Articles | Volume 15, issue 11
https://doi.org/10.5194/essd-15-4749-2023
© 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-15-4749-2023
© 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
| 
30 Oct 2023
Data description paper |
| 30 Oct 2023
| 30 Oct 2023
SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data
Zhuohong Li
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, PR China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, PR China
Mofan Cheng
School of Electronic Information, Wuhan University, Wuhan, 430079, PR China
Jingxin Hu
School of Electronic Information, Wuhan University, Wuhan, 430079, PR China
Guangyi Yang
School of Electronic Information, Wuhan University, Wuhan, 430079, PR China
Hongyan Zhang
CORRESPONDING AUTHOR
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, PR China
School of Computer Science, China University of Geosciences, Wuhan, 430074, PR China
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
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
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
Mapping sugarcane globally at 10 m resolution using GEDI and Sentinel-2
A 28 time-points cropland area change dataset in Northeast China from 1000 to 2020
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
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
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
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
Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data
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Estimating local agricultural gross domestic product (AgGDP) across the world
Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system
Harmonising the land-use flux estimates of global models and national inventories for 2000–2020
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-121, https://doi.org/10.5194/essd-2024-121, 2024
Revised manuscript accepted for ESSD
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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 10m 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 recall of our maps.
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-94, https://doi.org/10.5194/essd-2024-94, 2024
Revised manuscript accepted for ESSD
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One of the major manifestations of global change is the alteration of natural vegetation landscapes by human reclamation. Here we reconstruct a unified set of long-term time series cropland area change datasets with standardized criteria. The cropland in Northeast China exhibited phase changes of expansion-reduction-expansion over the past millennium. Compared to global historical LUCC datasets, our dataset has significant time resolution and reliability.
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
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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
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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
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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
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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
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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
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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
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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).
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
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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
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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
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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
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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.
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
Preprint under review for ESSD
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
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Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
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We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
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Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
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We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
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Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Xiaoyong Li, Hanqin Tian, Chaoqun Lu, and Shufen Pan
Earth Syst. Sci. Data, 15, 1005–1035, https://doi.org/10.5194/essd-15-1005-2023, https://doi.org/10.5194/essd-15-1005-2023, 2023
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We reconstructed land use and land cover (LULC) history for the conterminous United States during 1630–2020 by integrating multi-source data. The results show the widespread expansion of cropland and urban land and the shrinking of natural vegetation in the past four centuries. Forest planting and regeneration accelerated forest recovery since the 1920s. The datasets can be used to assess the LULC impacts on the ecosystem's carbon, nitrogen, and water cycles.
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, https://doi.org/10.5194/essd-15-821-2023, 2023
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The accuracy of regional grassland aboveground biomass (AGB) is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37 000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m2. Our new dataset provides important input parameters for understanding the Qinghai–Tibet Plateau during global climate change.
Huaqing Wu, Jing Zhang, Zhao Zhang, Jichong Han, Juan Cao, Liangliang Zhang, Yuchuan Luo, Qinghang Mei, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 791–808, https://doi.org/10.5194/essd-15-791-2023, https://doi.org/10.5194/essd-15-791-2023, 2023
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High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) for 1995–2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and much improved compared with previous datasets. AsiaRiceYield4km will fill the current data gap and better support agricultural monitoring systems.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-339, https://doi.org/10.5194/essd-2022-339, 2023
Revised manuscript accepted for ESSD
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Existing satellite-based forest maps remain large uncertainties, due to different forest definitions and mapping algorithms. To effectively manage the forest resources, timely and accurate annual forest maps at a high spatial resolution are demanded. This study improved the forest maps by integrating the PALSAR-2 and Landsat images. The annual evergreen and non-evergreen forest type maps were also generated. This critical information supports the Global Forest Resources Assessment.
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023, https://doi.org/10.5194/essd-15-681-2023, 2023
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Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
Cited articles
Audebert, N., Le Saux, B., and Lefèvre, S.: Joint learning from earth observation and openstreetmap data to get faster better semantic maps, In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 21–26 July 2017, Honolulu, HI, USA, 67–75, https://doi.org/10.1109/CVPRW.2017.199, 2017.
