65 citations as recorded by crossref.

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4. 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
5. Characterising the land surface phenology of Africa using 500 m MODIS EVI T. Adole et al. 10.1016/j.apgeog.2017.12.006
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7. 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
8. Seasonal, interannual and decadal drivers of tree and grass productivity in an Australian tropical savanna C. Moore et al. 10.1111/gcb.14072
9. 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
10. 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
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15. 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
16. 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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21. 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
22. 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
23. 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
24. 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
25. 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
26. Canopy leaf area of a mature evergreenEucalyptuswoodland does not respond to elevated atmospheric [CO2] but tracks water availability R. Duursma et al. 10.1111/gcb.13151
27. 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
28. 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
29. 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
30. Land surface albedo and vegetation feedbacks enhanced the millennium drought in south-east Australia J. Evans et al. 10.5194/hess-21-409-2017
31. 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
32. 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
33. Characterizing land surface phenology and responses to rainfall in the Sahara desert D. Yan et al. 10.1002/2016JG003441
34. 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
35. 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
36. 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
37. Mapping precipitation-corrected NDVI trends across Namibia V. Wingate et al. 10.1016/j.scitotenv.2019.05.158
38. 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
39. 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
40. 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
41. Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series L. Recuero et al. 10.3390/rs11212497
42. A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index Q. Xin et al. 10.1029/2019MS001935
43. Assessing woody vegetation trends in Sahelian drylands using MODIS based seasonal metrics M. Brandt et al. 10.1016/j.rse.2016.05.027
44. 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
45. Climatic and non-climatic vegetation cover changes in the rangelands of Africa F. D'Adamo et al. 10.1016/j.gloplacha.2021.103516
46. Variability and evolution of global land surface phenology over the past three decades (1982-2012) I. Garonna et al. 10.1111/gcb.13168
47. 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
48. Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) A. Burrell et al. 10.1016/j.rse.2017.05.018
49. 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
50. 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
51. Abrupt shifts in phenology and vegetation productivity under climate extremes X. Ma et al. 10.1002/2015JG003144
52. 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
53. 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
54. 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
55. Temporal Trends and Spatial Variability of Vegetation Phenology over the Northern Hemisphere during 1982-2012 S. Wang et al. 10.1371/journal.pone.0157134
56. Multi-climate mode interactions drive hydrological and vegetation responses to hydroclimatic extremes in Australia Z. Xie et al. 10.1016/j.rse.2019.111270
57. 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
58. 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
59. Woody plant cover estimation in drylands from Earth Observation based seasonal metrics M. Brandt et al. 10.1016/j.rse.2015.10.036
60. 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
61. The impact of dataset selection on land degradation assessment A. Burrell et al. 10.1016/j.isprsjprs.2018.08.017
62. Validation and Application of European Beech Phenological Metrics Derived from MODIS Data along an Altitudinal Gradient V. Lukasová et al. 10.3390/f10010060
63. Hydrological transformation coincided with megafaunal extinction in central Australia T. Cohen et al. 10.1130/G36346.1
64. Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change L. Chasmer et al. 10.3390/rs12081321
65. Plant phenology and climate change J. Fitchett et al. 10.1177/0309133315578940