Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-1625-2020
© Author(s) 2020. 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-12-1625-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Jul 2020
Data description paper |
| 15 Jul 2020
| 15 Jul 2020
Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform
Xiao Zhang
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
Changshan Wu
Department of Geography, University of Wisconsin-Milwaukee,
Milwaukee, WI, USA
Xidong Chen
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
Yuan Gao
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
College of Geomatics, Xi'an University of Science and Technology,
Xi'an 710054, China
Shuai Xie
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
Bing Zhang
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
Related authors
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Philippe Bousquet, Josep G. Canadell, Nick Davidson, Meng Ding, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Liangyun Liu, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, Xiao Zhang, and Michele Thieme
Earth Syst. Sci. Data, 17, 2277–2329, https://doi.org/10.5194/essd-17-2277-2025, https://doi.org/10.5194/essd-17-2277-2025, 2025
Short summary
Short summary
The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies – that is, the maximum extent – covers 18.2 × 106 km2, equivalent to 13.4 % of total global land area.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Wenhan Zhang, Linlin Guan, Ming Bai, and Xidong Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-73, https://doi.org/10.5194/essd-2025-73, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This work describes a novel global 10 m land-cover dataset with fine classification system, which contains 30 land-cover subcategories and achieves the fulfilling performance over the globe.
Liangyun Liu and Xiao Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 137–143, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, 2024
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
Short summary
Short summary
An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-277, https://doi.org/10.5194/essd-2022-277, 2022
Manuscript not accepted for further review
Short summary
Short summary
Leaf chlorophyll content (LCC) is an important plant physiological trait and a proxy for leaf photosynthetic capacity. We generated a global LCC dataset from ENVISAT MERIS and Sentinel-3 OLCI satellite data for the period 2003–2012 to 2018–2020 using a physically-based radiative transfer modeling approach. This new LCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling on a global scale.
Xidong Chen, Liangyun Liu, Xiao Zhang, Junsheng Li, Shenglei Wang, Yuan Gao, and Jun Mi
Hydrol. Earth Syst. Sci., 26, 3517–3536, https://doi.org/10.5194/hess-26-3517-2022, https://doi.org/10.5194/hess-26-3517-2022, 2022
Short summary
Short summary
A 30 m LAke Water Secchi Depth (LAWSD30) dataset of China was first developed for 1985–2020, and national-scale water clarity estimations of lakes in China over the past 35 years were analyzed. Lake clarity in China exhibited a significant downward trend before the 21st century, but improved after 2000. The developed LAWSD30 dataset and the evaluation results can provide effective guidance for water preservation and restoration.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
Short summary
Short summary
Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, and Jun Mi
Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, https://doi.org/10.5194/essd-13-2753-2021, 2021
Short summary
Short summary
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Bernhard Lehner, Mira Anand, Etienne Fluet-Chouinard, Florence Tan, Filipe Aires, George H. Allen, Philippe Bousquet, Josep G. Canadell, Nick Davidson, Meng Ding, C. Max Finlayson, Thomas Gumbricht, Lammert Hilarides, Gustaf Hugelius, Robert B. Jackson, Maartje C. Korver, Liangyun Liu, Peter B. McIntyre, Szabolcs Nagy, David Olefeldt, Tamlin M. Pavelsky, Jean-Francois Pekel, Benjamin Poulter, Catherine Prigent, Jida Wang, Thomas A. Worthington, Dai Yamazaki, Xiao Zhang, and Michele Thieme
Earth Syst. Sci. Data, 17, 2277–2329, https://doi.org/10.5194/essd-17-2277-2025, https://doi.org/10.5194/essd-17-2277-2025, 2025
Short summary
Short summary
The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies – that is, the maximum extent – covers 18.2 × 106 km2, equivalent to 13.4 % of total global land area.
Julien Lamour, Shawn P. Serbin, Alistair Rogers, Kelvin T. Acebron, Elizabeth Ainsworth, Loren P. Albert, Michael Alonzo, Jeremiah Anderson, Owen K. Atkin, Nicolas Barbier, Mallory L. Barnes, Carl J. Bernacchi, Ninon Besson, Angela C. Burnett, Joshua S. Caplan, Jérôme Chave, Alexander W. Cheesman, Ilona Clocher, Onoriode Coast, Sabrina Coste, Holly Croft, Boya Cui, Clément Dauvissat, Kenneth J. Davidson, Christopher Doughty, Kim S. Ely, Jean-Baptiste Féret, Iolanda Filella, Claire Fortunel, Peng Fu, Maquelle Garcia, Bruno O. Gimenez, Kaiyu Guan, Zhengfei Guo, David Heckmann, Patrick Heuret, Marney Isaac, Shan Kothari, Etsushi Kumagai, Thu Ya Kyaw, Liangyun Liu, Lingli Liu, Shuwen Liu, Joan Llusià, Troy Magney, Isabelle Maréchaux, Adam R. Martin, Katherine Meacham-Hensold, Christopher M. Montes, Romà Ogaya, Joy Ojo, Regison Oliveira, Alain Paquette, Josep Peñuelas, Antonia Debora Placido, Juan M. Posada, Xiaojin Qian, Heidi J. Renninger, Milagros Rodriguez-Caton, Andrés Rojas-González, Urte Schlüter, Giacomo Sellan, Courtney M. Siegert, Guangqin Song, Charles D. Southwick, Daisy C. Souza, Clément Stahl, Yanjun Su, Leeladarshini Sujeeun, To-Chia Ting, Vicente Vasquez, Amrutha Vijayakumar, Marcelo Vilas-Boas, Diane R. Wang, Sheng Wang, Han Wang, Jing Wang, Xin Wang, Andreas P. M. Weber, Christopher Y. S. Wong, Jin Wu, Fengqi Wu, Shengbiao Wu, Zhengbing Yan, Dedi Yang, and Yingyi Zhao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-213, https://doi.org/10.5194/essd-2025-213, 2025
Preprint under review for ESSD
Short summary
Short summary
We present the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. This repository provides a unique source of information for creating hyperspectral models for predicting photosynthetic traits and associated leaf traits in terrestrial plants.
Chu Zou, Shanshan Du, Xinjie Liu, and Liangyun Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-94, https://doi.org/10.5194/essd-2025-94, 2025
Preprint under review for ESSD
Short summary
Short summary
Understanding plant sunlight absorption is crucial for tracking global ecosystem health. We developed a 1995–2023 dataset that enhances satellite-based plant activity measurements by resolving data inconsistencies and improving resolution. Using advanced modeling, we harmonized signals from multiple satellites, cutting errors by 45 %. This offers clearer global photosynthesis trends, aiding climate research and vegetation monitoring.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Wenhan Zhang, Linlin Guan, Ming Bai, and Xidong Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-73, https://doi.org/10.5194/essd-2025-73, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This work describes a novel global 10 m land-cover dataset with fine classification system, which contains 30 land-cover subcategories and achieves the fulfilling performance over the globe.
Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3672, https://doi.org/10.5194/egusphere-2024-3672, 2025
Short summary
Short summary
The Russia-Ukraine war in 2022 severely disrupted Ukraine’s economy, with significant reductions in industrial, transportation, and residential activities. Our research used satellite data to track changes in nitrogen oxide emissions, a key indicator of human activity, during the war. We found a 28 % decline in emissions, which was twice of the decrease caused by the COVID-19 pandemic. This study highlights how modern warfare can deeply impact both the environment and economic stability.
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-574, https://doi.org/10.5194/essd-2024-574, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Forest age is critical for carbon cycle modelling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's Annual Forest Age Dataset (CAFA), spanning 1986–2022 at 30-m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger for undisturbed forests. CAFA can enhance carbon cycle modelling and forest management in China.
Dianrun Zhao, Shanshan Du, Chu Zou, Longfei Tian, Meng Fan, Yulu Du, and Liangyun Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3118, https://doi.org/10.5194/egusphere-2024-3118, 2024
Short summary
Short summary
The TanSat-2 satellite is designed for global carbon monitoring. It provides high-resolution, dual-band observations of solar-induced chlorophyll fluorescence, a key indicator of plant photosynthesis. Through simulations, we optimized the satellite's data processing and found it can retrieve this fluorescence with great accuracy. These findings suggest that TanSat-2 will enhance global monitoring of carbon cycles and vegetation health, offering valuable insights for climate change research.
