Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-555-2023
© Author(s) 2023. 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-15-555-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
03 Feb 2023
Data description paper |
| 03 Feb 2023
| 03 Feb 2023
UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework
Qian Shi
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
Andrea Marinoni
Department of Physics and Technology, UiT – The Arctic University of Norway, 9019 Tromsø, Norway
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
Xiaoping Liu
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
Related authors
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-217, https://doi.org/10.5194/essd-2024-217, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Given the limited coverage or spatial resolution of existing datasets, we develop the first global building height map (3D-GloBFP) at the building footprint scale using Earth observation datasets and advanced machine learning techniques. Our map reveals the complex 3-D morphology of the world's building heights at a finer scale and provides reliable results (i.e., R2: 0.66–0.96, RMSEs: 1.9 m–14.6 m) over global regions 3D-GloBFP has great potential to support both macro- and micro-urban analysis
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
Short summary
Short summary
A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Y. Cai, Q. Shi, and X. Liu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-W1-2022, 1–6, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, 2022
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
Preprint under review 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.
Yifan Cheng, Lei Zhao, Tirthankar Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-416, https://doi.org/10.5194/essd-2024-416, 2024
Preprint under review for ESSD
Short summary
Short summary
Absence of globally consistent and spatially continuous urban surface properties have long prevented large-scale high-resolution urban climate modeling. We developed the U-Surf data, a 1km-resolution dataset that provides key urban surface properties worldwide. U-Surf enhances urban representation in models, enables city-to-city comparison, and supports kilometer-scale Earth system modeling. Its broader applications can be extended to machine learning and many other non-climatic practices.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-217, https://doi.org/10.5194/essd-2024-217, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Given the limited coverage or spatial resolution of existing datasets, we develop the first global building height map (3D-GloBFP) at the building footprint scale using Earth observation datasets and advanced machine learning techniques. Our map reveals the complex 3-D morphology of the world's building heights at a finer scale and provides reliable results (i.e., R2: 0.66–0.96, RMSEs: 1.9 m–14.6 m) over global regions 3D-GloBFP has great potential to support both macro- and micro-urban analysis
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
Short summary
Short summary
Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
Short summary
Short summary
A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Y. Cai, Q. Shi, and X. Liu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-W1-2022, 1–6, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, 2022
Qinchuan Xin, Yongjiu Dai, and Xiaoping Liu
Biogeosciences, 16, 467–484, https://doi.org/10.5194/bg-16-467-2019, https://doi.org/10.5194/bg-16-467-2019, 2019
Short summary
Short summary
Terrestrial biosphere models that simulate both leaf dynamics and canopy photosynthesis are required to understand vegetation–climate interactions. A time-stepping scheme is proposed to simulate leaf area index, phenology, and gross primary production via climate variables. The method performs well on simulating deciduous broadleaf forests across the eastern United States; it provides a simplified and improved version of the growing production day model for use in land surface modeling.
L. M. Jiao, X. Tang, and X. P. Liu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1203–1211, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1203-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1203-2017, 2017
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
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
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
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
3D-GloBFP: the first global three-dimensional building footprint dataset
Map of forest tree species for Poland based on Sentinel-2 data
Global 30-m seamless data cube (2000–2022) of land surface reflectance generated from Landsat-5,7,8,9 and MODIS Terra constellations
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
A flux tower site attribute dataset intended for land surface 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
Advancements in LUCAS Copernicus 2022: Enhancing Earth Observation with Comprehensive In-Situ Data on EU Land Cover and Use
Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images
Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products
The first map of crop sequence types in Europe over 2012–2018
WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping
High-resolution mapping of global winter-triticeae crops using a sample-free identification method
Mapping Rangeland Health Indicators in East Africa from 2000 to 2022
A new cropland area database by country circa 2020
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach
SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data
HISDAC-ES: historical settlement data compilation for Spain (1900–2020)
LCM2021 – the UK Land Cover Map 2021
ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
Refined fine-scale mapping of tree cover using time series of Planet-NICFI and Sentinel-1 imagery for Southeast Asia (2016–2021)
High-resolution global map of closed-canopy coconut palm
High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe
Global urban fractional changes at a 1 km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs)
China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery
High-resolution distribution maps of single-season rice in China from 2017 to 2022
Mapping global non-floodplain wetlands
An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach
Annual emissions of carbon from land use, land-use change, and forestry from 1850 to 2020
An open-source automatic survey of green roofs in London using segmentation of aerial imagery
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.