Bartholomé, E. and Belward, A. S.: GLC2000: a new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, https://doi.org/10.1080/01431160412331291297, 2007.
Boguszewski, A., Batorski, D., Ziemba-Jankowska, N., Dziedzic, T., and Zambrzycka, A.: LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery, In Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 19–20 June 2022, New Orleans, LA, USA, 1102–1110, https://doi.org/10.1109/cvprw53098.2021.00121, 2020.
Cao, Y., and Huang, X.: A coarse-to-fine weakly supervised learning method for green plastic cover segmentation using high-resolution remote sensing images, ISPRS J. Photogramm. Remote Sens., 188, 157–176, https://doi.org/10.1016/j.isprsjprs.2022.04.012, 2022.
Chang, G. H. and Brada, J. C.: The paradox of China's growing under-urbanization, Econ. Syst., 30, 24–40, https://doi.org/10.1016/j.ecosys.2005.07.002, 2006.
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. Remote Sens., 103, 7–27, https://doi.org/10.1016/j.isprsjprs.2014.09.002, 2015.
Coltri, P. P., Zullo, J., Gonçalves, R. R. do V., Romani, L. A. S., and Pinto, H. S.: Coffee Crop's Biomass and Carbon Stock Estimation With Usage of High Resolution Satellites Images, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 6, 1786–1795, https://doi.org/10.1109/JSTARS.2013.2262767, 2013.
Defourny, P., Vancutsem, C., Bicheron, C., Brockmann, F., Nino, L., Schouten, and Leroy, M.: GLOBECOVER: a 300 m global land cover product for 2005 using Envisat MERIS time series, in: Proceedings of ISPRS Commission VII Mid-Term Symposium: Remote Sensing: from Pixels to Processes, Ensched, the Netherlands, May 2006, 8–11, https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=abeb2ddafabe2b945a9b3d425e68606c2bd92c70 (last access: 4 October 2023), 2007.
Du, S., Du, S., Liu, B., Zhang, X., and Zheng, Z.: Large-scale urban functional zone mapping by integrating remote sensing images and open social data, GISci. Remote Sens., 57, 411–430, https://doi.org/10.1080/15481603.2020.1724707, 2020.
Feng, M. and Li, X.: Land cover mapping toward finer scales, Sci. Bulletin, 65, 1604–1606, https://doi.org/10.1016/j.scib.2020.06.014, 2020.
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.
Friedl, M. A., Sulla-menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X.: Remote Sensing of Environment MODIS Collection 5 global land cover: Algorithm re fi nements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Gómez, C., White, J. C., and Wulder, M. A.: Optical remotely sensed time series data for land cover classification: A review, ISPRS J. Photogramm. Remote Sens., 116, 55–72, https://doi.org/10.1016/j.isprsjprs.2016.03.008, 2016.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., 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., Li, X., and Zhang, W.: 40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing, Sci. Bulletin, 64, 756–763, https://doi.org/10.1016/j.scib.2019.04.024, 2019a.
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., and Yuan, C.: 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. Bulletin, 64, 370–373, https://doi.org/10.1016/j.scib.2019.03.002, 2019b.
Griffiths, P., Nendel, C., and Hostert, P.: Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping, Remote Sens. Environ., 220, 135–151, https://doi.org/10.1016/j.rse.2018.10.031, 2019.
Guan, X., Wei, H., Lu, S., Dai, Q., and Su, H.: Assessment on the urbanization strategy in China: Achievements, challenges and reflections, Habitat Int., 71, 97–109, 2018.
Guo, Z., Shao, X., Xu, Y., Miyazaki, H., Ohira, W., and Shibasaki, R.: Identification of village building via Google Earth images and supervised machine learning methods, Remote Sens., 8, 271, https://doi.org/10.3390/rs8040271, 2016.
Hu, J., Liu, R., Hong, D., Camero, A., Yao, J., Schneider, M., Kurz, F., Segl, K., and Zhu, X. X.: MDAS: a new multimodal benchmark dataset for remote sensing, Earth Syst. Sci. Data, 15, 113–131, https://doi.org/10.5194/essd-15-113-2023, 2023.