Chu Zou, Shanshan Du, Xinjie Liu, and Liangyun Liu
Earth Syst. Sci. Data, 16, 2789–2809, https://doi.org/10.5194/essd-16-2789-2024, https://doi.org/10.5194/essd-16-2789-2024, 2024
Short summary
Short summary
To obtain a temporally consistent satellite solar-induced chlorophyll fluorescence
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
(SIF) product (TCSIF), we corrected for time degradation of GOME-2A using a pseudo-invariant method. After the correction, the global SIF grew by 0.70 % per year from 2007 to 2021, and 62.91 % of vegetated regions underwent an increase in SIF. The dataset is a promising tool for monitoring global vegetation variation and will advance our understanding of vegetation's photosynthetic activities at a global scale.
Liangyun Liu and Xiao Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 137–143, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-137-2024, 2024
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
Short summary
Short summary
An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Shijun Zheng, Dailiang Peng, Bing Zhang, Yuhao Pan, Le Yu, Yan Wang, Xuxiang Feng, and Changyong Dou
EGUsphere, https://doi.org/10.5194/egusphere-2022-1110, https://doi.org/10.5194/egusphere-2022-1110, 2022
Preprint archived
Short summary
Short summary
This study observed the marked interannual differences in the vegetation response to the trend towards a warmer and wetter climate in northwest China. And found that the influence of precipitation to vegetation has gradually become stronger from 1982 to 2019 in northwest China, whereas which of temperature has gradually become weaker.
Xiaojin Qian, Liangyun Liu, Xidong Chen, Xiao Zhang, Siyuan Chen, and Qi Sun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-277, https://doi.org/10.5194/essd-2022-277, 2022
Manuscript not accepted for further review
Short summary
Short summary
Leaf chlorophyll content (LCC) is an important plant physiological trait and a proxy for leaf photosynthetic capacity. We generated a global LCC dataset from ENVISAT MERIS and Sentinel-3 OLCI satellite data for the period 2003–2012 to 2018–2020 using a physically-based radiative transfer modeling approach. This new LCC dataset spanning nearly 20 years will provide a valuable opportunity for the monitoring of vegetation growth and terrestrial carbon cycle modeling on a global scale.
Linan Guo, Hongxing Zheng, Yanhong Wu, Lanxin Fan, Mengxuan Wen, Junsheng Li, Fangfang Zhang, Liping Zhu, and Bing Zhang
Earth Syst. Sci. Data, 14, 3411–3422, https://doi.org/10.5194/essd-14-3411-2022, https://doi.org/10.5194/essd-14-3411-2022, 2022
Short summary
Short summary
Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an indicator of climate change. By combining the strengths of satellites and models, we produced an integrated dataset on daily LSWT of 160 large lakes across the Tibetan Plateau (TP) for the period 1978–2017. LSWT increased significantly at a rate of 0.01–0.47° per 10 years. The dataset can contribute to research on water and heat balance changes and their ecological effects in the TP.
Xidong Chen, Liangyun Liu, Xiao Zhang, Junsheng Li, Shenglei Wang, Yuan Gao, and Jun Mi
Hydrol. Earth Syst. Sci., 26, 3517–3536, https://doi.org/10.5194/hess-26-3517-2022, https://doi.org/10.5194/hess-26-3517-2022, 2022
Short summary
Short summary
A 30 m LAke Water Secchi Depth (LAWSD30) dataset of China was first developed for 1985–2020, and national-scale water clarity estimations of lakes in China over the past 35 years were analyzed. Lake clarity in China exhibited a significant downward trend before the 21st century, but improved after 2000. The developed LAWSD30 dataset and the evaluation results can provide effective guidance for water preservation and restoration.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
Short summary
Short summary
Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, Shuai Xie, and Jun Mi
Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, https://doi.org/10.5194/essd-13-2753-2021, 2021
Short summary
Short summary
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Xiaojin Qian, Liangyun Liu, Holly Croft, and Jingming Chen
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-228, https://doi.org/10.5194/bg-2019-228, 2019
Preprint withdrawn
Short summary
Short summary
The leaf maximum carboxylation rate (Vcmax) is a key photosynthesis parameter. We attempt to investigate whether a universal and stable relationship exists between leaf Vcmax25 and chlorophyll content across different C3 plant types from a plant physiological perspective and verify it using field experiments. The results confirm that leaf chlorophyll can be a reliable proxy for estimating Vcmax25, providing an operational approach for the global mapping of Vcmax25 across different plant types.
Y. Deng and C. Wu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 267–270, https://doi.org/10.5194/isprs-archives-XLII-3-267-2018, https://doi.org/10.5194/isprs-archives-XLII-3-267-2018, 2018
Related subject area
Land Cover and Land Use
CCD-Rice: a long-term paddy rice distribution dataset in China at 30 m resolution
U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
The Earth Topography 2022 (ETOPO 2022) global DEM dataset
The 20 m Africa rice distribution map of 2023
A 30m resolution annual cropland extent dataset of Africa in recent decades of the 21st century
Revised and updated geospatial monitoring of 21st century forest carbon fluxes
ChatEarthNet: a global-scale image–text dataset empowering vision–language geo-foundation models
GLC_FCS10: a global 10-m land-cover dataset with a fine classification system from Sentinel-1 and Sentinel-2 time-series data in Google Earth Engine
Aboveground biomass dataset from SMOS L-band vegetation optical depth and reference maps
Statistical Atlas of European Agriculture: Gridded Data from the Agricultural Census 2020 and the Spatial Distribution of CAP Contextual Indicators
GMIE: a global maximum irrigation extent and central pivot irrigation system dataset derived via irrigation performance during drought stress and deep learning methods
Annual vegetation maps in the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 based on MODIS series satellite imagery
Time series of Landsat-based bimonthly and annual spectral indices for continental Europe for 2000–2022
EARice10: a 10 m resolution annual rice distribution map of East Asia for 2023
A Sentinel-2 machine learning dataset for tree species classification in Germany
High-resolution mapping of global winter-triticeae crops using a sample-free identification method
A flux tower site attribute dataset intended for land surface modeling
An annual 30 m cultivated pasture dataset of the Tibetan Plateau from 1988 to 2021
Large-scale forest stand height mapping in the northeastern U.S. and China using L-band spaceborne repeat-pass InSAR and GEDI LiDAR data
Advances in LUCAS Copernicus 2022: enhancing Earth observations with comprehensive in situ data on EU land cover and use
Global 30 m seamless data cube (2000–2022) of land surface reflectance generated from Landsat 5, 7, 8, and 9 and MODIS Terra constellations
Mapping rangeland health indicators in eastern Africa from 2000 to 2022
3D-GloBFP: the first global three-dimensional building footprint dataset
Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data
Annual high-resolution grazing-intensity maps on the Qinghai–Tibet Plateau from 1990 to 2020
Global mapping of oil palm planting year from 1990 to 2021
A 28-time-point cropland area change dataset in Northeast China from 1000 to 2020
Mapping sugarcane globally at 10 m resolution using Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2
GloUCP: A global 1 km spatially continuous urban canopy parameters for the WRF model
The GIEMS-MethaneCentric database: a dynamic and comprehensive global product of methane-emitting aquatic areas
Annual maps of forest and evergreen forest in the contiguous United States during 2015–2017 from analyses of PALSAR-2 and Landsat images
Monsoon Asia Rice Calendar (MARC): a gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images
A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data
Global agricultural lands in the year 2015
Annual time-series 1 km maps of crop area and types in the conterminous US (CropAT-US): cropping diversity changes during 1850–2021
Retrieval of dominant methane (CH4) emission sources, the first high-resolution (1–2 m) dataset of storage tanks of China in 2000–2021
A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types
ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021
Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset
Map of forest tree species for Poland based on Sentinel-2 data
The ABoVE L-band and P-band airborne synthetic aperture radar surveys
A 30 m annual cropland dataset of China from 1986 to 2021
Global 1 km land surface parameters for kilometer-scale Earth system modeling
ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China
Harmonized European Union subnational crop statistics can reveal climate impacts and crop cultivation shifts
GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method
A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022
A 2020 forest age map for China with 30 m resolution
Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry
A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): nitrogen, phosphorus and potassium
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 17, 2193–2216, https://doi.org/10.5194/essd-17-2193-2025, https://doi.org/10.5194/essd-17-2193-2025, 2025
Short summary
Short summary
Rice is a vital staple crop that plays a crucial role in food security in China. However, long-term high-resolution rice distribution maps in China are lacking. This study developed a new rice-mapping method, mitigating the impact of cloud contamination and missing data in optical remote sensing observations on rice mapping. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed a strong correlation with statistical data.
