91 citations as recorded by crossref.

1. Assessing the performance of smoothing functions to estimate land surface phenology on temperate grassland B. Lara & M. Gandini 10.1080/2150704X.2016.1168945
2. Codominant water control on global interannual variability and trends in land surface phenology and greenness M. Forkel et al. 10.1111/gcb.12950
3. Beyond a ‘just add water’ perspective: environmental water management for vegetation outcomes C. Campbell et al. 10.1016/j.jenvman.2023.119499
4. Analyzing the Influence of Climate Variables on Native Pasture Phenology in the Fitzroy Basin, Australia M. Aditya 10.1080/23754931.2022.2081815
5. Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region M. Tulbure et al. 10.1016/j.rse.2016.02.034
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
7. Comprehensive reassessment of Australia's land-surface phenology trends (1982–2022) using circular statistics and a harmonised NDVI dataset C. Burton et al. 10.1016/j.rse.2025.114940
8. Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006 W. De Keersmaecker et al. 10.3390/rs9010034
9. Land surface phenology indicators retrieved across diverse ecosystems using a modified threshold algorithm Q. Xie et al. 10.1016/j.ecolind.2023.110000
10. Phenological Divergences in Vegetation with Land Surface Temperature Changes in Different Geographical Zones Y. Tian & B. Liu 10.3390/land14030562
11. Holistic Reduction to Compare and Create New Indices for Global Inter-Seasonal Monitoring: Case Study for High Resolution Surface Water Mapping R. Aravena et al. 10.3390/rs15082063
12. Characterising the land surface phenology of Africa using 500 m MODIS EVI T. Adole et al. 10.1016/j.apgeog.2017.12.006
13. Satellite-observed shifts in C3/C4 abundance in Australian grasslands are associated with rainfall patterns Q. Xie et al. 10.1016/j.rse.2022.112983
14. Carbon cycle responses of semi‐arid ecosystems to positive asymmetry in rainfall V. Haverd et al. 10.1111/gcb.13412
15. Evaluation of impact of climate extremes on vegetation change in Southwest China considering time-lag effect G. Qi et al. 10.1016/j.gecco.2025.e03497
16. 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
17. High Resolution Forest Masking for Seasonal Monitoring with a Regionalized and Colourimetrically Assisted Chorologic Typology R. Aravena et al. 10.3390/rs15143457
18. Seasonal, interannual and decadal drivers of tree and grass productivity in an Australian tropical savanna C. Moore et al. 10.1111/gcb.14072
19. Assessment of FSDAF Accuracy on Cotton Yield Estimation Using Different MODIS Products and Landsat Based on the Mixed Degree Index with Different Surroundings L. Meng et al. 10.3390/s21155184
20. A spatio-temporal analysis of canopy dynamics and intra-stand competition in a riparian forest, south-eastern Australia E. Curtis et al. 10.1016/j.foreco.2018.08.044
21. Essential dryland ecosystem variables B. Wu et al. 10.1016/j.cosust.2020.10.007
22. Changes in foliage projective cover and its implications for mapping groundwater dependent vegetation across a precipitation gradient B. Parker et al. 10.1002/eco.1937
23. Understanding the Land Carbon Cycle with Space Data: Current Status and Prospects J. Exbrayat et al. 10.1007/s10712-019-09506-2
24. Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities W. Smith et al. 10.1016/j.rse.2019.111401
25. A risk-benefit model to simulate vegetation spring onset in response to multi-decadal climate variability: Theoretical basis and applications from the field to the Northern Hemisphere Q. Xin 10.1016/j.agrformet.2016.06.017
26. Wide-area invasive species propagation mapping is possible using phenometric trends T. Landmann et al. 10.1016/j.isprsjprs.2019.10.016
27. 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
28. Recalculating Australian water scarcity characterisation factors using the AWARE method P. Bontinck et al. 10.1007/s11367-021-01952-8
29. Unraveling the Spatiotemporal Dynamics of Rubber Phenology in Hainan Island, China: A Multi-Sensor Remote Sensing and Climate Drivers Analysis H. Lai et al. 10.3390/rs17142403
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. Phenological Spatial Divergences Promoted by Climate, Terrain, and Forest Height in a Cold Temperate Forest Landscape: A Case Study of the Greater Khingan Mountain in Hulun Buir, China Y. Tian et al. 10.3390/f16030490
