94 citations as recorded by crossref.

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26. Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics A. Lausch et al. https://doi.org/10.3390/rs8121029
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40. Revealing forest phenomenological heterogeneity in Yunnan using ICESat-2-derived canopy density and MODIS time series S. Ma et al. https://doi.org/10.1080/15481603.2025.2547124
41. 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. https://doi.org/10.1016/j.jag.2021.102487
42. Simulation of the Carbon Cycle’s Spatiotemporal Dynamics in the Hangzhou Forest Ecosystem and How It Responds to Phenology M. Hu et al. https://doi.org/10.3390/rs17091531
43. Abrupt shifts in phenology and vegetation productivity under climate extremes X. Ma et al. https://doi.org/10.1002/2015JG003144
44. Using Multi-Temporal Satellite Data to Analyse Phenological Responses of Rubber (Hevea brasiliensis) to Climatic Variations in South Sumatra, Indonesia F. Azizan et al. https://doi.org/10.3390/rs13152932
45. Hydrologic connectivity drives extremes and high variability in vegetation productivity across Australian arid and semi-arid ecosystems A. Norton et al. https://doi.org/10.1016/j.rse.2022.112937
46. Remote sensing monitoring of the spatiotemporal dynamics of urban forest phenology and its response to climate and urbanization M. Hu et al. https://doi.org/10.1016/j.uclim.2024.101810
47. Temporal Trends and Spatial Variability of Vegetation Phenology over the Northern Hemisphere during 1982-2012 S. Wang et al. https://doi.org/10.1371/journal.pone.0157134
48. Attribution of NDVI Dynamics over the Globe from 1982 to 2015 C. Liu et al. https://doi.org/10.3390/rs14112706
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50. Toward a Novel Method for General On-Orbit Earth Surface Anomaly Detection Leveraging Large Vision Models and Lightweight Priors J. Xu et al. https://doi.org/10.1109/TGRS.2024.3432749
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52. The impact of dataset selection on land degradation assessment A. Burrell et al. https://doi.org/10.1016/j.isprsjprs.2018.08.017
53. 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. https://doi.org/10.1071/MF24016
54. Assessing the performance of smoothing functions to estimate land surface phenology on temperate grassland B. Lara & M. Gandini https://doi.org/10.1080/2150704X.2016.1168945
55. Codominant water control on global interannual variability and trends in land surface phenology and greenness M. Forkel et al. https://doi.org/10.1111/gcb.12950
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58. A river runs through it: Robust automated mapping of riparian woodlands and land surface phenology across dryland regions C. McMahon et al. https://doi.org/10.1016/j.rse.2024.114056
59. Comprehensive reassessment of Australia's land-surface phenology trends (1982–2022) using circular statistics and a harmonised NDVI dataset C. Burton et al. https://doi.org/10.1016/j.rse.2025.114940
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