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025, https://doi.org/10.5194/essd-17-2147-2025, 2025
Short summary
Short summary
The absence of globally consistent and spatially continuous urban surface input has long hindered large-scale high-resolution urban climate modeling. Using remote sensing, cloud computing, and machine learning, we developed U-Surf, a 1 km dataset providing key urban surface properties worldwide. U-Surf enhances urban representation across scales and supports kilometer-scale urban-resolving Earth system modeling unprecedentedly, with broader applications in urban studies and beyond.
Michael MacFerrin, Christopher Amante, Kelly Carignan, Matthew Love, and Elliot Lim
Earth Syst. Sci. Data, 17, 1835–1849, https://doi.org/10.5194/essd-17-1835-2025, https://doi.org/10.5194/essd-17-1835-2025, 2025
Short summary
Short summary
Here we present Earth TOPOgraphy (ETOPO) 2022, the latest iteration of NOAA's global seamless topographic–bathymetric dataset. ETOPO 2022 is a significant upgrade regarding resolution and accuracy from previous ETOPO releases and is freely available in multiple data formats and resolutions for all uses (public or private), excepting navigation.
Jingling Jiang, Hong Zhang, Ji Ge, Lijun Zuo, Lu Xu, Mingyang Song, Yinhaibin Ding, Yazhe Xie, and Wenjiang Huang
Earth Syst. Sci. Data, 17, 1781–1805, https://doi.org/10.5194/essd-17-1781-2025, https://doi.org/10.5194/essd-17-1781-2025, 2025
Short summary
Short summary
This study uses temporal synthetic aperture radar (SAR) data and optical imagery to conduct rice-mapping experiments in 34 African countries with rice-planting areas exceeding 5000 ha in 2022, achieving a 20 m resolution spatial distribution mapping for 2023. The average classification accuracy based on the validation set exceeded 85 %, and the R2; values for linear fitting with existing statistical data all surpassed 0.9, demonstrating the effectiveness of the proposed mapping method.
Zihang Lou, Dailiang Peng, Zhou Shi, Hongyan Wang, Yaqiong Zhang, Xue Yan, Zhongxing Chen, Su Ye, Le Yu, Jinkang Hu, Yulong Lv, Hao Peng, Yizhou Zhang, and Bing Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-133, https://doi.org/10.5194/essd-2025-133, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This study created the first detailed annual maps of Africa's cropland extent from 2000 to 2022 in 30 m resolution to support global efforts against hunger and sustainable farming. Our findings show Africa's cropland grew by 8.5 % over two decade, while 11.5 % of cropland was abandoned by 2018 – revealing hidden challenges in agricultural sustainability. These yearly, field-sized maps help governments track where farming grows or shrinks, plan food supplies, and protect vital cropland.
David A. Gibbs, Melissa Rose, Giacomo Grassi, Joana Melo, Simone Rossi, Viola Heinrich, and Nancy L. Harris
Earth Syst. Sci. Data, 17, 1217–1243, https://doi.org/10.5194/essd-17-1217-2025, https://doi.org/10.5194/essd-17-1217-2025, 2025
Short summary
Short summary
Updated global maps of greenhouse gas (GHG) emissions and sequestration by forests from 2001 onwards using satellite-derived data show that forests are strong net carbon sinks, capturing about as much CO2 each year on average as the USA emitted from fossil fuels in 2019. After reclassifying fluxes to countries’ reporting categories for national GHG inventories, we found that roughly two-thirds of the net CO2 flux from forests is anthropogenic and one-third is non-anthropogenic.
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 17, 1245–1263, https://doi.org/10.5194/essd-17-1245-2025, https://doi.org/10.5194/essd-17-1245-2025, 2025
Short summary
Short summary
ChatEarthNet is an image–text dataset that provides high-quality, detailed natural language descriptions for global-scale satellite data. It consists of 163 488 image-text pairs with captions generated by ChatGPT-3.5 and an additional 10 000 image-text pairs with captions generated by ChatGPT-4V(ision). This dataset has significant potential for training and evaluating vision–language geo-foundation models in remote sensing.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Wenhan Zhang, Linlin Guan, Ming Bai, and Xidong Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-73, https://doi.org/10.5194/essd-2025-73, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This work describes a novel global 10 m land-cover dataset with fine classification system, which contains 30 land-cover subcategories and achieves the fulfilling performance over the globe.
Simon Boitard, Arnaud Mialon, Stéphane Mermoz, Nemesio J. Rodríguez-Fernández, Philippe Richaume, Julio César Salazar-Neira, Stéphane Tarot, and Yann H. Kerr
Earth Syst. Sci. Data, 17, 1101–1119, https://doi.org/10.5194/essd-17-1101-2025, https://doi.org/10.5194/essd-17-1101-2025, 2025
Short summary
Short summary
Aboveground biomass (AGB) is a critical component of the Earth's carbon cycle. The presented dataset aims to help monitor this essential climate variable with AGB time series from 2011 onward, derived with a carefully calibrated spatial relationship between the measurements of the Soil Moisture and Ocean Salinity (SMOS) mission and pre-existing AGB maps. The produced dataset has been extensively compared with other available AGB time series and can be used in AGB studies.
Nicolas Lampach, Jon Olav Skoien, Helena Ramos, Julien Gaffuri, Renate Koeble, Linda See, and Marijn Van der Velde
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-59, https://doi.org/10.5194/essd-2025-59, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Eurostat and JRC developed a new methodology to make geospatial data from agricultural census available to users, while ensuring that no confidential information from individuals is disclosed. The geospatial data presented in the article correspond to the contextual indicators of the monitoring and evaluation framework of the Common Agricultural Policy. Our exploratory analysis reveals several interesting patterns which contribute to the broader debate on the future of European agriculture.
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data, 17, 855–880, https://doi.org/10.5194/essd-17-855-2025, https://doi.org/10.5194/essd-17-855-2025, 2025
Short summary
Short summary
Our study introduces GMIE, a high-resolution global map of irrigated cropland at 100 m resolution, covering 403.17 Mha and utilizing irrigation performance under drought stress. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the United States, and Pakistan. We identified the distribution of central pivot systems commonly used in the United States and Saudi Arabia. This new map can better support water management and food security globally.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data, 17, 773–797, https://doi.org/10.5194/essd-17-773-2025, https://doi.org/10.5194/essd-17-773-2025, 2025
Short summary
Short summary
This study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the Qinghai–Tibet Plateau (QTP) from 2000 to 2022 using the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 83.3%, with the reference annual 2020 data reaching an accuracy of 83.3% and a kappa coefficient of 0.82. The study supports the use of remote sensing data to mapping long-time continuous annual vegetation.
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data, 17, 741–772, https://doi.org/10.5194/essd-17-741-2025, https://doi.org/10.5194/essd-17-741-2025, 2025
Short summary
Short summary
Our study introduces a Landsat-based data cube simplifying access to detailed environmental data across Europe from 2000 to 2022, covering vegetation, water, soil, and crops. Our experiments demonstrate its effectiveness in developing environmental models and maps. Tailored feature selection is crucial for its effective use in environmental modeling. It aims to support comprehensive environmental monitoring and analysis, helping researchers and policy-makers in managing environmental resources.
Mingyang Song, Lu Xu, Ji Ge, Hong Zhang, Lijun Zuo, Jingling Jiang, Yinhaibin Ding, Yazhe Xie, and Fan Wu
Earth Syst. Sci. Data, 17, 661–683, https://doi.org/10.5194/essd-17-661-2025, https://doi.org/10.5194/essd-17-661-2025, 2025
Short summary
Short summary
We created a 10 m resolution rice distribution map for East Asia in 2023 (EARice10), achieving an overall accuracy (OA) of 90.48 % on validation samples. EARice10 shows strong consistency with statistical data (coefficient of determination, R2: 0.94–0.98) and existing datasets (R2: 0.79–0.98). It is the most up-to-date map, covering the four major rice-producing countries in East Asia at 10 m resolution.