Huang, X., Wang, Y., Li, J., Chang, X., Cao, Y., Xie, J., and Gong, J.: High-resolution urban land-cover mapping and landscape analysis of the 42 major cities in China using ZY-3 satellite images, Sci. Bulletin, 65, 1039–1048, https://doi.org/10.1016/j.scib.2020.03.003, 2020.
Jalan, S.: Exploring the potential of object based image analysis for mapping urban land cover, J. Indian Soc. Remote, 40, 507–518, 2012.
Karra, K., Kontgis, C., Statman-weil, Z., Mazzariello, J. C., Mathis, M., and Steven, P.: Global land use/land cover with Sentinel 2 and deep learning, in: 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brusseles, Belgium, 11–16 July 2021, 4704–4707, https://doi.org/10.1109/IGARSS47720.2021.9553499, 2021.
Li, W., Dong, R., Fu, H., Wang, J., Yu, L., and Gong, P.: Integrating Google Earth imagery with Landsat data to improve 30 m resolution land cover mapping, Remote Sens. Environ., 237, 111563, https://doi.org/10.1016/j.rse.2019.111563, 2020.
Li, Z., Lu, F., Zhang, H., Yang, G., and Zhang, L.: Change cross-detection based on label improvements and multi-model fusion for multi-temporal remote sensing images, in: 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brusseles, Belgium, 11–16 July 2021, 2054–2057, https://doi.org/10.1109/IGARSS47720.2021.9553120, 2021.
Li, Z., Zou, J., Lu, F., and Zhang, H.: Multi-Stage Pseudo-Label Iteration Framework for Semi-Supervised Land-Cover Mapping, in: 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 4607–4610, https://doi.org/10.1109/IGARSS46834.2022.9884345, 2022a.
Li, Z., Lu, F., Zhang, H., Tu, L., Li, J., Huang, X., Robinson, C., Malkin, N., Jojic, N., Ghamisi, P., Hansch, R., and Yokoya, N.: The Outcome of the 2021 IEEE GRSS Data Fusion Contest – Track MSD: Multitemporal Semantic Change Detection, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 15, 1643–1655, https://doi.org/10.1109/JSTARS.2022.3144318, 2022b.
Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G., and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, ISPRS J. Photogramm. Remote Sens., 192, 244–267, https://doi.org/10.1016/j.isprsjprs.2022.08.008, 2022c.
Li, Z., Zhang, H., He, W., Chen, M., Hu, J., An, X., Yan, H., and Yang, G.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with the deep learning framework and open-access data, Zenodo [data set], https://doi.org/10.5281/zenodo.7707461, 2023.
Liang-Chieh, C., Zhu, Y., Papandreou, G., Schroff, F., and Hartwig, A.: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, Proc. Eur. Conf. Comput. Vis., 34, 137–143, 2018.
Lin, G. C. S.: The growth and structural change of Chinese cities: a contextual and geographic analysis, Cities, 19, 299–316, https://doi.org/10.1016/S0264-2751(02)00039-2, 2002.
Lin, G. C. S. and Ho, S. P. S.: China's land resources and land-use change: Insights from the 1996 land survey, Land Use Policy, 20, 87–107, https://doi.org/10.1016/S0264-8377(03)00007-3, 2003.
Liu, J., Kuang, W., Zhang, Z., Xu, X., Qin, Y., Ning, J., Zhou, W., Zhang, S., Li, R., and Yan, C.: Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s, J. Geogr. Sci., 24, 195–210, 2014.
Liu, L., Gao, Y., Zhang, X., Chen, X., and Xie, S.: A Dataset of Global Land Cover Validation Samples, Zenodo [data set], https://doi.org/10.5281/ZENODO.3551995, 2019.
Liu, T., Liu, H., and Qi, Y.: Construction land expansion and cultivated land protection in urbanizing China: Insights from national land surveys, 1996–2006, Habitat Int., 46, 13–22, https://doi.org/10.1016/j.habitatint.2014.10.019, 2015.
Liu, Y., Zhong, Y., Ma, A., Zhao, J., and Zhang, L.: Cross-resolution national-scale land-cover mapping based on noisy label learning: A case study of China, Int. J. Appl. Earth Obs., 118, 103265, https://doi.org/10.1016/j.jag.2023.103265, 2023.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., and Yang, L.: Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330, https://doi.org/10.1080/014311600210191, 2010.
