Articles | Volume 12, issue 3
https://doi.org/10.5194/essd-12-1625-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-1625-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Jul 2020
Data description paper |
| 15 Jul 2020
| 15 Jul 2020
Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform
Xiao Zhang
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
Changshan Wu
Department of Geography, University of Wisconsin-Milwaukee,
Milwaukee, WI, USA
Xidong Chen
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
Yuan Gao
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
College of Geomatics, Xi'an University of Science and Technology,
Xi'an 710054, China
Shuai Xie
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
Bing Zhang
State Key Laboratory of Remote Sensing Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
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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.
Charles 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 Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-172, https://doi.org/10.5194/essd-2021-172, 2023
Revised manuscript accepted for ESSD
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NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 4 million km2 in Alaska and northwestern Canada during 2017, 2018, and 2019. This paper summarizes those results and gives 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.
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.
Lingcheng Li, Gautam Bisht, Dalei Hao, and Lai-Yung Ruby Leung
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-242, https://doi.org/10.5194/essd-2023-242, 2023
Revised manuscript accepted for ESSD
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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.
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.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Peng Gong, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-190, https://doi.org/10.5194/essd-2023-190, 2023
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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. China’s total cropland area expanded by 30,300 km2 (1.79 %) during the study period, with significant expansions observed in the northwest but substantial losses along the eastern coastal region. Our fine-resolution cropland maps offer valuable information for diverse applications and decision makings in the future.
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.
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.
Qian Shi, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu
Earth Syst. Sci. Data, 15, 555–577, https://doi.org/10.5194/essd-15-555-2023, https://doi.org/10.5194/essd-15-555-2023, 2023
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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.
Raphaël d'Andrimont, Martin Claverie, Pieter Kempeneers, Davide Muraro, Momchil Yordanov, Devis Peressutti, Matej Batič, and François Waldner
Earth Syst. Sci. Data, 15, 317–329, https://doi.org/10.5194/essd-15-317-2023, https://doi.org/10.5194/essd-15-317-2023, 2023
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AI4boundaries is an open AI-ready data set to map field boundaries with Sentinel-2 and aerial photography provided with harmonised labels covering seven countries and 2.5 M parcels in Europe.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
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An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023, https://doi.org/10.5194/essd-15-133-2023, 2023
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Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 15, 113–131, https://doi.org/10.5194/essd-15-113-2023, https://doi.org/10.5194/essd-15-113-2023, 2023
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Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.
Furong Li, Marie-José Gaillard, Xianyong Cao, Ulrike Herzschuh, Shinya Sugita, Jian Ni, Yan Zhao, Chengbang An, Xiaozhong Huang, Yu Li, Hongyan Liu, Aizhi Sun, and Yifeng Yao
Earth Syst. Sci. Data, 15, 95–112, https://doi.org/10.5194/essd-15-95-2023, https://doi.org/10.5194/essd-15-95-2023, 2023
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The objective of this study is present the first gridded and temporally continuous quantitative plant-cover reconstruction for temperate and northern subtropical China over the last 12 millennia. The reconstructions are based on 94 pollen records and include estimates for 27 plant taxa, 10 plant functional types, and 3 land-cover types. The dataset is suitable for palaeoclimate modelling and the evaluation of simulated past vegetation cover and anthropogenic land-cover change from models.
Jose Luis Gómez-Dans, Philip Edward Lewis, Feng Yin, Kofi Asare, Patrick Lamptey, Kenneth Kobina Yedu Aidoo, Dilys Sefakor MacCarthy, Hongyuan Ma, Qingling Wu, Martin Addi, Stephen Aboagye-Ntow, Caroline Edinam Doe, Rahaman Alhassan, Isaac Kankam-Boadu, Jianxi Huang, and Xuecao Li
Earth Syst. Sci. Data, 14, 5387–5410, https://doi.org/10.5194/essd-14-5387-2022, https://doi.org/10.5194/essd-14-5387-2022, 2022
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We provide a data set to support mapping croplands in smallholder landscapes in Ghana. The data set contains information on crop location on three agroecological zones for 2 years, temporal series of measurements of leaf area index and leaf chlorophyll concentration for maize canopies and yield. We demonstrate the use of these data to validate cropland masks, create a maize mask using satellite data and explore the relationship between satellite measurements and yield.