Maximilian Freudenberg, Sebastian Schnell, and Paul Magdon
Earth Syst. Sci. Data, 17, 351–367, https://doi.org/10.5194/essd-17-351-2025, https://doi.org/10.5194/essd-17-351-2025, 2025
Short summary
Short summary
Classifying tree species in satellite images is an important task for environmental monitoring and forest management. Here we present a dataset containing Sentinel-2 satellite pixel time series of individual trees intended for training machine learning models. The dataset was created by merging information from the German National Forest Inventory in 2012 with satellite data. It sparsely covers the whole of Germany for the years 2015 to 2022 and comprises 48 species and 3 species groups.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data, 17, 95–115, https://doi.org/10.5194/essd-17-95-2025, https://doi.org/10.5194/essd-17-95-2025, 2025
Short summary
Short summary
This study proposed the Winter-Triticeae Crops Index (WTCI), which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 66 countries worldwide, with an overall accuracy of 87.7 %. The first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 were further produced based on the WTCI method. The product can serve as an important basis for agricultural applications.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
Short summary
Short summary
Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Binghong Han, Jian Bi, Shengli Tao, Tong Yang, Yongli Tang, Mengshuai Ge, Hao Wang, Zhenong Jin, Jinwei Dong, Zhibiao Nan, and Jin-Sheng He
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-620, https://doi.org/10.5194/essd-2024-620, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
The Tibetan Plateau is an important pastoral area where cultivated pastures play an increasingly important role. However, little is known about the spatial distribution of the cultivated pasture due to the difficulty in distinguishing them from natural grasslands with remote sensing. For the first time, we have mapped the cultivated pastures on the plateau at a resolution of 30 meters with decent accuracy. This data set is valuable to scientists, policy makers, conservationists and pastoralists.
Yanghai Yu, Yang Lei, Paul Siqueira, Xiaotong Liu, Denuo Gu, Anmin Fu, Yong Pang, Wenli Huang, and Jiancheng Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-596, https://doi.org/10.5194/essd-2024-596, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper presents a global-to-local method to improve forest height estimates by fusing InSAR and GEDI data. The large-scale ability was tested on open-access ALOS-1 data, where a two-fold solution is used to address temporal gap between GEDI and ALOS data. Produced products of 30 m gridded forest height mosaics for the northeastern U.S. and China show improved accuracy at 3–4 m/ha and 20 % enhancement over interpolated GEDI maps. The prototype is promising to fuse GEDI and future NISAR data.
Raphaël d'Andrimont, Momchil Yordanov, Fernando Sedano, Astrid Verhegghen, Peter Strobl, Savvas Zachariadis, Flavia Camilleri, Alessandra Palmieri, Beatrice Eiselt, Jose Miguel Rubio Iglesias, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 5723–5735, https://doi.org/10.5194/essd-16-5723-2024, https://doi.org/10.5194/essd-16-5723-2024, 2024
Short summary
Short summary
The Land Use/Cover Area frame Survey (LUCAS) Copernicus 2022 is a large and systematic in situ field survey of 137 966 polygons over the European Union in 2022. The data contain 82 land cover classes and 40 land use classes.
Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, and Peng Gong
Earth Syst. Sci. Data, 16, 5449–5475, https://doi.org/10.5194/essd-16-5449-2024, https://doi.org/10.5194/essd-16-5449-2024, 2024
Short summary
Short summary
The inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses challenges to large-scale land monitoring. We developed a global 30 m 23-year (2000–2022) daily seamless data cube (SDC) of surface reflectance based on Landsat 5, 7, 8, and 9 and MODIS products. The SDC exhibits enhanced capabilities for monitoring land cover changes and robust consistency in both spatial and temporal dimensions, which are important for global environmental monitoring.
Gerardo E. Soto, Steven W. Wilcox, Patrick E. Clark, Francesco P. Fava, Nathaniel D. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher B. Barrett
Earth Syst. Sci. Data, 16, 5375–5404, https://doi.org/10.5194/essd-16-5375-2024, https://doi.org/10.5194/essd-16-5375-2024, 2024
Short summary
Short summary
This paper uses machine learning and linear unmixing to produce rangeland health indicators: Landsat time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground in arid and semi-arid Kenya, Ethiopia, and Somalia. This represents the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangeland health in the arid and semi-arid rangelands of this portion of eastern Africa.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
Short summary
Short summary
Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo
Earth Syst. Sci. Data, 16, 5267–5285, https://doi.org/10.5194/essd-16-5267-2024, https://doi.org/10.5194/essd-16-5267-2024, 2024
Short summary
Short summary
The national-scale continuous maps of arithmetic mean height and weighted mean height across China address the challenges of accurately estimating forest stand mean height using a tree-based approach. These maps produced in this study provide critical datasets for forest sustainable management in China, including climate change mitigation (e.g., terrestrial carbon estimation), forest ecosystem assessment, and forest inventory practices.
Jia Zhou, Jin Niu, Ning Wu, and Tao Lu
Earth Syst. Sci. Data, 16, 5171–5189, https://doi.org/10.5194/essd-16-5171-2024, https://doi.org/10.5194/essd-16-5171-2024, 2024
Short summary
Short summary
The study provided an annual 100 m resolution glimpse into the grazing activities across the Qinghai–Tibet Plateau. The newly minted Gridded Dataset of Grazing Intensity (GDGI) not only boasts exceptional accuracy but also acts as a pivotal resource for further research and strategic planning, with the potential to shape sustainable grazing practices, guide informed environmental stewardship, and ensure the longevity of the region’s precious ecosystems.
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data, 16, 5111–5129, https://doi.org/10.5194/essd-16-5111-2024, https://doi.org/10.5194/essd-16-5111-2024, 2024
Short summary
Short summary
This study provides a 10 m global oil palm extent layer for 2021 and a 30 m oil palm planting-year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data, 16, 4971–4994, https://doi.org/10.5194/essd-16-4971-2024, https://doi.org/10.5194/essd-16-4971-2024, 2024
Short summary
Short summary
We reconstructed a cropland area change dataset in Northeast China over the past millennium by integrating multisource data with a unified standard using the historical and archaeological record, statistical yearbook, and national land survey. Cropland in Northeast China exhibited phases of expansion–reduction–expansion over the past millennium. This dataset can be used for improving the land use and land cover change (LUCC) dataset and assessing LUCC-induced carbon emission and climate change.
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data, 16, 4931–4947, https://doi.org/10.5194/essd-16-4931-2024, https://doi.org/10.5194/essd-16-4931-2024, 2024
Short summary
Short summary
Sugarcane plays a vital role in food, biofuel, and farmer income globally, yet its cultivation faces numerous social and environmental challenges. Despite its significance, accurate mapping remains limited. Our study addresses this gap by introducing a novel 10 m global dataset of sugarcane maps spanning 2019–2022. Comparisons with field data, pre-existing maps, and official government statistics all indicate the high precision and high recall of our maps.
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-408, https://doi.org/10.5194/essd-2024-408, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP), which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, Elodie Salmon, Philippe Ciais, Zhen Zhang, Shushi Peng, and Marielle Saunois
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-466, https://doi.org/10.5194/essd-2024-466, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Wetlands are responsible for about a third of global emissions of methane, a potent greenhouse gas. We have developed the GIEMS-MethaneCentric (GIEMS-MC) dataset to represent the dynamics of wetland extent on a global scale (0.25°x0.25° resolution, monthly time step). This updated resource combines satellite data and existing wetland databases, covering 1992 to 2020. Consistent maps of other methane-emitting surface waters (lakes, rivers, reservoirs, rice paddies) are also provided.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data, 16, 4619–4639, https://doi.org/10.5194/essd-16-4619-2024, https://doi.org/10.5194/essd-16-4619-2024, 2024
Short summary
Short summary
Existing satellite-based forest maps have large uncertainties due to different forest definitions and mapping algorithms. To effectively manage forest resources, timely and accurate annual forest maps at a high spatial resolution are needed. This study improved forest maps by integrating PALSAR-2 and Landsat images. Annual evergreen and non-evergreen forest-type maps were also generated. This critical information supports the Global Forest Resources Assessment.
Xin Zhao, Kazuya Nishina, Haruka Izumisawa, Yuji Masutomi, Seima Osako, and Shuhei Yamamoto
Earth Syst. Sci. Data, 16, 3893–3911, https://doi.org/10.5194/essd-16-3893-2024, https://doi.org/10.5194/essd-16-3893-2024, 2024
Short summary
Short summary
Mapping a rice calendar in a spatially explicit manner with a consistent framework remains challenging at a global or continental scale. We successfully developed a new gridded rice calendar for monsoon Asia based on Sentinel-1 and Sentinel-2 images, which characterize transplanting and harvesting dates and the number of rice croppings in a comprehensive framework. Our rice calendar will be beneficial for rice management, production prediction, and the estimation of greenhouse gas emissions.