Luo, M. and Ji, S.: Cross-spatiotemporal land-cover classification from VHR remote sensing images with deep learning based domain adaptation, ISPRS J. Photogramm. Remote Sens., 191, 105–128, https://doi.org/10.1016/j.isprsjprs.2022.07.011, 2022.
Ma, A., Chen, D., Zhong, Y., Zheng, Z., and Zhang, L.: National-scale greenhouse mapping for high spatial resolution remote sensing imagery using a dense object dual-task deep learning framework: A case study of China, ISPRS J. Photogramm. Remote Sens., 181, 279–294, https://doi.org/10.1016/j.isprsjprs.2021.08.024, 2021.
Malarvizhi, K., Kumar, S. V., and Porchelvan, P.: Use of high resolution Google Earth satellite imagery in landuse map preparation for urban related applications, Proc. Tech., 24, 1835–1842, 2016.
Morton, R. D., Marston, C. G., O'Neil, A. W., and Rowland, C. S.: Land Cover Map 2020 (10 m classified pixels, GB), NERC EDS Environmental Information Data Centre [data set], https://doi.org/10.5285/35c7d0e5-1121-4381-9940-75f7673c98f7, 2021.
Ning, Y., Liu, S., Zhao, S., Liu, M., Gao, H., and Gong, P.: Urban growth rates, trajectories, and multi-dimensional disparities in China, Cities, 126, 103717, https://doi.org/10.1016/j.cities.2022.103717, 2022.
Olofsson, P., Foody, G. M., Stehman, S. V, and Woodcock, C. E.: Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation, Remote Sens. Environ., 129, 122–131, https://doi.org/10.1016/j.rse.2012.10.031, 2013.
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.
Osses, M., Rojas, N., Ibarra, C., Valdebenito, V., Laengle, I., Pantoja, N., Osses, D., Basoa, K., Tolvett, S., Huneeus, N., Gallardo, L., and Gómez, B.: High-resolution spatial-distribution maps of road transport exhaust emissions in Chile, 1990–2020, Earth Syst. Sci. Data, 14, 1359–1376, https://doi.org/10.5194/essd-14-1359-2022, 2022.
Otsu, N.: A threshold selection method from gray-level histograms, IEEE T. Syst. Man. Cyb., 9, 62–66, 1979.
Pengra, B., Long, J., Dahal, D., Stehman, S. V., and Loveland, T. R.: A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30 m continuous field tree cover data, Remote Sens. Environ., 165, 234–248, https://doi.org/10.1016/j.rse.2015.01.018, 2015.
Pilant, A., Endres, K., Rosenbaum, D., and Gundersen, G.: US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance, Remote Sens., 12, 1909, https://doi.org/10.3390/rs12121909, 2020.
Pulighe, G., Baiocchi, V., and Lupia, F.: Horizontal accuracy assessment of very high resolution Google Earth images in the city of Rome, Italy, Int. J. Digit. Earth, 9, 342–362, 2016.
Rahman, A., Aggarwal, S. P., Netzband, M., and Fazal, S.: Monitoring urban sprawl using remote sensing and GIS techniques of a fast growing urban centre, India, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 4, 56–64, 2010.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention, Munich, Germany, 5–9 October 2015, 234–241, https://doi.org/10.1007/978-3-319-24574-4, 2015.
Roy, D. P., Huang, H., Houborg, R., and Martins, V. S.: A global analysis of the temporal availability of PlanetScope high spatial resolution multi-spectral imagery, Remote Sens. Environ., 264, 112586, https://doi.org/10.1016/j.rse.2021.112586, 2021.
Song, W. and Deng, X.: Land-use/land-cover change and ecosystem service provision in China, Sci. Total Environ., 576, 705–719, https://doi.org/10.1016/j.scitotenv.2016.07.078, 2017.
Srivastava, S., Vargas-Muñoz, J. E., and Tuia, D.: Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution, Remote Sens. Environ., 228, 129–143, https://doi.org/10.1016/j.rse.2019.04.014, 2019.