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.
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.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-217, https://doi.org/10.5194/essd-2024-217, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Given the limited coverage or spatial resolution of existing datasets, we develop the first global building height map (3D-GloBFP) at the building footprint scale using Earth observation datasets and advanced machine learning techniques. Our map reveals the complex 3-D morphology of the world's building heights at a finer scale and provides reliable results (i.e., R2: 0.66–0.96, RMSEs: 1.9 m–14.6 m) over global regions 3D-GloBFP has great potential to support both macro- and micro-urban analysis
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.
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 Discuss., https://doi.org/10.5194/essd-2024-178, https://doi.org/10.5194/essd-2024-178, 2024
Revised manuscript accepted for ESSD
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), and daily Seamless Data Cube (SDC) of surface reflectance based on Landsat 5,7,8,9 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.
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.
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 Discuss., https://doi.org/10.5194/essd-2024-77, https://doi.org/10.5194/essd-2024-77, 2024
Revised manuscript accepted for ESSD
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.
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).
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 Discuss., https://doi.org/10.5194/essd-2023-494, https://doi.org/10.5194/essd-2023-494, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
LUCAS 2022 Copernicus is a large an systematic in-situ field survey of 137,966 polygons over the EU in 2022. The data holds 82 land cover classes and 40 land use classes.
Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III
Earth Syst. Sci. Data, 16, 321–336, https://doi.org/10.5194/essd-16-321-2024, https://doi.org/10.5194/essd-16-321-2024, 2024
Short summary
Short summary
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
Xiangan Liang, Qiang Liu, Jie Wang, Shuang Chen, and Peng Gong
Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, https://doi.org/10.5194/essd-16-177-2024, 2024
Short summary
Short summary
The state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps, which make it difficult to characterize the continuous variation of the terrestrial surface. We proposed a framework for generating the first global 500 m daily seamless data cubes (SDC500), covering the period from 2000 to 2022. We believe that the SDC500 dataset can interest other researchers who study land cover mapping, quantitative remote sensing, and ecological science.
Rémy Ballot, Nicolas Guilpart, and Marie-Hélène Jeuffroy
Earth Syst. Sci. Data, 15, 5651–5666, https://doi.org/10.5194/essd-15-5651-2023, https://doi.org/10.5194/essd-15-5651-2023, 2023
Short summary
Short summary
Assessing the benefits of crop diversification – a key element of agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of Europe. To fill this gap, we used a dataset that provides temporally and spatially incomplete land cover information to create a map of dominant crop sequence types for Europe over 2012–2018. This map is a useful baseline for assessing the benefits of future crop diversification.
Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Daniele Zanaga, Marjorie Battude, Alex Grosu, Joost Brombacher, Myroslava Lesiv, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Inbal Becker-Reshef, Belén Franch, Bertran Mollà-Bononad, Hendrik Boogaard, Arun Kumar Pratihast, Benjamin Koetz, and Zoltan Szantoi
Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, https://doi.org/10.5194/essd-15-5491-2023, 2023
Short summary
Short summary
WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-432, https://doi.org/10.5194/essd-2023-432, 2023
Revised manuscript accepted for ESSD
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 65 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.
Gerardo E. Soto, Steven Wilcox, Patrick E. Clark, Francesco P. Fava, Nathan M. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher Barrett
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-217, https://doi.org/10.5194/essd-2023-217, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Using machine learning classification and linear unmixing, this paper produced Landsat-based time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground. This dataset represents a first multi-decadal high-resolution dataset specifically designed for mapping and monitoring rangelands health in East Africa including Kenya, Ethiopia, and Somalia, which are dominated by arid and semi-arid extensive rangeland systems.