Yuehong Chen, Congcong Xu, Yong Ge, Xiaoxiang Zhang, and Ya'nan Zhou
Earth Syst. Sci. Data, 16, 3705–3718, https://doi.org/10.5194/essd-16-3705-2024, https://doi.org/10.5194/essd-16-3705-2024, 2024
Short summary
Short summary
Population data is crucial for human–nature interactions. Gridded population data can address limitations of census data in irregular units. In China, rapid urbanization necessitates timely and accurate population grids. However, existing datasets for China are either outdated or lack recent census data. Hence, a novel approach was developed to disaggregate China’s seventh census data into 100 m population grids. The resulting dataset outperformed the existing LandScan and WorldPop datasets.
Zia Mehrabi, Kaitai Tong, Julie Fortin, Radost Stanimirova, Mark Friedl, and Navin Ramankutty
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-279, https://doi.org/10.5194/essd-2024-279, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We present a global geospatial database of cropland and pastures representing the year 2015. We made it using machine learning models to merge satellite-based land cover data with agricultural census data that we compiled. This database is an update to an earlier version representing the year 2000. It can be used to study issues such as land use, food security, climate change and biodiversity loss. We provide a reproducible code base to easily update the product for future years.
Shuchao Ye, Peiyu Cao, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 3453–3470, https://doi.org/10.5194/essd-16-3453-2024, https://doi.org/10.5194/essd-16-3453-2024, 2024
Short summary
Short summary
We reconstructed annual cropland density and crop type maps, including nine major crop types (corn, soybean, winter wheat, spring wheat, durum wheat, cotton, sorghum, barley, and rice), from 1850 to 2021 at 1 km × 1 km resolution. We found that the US total crop acreage has increased by 118 × 106 ha (118 Mha), mainly driven by corn (30 Mha) and soybean (35 Mha). Additionally, the US cropping diversity experienced an increase in the 1850s–1960s, followed by a decline over the past 6 decades.
Fang Chen, Lei Wang, Yu Wang, Haiying Zhang, Ning Wang, Pengfei Ma, and Bo Yu
Earth Syst. Sci. Data, 16, 3369–3382, https://doi.org/10.5194/essd-16-3369-2024, https://doi.org/10.5194/essd-16-3369-2024, 2024
Short summary
Short summary
Storage tanks are responsible for approximately 25 % of CH4 emissions in the atmosphere, exacerbating climate warming. Currently there is no publicly accessible storage tank inventory. We generated the first high-spatial-resolution (1–2 m) storage tank dataset (STD) over 92 typical cities in China in 2021, totaling 14 461 storage tanks with the construction year from 2000–2021. It shows significant agreement with CH4 emission spatially and temporally, promoting the CH4 control strategy proposal.
Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian
Earth Syst. Sci. Data, 16, 3307–3332, https://doi.org/10.5194/essd-16-3307-2024, https://doi.org/10.5194/essd-16-3307-2024, 2024
Short summary
Short summary
The Tibetan Plateau, with its diverse vegetation ranging from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the map TP_LC10-2022. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Qinghang Mei, Zhao Zhang, Jichong Han, Jie Song, Jinwei Dong, Huaqing Wu, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 16, 3213–3231, https://doi.org/10.5194/essd-16-3213-2024, https://doi.org/10.5194/essd-16-3213-2024, 2024
Short summary
Short summary
In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
Yavar Pourmohamad, John T. Abatzoglou, Erin J. Belval, Erica Fleishman, Karen Short, Matthew C. Reeves, Nicholas Nauslar, Philip E. Higuera, Eric Henderson, Sawyer Ball, Amir AghaKouchak, Jeffrey P. Prestemon, Julia Olszewski, and Mojtaba Sadegh
Earth Syst. Sci. Data, 16, 3045–3060, https://doi.org/10.5194/essd-16-3045-2024, https://doi.org/10.5194/essd-16-3045-2024, 2024
Short summary
Short summary
The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
Short summary
Short summary
We accurately mapped 16 dominant tree species and genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved more than 80% accuracy in country-wide forest species mapping, with variation based on species, region, and observation frequency. Freely accessible resources, including the forest tree species map and training and test data, can be found at https://doi.org/10.5281/zenodo.10180469.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
Short summary
Short summary
NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
Short summary
Short summary
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
Short summary
Short summary
This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
Hui Li, Xiaobo Wang, Shaoqiang Wang, Jinyuan Liu, Yuanyuan Liu, Zhenhai Liu, Shiliang Chen, Qinyi Wang, Tongtong Zhu, Lunche Wang, and Lizhe Wang
Earth Syst. Sci. Data, 16, 1689–1701, https://doi.org/10.5194/essd-16-1689-2024, https://doi.org/10.5194/essd-16-1689-2024, 2024
Short summary
Short summary
Utilizing satellite remote sensing data, we established a multi-season rice calendar dataset named ChinaRiceCalendar. It exhibits strong alignment with field observations collected by agricultural meteorological stations across China. ChinaRiceCalendar stands as a reliable dataset for investigating and optimizing the spatiotemporal dynamics of rice phenology in China, particularly in the context of climate and land use changes.
Giulia Ronchetti, Luigi Nisini Scacchiafichi, Lorenzo Seguini, Iacopo Cerrani, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 1623–1649, https://doi.org/10.5194/essd-16-1623-2024, https://doi.org/10.5194/essd-16-1623-2024, 2024
Short summary
Short summary
We present a dataset of EU-wide harmonized subnational crop area, production, and yield statistics with information on data sources, processing steps, missing and derived data, and quality checks. Statistical records (344 282) collected from 1975 to 2020 for soft and durum wheat, winter and spring barley, grain maize, sunflower, and sugar beet were aligned with the EUROSTAT crop legend and the 2016 territorial classification for 961 regions. Time series have a median length of 21 years.
Xiao Zhang, Tingting Zhao, Hong Xu, Wendi Liu, Jinqing Wang, Xidong Chen, and Liangyun Liu
Earth Syst. Sci. Data, 16, 1353–1381, https://doi.org/10.5194/essd-16-1353-2024, https://doi.org/10.5194/essd-16-1353-2024, 2024
Short summary
Short summary
This work describes GLC_FCS30D, the first global 30 m land-cover dynamics monitoring dataset, which contains 35 land-cover subcategories and covers the period of 1985–2022 in 26 time steps (its maps are updated every 5 years before 2000 and annually after 2000).
Qiangqiang Sun, Ping Zhang, Xin Jiao, Xin Lin, Wenkai Duan, Su Ma, Qidi Pan, Lu Chen, Yongxiang Zhang, Shucheng You, Shunxi Liu, Jinmin Hao, Hong Li, and Danfeng Sun
Earth Syst. Sci. Data, 16, 1333–1351, https://doi.org/10.5194/essd-16-1333-2024, https://doi.org/10.5194/essd-16-1333-2024, 2024
Short summary
Short summary
To provide multifaceted changes under climate change and anthropogenic impacts, we estimated monthly vegetation and soil fractions in 2001–2022, providing an accurate estimate of surface heterogeneous composition, better than vegetation index and vegetation continuous-field products. We find a greening trend on Earth except for the tropics. A combination of interactive changes in vegetation and soil can be adopted as a valuable measurement of climate change and anthropogenic impacts.
Kai Cheng, Yuling Chen, Tianyu Xiang, Haitao Yang, Weiyan Liu, Yu Ren, Hongcan Guan, Tianyu Hu, Qin Ma, and Qinghua Guo
Earth Syst. Sci. Data, 16, 803–819, https://doi.org/10.5194/essd-16-803-2024, https://doi.org/10.5194/essd-16-803-2024, 2024
Short summary
Short summary
To quantify forest carbon stock and its future potential accurately, we generated a 30 m resolution forest age map for China in 2020 using multisource remote sensing datasets based on machine learning and time series analysis approaches. Validation with independent field samples indicated that the mapped forest age had an R2 of 0.51--0.63. Nationally, the average forest age is 56.1 years (standard deviation of 32.7 years).
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
Short summary
Short summary
We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cameron I. Ludemann, Nathan Wanner, Pauline Chivenge, Achim Dobermann, Rasmus Einarsson, Patricio Grassini, Armelle Gruere, Kevin Jackson, Luis Lassaletta, Federico Maggi, Griffiths Obli-Laryea, Martin K. van Ittersum, Srishti Vishwakarma, Xin Zhang, and Francesco N. Tubiello
Earth Syst. Sci. Data, 16, 525–541, https://doi.org/10.5194/essd-16-525-2024, https://doi.org/10.5194/essd-16-525-2024, 2024
Short summary
Short summary
Nutrient budgets help identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow the calculation of indicators, such as the nutrient balance (surplus or deficit) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability. This article describes a global cropland nutrient budget that provides data on 205 countries and territories from 1961 to 2020 (data available at https://www.fao.org/faostat/en/#data/ESB).
