82 citations as recorded by crossref.

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23. Wide-area invasive species propagation mapping is possible using phenometric trends T. Landmann et al. 10.1016/j.isprsjprs.2019.10.016
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25. Recalculating Australian water scarcity characterisation factors using the AWARE method P. Bontinck et al. 10.1007/s11367-021-01952-8
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. Land surface albedo and vegetation feedbacks enhanced the millennium drought in south-east Australia J. Evans et al. 10.5194/hess-21-409-2017
39. 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
40. 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
41. 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
42. Characterizing land surface phenology and responses to rainfall in the Sahara desert D. Yan et al. 10.1002/2016JG003441
43. 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
44. 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
45. 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
46. 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
47. Mapping precipitation-corrected NDVI trends across Namibia V. Wingate et al. 10.1016/j.scitotenv.2019.05.158
48. 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
49. 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
50. Identification of Rubber Plantations in Southwestern China Based on Multi-Source Remote Sensing Data and Phenology Windows G. Chen et al. 10.3390/rs15051228
51. 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
52. Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series L. Recuero et al. 10.3390/rs11212497
53. A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index Q. Xin et al. 10.1029/2019MS001935
54. 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
55. Assessing woody vegetation trends in Sahelian drylands using MODIS based seasonal metrics M. Brandt et al. 10.1016/j.rse.2016.05.027
56. 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
57. Climatic and non-climatic vegetation cover changes in the rangelands of Africa F. D'Adamo et al. 10.1016/j.gloplacha.2021.103516
58. Variability and evolution of global land surface phenology over the past three decades (1982–2012) I. Garonna et al. 10.1111/gcb.13168
59. 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
60. Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) A. Burrell et al. 10.1016/j.rse.2017.05.018
61. 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
62. 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
63. Abrupt shifts in phenology and vegetation productivity under climate extremes X. Ma et al. 10.1002/2015JG003144
64. 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
65. Sentinel-2 time series: a promising tool in monitoring temperate species spring phenology E. Grabska-Szwagrzyk et al. 10.1093/forestry/cpad039
66. 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
67. 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
68. 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
69. 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
70. Temporal Trends and Spatial Variability of Vegetation Phenology over the Northern Hemisphere during 1982-2012 S. Wang et al. 10.1371/journal.pone.0157134
71. Multi-climate mode interactions drive hydrological and vegetation responses to hydroclimatic extremes in Australia Z. Xie et al. 10.1016/j.rse.2019.111270
72. Attribution of NDVI Dynamics over the Globe from 1982 to 2015 C. Liu et al. 10.3390/rs14112706
73. 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
74. 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
75. 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
76. Woody plant cover estimation in drylands from Earth Observation based seasonal metrics M. Brandt et al. 10.1016/j.rse.2015.10.036
77. 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
78. The impact of dataset selection on land degradation assessment A. Burrell et al. 10.1016/j.isprsjprs.2018.08.017
79. Validation and Application of European Beech Phenological Metrics Derived from MODIS Data along an Altitudinal Gradient V. Lukasová et al. 10.3390/f10010060
80. Hydrological transformation coincided with megafaunal extinction in central Australia T. Cohen et al. 10.1130/G36346.1
81. Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change L. Chasmer et al. 10.3390/rs12081321
82. Plant phenology and climate change J. Fitchett et al. 10.1177/0309133315578940