Francesco N. Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data, 15, 4997–5015, https://doi.org/10.5194/essd-15-4997-2023, https://doi.org/10.5194/essd-15-4997-2023, 2023
Short summary
Short summary
We describe a new dataset of cropland area circa the year 2020, with global coverage and country detail. Data are generated from geospatial information on the agreement characteristics of six high-resolution cropland maps. By helping to highlight features of cropland characteristics and underlying causes for agreement across land cover products, the dataset can be used as a tool to help guide future mapping efforts towards improved agricultural monitoring.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data, 15, 4927–4945, https://doi.org/10.5194/essd-15-4927-2023, https://doi.org/10.5194/essd-15-4927-2023, 2023
Short summary
Short summary
As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data, 15, 4749–4780, https://doi.org/10.5194/essd-15-4749-2023, https://doi.org/10.5194/essd-15-4749-2023, 2023
Short summary
Short summary
Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1 m resolution LC map of China, SinoLC-1, was built. The results showed that SinoLC-1 had an overall accuracy of 73.61 % and conformed to the official survey reports. Comparison with other datasets suggests that SinoLC-1 can be a better support for downstream applications and provide more accurate LC information to users.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José A. Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data, 15, 4713–4747, https://doi.org/10.5194/essd-15-4713-2023, https://doi.org/10.5194/essd-15-4713-2023, 2023
Short summary
Short summary
Historical, fine-grained geospatial datasets on built-up areas are rarely available, constraining studies of urbanization, settlement evolution, or the dynamics of human–environment interactions to recent decades. In order to provide such historical data, we used publicly available cadastral building data for Spain and created a series of gridded surfaces, measuring age, physical, and land-use-related features of the built environment in Spain and the evolution of settlements from 1900 to 2020.
Christopher G. Marston, Aneurin W. O'Neil, R. Daniel Morton, Claire M. Wood, and Clare S. Rowland
Earth Syst. Sci. Data, 15, 4631–4649, https://doi.org/10.5194/essd-15-4631-2023, https://doi.org/10.5194/essd-15-4631-2023, 2023
Short summary
Short summary
The UK Land Cover Map 2021 (LCM2021) is a UK-wide land cover data set, with 21- and 10-class versions. It is intended to support a broad range of UK environmental research, including ecological and hydrological research. LCM2021 was produced by classifying Sentinel-2 satellite imagery. LCM2021 is distributed as a suite of products to facilitate easy use for a range of applications. To support research at different spatial scales it includes 10 m, 25 m and 1 km resolution products.
Yu Zhao, Shaoyu Han, Jie Zheng, Hanyu Xue, Zhenhai Li, Yang Meng, Xuguang Li, Xiaodong Yang, Zhenhong Li, Shuhong Cai, and Guijun Yang
Earth Syst. Sci. Data, 15, 4047–4063, https://doi.org/10.5194/essd-15-4047-2023, https://doi.org/10.5194/essd-15-4047-2023, 2023
Short summary
Short summary
In the present study, we generated a 30 m Chinese winter wheat yield dataset from 2016 to 2021, called ChinaWheatYield30m. The dataset has high spatial resolution and great accuracy. It is the highest-resolution yield dataset known. Such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling or regional climate evaluation.
Feng Yang and Zhenzhong Zeng
Earth Syst. Sci. Data, 15, 4011–4021, https://doi.org/10.5194/essd-15-4011-2023, https://doi.org/10.5194/essd-15-4011-2023, 2023
Short summary
Short summary
We generated a 4.77 m resolution annual tree cover map product for Southeast Asia (SEA) for 2016–2021 using Planet-NICFI and Sentinel-1 imagery. Maps were created with good accuracy and high consistency during 2016–2021. The baseline maps at 4.77 m can be converted to forest cover maps for SEA at various resolutions to meet different users’ needs. Our products can help resolve rounding errors in forest cover mapping by counting isolated trees and monitoring long, narrow forest cover removal.
Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard
Earth Syst. Sci. Data, 15, 3991–4010, https://doi.org/10.5194/essd-15-3991-2023, https://doi.org/10.5194/essd-15-3991-2023, 2023
Short summary
Short summary
The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
Short summary
Short summary
Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Zeping Liu, Hong Tang, Lin Feng, and Siqing Lyu
Earth Syst. Sci. Data, 15, 3547–3572, https://doi.org/10.5194/essd-15-3547-2023, https://doi.org/10.5194/essd-15-3547-2023, 2023
Short summary
Short summary
Large-scale maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In this paper, we propose a deep-learning method for high-resolution BRA mapping (2.5 m) from Sentinel-2 imagery (10 m). The resulting China building rooftop area dataset (CBRA) is the first multi-annual (2016–2021) and high-resolution (2.5 m) BRA dataset in China. Cross-comparisons show that the CBRA achieves the best performance in capturing the spatiotemporal information.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
Short summary
Short summary
Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Charles R. Lane, Ellen D'Amico, Jay R. Christensen, Heather E. Golden, Qiusheng Wu, and Adnan Rajib
Earth Syst. Sci. Data, 15, 2927–2955, https://doi.org/10.5194/essd-15-2927-2023, https://doi.org/10.5194/essd-15-2927-2023, 2023
Short summary
Short summary
Non-floodplain wetlands (NFWs) – wetlands located outside floodplains – confer watershed-scale resilience to hydrological, biogeochemical, and biotic disturbances. Although they are frequently unmapped, we identified ~ 33 million NFWs covering > 16 × 10 km2 across the globe. NFWs constitute the majority of the world's wetlands (53 %). Despite their small size (median 0.039 km2), these imperiled systems have an outsized impact on watershed functions and sustainability and require protection.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
Short summary
Short summary
A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data, 15, 2025–2054, https://doi.org/10.5194/essd-15-2025-2023, https://doi.org/10.5194/essd-15-2025-2023, 2023
Short summary
Short summary
We update a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry from 1850 to 2020. We use data from the latest (2020) Global Forest Resources Assessment, incorporate shifting cultivation, and include improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade from 2011 to 2020 (mean of 0.96 Pg C yr−1), falling below 1.0 Pg C yr−1 for the first time in 30 years.
Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside
Earth Syst. Sci. Data, 15, 1521–1541, https://doi.org/10.5194/essd-15-1521-2023, https://doi.org/10.5194/essd-15-1521-2023, 2023
Short summary
Short summary
Adding plants to roofs of buildings can reduce indoor and outdoor temperatures and so can reduce urban overheating, which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already.
We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.
Cited articles
Badrinarayanan, V., Kendall, A., and Cipolla, R.: Segnet: A deep convolutional
encoder-decoder architecture for image segmentation, IEEE T.
Pattern Anal., 39, 2481–2495,
https://doi.org/10.1109/TPAMI.2016.2644615, 2017. a, b
Cao, Y. and Huang, X.: A deep learning method for building height estimation
using high-resolution multi-view imagery over urban areas: A case study of 42
Chinese cities, Remote Sens. Environ., 264, 112590, https://doi.org/10.1016/j.rse.2021.112590, 2021. a
Chen, B., Tu, Y., Wu, S., Song, Y., Jin, Y., Webster, C., Xu, B., and Gong, P.:
Beyond green environments: multi-scale difference in human exposure to
greenspace in China, Environ. Int., 166, 107348, https://doi.org/10.1016/j.envint.2022.107348,
2022a. a
Chen, B., Wu, S., Song, Y., Webster, C., Xu, B., and Gong, P.: Contrasting
inequality in human exposure to greenspace between cities of Global North and
Global South, Nat. Commun., 13, 1–9, 2022b. a
Chen, J., Cao, X., Peng, S., and Ren, H.: Analysis and applications of
GlobeLand30: a review, ISPRS Int. J. Geo-Inf., 6,
230, https://doi.org/10.3390/ijgi6080230, 2017. a
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.:
Encoder-decoder with atrous separable convolution for semantic image
segmentation, in: Computer Vision – ECCV 2018, edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., Springer International Publishing, Cham, 833–851, https://doi.org/10.1007/978-3-030-01234-2_49, 2018. a
Chen, W., Huang, H., Dong, J., Zhang, Y., Tian, Y., and Yang, Z.: Social
functional mapping of urban green space using remote sensing and social
sensing data, ISPRS J. Photogramm. Remote, 146,
436–452, 2018. a
Daudt, R. C., Saux, B. L., and Boulch, A.: Fully Convolutional Siamese Networks
for Change Detection, in: 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, 4063–4067, https://doi.org/10.1109/ICIP.2018.8451652, 2018. a
Deng, L. and Yu, D.: Deep Learning: Methods and Applications, Foundations &
Trends in Signal Processing, 7, 197–387, 2014. a
De Ridder, K., Adamec, V., Bañuelos, A., Bruse, M., Bürger, M.,
Damsgaard, O., Dufek, J., Hirsch, J., Lefebre, F., Pérez-Lacorzana, J. M., Thierry, A., and Weber,
C.:
An integrated methodology to assess the benefits of urban green space,
Sci. Total Environ., 334, 489–497,
https://doi.org/10.1016/j.scitotenv.2004.04.054, 2004. a
Devlin, J., Chang, M., Lee, K., and Toutanova, K.: BERT: Pre-training of Deep
Bidirectional Transformers for Language Understanding, CoRR, abs/1810.04805,
http://arxiv.org/abs/1810.04805, 2018. a
Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., and
Zisserman, A.: The pascal visual object classes challenge: A retrospective,
Int. J. Comput. Vision, 111, 98–136,
https://doi.org/10.1007/s11263-014-0733-5, 2015. a
Fuller, R. A., Irvine, K. N., Devine-Wright, P., Warren, P. H., and Gaston,
K. J.: Psychological benefits of greenspace increase with biodiversity,
Biol. Lett., 3, 390–394, https://doi.org/10.1098/rsbl.2007.0149,
2007. a
General Office of the State Council, PRC: Guidelines on scientific greening,
https://www.mee.gov.cn/zcwj/gwywj/202106/t20210603_836084.shtml,
last access: 3 June 2021. a
Glorot, X., Bordes, A., and Bengio, Y.: Deep Sparse Rectifier Neural Networks,
J. Mach. Learn. Res., 15, 315–323, 2011. a
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. a
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang,
J., Zhang, W., and Zhou, Y.: Annual maps of global artificial impervious area
(GAIA) between 1985 and 2018, Remote Sens. Environ., 236, 111510,
https://doi.org/10.1016/j.rse.2019.111510, 2020. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image
recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a
Helber, P., Bischke, B., Dengel, A., and Borth, D.: Eurosat: A novel dataset
and deep learning benchmark for land use and land cover classification, IEEE
J. Sel. Top. Appl. Earth Obs., 12, 2217–2226, https://doi.org/10.1109/JSTARS.2019.2918242,
2019. a
Hou, Q., Zhou, D., and Feng, J.: Coordinate Attention for Efficient Mobile
Network Design, in: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), 13708–13717, https://doi.org/10.1109/CVPR46437.2021.01350, 2021. a
Huang, C., Yang, J., Lu, H., Huang, H., and Yu, L.: Green spaces as an
indicator of urban health: evaluating its changes in 28 mega-cities, Remote
Sens., 9, 1266, https://doi.org/10.3390/rs9121266, 2017. a, b
Huang, C., Yang, J., and Jiang, P.: Assessing impacts of urban form on
landscape structure of urban green spaces in China using Landsat images based
on Google Earth Engine, Remote Sens., 10, 1569,
https://doi.org/10.3390/rs10101569, 2018. a
Ioffe, S. and Szegedy, C.: Batch Normalization: Accelerating Deep Network
Training by Reducing Internal Covariate Shift, CoRR, abs/1502.03167,
http://arxiv.org/abs/1502.03167, 2015. a
Jun, C., Ban, Y., and Li, S.: China: Open access to Earth land-cover map,
Nature, 514, 434–434, https://doi.org/10.1038/514434c, 2014. a
Kirillov, A., Wu, Y., He, K., and Girshick, R.: PointRend: Image Segmentation
As Rendering, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), 9796–9805, https://doi.org/10.1109/CVPR42600.2020.00982, 2020. a
Kong, F., Yin, H., James, P., Hutyra, L. R., and He, H. S.: Effects of spatial
pattern of greenspace on urban cooling in a large metropolitan area of