Zhen Yu, Jing Liu, and Giri Kattel
Earth Syst. Sci. Data, 14, 5179–5194, https://doi.org/10.5194/essd-14-5179-2022, https://doi.org/10.5194/essd-14-5179-2022, 2022
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We developed a 5 km annual nitrogen (N) fertilizer use dataset in China, covering the period from 1952 to 2018. We found that previous FAO-data-based N fertilizer products overestimated the N use in low, but underestimated in high, cropland coverage areas in China. The new dataset has improved the spatial distribution and corrected the existing biases, which is beneficial for biogeochemical cycle simulations in China, such as the assessment of greenhouse gas emissions and food production.
Femke van Geffen, Birgit Heim, Frederic Brieger, Rongwei Geng, Iuliia A. Shevtsova, Luise Schulte, Simone M. Stuenzi, Nadine Bernhardt, Elena I. Troeva, Luidmila A. Pestryakova, Evgenii S. Zakharov, Bringfried Pflug, Ulrike Herzschuh, and Stefan Kruse
Earth Syst. Sci. Data, 14, 4967–4994, https://doi.org/10.5194/essd-14-4967-2022, https://doi.org/10.5194/essd-14-4967-2022, 2022
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SiDroForest is an attempt to remedy data scarcity regarding vegetation data in the circumpolar region, whilst providing adjusted and labeled data for machine learning and upscaling practices. SiDroForest contains four datasets that include SfM point clouds, individually labeled trees, synthetic tree crowns and labeled Sentinel-2 patches that provide insights into the vegetation composition and forest structure of two important vegetation transition zones in Siberia, Russia.
Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, https://doi.org/10.5194/essd-14-4551-2022, 2022
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Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
Raphaël d'Andrimont, Momchil Yordanov, Laura Martinez-Sanchez, Peter Haub, Oliver Buck, Carsten Haub, Beatrice Eiselt, and Marijn van der Velde
Earth Syst. Sci. Data, 14, 4463–4472, https://doi.org/10.5194/essd-14-4463-2022, https://doi.org/10.5194/essd-14-4463-2022, 2022
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Between 2006 and 2018, 875 661 LUCAS cover (i.e. close-up) photos were taken over a systematic sample of the European Union. This geo-located photo dataset has been curated and is being made available along with the surveyed label data, including land cover and plant species.
Cited articles
Bai, Y., Feng, M., Jiang, H., Wang, J., and Liu, Y.: Validation of Land
Cover Maps in China Using a Sampling-Based Labeling Approach, Remote
Sens., 7, 10589–10606, https://doi.org/10.3390/rs70810589, 2015.
Ban, Y., Jacob, A., and Gamba, P.: Spaceborne SAR data for global urban
mapping at 30 m resolution using a robust urban extractor, ISPRS J. Photogramm., 103, 28–37,
https://doi.org/10.1016/j.isprsjprs.2014.08.004, 2015.
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review
of applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011,
2016.
Bennett, M. M. and Smith, L. C.: Advances in using multitemporal night-time
lights satellite imagery to detect, estimate, and monitor socioeconomic
dynamics, Remote Sens. Environ., 192, 176–197,
https://doi.org/10.1016/j.rse.2017.01.005, 2017.
Berger, M., Moreno, J., Johannessen, J. A., Levelt, P. F., and Hanssen, R.
F.: ESA's sentinel missions in support of Earth system science, Remote Sens. Environ., 120, 84–90,
https://doi.org/10.1016/j.rse.2011.07.023, 2012.
