60 citations as recorded by crossref.

1. Influence of Varying Solar Zenith Angles on Land Surface Phenology Derived from Vegetation Indices: A Case Study in the Harvard Forest Y. Li et al. 10.3390/rs13204126
2. A Threshold Method for Robust and Fast Estimation of Land-Surface Phenology Using Google Earth Engine A. Descals et al. 10.1109/JSTARS.2020.3039554
3. A 30 m annual maize phenology dataset from 1985 to 2020 in China Q. Niu et al. 10.5194/essd-14-2851-2022
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
7. Phenological Dynamics Characterization of Alignment Trees with Sentinel-2 Imagery: A Vegetation Indices Time Series Reconstruction Methodology Adapted to Urban Areas C. Granero-Belinchon et al. 10.3390/rs12040639
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
9. Comparison of change-based and shape-based data fusion methods in fine-resolution land surface phenology monitoring with Landsat and Sentinel-2 data C. Wang et al. 10.1016/j.scitotenv.2024.172014
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. An assessment of the relationship between spring frost indicators and global crop yield losses W. Guo et al. 10.1016/j.scitotenv.2024.176560
12. Ecological Gate Water Control and Its Influence on Surface Water Dynamics and Vegetation Restoration: A Case Study from the Middle Reaches of the Tarim River J. Wu et al. 10.3390/f15112005
13. Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems J. Fang et al. 10.1038/s41597-023-02224-w
14. Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression M. Salinero-Delgado et al. 10.3390/rs14010146
15. Characterizing seasonal variation in foliar biochemistry with airborne imaging spectroscopy A. Chlus & P. Townsend 10.1016/j.rse.2022.113023
16. 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
17. Floating in the air: forecasting allergenic pollen concentration for managing urban public health X. Zhu et al. 10.1080/17538947.2024.2306894
18. Evaluation of Urban Vegetation Phenology Using 250 m MODIS Vegetation Indices H. Zhang et al. 10.14358/PERS.21-00049R3
19. 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
20. Reading Greenness in Urban Areas: Possible Roles of Phenological Metrics from the Copernicus HR-VPP Dataset E. Borgogno-Mondino & V. Fissore 10.3390/rs14184517
21. 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
22. Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine X. Zhang et al. 10.5194/essd-14-3743-2022
23. Mapping Phenology of Complicated Wetland Landscapes through Harmonizing Landsat and Sentinel-2 Imagery C. Fan et al. 10.3390/rs15092413
24. Time series sUAV data reveal moderate accuracy and large uncertainties in spring phenology metric of deciduous broadleaf forest as estimated by vegetation index-based phenological models L. Pan et al. 10.1016/j.isprsjprs.2024.09.023
25. 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
26. 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
27. Widespread drought‐induced leaf shedding and legacy effects on productivity in European deciduous forests A. Descals et al. 10.1002/rse2.296
28. Divergent responses of spring phenology to daytime and nighttime warming L. Meng et al. 10.1016/j.agrformet.2019.107832
29. 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
30. Multi-sensor detection of spring breakup phenology of Canada's lakes X. Giroux-Bougard et al. 10.1016/j.rse.2023.113656
31. 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
32. Reconstructed NDVI and EVI datasets in China (ReVIChina) generated by a spatial-interannual reconstruction method R. Yao et al. 10.1080/17538947.2023.2283492
33. 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
34. 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
35. 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
36. geemap: A Python package for interactive mapping with Google Earth Engine Q. Wu 10.21105/joss.02305
37. 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
38. 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
39. Impacts of the scale effect on quantifying the response of spring vegetation phenology to urban intensity Z. Peng et al. 10.1016/j.rse.2024.114485
40. Understanding urban plant phenology for sustainable cities and planet Y. Zhou 10.1038/s41558-022-01331-7
41. 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
42. Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology M. Weber et al. 10.3390/rs12152384
43. Synergistic Impacts of Built-Up Characteristics and Background Climate on Urban Vegetation Phenology: Evidence from Beijing, China X. Fu & B. He 10.3390/f15040728
44. Sentinel-2 time series: a promising tool in monitoring temperate species spring phenology E. Grabska-Szwagrzyk et al. 10.1093/forestry/cpad039
45. Waveform LiDAR concepts and applications for potential vegetation phenology monitoring and modeling: a comprehensive review E. Salas 10.1080/10095020.2020.1761763
46. 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
47. Near Real-time Fine-resolution Land Surface Phenological Prediction Using Convolutional Neural Network and Data Fusion K. Xiao et al. 10.1051/e3sconf/202235001008
48. 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
49. An improved urban cellular automata model by using the trend-adjusted neighborhood X. Li et al. 10.1186/s13717-020-00234-9
50. 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
51. 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
52. Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks M. Ortega Adarme et al. 10.3390/rs14143290
53. Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment L. Li et al. 10.1016/j.envres.2021.111937
54. 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
55. 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
56. phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data Y. Ruan et al. 10.3389/fenvs.2023.1097249
57. Distinct latitudinal patterns of shifting spring phenology across the Appalachian Trail Corridor J. Tourville et al. 10.1002/ecy.4403
58. 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
59. 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
60. A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States X. Li et al. 10.5194/essd-11-881-2019