eastern China, Landscape Urban Plan., 128, 35–47,
https://doi.org/10.1016/j.landurbplan.2014.04.018, 2014. a
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: Imagenet classification with
deep convolutional neural networks, Adv. Neur. In., 25, 1097–1105, 2012. a
Kuang, W. and Dou, Y.: Investigating the patterns and dynamics of urban green
space in China's 70 major cities using satellite remote sensing, Remote
Sens., 12, 1929, https://doi.org/10.3390/rs12121929, 2020. a
Kuang, W., Zhang, S., Li, X., and Lu, D.: A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018, Earth Syst. Sci. Data, 13, 63–82, https://doi.org/10.5194/essd-13-63-2021, 2021. a
Li, X., Gong, P., Zhou, Y., Wang, J., Bai, Y., Chen, B., Hu, T., Xiao, Y., Xu,
B., Yang, J., Liu, X., Cai, W., Huang, H., Wu, T., Wang, X., Lin, P., Li, X.,
Chen, J., He, C., Li, X., Yu, L., Clinton, N., and Zhu, Z.: Mapping global
urban boundaries from the global artificial impervious area (GAIA) data,
Environ. Res. Lett., 15, 094044, https://doi.org/10.1088/1748-9326/ab9be3,
2020. a, b, c, d, e
Liao, C., Dai, T., Cai, H., and Zhang, W.: Examining the driving factors
causing rapid urban expansion in china: an analysis based on globeland30
data, ISPRS Int. J. Geo-Inf., 6, 264, https://doi.org/10.3390/ijgi6090264, 2017. a
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F.,
Ghafoorian, M., van der Laak, J. A., van Ginneken, B., and Sánchez,
C. I.: A survey on deep learning in medical image analysis, Medical Image
Analysis, 42, 60–88, https://doi.org/10.1016/j.media.2017.07.005,
2017. a
Liu, M.: liumency/UGS-1m: v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7581694, 2023. a
Liu, M., Shi, Q., Marinoni, A., He, D., Liu, X., and Zhang, L.: Super-Resolution-Based Change
Detection Network With Stacked Attention Module for Images With Different Resolutions,
IEEE T. Geosci. Remote, 60, 4403718,
https://doi.org/10.1109/TGRS.2021.3091758, 2022. a
Liu, P., Liu, X., Liu, M., Shi, Q., Yang, J., Xu, X., and Zhang, Y.: Building
Footprint Extraction from High-Resolution Images via Spatial Residual
Inception Convolutional Neural Network, Remote Sens., 11, 830, https://doi.org/10.3390/rs11070830,
2019. a
Liu, W., Yue, A., Shi, W., Ji, J., and Deng, R.: An Automatic Extraction
Architecture of Urban Green Space Based on DeepLabv3plus Semantic
Segmentation Model, in: 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), 311–315, https://doi.org/10.1109/ICIVC47709.2019.8981007, 2019. a, b
Mathieu, R., Aryal, J., and Chong, A. K.: Object-based classification of Ikonos
imagery for mapping large-scale vegetation communities in urban areas,
Sensors, 7, 2860–2880, https://doi.org/10.3390/s7112860, 2007. a
Ministry of Housing and Urban-Rural Development, PRC: Urban Green Space
Planning Standard (GB/T51346-2019),
https://www.mohurd.gov.cn/gongkai/fdzdgknr/tzgg/201910/20191012_242194.html,
last access: 9 April 2019. a
Ronneberger, O., Fischer, P., and Brox, T.: U-net: Convolutional networks for
biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Springer International Publishing, Cham, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a, b
Schmidt-Traub, G., Kroll, C., Teksoz, K., Durand-Delacre, D., and Sachs, J. D.:
National baselines for the Sustainable Development Goals assessed in the SDG
Index and Dashboards, Nat. Geosci., 10, 547–555, 2017. a
Shi, Q., Liu, M., Li, S., Liu, X., Wang, F., and Zhang, L.: A deeply supervised
attention metric-based network and an open aerial image dataset for remote
sensing change detection, IEEE T. Geosci. Remote, 60, 5604816,
https://doi.org/10.1109/TGRS.2021.3085870, 2021. a
Shi, Q., Liu, M., Marinoni, A., and Liu, X.: UGS-1m: Fine-grained urban green
space mapping of 31 major cities in China based on the deep learning
framework, Science Data Bank [data set], https://doi.org/10.57760/sciencedb.07049, 2023. a, b, c
Sun, J., Wang, X., Chen, A., Ma, Y., Cui, M., and Piao, S.: NDVI indicated
characteristics of vegetation cover change in China's metropolises over the
last three decades, Environ. Monit. A., 179, 1–14,
https://doi.org/10.1007/s10661-010-1715-x, 2011. a
Tatem, A. J.: WorldPop, open data for spatial demography, Sci. Data, 4,
