Agricultural Research Using Social Media Data S. Zipper 10.2134/agronj2017.08.0495
143. Calibrating PhenoCam Data with Phenological Observations of a Black Spruce Stand S. Zhang et al. 10.1080/07038992.2020.1761251
144. Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem D. Browning et al. 10.3390/rs9101071
145. Mangrove Phenology and Environmental Drivers Derived from Remote Sensing in Southern Thailand V. Songsom et al. 10.3390/rs11080955
146. 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
147. Cross-scale phenological monitoring in forest ecosystems: a content-analysis-based review E. Reyes-González et al. 10.1007/s00484-021-02173-2
148. Comparison of land surface phenology in the Northern Hemisphere based on AVHRR GIMMS3g and MODIS datasets J. Zhang et al. 10.1016/j.isprsjprs.2020.08.020
149. Investigation of land surface phenology detections in shrublands using multiple scale satellite data D. Peng et al. 10.1016/j.rse.2020.112133
150. 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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152. Using Digital Photography to Track Understory Phenology in Mediterranean Cork Oak Woodlands C. Jorge et al. 10.3390/rs13040776
153. Change point estimation of deciduous forest land surface phenology Y. Xie & A. Wilson 10.1016/j.rse.2020.111698
154. Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery M. Moon et al. 10.1016/j.rse.2021.112716
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156. Analysis of canopy phenology in man-made forests using near-earth remote sensing P. Guan et al. 10.1186/s13007-021-00803-9
157. Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages C. Diao 10.1016/j.rse.2020.111960
158. Prediction of vegetation phenology with atmospheric reanalysis over semiarid grasslands in Inner Mongolia X. Ma et al. 10.1016/j.scitotenv.2021.152462
159. Standardized flux seasonality metrics: a companion dataset for FLUXNET annual product L. Yang & A. Noormets 10.5194/essd-13-1461-2021
160. Detection of autumn leaf phenology and color brightness from repeat photography: Accurate, robust, and sensitive indexes and modeling under unstable field observations D. Koide et al. 10.1016/j.ecolind.2019.105482
161. Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review J. Caparros-Santiago et al. 10.1016/j.isprsjprs.2020.11.019
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164. Networked web-cameras monitor congruent seasonal development of birches with phenological field observations M. Peltoniemi et al. 10.1016/j.agrformet.2017.10.008
165. 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
166. 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
167. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery A. Richardson et al. 10.1038/sdata.2018.28
168. Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework D. Browning et al. 10.1016/j.ecolind.2021.108147
169. What leads to rubber leaf senescence in the northern edge of the Asian tropics? Y. Chen et al. 10.1016/j.indcrop.2022.114617
170. Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network D. Yan et al. 10.1016/j.jag.2019.02.011
171. 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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173. Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies G. Song et al. 10.1016/j.isprsjprs.2021.10.023
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