Bontemps, S., Defourny, P., Van Bogaert, E., Arino, O., Kalogirou, V., and
Perez, J. R.: GLOBCOVER 2009-Products description and validation report,
available at: http://due.esrin.esa.int/files/GLOBCOVER2009_Validation_Report_2.2.pdf (last access: 8 July 2020), 2011.
Brown de Colstoun, E. C., Huang, C., Wang, P., Tilton, J. C., Tan, B.,
Phillips, J., Niemczura, S., Ling, P.-Y., and Wolfe, R. E.: Global Man-made
Impervious Surface (GMIS) Dataset From Landsat. NASA Socioeconomic Data and
Applications Center (SEDAC), Palisades, NY,
https://doi.org/10.7927/H4P55KKF, 2017.
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G.,
Peng, S., Lu, M., Zhang, W., Tong, X., and Mills, J.: Global land cover
mapping at 30 m resolution: A POK-based operational approach, ISPRS J. Photogramm., 103, 7–27,
https://doi.org/10.1016/j.isprsjprs.2014.09.002, 2015.
Chen, X., Cao, X., Liao, A., Chen, L., Peng, S., Lu, M., Chen, J., Zhang,
W., Zhang, H., and Han, G.: Global mapping of artificial surfaces at 30 m
resolution, Sci. China Earth Sci., 59, 2295–2306,
https://doi.org/10.1007/s11430-016-5291-y, 2016.
Clarke, K. C., Hoppen, S., and Gaydos, L.: A self-modifying cellular
automaton model of historical urbanization in the San Francisco Bay area,
Environ. Plann. B, 24, 247–261,
https://doi.org/10.1068/b240247, 1997.
Deng, C. and Wu, C.: BCI: A biophysical composition index for remote sensing
of urban environments, Remote Sens. Environ., 127, 247–259,
https://doi.org/10.1016/j.rse.2012.09.009, 2012.
Didan, K., Munoz, A. B., Solano, R., and Huete, A.: MODIS vegetation index
user's guide (MOD13 series), Vegetation Index and Phenology Lab, The
University of Arizona, 1–38, https://doi.org/10.5067/MODIS/MYD13Q1.006,
2015.
Du, P., Samat, A., Waske, B., Liu, S., and Li, Z.: Random Forest and
Rotation Forest for fully polarized SAR image classification using
polarimetric and spatial features, ISPRS J. Photogramm., 105, 38–53, https://doi.org/10.1016/j.isprsjprs.2015.03.002,
2015.
Elvidge, C. D., Tuttle, B. T., Sutton, P. C., Baugh, K. E., Howard, A. T.,
Milesi, C., Bhaduri, B. L., and Nemani, R.: Global Distribution and Density
of Constructed Impervious Surfaces, Sensors, 7, 1962–1979,
https://doi.org/10.3390/s7091962, 2007.
Elvidge, C. D., Baugh, K., Zhizhin, M., Feng, C. H., and Ghosh, T.: VIIRS
night-time lights, Int. J. Remote Sens., 38, 5860–5879,
https://doi.org/10.1080/01431161.2017.1342050, 2017.
ESA: Sentinel-1 SAR User Guide Introduction, availabe at:
https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar (last access: 26 December 2019), 2016.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004,
https://doi.org/10.1029/2005rg000183, 2007.
Florczyk, A., Corbane, C., Ehrlich, D., Freire, S., Kemper, T., Maffenini,
L., Melchiorri, M., Pesaresi, M., Politis, P., and Schiavina, M.: GHSL Data
Package 2019, Luxembourg, EUR, 29788, https://doi.org/10.2760/290498, 2019.
Foody, G. M. and Mathur, A.: Toward intelligent training of supervised image
classifications: directing training data acquisition for SVM classification,
Remote Sens. Environ., 93, 107–117,
https://doi.org/10.1016/j.rse.2004.06.017, 2004.
Fu, P. and Weng, Q.: A time series analysis of urbanization induced land use
and land cover change and its impact on land surface temperature with
Landsat imagery, Remote Sens. Environ., 175, 205–214,
https://doi.org/10.1016/j.rse.2015.12.040, 2016.