Tong, X.-Y., Xia, G.-S., Lu, Q., Shen, H., Li, S., You, S., and Zhang, L.: Land-cover classification with high-resolution remote sensing images using transferable deep models, Remote Sens. Environ., 237, 111322, https://doi.org/10.1016/j.rse.2019.111322, 2020.
Van De Kerchove, R., Zanaga, D., Keersmaecker, W., Souverijns, N., Wevers, J., Brockmann, C., Grosu, A., Paccini, A., Cartus, O., and Santoro, M.: ESA WorldCover: Global land cover mapping at 10 m resolution for 2020 based on Sentinel-1 and 2 data, AGU Fall Meeting Abstracts, 2021, GC45I-0915, https://ui.adsabs.harvard.edu/abs/2021AGUFMGC45I0915V (last access: 4 October 2023), 2021.
Wang, J., Zheng, Z., Lu, X., and Zhong, Y.: LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation, arXiv [preprint], https://doi.org/10.48550/arXiv.2110.08733, 2021.
Wang, J., Ma, A., Zhong, Y., Zheng, Z., and Zhang, L.: Cross-sensor domain adaptation for high spatial resolution urban land-cover mapping: From airborne to spaceborne imagery, Remote Sens. Environ., 277, 113058, https://doi.org/10.1016/j.rse.2022.113058, 2022.
Wickham, J., Stehman, S. V, Sorenson, D. G., Gass, L., and Dewitz, J. A.: Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States, Remote Sens. Environ., 257, 112357, https://doi.org/10.1016/j.rse.2021.112357, 2021.
Xia, J., Yokoya, N., Adriano, B., and Broni-Bediako, C.: OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 2–7 January 2023, 6254–6264, https://doi.org/10.1109/WACV56688.2023.00619, 2023.
Yang, J. and Huang, X.: The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019, Earth Syst. Sci. Data, 13, 3907–3925, https://doi.org/10.5194/essd-13-3907-2021, 2021.
Yang, Y., Wu, T., Wang, S., and Li, H.: Fractional evergreen forest cover mapping by MODIS time-series FEVC-CV methods at sub-pixel scales, ISPRS J. Photogramm. Remote Sens., 163, 272–283, https://doi.org/10.1016/j.isprsjprs.2020.03.012, 2020.
Yue, T. X., Fan, Z. M., and Liu, J. Y.: Scenarios of land cover in China, Global Planet. Change, 55, 317–342, https://doi.org/10.1016/j.gloplacha.2006.10.002, 2007.
Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., and Atkinson, P. M.: An object-based convolutional neural network (OCNN) for urban land use classification, Remote Sens. Environ., 216, 57–70, https://doi.org/10.1016/j.rse.2018.06.034, 2018.
Zhang, C., Yue, P., Tapete, D., Jiang, L., Shangguan, B., Huang, L., and Liu, G.: A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images, ISPRS J. Photogramm. Remote Sens., 166, 183–200, https://doi.org/10.1016/j.isprsjprs.2020.06.003, 2020.
Zhang, J. and Zhang, Y.: Remote sensing research issues of the National Land Use Change Program of China, ISPRS J. Photogramm. Remote Sens., 62, 461–472, https://doi.org/10.1016/j.isprsjprs.2007.07.002, 2007.
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.
Zhao, Y., Gong, P., Yu, L., Hu, L., Li, X., Li, C., Zhang, H., Zheng, Y., Wang, J., Zhao, Y., Cheng, Q., Liu, C., Liu, S., and Wang, X.: Towards a common validation sample set for global land-cover mapping, Int. J. Remote Sens., 35, 4795–4814, https://doi.org/10.1080/01431161.2014.930202, 2014.
Zhong, Y., Su, Y., Wu, S., Zheng, Z., Zhao, J., Ma, A., Zhu, Q., Ye, R., Li, X., Pellikka, P., and Zhang, L.: Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities, Remote Sens. Environ., 247, 111838, https://doi.org/10.1016/j.rse.2020.111838, 2020.
Zhu, Q., Lei, Y., Sun, X., Guan, Q., Zhong, Y., Zhang, L., and Li, D.: Knowledge-guided land pattern depiction for urban land use mapping: A case study of Chinese cities, Remote Sens. Environ., 272, 112916, https://doi.org/10.1016/j.rse.2022.112916, 2022.
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.
Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable...
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