Articles | Volume 11, issue 2
https://doi.org/10.5194/essd-11-881-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-11-881-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States
Xuecao Li
Department of Geological and Atmospheric Sciences, Iowa State
University, Ames, IA, 50011, USA
Department of Geological and Atmospheric Sciences, Iowa State
University, Ames, IA, 50011, USA
Lin Meng
Department of Geological and Atmospheric Sciences, Iowa State
University, Ames, IA, 50011, USA
Ghassem R. Asrar
Joint Global Change Research Institute, Pacific Northwest National
Lab, College Park, MD, 20740, USA
Chaoqun Lu
Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
Qiusheng Wu
Department of Geography, University of Tennessee, Knoxville, TN,
37996, USA
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Cited
44 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- A 30 m annual maize phenology dataset from 1985 to 2020 in China Q. Niu et al. 10.5194/essd-14-2851-2022
- Sustainable urban systems: from landscape to ecological processes Y. Zhou et al. 10.1186/s13717-022-00371-3
- Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine S. Wang et al. 10.3390/rs14092055
- 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
- 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
- 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
- Characterizing Spring Phenological Changes of the Land Surface across the Conterminous United States from 2001 to 2021 W. Wu & Q. Xin 10.3390/rs15030737
- Understanding urban plant phenology for sustainable cities and planet Y. Zhou 10.1038/s41558-022-01331-7
- 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
- Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems J. Fang et al. 10.1038/s41597-023-02224-w
- Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology M. Weber et al. 10.3390/rs12152384
- Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression M. Salinero-Delgado et al. 10.3390/rs14010146
- Characterizing seasonal variation in foliar biochemistry with airborne imaging spectroscopy A. Chlus & P. Townsend 10.1016/j.rse.2022.113023
- Evaluation of Urban Vegetation Phenology Using 250 m MODIS Vegetation Indices H. Zhang et al. 10.14358/PERS.21-00049R3
- 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
- Reading Greenness in Urban Areas: Possible Roles of Phenological Metrics from the Copernicus HR-VPP Dataset E. Borgogno-Mondino & V. Fissore 10.3390/rs14184517
- 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
- Sentinel-2 time series: a promising tool in monitoring temperate species spring phenology E. Grabska-Szwagrzyk et al. 10.1093/forestry/cpad039
- Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine X. Zhang et al. 10.5194/essd-14-3743-2022
- Waveform LiDAR concepts and applications for potential vegetation phenology monitoring and modeling: a comprehensive review E. Salas 10.1080/10095020.2020.1761763
- Mapping Phenology of Complicated Wetland Landscapes through Harmonizing Landsat and Sentinel-2 Imagery C. Fan et al. 10.3390/rs15092413
- 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
- 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
- Near Real-time Fine-resolution Land Surface Phenological Prediction Using Convolutional Neural Network and Data Fusion K. Xiao et al. 10.1051/e3sconf/202235001008
- Widespread drought‐induced leaf shedding and legacy effects on productivity in European deciduous forests A. Descals et al. 10.1002/rse2.296
- Divergent responses of spring phenology to daytime and nighttime warming L. Meng et al. 10.1016/j.agrformet.2019.107832
- 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
- Multi-sensor detection of spring breakup phenology of Canada's lakes X. Giroux-Bougard et al. 10.1016/j.rse.2023.113656
- An improved urban cellular automata model by using the trend-adjusted neighborhood X. Li et al. 10.1186/s13717-020-00234-9
- 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
- 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
- Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks M. Ortega Adarme et al. 10.3390/rs14143290
- Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment L. Li et al. 10.1016/j.envres.2021.111937
- 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
- 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
- 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
- phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data Y. Ruan et al. 10.3389/fenvs.2023.1097249
- 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
- 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
- geemap: A Python package for interactive mapping with Google Earth Engine Q. Wu 10.21105/joss.02305
44 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- A 30 m annual maize phenology dataset from 1985 to 2020 in China Q. Niu et al. 10.5194/essd-14-2851-2022
- Sustainable urban systems: from landscape to ecological processes Y. Zhou et al. 10.1186/s13717-022-00371-3
- Fitting Nonlinear Equations with the Levenberg–Marquardt Method on Google Earth Engine S. Wang et al. 10.3390/rs14092055
- 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
- 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
- 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
- Characterizing Spring Phenological Changes of the Land Surface across the Conterminous United States from 2001 to 2021 W. Wu & Q. Xin 10.3390/rs15030737
- Understanding urban plant phenology for sustainable cities and planet Y. Zhou 10.1038/s41558-022-01331-7
- 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
- Vegetation photosynthetic phenology dataset in northern terrestrial ecosystems J. Fang et al. 10.1038/s41597-023-02224-w
- Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology M. Weber et al. 10.3390/rs12152384
- Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression M. Salinero-Delgado et al. 10.3390/rs14010146
- Characterizing seasonal variation in foliar biochemistry with airborne imaging spectroscopy A. Chlus & P. Townsend 10.1016/j.rse.2022.113023
- Evaluation of Urban Vegetation Phenology Using 250 m MODIS Vegetation Indices H. Zhang et al. 10.14358/PERS.21-00049R3
- 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
- Reading Greenness in Urban Areas: Possible Roles of Phenological Metrics from the Copernicus HR-VPP Dataset E. Borgogno-Mondino & V. Fissore 10.3390/rs14184517
- 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
- Sentinel-2 time series: a promising tool in monitoring temperate species spring phenology E. Grabska-Szwagrzyk et al. 10.1093/forestry/cpad039
- Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine X. Zhang et al. 10.5194/essd-14-3743-2022
- Waveform LiDAR concepts and applications for potential vegetation phenology monitoring and modeling: a comprehensive review E. Salas 10.1080/10095020.2020.1761763
- Mapping Phenology of Complicated Wetland Landscapes through Harmonizing Landsat and Sentinel-2 Imagery C. Fan et al. 10.3390/rs15092413
- 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
- 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
- Near Real-time Fine-resolution Land Surface Phenological Prediction Using Convolutional Neural Network and Data Fusion K. Xiao et al. 10.1051/e3sconf/202235001008
- Widespread drought‐induced leaf shedding and legacy effects on productivity in European deciduous forests A. Descals et al. 10.1002/rse2.296
- Divergent responses of spring phenology to daytime and nighttime warming L. Meng et al. 10.1016/j.agrformet.2019.107832
- 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
- Multi-sensor detection of spring breakup phenology of Canada's lakes X. Giroux-Bougard et al. 10.1016/j.rse.2023.113656
- An improved urban cellular automata model by using the trend-adjusted neighborhood X. Li et al. 10.1186/s13717-020-00234-9
- 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
- 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
- Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks M. Ortega Adarme et al. 10.3390/rs14143290
- Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment L. Li et al. 10.1016/j.envres.2021.111937
- 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
- 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
- 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
- phenoC++: An open-source tool for retrieving vegetation phenology from satellite remote sensing data Y. Ruan et al. 10.3389/fenvs.2023.1097249
- 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
- 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
- geemap: A Python package for interactive mapping with Google Earth Engine Q. Wu 10.21105/joss.02305
Latest update: 28 Sep 2023
Short summary
We generated a long-term (1985–2015) and medium-resolution (30 m) product of phenology indicators in urban domains in the conterminous US using Landsat satellite observations. The derived phenology indicators agree well with in situ observations and provide more spatial details in complex urban areas compared to the existing coarse resolution phenology products (e.g., MODIS). The published data are of great use for urban phenology studies (e.g., pollen-induced respiratory allergies).
We generated a long-term (1985–2015) and medium-resolution (30 m) product of phenology...