Gao, F., Colstoun, E. B. d., Ma, R., Weng, Q., Masek, J. G., Chen, J., Pan,
Y., and Song, C.: Mapping impervious surface expansion using
medium-resolution satellite image time series: a case study in the Yangtze
River Delta, China, Int. J. Remote Sens., 33, 7609–7628,
https://doi.org/10.1080/01431161.2012.700424, 2012.
Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R.: Random Forests
for land cover classification, Pattern Recogn. Lett., 27, 294–300,
https://doi.org/10.1016/j.patrec.2005.08.011, 2006.
Goldblatt, R., Stuhlmacher, M. F., Tellman, B., Clinton, N., Hanson, G.,
Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A. K., Cheng,
W.-H., and Balling, R. C.: Using Landsat and nighttime lights for supervised
pixel-based image classification of urban land cover, Remote Sens. Environ., 205, 253–275, https://doi.org/10.1016/j.rse.2017.11.026, 2018.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang,
X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X.,
Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng,
Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu,
X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C.,
Clinton, N., Zhu, Z., Chen, J., and Chen, J.: Finer resolution observation
and monitoring of global land cover: first mapping results with Landsat TM
and ETM+ data, Int. J. Remote Sens., 34, 2607–2654,
https://doi.org/10.1080/01431161.2012.748992, 2013.
Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., Clinton, N., Ji,
L., Li, W., Bai, Y., Chen, B., Xu, B., Zhu, Z., Yuan, C., Ping Suen, H.,
Guo, J., Xu, N., Li, W., Zhao, Y., Yang, J., Yu, C., Wang, X., Fu, H., Yu,
L., Dronova, I., Hui, F., Cheng, X., Shi, X., Xiao, F., Liu, Q., and Song,
L.: Stable classification with limited sample: transferring a 30 m
resolution sample set collected in 2015 to mapping 10 m resolution global
land cover in 2017, Sci. Bull., 64, 370–373,
https://doi.org/10.1016/j.scib.2019.03.002, 2019.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27,
https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Hansen, M. C., Egorov, A., Potapov, P. V., Stehman, S. V., Tyukavina, A.,
Turubanova, S. A., Roy, D. P., Goetz, S. J., Loveland, T. R., Ju, J.,
Kommareddy, A., Kovalskyy, V., Forsyth, C., and Bents, T.: Monitoring
conterminous United States (CONUS) land cover change with Web-Enabled
Landsat Data (WELD), Remote Sens. Environ., 140, 466–484,
https://doi.org/10.1016/j.rse.2013.08.014, 2014.
Homer, C., Huang, C., Yang, L., Wylie, B., and Coan, M.: Development of a
2001 national land-cover database for the United States, Photogramm. Eng.
Rem. S., 70, 829–840,
https://doi.org/10.14358/PERS.70.7.829, 2004.
Homer, C., Dewitz, J., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston,
J., Herold, N., Wickham, J., and Megown, K.: Completion of the 2011 National
Land Cover Database for the conterminous United States–representing a
decade of land cover change information, Photogramm. Eng.
Rem. S., 81, 345–354, https://doi.org/10.1016/S0099-1112(15)30100-2,
2015.
Hu, Y., Liu, L., Liu, L., Peng, D., Jiao, Q., and Zhang, H.: A Landsat-5
atmospheric correction based on MODIS atmosphere products and 6S model, IEEE J. Sel. Top. Appl.,
7, 1609–1615, https://doi.org/10.1109/JSTARS.2013.2290028, 2014.
Huang, X., Schneider, A., and Friedl, M. A.: Mapping sub-pixel urban
expansion in China using MODIS and DMSP/OLS nighttime lights, Remote Sens. Environ., 175, 92–108, https://doi.org/10.1016/j.rse.2015.12.042,
2016.
