84 citations as recorded by crossref.

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6. A river runs through it: Robust automated mapping of riparian woodlands and land surface phenology across dryland regions C. McMahon et al. 10.1016/j.rse.2024.114056
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24. Wide-area invasive species propagation mapping is possible using phenometric trends T. Landmann et al. 10.1016/j.isprsjprs.2019.10.016
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. Recalculating Australian water scarcity characterisation factors using the AWARE method P. Bontinck et al. 10.1007/s11367-021-01952-8
27. A steady-state approximation approach to simulate seasonal leaf dynamics of deciduous broadleaf forests via climate variables Q. Xin et al. 10.1016/j.agrformet.2017.11.025
28. A spatially explicit land surface phenology data product for science, monitoring and natural resources management applications M. Broich et al. 10.1016/j.envsoft.2014.11.017
29. Dryland vegetation response to wet episode, not inherent shift in sensitivity to rainfall, behind Australia's role in 2011 global carbon sink anomaly V. Haverd et al. 10.1111/gcb.13202
30. 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
31. Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics Q. Xin et al. 10.1016/j.rse.2015.02.003
32. Sensitivity of remote sensing-based vegetation proxies to climate and sea surface temperature variabilities in Australia and parts of Southeast Asia S. Arjasakusuma et al. 10.1080/01431161.2020.1782509
33. Application of time series of remotely sensed normalized difference water, vegetation and moisture indices in characterizing flood dynamics of large-scale arid zone floodplains A. Mohammadi et al. 10.1016/j.rse.2016.12.003
34. Enhanced spring phenological temperature sensitivity explains the extension of carbon uptake period in temperate forest protected areas J. Ma et al. 10.1016/j.foreco.2019.117679
35. Canopy leaf area of a mature evergreen Eucalyptus woodland does not respond to elevated atmospheric [CO2] but tracks water availability R. Duursma et al. 10.1111/gcb.13151
36. Wide‐area estimates of evapotranspiration by red gum (Eucalyptus camaldulensis) and associated vegetation in the Murray–Darling River Basin, Australia P. Nagler et al. 10.1002/hyp.10734
37. Quantifying Australia's dryland vegetation response to flooding and drought at sub-continental scale M. Broich et al. 10.1016/j.rse.2018.04.032
38. Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992–2012 X. Tong et al. 10.1016/j.rse.2019.111307
39. Land surface albedo and vegetation feedbacks enhanced the millennium drought in south-east Australia J. Evans et al. 10.5194/hess-21-409-2017
40. Modeling photosynthesis of discontinuous plant canopies by linking the Geometric Optical Radiative Transfer model with biochemical processes Q. Xin et al. 10.5194/bg-12-3447-2015
41. Climatic Drivers of the Complex Phenology of the Mediterranean Semi-Deciduous Shrub Phlomis fruticosa Based on Satellite-Derived EVI A. Kyparissis & E. Levizou 10.3390/plants11050584
42. A web-based application for beekeepers to visualise patterns of growth in floral resources using MODIS data J. Arundel et al. 10.1016/j.envsoft.2016.05.010
43. Characterizing land surface phenology and responses to rainfall in the Sahara desert D. Yan et al. 10.1002/2016JG003441
44. Regional patterns of ecosystem functional diversity in the Argentina Pampas using MODIS time-series B. Lara et al. 10.1016/j.ecoinf.2017.11.004
45. 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
46. 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
47. Assessing spatial and temporal patterns in land surface phenology for the Australian Alps (2000–2014) J. Thompson & D. Paull 10.1016/j.rse.2017.06.032
48. Mapping precipitation-corrected NDVI trends across Namibia V. Wingate et al. 10.1016/j.scitotenv.2019.05.158
49. 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
50. Using phase-spaces to characterize land surface phenology in a seasonally snow-covered landscape J. Thompson et al. 10.1016/j.rse.2015.04.008
51. Identification of Rubber Plantations in Southwestern China Based on Multi-Source Remote Sensing Data and Phenology Windows G. Chen et al. 10.3390/rs15051228
52. Time series harmonic regression analysis reveals seasonal vegetation productivity trends in semi-arid savannas D. LeVine & K. Crews 10.1016/j.jag.2019.04.007
53. Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series L. Recuero et al. 10.3390/rs11212497
54. A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index Q. Xin et al. 10.1029/2019MS001935
55. Analysis and prediction of rubber tree phenological changes during Pestalotiopsis infection using Sentinel-2 imagery and random forest Y. Herdiyeni et al. 10.1117/1.JRS.18.014524
56. 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
57. Assessing woody vegetation trends in Sahelian drylands using MODIS based seasonal metrics M. Brandt et al. 10.1016/j.rse.2016.05.027
58. 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
59. Climatic and non-climatic vegetation cover changes in the rangelands of Africa F. D'Adamo et al. 10.1016/j.gloplacha.2021.103516
60. Variability and evolution of global land surface phenology over the past three decades (1982–2012) I. Garonna et al. 10.1111/gcb.13168
61. 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
62. Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) A. Burrell et al. 10.1016/j.rse.2017.05.018
63. 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
64. 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
65. Abrupt shifts in phenology and vegetation productivity under climate extremes X. Ma et al. 10.1002/2015JG003144
66. Land cover change during a period of extensive landscape restoration in Ningxia Hui Autonomous Region, China A. Cadavid Restrepo et al. 10.1016/j.scitotenv.2017.04.124
67. Sentinel-2 time series: a promising tool in monitoring temperate species spring phenology E. Grabska-Szwagrzyk et al. 10.1093/forestry/cpad039
68. A remote sensing spatio-temporal framework for interpreting sparse indicators in highly variable arid landscapes E. Lawley et al. 10.1016/j.ecolind.2015.01.042
69. Using Multi-Temporal Satellite Data to Analyse Phenological Responses of Rubber (Hevea brasiliensis) to Climatic Variations in South Sumatra, Indonesia F. Azizan et al. 10.3390/rs13152932
70. Hydrologic connectivity drives extremes and high variability in vegetation productivity across Australian arid and semi-arid ecosystems A. Norton et al. 10.1016/j.rse.2022.112937
71. Remote sensing monitoring of the spatiotemporal dynamics of urban forest phenology and its response to climate and urbanization M. Hu et al. 10.1016/j.uclim.2024.101810
72. Temporal Trends and Spatial Variability of Vegetation Phenology over the Northern Hemisphere during 1982-2012 S. Wang et al. 10.1371/journal.pone.0157134
73. Multi-climate mode interactions drive hydrological and vegetation responses to hydroclimatic extremes in Australia Z. Xie et al. 10.1016/j.rse.2019.111270
74. Attribution of NDVI Dynamics over the Globe from 1982 to 2015 C. Liu et al. 10.3390/rs14112706
75. The Addition of Temperature to the TSS-RESTREND Methodology Significantly Improves the Detection of Dryland Degradation A. Burrell et al. 10.1109/JSTARS.2019.2906466
76. Impacts of extreme climate on Australia's green cover (2003–2018): A MODIS and mascon probe A. Saleem et al. 10.1016/j.scitotenv.2020.142567
77. Spatiotemporal patterns of remotely sensed phenology and their response to climate change and topography in subtropical bamboo forests during 2001-2017: a case study in Zhejiang Province, China X. Li et al. 10.1080/15481603.2022.2163575
78. Toward a Novel Method for General On-Orbit Earth Surface Anomaly Detection Leveraging Large Vision Models and Lightweight Priors J. Xu et al. 10.1109/TGRS.2024.3432749
79. Woody plant cover estimation in drylands from Earth Observation based seasonal metrics M. Brandt et al. 10.1016/j.rse.2015.10.036
80. Spatiotemporal patterns of vegetation phenology change and relationships with climate in the two transects of East China X. Wang et al. 10.1016/j.gecco.2017.01.010
81. The impact of dataset selection on land degradation assessment A. Burrell et al. 10.1016/j.isprsjprs.2018.08.017
82. Predicting trajectories of dryland wetland vegetation transformation under climate change: a case study of the northern Murray–Darling Basin, Australia J. Johnston-Bates et al. 10.1071/MF24016
83. Hydrological transformation coincided with megafaunal extinction in central Australia T. Cohen et al. 10.1130/G36346.1
84. Plant phenology and climate change J. Fitchett et al. 10.1177/0309133315578940