1–4, 2017. a
Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., and Chandraker,
M.: Learning to Adapt Structured Output Space for Semantic Segmentation, in:
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7472–7481, https://doi.org/10.1109/CVPR.2018.00780, 2018. a, b
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,
Kaiser, Ł., and Polosukhin, I.: Attention is all you need, Advances in
neural information processing systems, Curran Associates Inc., Long Beach, California, USA, 6000–6010, https://doi.org/10.5555/3295222.3295349, 2017. a
Woo, S., Park, J., Lee, J.-Y., and Kweon, I. S.: Cbam: Convolutional block
attention module, in: Proceedings of the European conference on computer
vision (ECCV), 3–19, 2018. a
Wu, F., Wang, C., Zhang, H., Li, J., Li, L., Chen, W., and Zhang, B.: Built-up
area mapping in China from GF-3 SAR imagery based on the framework of deep
learning, Remote Sens. Environ., 262, 112515,
https://doi.org/10.1016/j.rse.2021.112515, 2021. a
Wu, Z., Chen, R., Meadows, M. E., Sengupta, D., and Xu, D.: Changing urban
green spaces in Shanghai: Trends, drivers and policy implications, Land use
policy, 87, 104080, https://doi.org/10.1016/j.landusepol.2019.104080, 2019. a
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified Perceptual Parsing for Scene Understanding, in: Computer Vision – ECCV 2018,
edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., Springer International Publishing, Cham, 432–448, https://doi.org/10.1007/978-3-030-01228-1_26, 2018. a
Xu, Z., Zhou, Y., Wang, S., Wang, L., Li, F., Wang, S., and Wang, Z.: A novel
intelligent classification method for urban green space based on
high-resolution remote sensing images, Remote Sens., 12, 3845,
https://doi.org/10.3390/rs12223845, 2020. a
Yang, J., Huang, C., Zhang, Z., and Wang, L.: The temporal trend of urban green
coverage in major Chinese cities between 1990 and 2010, Urban Forestry &
Urban Greening, 13, 19–27, https://doi.org/10.1016/j.ufug.2013.10.002,
2014. a
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and
Sang, N.: BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation, in: Computer Vision – ECCV 2018, edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., Springer International Publishing, Cham, 334–349, https://doi.org/10.1007/978-3-030-01261-8_20, 2018. a
Zhang, B., Xie, G.-D., Li, N., and Wang, S.: Effect of urban green space changes on
the role of rainwater runoff reduction in Beijing, China, Landscape Urban
Plan., 140, 8–16,
https://doi.org/10.1016/j.landurbplan.2015.03.014, 2015. a, b
Zhang, Q., Yang, L. T., Chen, Z., and Li, P.: A survey on deep learning for big
data, Information Fusion, 42, 146–157, 2018. a
Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., and Mi, J.: GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery, Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, 2021.
a, b, c
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia,
J.: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), in: Pyramid Scene Parsing Network, 6230–6239, https://doi.org/10.1109/CVPR.2017.660, 2017. a
Zhao, J., Ouyang, Z., Zheng, H., Zhou, W., Wang, X., Xu, W., and Ni, Y.: Plant
species composition in green spaces within the built-up areas of Beijing,
China, Plant Ecol., 209, 189–204,
https://doi.org/10.1007/s11258-009-9675-3, 2010. a
Zhao, J., Chen, S., Jiang, B., Ren, Y., Wang, H., Vause, J., and Yu, H.:
Temporal trend of green space coverage in China and its relationship with
urbanization over the last two decades, Sci. Total Environ.,
442, 455–465, 2013. a
Zhou, W., Wang, J., Qian, Y., Pickett, S. T., Li, W., and Han, L.: The rapid
but “invisible” changes in urban greenspace: A comparative study of nine
Chinese cities, Sci. Total Environ., 627, 1572–1584,
https://doi.org/10.1016/j.scitotenv.2018.01.335, 2018. a
Zhou, X. and Wang, Y.-C.: Spatial-temporal dynamics of urban green space in
response to rapid urbanization and greening policies, Landscape Urban Plan., 100, 268–277,
https://doi.org/10.1016/j.landurbplan.2010.12.013, 2011. a
Short summary
A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in...
Altmetrics
Final-revised paper
Preprint