Im, J., Lu, Z., Rhee, J., and Quackenbush, L. J.: Impervious surface
quantification using a synthesis of artificial immune networks and
decision/regression trees from multi-sensor data, Remote Sens. Environ., 117, 102–113, https://doi.org/10.1016/j.rse.2011.06.024, 2012.
Jokar Arsanjani, J., Tayyebi, A., and Vaz, E.: GlobeLand30 as an alternative
fine-scale global land cover map: Challenges, possibilities, and
implications for developing countries, Habitat Int., 55, 25–31,
https://doi.org/10.1016/j.habitatint.2016.02.003, 2016.
Langanke, T., Moran, A., Dulleck, B., and Schleicher, C.: Copernicus Land
Monitoring Service–High Resolution Layer Water and Wetness Product
Specifications Document, Copernicus team at EEA, 2016.
Li, C., Peng, G., Wang, J., Zhu, Z., Biging, G. S., Yuan, C., Hu, T., Zhang,
H., Wang, Q., and Li, X.: The first all-season sample set for mapping global
land cover with Landsat-8 data, Sci. Bull., 62, 508–515,
https://doi.org/10.1016/j.scib.2017.03.011, 2017.
Li, X. and Zhou, Y.: Urban mapping using DMSP/OLS stable night-time light: a
review, Int. J. Remote Sens., 38, 6030–6046,
https://doi.org/10.1080/01431161.2016.1274451, 2017.
Li, X., Gong, P., and Liang, L.: A 30-year (1984–2013) record of annual
urban dynamics of Beijing City derived from Landsat data, Remote Sens. Environ., 166, 78–90, https://doi.org/10.1016/j.rse.2015.06.007, 2015.
Li, X., Zhou, Y., Zhu, Z., Liang, L., Yu, B., and Cao, W.: Mapping annual
urban dynamics (1985–2015) using time series of Landsat data, Remote Sens. Environ., 216, 674–683,
https://doi.org/10.1016/j.rse.2018.07.030, 2018.
Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., and Wang, S.:
High-resolution multi-temporal mapping of global urban land using Landsat
images based on the Google Earth Engine Platform, Remote Sens. Environ., 209, 227–239, https://doi.org/10.1016/j.rse.2018.02.055, 2018.
Lu, D. and Weng, Q.: Use of impervious surface in urban land-use
classification, Remote Sens. Environ., 102, 146–160,
https://doi.org/10.1016/j.rse.2006.02.010, 2006.
Massey, R., Sankey, T. T., Yadav, K., Congalton, R. G., and Tilton, J. C.:
Integrating cloud-based workflows in continental-scale cropland extent
classification, Remote Sens. Environ., 219, 162–179,
https://doi.org/10.1016/j.rse.2018.10.013, 2018.
Okujeni, A., van der Linden, S., Tits, L., Somers, B., and Hostert, P.:
Support vector regression and synthetically mixed training data for
quantifying urban land cover, Remote Sens. Environ., 137, 184–197,
https://doi.org/10.1016/j.rse.2013.06.007, 2013.
Okujeni, A., Canters, F., Cooper, S. D., Degerickx, J., Heiden, U., Hostert,
P., Priem, F., Roberts, D. A., Somers, B., and van der Linden, S.:
Generalizing machine learning regression models using multi-site spectral
libraries for mapping vegetation-impervious-soil fractions across multiple
cities, Remote Sens. Environ., 216, 482–496,
https://doi.org/10.1016/j.rse.2018.07.011, 2018.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and
Wulder, M. A.: Good practices for estimating area and assessing accuracy of
land change, Remote Sens. Environ., 148, 42–57,
https://doi.org/10.1016/j.rse.2014.02.015, 2014.
Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540,
418–422, https://doi.org/10.1038/nature20584, 2016.
Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Freire, S., Halkia, M.,
Julea, A., Kemper, T., Soille, P., and Syrris, V.: Operating procedure for
the production of the Global Human Settlement Layer from Landsat data of the
epochs 1975, 1990, 2000, and 2014, Publications Office of the European
Union, 1–62, https://doi.org/10.2788/253582, 2016.
