77 citations as recorded by crossref.

1. Use of Cool Roofs and Vegetation to Mitigate Urban Heat and Improve Human Thermal Stress in Melbourne, Australia S. Jacobs et al. 10.1175/JAMC-D-17-0243.1
2. 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
3. Evaluation of PlanetScope-detected plant-specific phenology using infrared-enabled PhenoCam observations in semi-arid ecosystems Y. Liu et al. 10.1016/j.isprsjprs.2024.03.017
4. A deep semantic vegetation health monitoring platform for citizen science imaging data A. Khan et al. 10.1371/journal.pone.0270625
5. 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
6. Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology C. Diao & G. Li 10.3390/rs14091957
7. Detection of year-to-year spring and autumn bio-meteorological variations in siberian ecosystems S. Nagai et al. 10.1016/j.polar.2020.100534
8. Long-Term Effects of Fire Severity and Climatic Factors on Post-Forest-Fire Vegetation Recovery B. Hao et al. 10.3390/f13060883
9. Satellite Observations of the Contrasting Response of Trees and Grasses to Variations in Water Availability S. Walther et al. 10.1029/2018GL080535
10. An introduction to the Australian and New Zealand flux tower network – OzFlux J. Beringer et al. 10.5194/bg-13-5895-2016
11. Evergreen and ever growing – Stem and canopy growth dynamics of a temperate eucalypt forest A. Griebel et al. 10.1016/j.foreco.2016.12.017
12. Current issues in tropical phenology: a synthesis K. Abernethy et al. 10.1111/btp.12558
13. Ozone–vegetation feedback through dry deposition and isoprene emissions in a global chemistry–carbon–climate model C. Gong et al. 10.5194/acp-20-3841-2020
14. Predicting autumn phenology: How deciduous tree species respond to weather stressors Y. Xie et al. 10.1016/j.agrformet.2017.12.259
15. 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
16. Multi-Scale Phenology of Temperate Grasslands: Improving Monitoring and Management With Near-Surface Phenocams C. Watson et al. 10.3389/fenvs.2019.00014
17. Challenges and opportunities in land surface modelling of savanna ecosystems R. Whitley et al. 10.5194/bg-14-4711-2017
18. Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery A. Richardson 10.1111/nph.15591
19. 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
20. Impact of a Tropical Cyclone on Terrestrial Inputs and Bio-Optical Properties in Princess Charlotte Bay (Great Barrier Reef Lagoon) K. Oubelkheir et al. 10.3390/rs15030652
21. Utilizing machine learning for detecting flowering in mid-range digital repeat photography T. Kim et al. 10.1016/j.ecolmodel.2020.109419
22. 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
23. Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest J. Wang et al. 10.1016/j.rse.2020.111865
24. Continuous observations of forest canopy structure using low-cost digital camera traps F. Chianucci et al. 10.1016/j.agrformet.2021.108516
25. Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network J. Beringer et al. 10.1111/gcb.16141
26. An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites J. Wang et al. 10.1016/j.rse.2022.113429
27. Evaluating the Ability of the Sentinel-1 Cross-Polarization Ratio to Detect Spring Maize Phenology Using Adaptive Dynamic Threshold Y. Ma et al. 10.3390/rs16050826
28. 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
29. Soundscape phenology: The effect of environmental and climatic factors on birds and insects in a subtropical woodland M. Scarpelli et al. 10.1016/j.scitotenv.2023.163080
30. Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI C. Burton et al. 10.5194/essd-16-4389-2024
31. Seasonal ensemble forecasts for soil moisture, evapotranspiration and runoff across Australia E. Vogel et al. 10.1016/j.jhydrol.2021.126620
32. Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques R. Bell & J. Callow 10.3390/rs12040669
33. Automatic detection of woody vegetation in repeat landscape photographs using a convolutional neural network U. Bayr & O. Puschmann 10.1016/j.ecoinf.2019.01.012
34. Analyzing the Influence of Climate Variables on Native Pasture Phenology in the Fitzroy Basin, Australia M. Aditya 10.1080/23754931.2022.2081815
35. Testing Hopkins’ Bioclimatic Law with PhenoCam data A. Richardson et al. 10.1002/aps3.1228
36. Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset B. Seyednasrollah et al. 10.1038/s41597-019-0229-9
37. Using dense Sentinel-2 time series to explore combined fire and drought impacts in eucalypt forests S. Hislop et al. 10.3389/ffgc.2023.1018936
38. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data L. Zeng et al. 10.1016/j.rse.2019.111511
39. Vehicle Detection in High Resolution Satellite Remote Sensing Images Based on Deep Learning Q. Tan et al. 10.1109/ACCESS.2020.3017894
40. Land surface phenology indicators retrieved across diverse ecosystems using a modified threshold algorithm Q. Xie et al. 10.1016/j.ecolind.2023.110000
41. Preface: OzFlux: a network for the study of ecosystem carbon and water dynamics across Australia and New Zealand E. van Gorsel et al. 10.5194/bg-15-349-2018