Cited articles
Bai, Y., Feng, M., Jiang, H., Wang, J., and Liu, Y.: Validation of Land
Cover Maps in China Using a Sampling-Based Labeling Approach, Remote
Sens., 7, 10589–10606, https://doi.org/10.3390/rs70810589, 2015.
Ban, Y., Jacob, A., and Gamba, P.: Spaceborne SAR data for global urban
mapping at 30 m resolution using a robust urban extractor, ISPRS J. Photogramm., 103, 28–37,
https://doi.org/10.1016/j.isprsjprs.2014.08.004, 2015.
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review
of applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011,
2016.
Bennett, M. M. and Smith, L. C.: Advances in using multitemporal night-time
lights satellite imagery to detect, estimate, and monitor socioeconomic
dynamics, Remote Sens. Environ., 192, 176–197,
https://doi.org/10.1016/j.rse.2017.01.005, 2017.
Berger, M., Moreno, J., Johannessen, J. A., Levelt, P. F., and Hanssen, R.
F.: ESA's sentinel missions in support of Earth system science, Remote Sens. Environ., 120, 84–90,
https://doi.org/10.1016/j.rse.2011.07.023, 2012.
Bontemps, S., Defourny, P., Van Bogaert, E., Arino, O., Kalogirou, V., and
Perez, J. R.: GLOBCOVER 2009-Products description and validation report,
available at: http://due.esrin.esa.int/files/GLOBCOVER2009_Validation_Report_2.2.pdf (last access: 8 July 2020), 2011.
Brown de Colstoun, E. C., Huang, C., Wang, P., Tilton, J. C., Tan, B.,
Phillips, J., Niemczura, S., Ling, P.-Y., and Wolfe, R. E.: Global Man-made
Impervious Surface (GMIS) Dataset From Landsat. NASA Socioeconomic Data and
Applications Center (SEDAC), Palisades, NY,
https://doi.org/10.7927/H4P55KKF, 2017.
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G.,
Peng, S., Lu, M., Zhang, W., Tong, X., and Mills, J.: Global land cover
mapping at 30 m resolution: A POK-based operational approach, ISPRS J. Photogramm., 103, 7–27,
https://doi.org/10.1016/j.isprsjprs.2014.09.002, 2015.
Chen, X., Cao, X., Liao, A., Chen, L., Peng, S., Lu, M., Chen, J., Zhang,
W., Zhang, H., and Han, G.: Global mapping of artificial surfaces at 30 m
resolution, Sci. China Earth Sci., 59, 2295–2306,
https://doi.org/10.1007/s11430-016-5291-y, 2016.
Clarke, K. C., Hoppen, S., and Gaydos, L.: A self-modifying cellular
automaton model of historical urbanization in the San Francisco Bay area,
Environ. Plann. B, 24, 247–261,
https://doi.org/10.1068/b240247, 1997.
Deng, C. and Wu, C.: BCI: A biophysical composition index for remote sensing
of urban environments, Remote Sens. Environ., 127, 247–259,
https://doi.org/10.1016/j.rse.2012.09.009, 2012.
Didan, K., Munoz, A. B., Solano, R., and Huete, A.: MODIS vegetation index
user's guide (MOD13 series), Vegetation Index and Phenology Lab, The
University of Arizona, 1–38, https://doi.org/10.5067/MODIS/MYD13Q1.006,
2015.
Du, P., Samat, A., Waske, B., Liu, S., and Li, Z.: Random Forest and
Rotation Forest for fully polarized SAR image classification using
polarimetric and spatial features, ISPRS J. Photogramm., 105, 38–53, https://doi.org/10.1016/j.isprsjprs.2015.03.002,
2015.
Elvidge, C. D., Tuttle, B. T., Sutton, P. C., Baugh, K. E., Howard, A. T.,
Milesi, C., Bhaduri, B. L., and Nemani, R.: Global Distribution and Density
of Constructed Impervious Surfaces, Sensors, 7, 1962–1979,
https://doi.org/10.3390/s7091962, 2007.
Elvidge, C. D., Baugh, K., Zhizhin, M., Feng, C. H., and Ghosh, T.: VIIRS
night-time lights, Int. J. Remote Sens., 38, 5860–5879,
https://doi.org/10.1080/01431161.2017.1342050, 2017.
ESA: Sentinel-1 SAR User Guide Introduction, availabe at:
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar (last access: 26 December 2019), 2016.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004,
https://doi.org/10.1029/2005rg000183, 2007.
Florczyk, A., Corbane, C., Ehrlich, D., Freire, S., Kemper, T., Maffenini,
L., Melchiorri, M., Pesaresi, M., Politis, P., and Schiavina, M.: GHSL Data
Package 2019, Luxembourg, EUR, 29788, https://doi.org/10.2760/290498, 2019.
Foody, G. M. and Mathur, A.: Toward intelligent training of supervised image
classifications: directing training data acquisition for SVM classification,
Remote Sens. Environ., 93, 107–117,
https://doi.org/10.1016/j.rse.2004.06.017, 2004.
Fu, P. and Weng, Q.: A time series analysis of urbanization induced land use
and land cover change and its impact on land surface temperature with
Landsat imagery, Remote Sens. Environ., 175, 205–214,
https://doi.org/10.1016/j.rse.2015.12.040, 2016.
Gao, F., Colstoun, E. B. d., Ma, R., Weng, Q., Masek, J. G., Chen, J., Pan,
Y., and Song, C.: Mapping impervious surface expansion using
medium-resolution satellite image time series: a case study in the Yangtze
River Delta, China, Int. J. Remote Sens., 33, 7609–7628,
https://doi.org/10.1080/01431161.2012.700424, 2012.
Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R.: Random Forests
for land cover classification, Pattern Recogn. Lett., 27, 294–300,
https://doi.org/10.1016/j.patrec.2005.08.011, 2006.
Goldblatt, R., Stuhlmacher, M. F., Tellman, B., Clinton, N., Hanson, G.,
Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A. K., Cheng,
W.-H., and Balling, R. C.: Using Landsat and nighttime lights for supervised
pixel-based image classification of urban land cover, Remote Sens. Environ., 205, 253–275, https://doi.org/10.1016/j.rse.2017.11.026, 2018.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang,
X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X.,
Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng,
Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu,
X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C.,
Clinton, N., Zhu, Z., Chen, J., and Chen, J.: Finer resolution observation
and monitoring of global land cover: first mapping results with Landsat TM
and ETM+ data, Int. J. Remote Sens., 34, 2607–2654,
https://doi.org/10.1080/01431161.2012.748992, 2013.
Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., Clinton, N., Ji,
L., Li, W., Bai, Y., Chen, B., Xu, B., Zhu, Z., Yuan, C., Ping Suen, H.,
Guo, J., Xu, N., Li, W., Zhao, Y., Yang, J., Yu, C., Wang, X., Fu, H., Yu,
L., Dronova, I., Hui, F., Cheng, X., Shi, X., Xiao, F., Liu, Q., and Song,
L.: Stable classification with limited sample: transferring a 30 m
resolution sample set collected in 2015 to mapping 10 m resolution global
land cover in 2017, Sci. Bull., 64, 370–373,
https://doi.org/10.1016/j.scib.2019.03.002, 2019.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27,
https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Hansen, M. C., Egorov, A., Potapov, P. V., Stehman, S. V., Tyukavina, A.,
Turubanova, S. A., Roy, D. P., Goetz, S. J., Loveland, T. R., Ju, J.,
Kommareddy, A., Kovalskyy, V., Forsyth, C., and Bents, T.: Monitoring
conterminous United States (CONUS) land cover change with Web-Enabled
Landsat Data (WELD), Remote Sens. Environ., 140, 466–484,
https://doi.org/10.1016/j.rse.2013.08.014, 2014.
Homer, C., Huang, C., Yang, L., Wylie, B., and Coan, M.: Development of a
2001 national land-cover database for the United States, Photogramm. Eng.
Rem. S., 70, 829–840,
https://doi.org/10.14358/PERS.70.7.829, 2004.
Homer, C., Dewitz, J., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston,
J., Herold, N., Wickham, J., and Megown, K.: Completion of the 2011 National
Land Cover Database for the conterminous United States–representing a
decade of land cover change information, Photogramm. Eng.
Rem. S., 81, 345–354, https://doi.org/10.1016/S0099-1112(15)30100-2,
2015.