Pflugmacher, D., Cohen, W. B., Kennedy, R. E., and Yang, Z.: Using
Landsat-derived disturbance and recovery history and lidar to map forest
biomass dynamics, Remote Sens. Environ., 151, 124–137,
https://doi.org/10.1016/j.rse.2013.05.033, 2014.
Radoux, J., Lamarche, C., Van Bogaert, E., Bontemps, S., Brockmann, C., and
Defourny, P.: Automated training sample extraction for global land cover
mapping, Remote Sens., 6, 3965–3987, https://doi.org/10.3390/rs6053965,
2014.
Ridd, M. K.: Exploring a V-I-S (vegetation-impervious surface-soil) model
for urban ecosystem analysis through remote sensing: comparative anatomy for
cities, Int. J. Remote Sens., 16, 2165–2185,
https://doi.org/10.1080/01431169508954549, 1995.
Rodriguez-Galiano, V. F., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P.
M., and Jeganathan, C.: Random Forest classification of Mediterranean land
cover using multi-seasonal imagery and multi-seasonal texture, Remote Sens. Environ., 121, 93–107,
https://doi.org/10.1016/j.rse.2011.12.003, 2012.
Schneider, A., Friedl, M. A., and Potere, D.: A new map of global urban
extent from MODIS satellite data, Environ. Res. Lett., 4, 044003,
https://doi.org/10.1088/1748-9326/4/4/044003, 2009.
Schneider, A., Friedl, M. A., and Potere, D.: Mapping global urban areas
using MODIS 500 m data: New methods and datasets based on “urban
ecoregions”, Remote Sens. Environ., 114, 1733–1746,
https://doi.org/10.1016/j.rse.2010.03.003, 2010.
Schug, F., Okujeni, A., Hauer, J., Hostert, P., Nielsen, J. Ø., and van
der Linden, S.: Mapping patterns of urban development in Ouagadougou,
Burkina Faso, using machine learning regression modeling with bi-seasonal
Landsat time series, Remote Sens. Environ., 210, 217–228,
https://doi.org/10.1016/j.rse.2018.03.022, 2018.
Shaban, M. and Dikshit, O.: Improvement of classification in urban areas by
the use of textural features: the case study of Lucknow city, Uttar Pradesh,
Int. J. Remote Sens., 22, 565–593,
https://doi.org/10.1080/01431160050505865, 2001.
Shao, Z., Fu, H., Fu, P., and Yin, L.: Mapping Urban Impervious Surface by
Fusing Optical and SAR Data at the Decision Level, Remote Sens., 8, 945,
https://doi.org/10.3390/rs8110945, 2016.
Sun, G., Kong, Y., Jia, X., Zhang, A., Rong, J., and Ma, H.: Synergistic Use
of Optical and Dual-Polarized SAR Data With Multiple Kernel Learning for
Urban Impervious Surface Mapping, IEEE J. Sel. Top. Appl., 12, 223–236,
https://doi.org/10.1109/jstars.2018.2883654, 2019.
Sun, Z., Wang, C., Guo, H., and Shang, R.: A Modified Normalized Difference
Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat
Imagery, Remote Sens., 9, 942, https://doi.org/10.3390/rs9090942, 2017.
Sun, Z., Xu, R., Du, W., Wang, L., and Lu, D.: High-Resolution Urban Land
Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine,
Remote Sens., 11, 752, https://doi.org/10.3390/rs11070752, 2019.
Tachikawa, T., Kaku, M., Iwasaki, A., Gesch, D. B., Oimoen, M. J., Zhang,
Z., Danielson, J., Krieger, T., Curtis, B., Haase, J., Abrams, M., and Carabajal, C.: ASTER Global
Digital Elevation Model Version 2 – Summary of validation results, available at: https://pubs.er.usgs.gov/publication/70005960 (last access: 8 July 2020), 2011.
