53 citations as recorded by crossref.

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4. Sustainable urban systems: from landscape to ecological processes Y. Zhou et al. 10.1186/s13717-022-00371-3
5. Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine S. Wang et al. 10.3390/rs14092055
6. Change Analysis of Spring Vegetation Green-Up Date in Qinba Mountains under the Support of Spatiotemporal Data Cube J. Li et al. 10.1155/2020/6413654
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8. Mapping fine-spatial-resolution vegetation spring phenology from individual Landsat images using a convolutional neural network X. Kun et al. 10.1080/01431161.2023.2216846
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10. Characterizing Spring Phenological Changes of the Land Surface across the Conterminous United States from 2001 to 2021 W. Wu & Q. Xin 10.3390/rs15030737
11. Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems J. Fang et al. 10.1038/s41597-023-02224-w
12. Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression M. Salinero-Delgado et al. 10.3390/rs14010146
13. Characterizing seasonal variation in foliar biochemistry with airborne imaging spectroscopy A. Chlus & P. Townsend 10.1016/j.rse.2022.113023
14. Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020 M. Zhu et al. 10.5194/essd-16-277-2024
15. Floating in the air: forecasting allergenic pollen concentration for managing urban public health X. Zhu et al. 10.1080/17538947.2024.2306894
16. Evaluation of Urban Vegetation Phenology Using 250 m MODIS Vegetation Indices H. Zhang et al. 10.14358/PERS.21-00049R3
17. Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China X. Zhang et al. 10.3390/rs13193909
18. Reading Greenness in Urban Areas: Possible Roles of Phenological Metrics from the Copernicus HR-VPP Dataset E. Borgogno-Mondino & V. Fissore 10.3390/rs14184517
19. Study on the Spatial and Temporal Distribution of Urban Vegetation Phenology by Local Climate Zone and Urban–Rural Gradient Approach S. Li et al. 10.3390/rs15163957
20. Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine X. Zhang et al. 10.5194/essd-14-3743-2022
21. Mapping Phenology of Complicated Wetland Landscapes through Harmonizing Landsat and Sentinel-2 Imagery C. Fan et al. 10.3390/rs15092413
22. 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
23. Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022 M. Li et al. 10.5194/essd-15-4181-2023
24. Widespread drought‐induced leaf shedding and legacy effects on productivity in European deciduous forests A. Descals et al. 10.1002/rse2.296
25. Divergent responses of spring phenology to daytime and nighttime warming L. Meng et al. 10.1016/j.agrformet.2019.107832
26. A robust and unified land surface phenology algorithm for diverse biomes and growth cycles in China by using harmonized Landsat and Sentinel-2 imagery J. Yang et al. 10.1016/j.isprsjprs.2023.07.017
27. Multi-sensor detection of spring breakup phenology of Canada's lakes X. Giroux-Bougard et al. 10.1016/j.rse.2023.113656
28. A national dataset of 30 m annual urban extent dynamics (1985–2015) in the conterminous United States X. Li et al. 10.5194/essd-12-357-2020
29. Reconstructed NDVI and EVI datasets in China (ReVIChina) generated by a spatial-interannual reconstruction method R. Yao et al. 10.1080/17538947.2023.2283492
30. Scale matters: Spatial resolution impacts tropical leaf phenology characterized by multi-source satellite remote sensing with an ecological-constrained deep learning model G. Song et al. 10.1016/j.rse.2024.114027
31. Detection and attribution of long-term and fine-scale changes in spring phenology over urban areas: A case study in New York State L. Li et al. 10.1016/j.jag.2022.102815
32. Evaluating fine-scale phenology from PlanetScope satellites with ground observations across temperate forests in eastern North America Y. Zhao et al. 10.1016/j.rse.2022.113310
33. geemap: A Python package for interactive mapping with Google Earth Engine Q. Wu 10.21105/joss.02305
34. Toward 30 m Fine-Resolution Land Surface Phenology Mapping at a Large Scale Using Spatiotemporal Fusion of MODIS and Landsat Data Y. Ruan et al. 10.3390/su15043365
35. Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations S. Wu et al. 10.1016/j.isprsjprs.2020.10.017
36. Understanding urban plant phenology for sustainable cities and planet Y. Zhou 10.1038/s41558-022-01331-7
37. Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product G. Xian et al. 10.5194/essd-14-143-2022
38. Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology M. Weber et al. 10.3390/rs12152384
39. Sentinel-2 time series: a promising tool in monitoring temperate species spring phenology E. Grabska-Szwagrzyk et al. 10.1093/forestry/cpad039
40. Waveform LiDAR concepts and applications for potential vegetation phenology monitoring and modeling: a comprehensive review E. Salas 10.1080/10095020.2020.1761763
41. Quantitative estimation for the impact of mining activities on vegetation phenology and identifying its controlling factors from Sentinel-2 time series X. Sun et al. 10.1016/j.jag.2022.102814
42. Near Real-time Fine-resolution Land Surface Phenological Prediction Using Convolutional Neural Network and Data Fusion K. Xiao et al. 10.1051/e3sconf/202235001008
43. Effects of land surface temperatures on vegetation phenology along urban–rural local climate zone gradients J. Xie et al. 10.1007/s10980-024-01856-6
44. An improved urban cellular automata model by using the trend-adjusted neighborhood X. Li et al. 10.1186/s13717-020-00234-9
45. HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data K. Tran et al. 10.1038/s41597-023-02605-1
46. Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI E. Amin et al. 10.3390/rs14081812
47. Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks M. Ortega Adarme et al. 10.3390/rs14143290
48. Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment L. Li et al. 10.1016/j.envres.2021.111937
49. Evaluations and comparisons of rule-based and machine-learning-based methods to retrieve satellite-based vegetation phenology using MODIS and USA National Phenology Network data Q. Xin et al. 10.1016/j.jag.2020.102189
50. The Green Revolution from space: Mapping the historic dynamics of main rice types in one of the world's food bowls J. Peña-Arancibia et al. 10.1016/j.rsase.2020.100460
51. phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data Y. Ruan et al. 10.3389/fenvs.2023.1097249
52. Urban warming increases the temperature sensitivity of spring vegetation phenology at 292 cities across China L. Wang et al. 10.1016/j.scitotenv.2022.155154
53. Urban-rural gradient in vegetation phenology changes of over 1500 cities across China jointly regulated by urbanization and climate change Y. Ji et al. 10.1016/j.isprsjprs.2023.10.015