43. Land surface albedo and vegetation feedbacks enhanced the millennium drought in south-east Australia J. Evans et al. 10.5194/hess-21-409-2017
44. 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
45. 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
46. 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
47. Characterizing land surface phenology and responses to rainfall in the Sahara desert D. Yan et al. 10.1002/2016JG003441
48. 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
49. 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
50. 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
51. 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
52. Mapping precipitation-corrected NDVI trends across Namibia V. Wingate et al. 10.1016/j.scitotenv.2019.05.158
53. 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
54. 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
55. Identification of Rubber Plantations in Southwestern China Based on Multi-Source Remote Sensing Data and Phenology Windows G. Chen et al. 10.3390/rs15051228
56. 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
57. Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series L. Recuero et al. 10.3390/rs11212497
58. A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index Q. Xin et al. 10.1029/2019MS001935
59. 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
60. Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation index (NDVI) with MODIS NDVI C. Burton et al. 10.5194/essd-16-4389-2024
61. Assessing woody vegetation trends in Sahelian drylands using MODIS based seasonal metrics M. Brandt et al. 10.1016/j.rse.2016.05.027
62. 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
63. Climatic and non-climatic vegetation cover changes in the rangelands of Africa F. D'Adamo et al. 10.1016/j.gloplacha.2021.103516
64. Variability and evolution of global land surface phenology over the past three decades (1982–2012) I. Garonna et al. 10.1111/gcb.13168
65. 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
66. Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) A. Burrell et al. 10.1016/j.rse.2017.05.018
67. 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
68. Revealing forest phenomenological heterogeneity in Yunnan using ICESat-2-derived canopy density and MODIS time series S. Ma et al. 10.1080/15481603.2025.2547124
69. 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
70. Simulation of the Carbon Cycle’s Spatiotemporal Dynamics in the Hangzhou Forest Ecosystem and How It Responds to Phenology M. Hu et al. 10.3390/rs17091531
71. Abrupt shifts in phenology and vegetation productivity under climate extremes X. Ma et al. 10.1002/2015JG003144
72. 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
73. Sentinel-2 time series: a promising tool in monitoring temperate species spring phenology E. Grabska-Szwagrzyk et al. 10.1093/forestry/cpad039
74. 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
75. 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
76. 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
77. 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
78. Temporal Trends and Spatial Variability of Vegetation Phenology over the Northern Hemisphere during 1982-2012 S. Wang et al. 10.1371/journal.pone.0157134
79. Multi-climate mode interactions drive hydrological and vegetation responses to hydroclimatic extremes in Australia Z. Xie et al. 10.1016/j.rse.2019.111270
80. Attribution of NDVI Dynamics over the Globe from 1982 to 2015 C. Liu et al. 10.3390/rs14112706
81. 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
82. 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
83. 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
84. 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
85. Woody plant cover estimation in drylands from Earth Observation based seasonal metrics M. Brandt et al. 10.1016/j.rse.2015.10.036
86. 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
87. Large Area Crops Mapping by Phenological Horizon Attention Transformer (PHAT) Method Using MODIS Time-Series Imagery Q. Gao et al. 10.1109/JSTARS.2025.3559939
88. The impact of dataset selection on land degradation assessment A. Burrell et al. 10.1016/j.isprsjprs.2018.08.017
89. 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
90. Hydrological transformation coincided with megafaunal extinction in central Australia T. Cohen et al. 10.1130/G36346.1
91. Plant phenology and climate change J. Fitchett et al. 10.1177/0309133315578940