42. 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
43. Data extraction from digital repeat photography using xROI: An interactive framework to facilitate the process B. Seyednasrollah et al. 10.1016/j.isprsjprs.2019.04.009
44. Progress in Remote Sensing of Grass Senescence: A Review on the Challenges and Opportunities L. Royimani et al. 10.1109/JSTARS.2021.3098720
45. 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
46. The Effect of Bioclimatic Covariates on Ensemble Machine Learning Prediction of Total Soil Carbon in the Pannonian Biogeoregion D. Radočaj et al. 10.3390/agronomy13102516
47. A monitoring toolkit for banksia woodlands: comparison of different scale methods to measure recovery of vegetation after fire M. Brundrett et al. 10.1002/rse2.88
48. Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island A. Vrieling et al. 10.1016/j.rse.2018.03.014
49. Effects of climate change on the growing season of alpine grassland in Northern Tibet, China X. Zhang et al. 10.1016/j.gecco.2020.e01126
50. Mangrove Phenology and Water Influences Measured with Digital Repeat Photography V. Songsom et al. 10.3390/rs13020307
51. Cross-scale phenological monitoring in forest ecosystems: a content-analysis-based review E. Reyes-González et al. 10.1007/s00484-021-02173-2
52. High Resolution Forest Masking for Seasonal Monitoring with a Regionalized and Colourimetrically Assisted Chorologic Typology R. Aravena et al. 10.3390/rs15143457
53. Remote sensing for monitoring tropical dryland forests: a review of current research, knowledge gaps and future directions for Southern Africa R. David et al. 10.1088/2515-7620/ac5b84
54. Review: Monitoring of land cover changes and plant phenology by remote‐sensing in East Asia N. Shin et al. 10.1111/1440-1703.12371
55. Exploration of global spatiotemporal changes of fall foliage coloration in deciduous forests and shrubs using the VIIRS land surface phenology product Y. Ye & X. Zhang 10.1016/j.srs.2021.100030
56. On quantifying the apparent temperature sensitivity of plant phenology T. Keenan et al. 10.1111/nph.16114
57. Near surface camera informed agricultural land monitoring for climate smart agriculture L. Yu et al. 10.1016/j.csag.2024.100008
58. Field validation of NDVI to identify crop phenological signatures M. Bhatti et al. 10.1007/s11119-024-10165-6
59. Reduced model complexity for efficient characterisation of savanna woodland structure using terrestrial laser scanning L. Luck et al. 10.1016/j.jag.2023.103255
60. Assessing the Accuracy of Forest Phenological Extraction from Sentinel-1 C-Band Backscatter Measurements in Deciduous and Coniferous Forests Y. Ling et al. 10.3390/rs14030674
61. Thirty-eight years of CO<sub>2</sub> fertilization has outpaced growing aridity to drive greening of Australian woody ecosystems S. Rifai et al. 10.5194/bg-19-491-2022
62. Phenology Dynamics of Dryland Ecosystems Along the North Australian Tropical Transect Revealed by Satellite Solar‐Induced Chlorophyll Fluorescence C. Wang et al. 10.1029/2019GL082716
63. Evergreen broadleaf greenness and its relationship with leaf flushing, aging, and water fluxes Y. Luo et al. 10.1016/j.agrformet.2022.109060
64. 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
65. U-Net Convolutional Neural Network for Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia T. Boston et al. 10.3390/jimaging10060143
66. Trail camera networks provide insights into satellite-derived phenology for ecological studies N. Liu et al. 10.1016/j.jag.2020.102291
67. Upside-down fluxes Down Under: CO<sub>2</sub> net sink in winter and net source in summer in a temperate evergreen broadleaf forest A. Renchon et al. 10.5194/bg-15-3703-2018
68. Seasonal variation of ecosystem photosynthetic capacity and its environmental drivers in global grasslands X. Chen et al. 10.3389/fevo.2023.1193607
69. Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities Z. Gong et al. 10.1016/j.isprsjprs.2024.08.011
70. 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
71. All models of satellite-derived phenology are wrong, but some are useful: A case study from northern Australia N. Younes et al. 10.1016/j.jag.2020.102285
72. Fire reduces eucalypt forest flowering phenology at the landscape-scale D. Dixon et al. 10.1016/j.scitotenv.2023.164828
73. Simultaneous monitoring of vegetation dynamics and wildlife activity with camera traps to assess habitat change C. Sun et al. 10.1002/rse2.222
74. Land surface phenology retrievals for arid and semi-arid ecosystems Q. Xie et al. 10.1016/j.isprsjprs.2022.01.017
75. Understorey productivity in temperate grassy woodland responds to soil water availability but not to elevated [CO2] L. Collins et al. 10.1111/gcb.14038
76. Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices A. Talucci et al. 10.3390/rs12182970
77. Temporal Dynamics of Canopy Properties and Carbon and Water Fluxes in a Temperate Evergreen Angiosperm Forest A. Renchon et al. 10.3390/f15050801