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E.,
Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I. N., Deghaye, P.,
Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L'Abbate, M., Croci, R.,
Pietropaolo, A., Huchler, M., and Rostan, F.: GMES Sentinel-1 mission,
Remote Sens. Environ., 120, 9–24,
https://doi.org/10.1016/j.rse.2011.05.028, 2012.
USGS: Landsat surface reflectance data, Reston, VA, Report 2015-3034,
2015.
USGS: Landsat 8 surface reflectance code (LaSRC) product, available
at:
https://www.usgs.gov/media/files/land-surface-reflectance-code-lasrc-product-guide (last access: 8 July 2020), 2018.
Vermote, E., Justice, C., Claverie, M., and Franch, B.: Preliminary analysis
of the performance of the Landsat 8/OLI land surface reflectance product,
Remote Sens. Environ., 185, 46–56,
https://doi.org/10.1016/j.rse.2016.04.008, 2016.
Wang, P., Huang, C., Brown de Colstoun, E., Tilton, J., and Tan, B.: Global
human built-up and settlement extent (HBASE) dataset from Landsat, NASA
Socioeconomic Data and Applications Center (SEDAC): Palisades, NY, USA,
https://doi.org/10.7927/H4DN434S, 2017a.
Wang, P., Huang, C., Tilton, J., Tan, B., and Brown de Colstoun, E.: HOTEX:
An approach for global mapping of human built-up and settlement extent, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017,
1562–1565, https://doi.org/10.1109/IGARSS.2017.8127268, 2017b.
Wang, Y., Liu, L., Hu, Y., Li, D., and Li, Z.: Development and validation of
the Landsat-8 surface reflectance products using a MODIS-based per-pixel
atmospheric correction method, Int. J. Remote Sens., 37,
1291–1314, https://doi.org/10.1080/01431161.2015.1104742, 2016.
Weng, Q.: A remote sensing-GIS evaluation of urban expansion and its impact
on surface temperature in the Zhujiang Delta, China, Int. J. Remote Sens., 22, 1999–2014, https://doi.org/10.1080/713860788, 2001.
Weng, Q.: Remote sensing of impervious surfaces in the urban areas:
Requirements, methods, and trends, Remote Sens. Environ., 117,
34–49, https://doi.org/10.1016/j.rse.2011.02.030, 2012.
Wetherley, E. B., Roberts, D. A., and McFadden, J. P.: Mapping spectrally
similar urban materials at sub-pixel scales, Remote Sens. Environ.,
195, 170–183, https://doi.org/10.1016/j.rse.2017.04.013, 2017.
Wu, C.: Normalized spectral mixture analysis for monitoring urban
composition using ETM+ imagery, Remote Sens. Environ., 93,
480–492, https://doi.org/10.1016/j.rse.2004.08.003, 2004.
Wu, C. and Murray, A. T.: Estimating impervious surface distribution by
spectral mixture analysis, Remote Sens. Environ., 84, 493–505,
https://doi.org/10.1016/s0034-4257(02)00136-0, 2003.
Xie, Y. and Weng, Q.: Spatiotemporally enhancing time-series DMSP/OLS
nighttime light imagery for assessing large-scale urban dynamics, ISPRS J. Photogramm., 128, 1–15,
https://doi.org/10.1016/j.isprsjprs.2017.03.003, 2017.
Xu, H.: Analysis of Impervious Surface and its Impact on Urban Heat
Environment using the Normalized Difference Impervious Surface Index
(NDISI), Photogramm. Eng. Rem. S., 76, 557–565,
https://doi.org/10.14358/pers.76.5.557, 2010.
Yang, J. and He, Y.: Automated mapping of impervious surfaces in urban and
suburban areas: Linear spectral unmixing of high spatial resolution imagery,
Int. J. Appl. Earth Obs., 54,
53–64, https://doi.org/10.1016/j.jag.2016.09.006, 2017.