Hu, Y., Liu, L., Liu, L., Peng, D., Jiao, Q., and Zhang, H.: A Landsat-5
atmospheric correction based on MODIS atmosphere products and 6S model, IEEE J. Sel. Top. Appl.,
7, 1609–1615, https://doi.org/10.1109/JSTARS.2013.2290028, 2014.
Huang, X., Schneider, A., and Friedl, M. A.: Mapping sub-pixel urban
expansion in China using MODIS and DMSP/OLS nighttime lights, Remote Sens. Environ., 175, 92–108, https://doi.org/10.1016/j.rse.2015.12.042,
2016.
Im, J., Lu, Z., Rhee, J., and Quackenbush, L. J.: Impervious surface
quantification using a synthesis of artificial immune networks and
decision/regression trees from multi-sensor data, Remote Sens. Environ., 117, 102–113, https://doi.org/10.1016/j.rse.2011.06.024, 2012.
Jokar Arsanjani, J., Tayyebi, A., and Vaz, E.: GlobeLand30 as an alternative
fine-scale global land cover map: Challenges, possibilities, and
implications for developing countries, Habitat Int., 55, 25–31,
https://doi.org/10.1016/j.habitatint.2016.02.003, 2016.
Langanke, T., Moran, A., Dulleck, B., and Schleicher, C.: Copernicus Land
Monitoring Service–High Resolution Layer Water and Wetness Product
Specifications Document, Copernicus team at EEA, 2016.
Li, C., Peng, G., Wang, J., Zhu, Z., Biging, G. S., Yuan, C., Hu, T., Zhang,
H., Wang, Q., and Li, X.: The first all-season sample set for mapping global
land cover with Landsat-8 data, Sci. Bull., 62, 508–515,
https://doi.org/10.1016/j.scib.2017.03.011, 2017.
Li, X. and Zhou, Y.: Urban mapping using DMSP/OLS stable night-time light: a
review, Int. J. Remote Sens., 38, 6030–6046,
https://doi.org/10.1080/01431161.2016.1274451, 2017.
Li, X., Gong, P., and Liang, L.: A 30-year (1984–2013) record of annual
urban dynamics of Beijing City derived from Landsat data, Remote Sens. Environ., 166, 78–90, https://doi.org/10.1016/j.rse.2015.06.007, 2015.
Li, X., Zhou, Y., Zhu, Z., Liang, L., Yu, B., and Cao, W.: Mapping annual
urban dynamics (1985–2015) using time series of Landsat data, Remote Sens. Environ., 216, 674–683,
https://doi.org/10.1016/j.rse.2018.07.030, 2018.
Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., and Wang, S.:
High-resolution multi-temporal mapping of global urban land using Landsat
images based on the Google Earth Engine Platform, Remote Sens. Environ., 209, 227–239, https://doi.org/10.1016/j.rse.2018.02.055, 2018.
Lu, D. and Weng, Q.: Use of impervious surface in urban land-use
classification, Remote Sens. Environ., 102, 146–160,
https://doi.org/10.1016/j.rse.2006.02.010, 2006.
Massey, R., Sankey, T. T., Yadav, K., Congalton, R. G., and Tilton, J. C.:
Integrating cloud-based workflows in continental-scale cropland extent
classification, Remote Sens. Environ., 219, 162–179,
https://doi.org/10.1016/j.rse.2018.10.013, 2018.
Okujeni, A., van der Linden, S., Tits, L., Somers, B., and Hostert, P.:
Support vector regression and synthetically mixed training data for
quantifying urban land cover, Remote Sens. Environ., 137, 184–197,
https://doi.org/10.1016/j.rse.2013.06.007, 2013.
Okujeni, A., Canters, F., Cooper, S. D., Degerickx, J., Heiden, U., Hostert,
P., Priem, F., Roberts, D. A., Somers, B., and van der Linden, S.:
Generalizing machine learning regression models using multi-site spectral
libraries for mapping vegetation-impervious-soil fractions across multiple
cities, Remote Sens. Environ., 216, 482–496,
https://doi.org/10.1016/j.rse.2018.07.011, 2018.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and
Wulder, M. A.: Good practices for estimating area and assessing accuracy of
land change, Remote Sens. Environ., 148, 42–57,
https://doi.org/10.1016/j.rse.2014.02.015, 2014.
Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540,
418–422, https://doi.org/10.1038/nature20584, 2016.
Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Freire, S., Halkia, M.,
Julea, A., Kemper, T., Soille, P., and Syrris, V.: Operating procedure for
the production of the Global Human Settlement Layer from Landsat data of the
epochs 1975, 1990, 2000, and 2014, Publications Office of the European
Union, 1–62, https://doi.org/10.2788/253582, 2016.
Pflugmacher, D., Cohen, W. B., Kennedy, R. E., and Yang, Z.: Using
Landsat-derived disturbance and recovery history and lidar to map forest
biomass dynamics, Remote Sens. Environ., 151, 124–137,
https://doi.org/10.1016/j.rse.2013.05.033, 2014.
Radoux, J., Lamarche, C., Van Bogaert, E., Bontemps, S., Brockmann, C., and
Defourny, P.: Automated training sample extraction for global land cover
mapping, Remote Sens., 6, 3965–3987, https://doi.org/10.3390/rs6053965,
2014.
Ridd, M. K.: Exploring a V-I-S (vegetation-impervious surface-soil) model
for urban ecosystem analysis through remote sensing: comparative anatomy for
cities, Int. J. Remote Sens., 16, 2165–2185,
https://doi.org/10.1080/01431169508954549, 1995.
Rodriguez-Galiano, V. F., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P.
M., and Jeganathan, C.: Random Forest classification of Mediterranean land
cover using multi-seasonal imagery and multi-seasonal texture, Remote Sens. Environ., 121, 93–107,
https://doi.org/10.1016/j.rse.2011.12.003, 2012.
Schneider, A., Friedl, M. A., and Potere, D.: A new map of global urban
extent from MODIS satellite data, Environ. Res. Lett., 4, 044003,
https://doi.org/10.1088/1748-9326/4/4/044003, 2009.
Schneider, A., Friedl, M. A., and Potere, D.: Mapping global urban areas
using MODIS 500 m data: New methods and datasets based on “urban
ecoregions”, Remote Sens. Environ., 114, 1733–1746,
https://doi.org/10.1016/j.rse.2010.03.003, 2010.
Schug, F., Okujeni, A., Hauer, J., Hostert, P., Nielsen, J. Ø., and van
der Linden, S.: Mapping patterns of urban development in Ouagadougou,
Burkina Faso, using machine learning regression modeling with bi-seasonal
Landsat time series, Remote Sens. Environ., 210, 217–228,
https://doi.org/10.1016/j.rse.2018.03.022, 2018.
Shaban, M. and Dikshit, O.: Improvement of classification in urban areas by
the use of textural features: the case study of Lucknow city, Uttar Pradesh,
Int. J. Remote Sens., 22, 565–593,
https://doi.org/10.1080/01431160050505865, 2001.
Shao, Z., Fu, H., Fu, P., and Yin, L.: Mapping Urban Impervious Surface by
Fusing Optical and SAR Data at the Decision Level, Remote Sens., 8, 945,
https://doi.org/10.3390/rs8110945, 2016.
Sun, G., Kong, Y., Jia, X., Zhang, A., Rong, J., and Ma, H.: Synergistic Use
of Optical and Dual-Polarized SAR Data With Multiple Kernel Learning for
Urban Impervious Surface Mapping, IEEE J. Sel. Top. Appl., 12, 223–236,
https://doi.org/10.1109/jstars.2018.2883654, 2019.
Sun, Z., Wang, C., Guo, H., and Shang, R.: A Modified Normalized Difference
Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat
Imagery, Remote Sens., 9, 942, https://doi.org/10.3390/rs9090942, 2017.
Sun, Z., Xu, R., Du, W., Wang, L., and Lu, D.: High-Resolution Urban Land
Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine,
Remote Sens., 11, 752, https://doi.org/10.3390/rs11070752, 2019.
Tachikawa, T., Kaku, M., Iwasaki, A., Gesch, D. B., Oimoen, M. J., Zhang,
Z., Danielson, J., Krieger, T., Curtis, B., Haase, J., Abrams, M., and Carabajal, C.: ASTER Global
Digital Elevation Model Version 2 – Summary of validation results, available at: https://pubs.er.usgs.gov/publication/70005960 (last access: 8 July 2020), 2011.
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E.,
Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I. N., Deghaye, P.,
Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L'Abbate, M., Croci, R.,
Pietropaolo, A., Huchler, M., and Rostan, F.: GMES Sentinel-1 mission,
Remote Sens. Environ., 120, 9–24,
https://doi.org/10.1016/j.rse.2011.05.028, 2012.
USGS: Landsat surface reflectance data, Reston, VA, Report 2015-3034,
2015.
USGS: Landsat 8 surface reflectance code (LaSRC) product, available
at:
https://www.usgs.gov/media/files/land-surface-reflectance-code-lasrc-product-guide (last access: 8 July 2020), 2018.
Vermote, E., Justice, C., Claverie, M., and Franch, B.: Preliminary analysis
of the performance of the Landsat 8/OLI land surface reflectance product,
Remote Sens. Environ., 185, 46–56,
https://doi.org/10.1016/j.rse.2016.04.008, 2016.
Wang, P., Huang, C., Brown de Colstoun, E., Tilton, J., and Tan, B.: Global
human built-up and settlement extent (HBASE) dataset from Landsat, NASA
Socioeconomic Data and Applications Center (SEDAC): Palisades, NY, USA,
https://doi.org/10.7927/H4DN434S, 2017a.
Wang, P., Huang, C., Tilton, J., Tan, B., and Brown de Colstoun, E.: HOTEX:
An approach for global mapping of human built-up and settlement extent, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017,
1562–1565, https://doi.org/10.1109/IGARSS.2017.8127268, 2017b.
Wang, Y., Liu, L., Hu, Y., Li, D., and Li, Z.: Development and validation of
the Landsat-8 surface reflectance products using a MODIS-based per-pixel
atmospheric correction method, Int. J. Remote Sens., 37,
1291–1314, https://doi.org/10.1080/01431161.2015.1104742, 2016.
Weng, Q.: A remote sensing-GIS evaluation of urban expansion and its impact
on surface temperature in the Zhujiang Delta, China, Int. J. Remote Sens., 22, 1999–2014, https://doi.org/10.1080/713860788, 2001.
Weng, Q.: Remote sensing of impervious surfaces in the urban areas:
Requirements, methods, and trends, Remote Sens. Environ., 117,
34–49, https://doi.org/10.1016/j.rse.2011.02.030, 2012.
Wetherley, E. B., Roberts, D. A., and McFadden, J. P.: Mapping spectrally
similar urban materials at sub-pixel scales, Remote Sens. Environ.,
195, 170–183, https://doi.org/10.1016/j.rse.2017.04.013, 2017.
Wu, C.: Normalized spectral mixture analysis for monitoring urban
composition using ETM+ imagery, Remote Sens. Environ., 93,
480–492, https://doi.org/10.1016/j.rse.2004.08.003, 2004.
Wu, C. and Murray, A. T.: Estimating impervious surface distribution by
spectral mixture analysis, Remote Sens. Environ., 84, 493–505,
https://doi.org/10.1016/s0034-4257(02)00136-0, 2003.
Xie, Y. and Weng, Q.: Spatiotemporally enhancing time-series DMSP/OLS
nighttime light imagery for assessing large-scale urban dynamics, ISPRS J. Photogramm., 128, 1–15,
https://doi.org/10.1016/j.isprsjprs.2017.03.003, 2017.
Xu, H.: Analysis of Impervious Surface and its Impact on Urban Heat
Environment using the Normalized Difference Impervious Surface Index
(NDISI), Photogramm. Eng. Rem. S., 76, 557–565,
https://doi.org/10.14358/pers.76.5.557, 2010.
Yang, J. and He, Y.: Automated mapping of impervious surfaces in urban and
suburban areas: Linear spectral unmixing of high spatial resolution imagery,
Int. J. Appl. Earth Obs., 54,
53–64, https://doi.org/10.1016/j.jag.2016.09.006, 2017.
Yang, Y., Xiao, P., Feng, X., and Li, H.: Accuracy assessment of seven
global land cover datasets over China, ISPRS J. Photogramm., 125, 156–173,
https://doi.org/10.1016/j.isprsjprs.2017.01.016, 2017.
Zhang, H., Zhang, Y., and Lin, H.: A comparison study of impervious surfaces
estimation using optical and SAR remote sensing images, Int. J. Appl. Earth Obs., 18, 148–156,
https://doi.org/10.1016/j.jag.2011.12.015, 2012.
Zhang, H., Zhang, Y., and Hui, L.: Seasonal effects of impervious surface
estimation in subtropical monsoon regions, Int. J. Digit.
Earth, 7, 746–760, https://doi.org/10.1080/17538947.2013.781241, 2014.
Zhang, H., Lin, H., Li, Y., Zhang, Y., and Fang, C.: Mapping urban
impervious surface with dual-polarimetric SAR data: An improved method,
Landscape Urban Plan., 151, 55–63,
https://doi.org/10.1016/j.landurbplan.2016.03.009, 2016.
Zhang, H., Lin, H., and Wang, Y.: A new scheme for urban impervious surface
classification from SAR images, ISPRS J. Photogramm., 139, 103–118, https://doi.org/10.1016/j.isprsjprs.2018.03.007,
2018.
Zhang, H. K. and Roy, D. P.: Using the 500 m MODIS land cover product to
derive a consistent continental scale 3 m Landsat land cover
classification, Remote Sens. Environ., 197, 15–34,
https://doi.org/10.1016/j.rse.2017.05.024, 2017.
Zhang, L. and Weng, Q.: Annual dynamics of impervious surface in the Pearl
River Delta, China, from 1988 to 2013, using time series Landsat imagery,
ISPRS J. Photogramm., 113, 86–96,
https://doi.org/10.1016/j.isprsjprs.2016.01.003, 2016.
Zhang, L., Zhang, M., and Yao, Y.: Mapping seasonal impervious surface
dynamics in Wuhan urban agglomeration, China from 2000 to 2016,
Int. J. Appl. Earth Obs., 70,
51–61, https://doi.org/10.1016/j.jag.2018.04.005, 2018.
Zhang, X. and Liu, L.: Development of a global 30 m impervious surface map
using multi-source and multi-temporal remote sensing datasets with the
Google Earth Engine platform, Zenodo, https://doi.org/10.5281/zenodo.3505079, 2019.
Zhang, X., Liu, L., Wang, Y., Hu, Y., and Zhang, B.: A SPECLib-based
operational classification approach: A preliminary test on China land cover
mapping at 30 m, Int. J. Appl. Earth Obs., 71, 83–94, https://doi.org/10.1016/j.jag.2018.05.006, 2018.
Zhang, X., Liu, L., Chen, X., Xie, S., and Gao, Y.: Fine Land-Cover Mapping
in China Using Landsat Datacube and an Operational SPECLib-Based Approach,
Remote Sens., 11, 1056, https://doi.org/10.3390/rs11091056, 2019.
Zhang, Y., Zhang, H., and Lin, H.: Improving the impervious surface
estimation with combined use of optical and SAR remote sensing images,
Remote Sens. Environ., 141, 155–167,
https://doi.org/10.1016/j.rse.2013.10.028, 2014.
Zhu, Z., Gallant, A. L., Woodcock, C. E., Pengra, B., Olofsson, P.,
Loveland, T. R., Jin, S., Dahal, D., Yang, L., and Auch, R. F.: Optimizing
selection of training and auxiliary data for operational land cover
classification for the LCMAP initiative, ISPRS J. Photogramm., 122, 206–221,
https://doi.org/10.1016/j.isprsjprs.2016.11.004, 2016.
Zhu, Z., Woodcock, C. E., Rogan, J., and Kellndorfer, J.: Assessment of
spectral, polarimetric, temporal, and spatial dimensions for urban and
peri-urban land cover classification using Landsat and SAR data, Remote Sens. Environ., 117, 72–82,
https://doi.org/10.1016/j.rse.2011.07.020, 2012.
Zhuo, L., Shi, Q., Tao, H., Zheng, J., and Li, Q.: An improved temporal
mixture analysis unmixing method for estimating impervious surface area
based on MODIS and DMSP-OLS data, ISPRS J. Photogramm., 142, 64–77, https://doi.org/10.1016/j.isprsjprs.2018.05.016, 2018.
Zhou, T., Zhao, M., Sun, C., and Pan, J.: Exploring the Impact of
Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and
GF-1 WFV Images in a Subtropical Monsoon-Climate Region, ISPRS Int. Geo-Inf., 7, 3, https://doi.org/10.3390/ijgi7010003, 2017.
Short summary
The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
The amount of impervious surface is an important indicator in the monitoring of the intensity of...
Altmetrics
Final-revised paper
Preprint