Yang, Y., Xiao, P., Feng, X., and Li, H.: Accuracy assessment of seven
global land cover datasets over China, ISPRS J. Photogramm., 125, 156–173,
https://doi.org/10.1016/j.isprsjprs.2017.01.016, 2017.
Zhang, H., Zhang, Y., and Lin, H.: A comparison study of impervious surfaces
estimation using optical and SAR remote sensing images, Int. J. Appl. Earth Obs., 18, 148–156,
https://doi.org/10.1016/j.jag.2011.12.015, 2012.
Zhang, H., Zhang, Y., and Hui, L.: Seasonal effects of impervious surface
estimation in subtropical monsoon regions, Int. J. Digit.
Earth, 7, 746–760, https://doi.org/10.1080/17538947.2013.781241, 2014.
Zhang, H., Lin, H., Li, Y., Zhang, Y., and Fang, C.: Mapping urban
impervious surface with dual-polarimetric SAR data: An improved method,
Landscape Urban Plan., 151, 55–63,
https://doi.org/10.1016/j.landurbplan.2016.03.009, 2016.
Zhang, H., Lin, H., and Wang, Y.: A new scheme for urban impervious surface
classification from SAR images, ISPRS J. Photogramm., 139, 103–118, https://doi.org/10.1016/j.isprsjprs.2018.03.007,
2018.
Zhang, H. K. and Roy, D. P.: Using the 500 m MODIS land cover product to
derive a consistent continental scale 3 m Landsat land cover
classification, Remote Sens. Environ., 197, 15–34,
https://doi.org/10.1016/j.rse.2017.05.024, 2017.
Zhang, L. and Weng, Q.: Annual dynamics of impervious surface in the Pearl
River Delta, China, from 1988 to 2013, using time series Landsat imagery,
ISPRS J. Photogramm., 113, 86–96,
https://doi.org/10.1016/j.isprsjprs.2016.01.003, 2016.
Zhang, L., Zhang, M., and Yao, Y.: Mapping seasonal impervious surface
dynamics in Wuhan urban agglomeration, China from 2000 to 2016,
Int. J. Appl. Earth Obs., 70,
51–61, https://doi.org/10.1016/j.jag.2018.04.005, 2018.
Zhang, X. and Liu, L.: Development of a global 30 m impervious surface map
using multi-source and multi-temporal remote sensing datasets with the
Google Earth Engine platform, Zenodo, https://doi.org/10.5281/zenodo.3505079, 2019.
Zhang, X., Liu, L., Wang, Y., Hu, Y., and Zhang, B.: A SPECLib-based
operational classification approach: A preliminary test on China land cover
mapping at 30 m, Int. J. Appl. Earth Obs., 71, 83–94, https://doi.org/10.1016/j.jag.2018.05.006, 2018.
Zhang, X., Liu, L., Chen, X., Xie, S., and Gao, Y.: Fine Land-Cover Mapping
in China Using Landsat Datacube and an Operational SPECLib-Based Approach,
Remote Sens., 11, 1056, https://doi.org/10.3390/rs11091056, 2019.
Zhang, Y., Zhang, H., and Lin, H.: Improving the impervious surface
estimation with combined use of optical and SAR remote sensing images,
Remote Sens. Environ., 141, 155–167,
https://doi.org/10.1016/j.rse.2013.10.028, 2014.
Zhu, Z., Gallant, A. L., Woodcock, C. E., Pengra, B., Olofsson, P.,
Loveland, T. R., Jin, S., Dahal, D., Yang, L., and Auch, R. F.: Optimizing
selection of training and auxiliary data for operational land cover
classification for the LCMAP initiative, ISPRS J. Photogramm., 122, 206–221,
https://doi.org/10.1016/j.isprsjprs.2016.11.004, 2016.
Zhu, Z., Woodcock, C. E., Rogan, J., and Kellndorfer, J.: Assessment of
spectral, polarimetric, temporal, and spatial dimensions for urban and
peri-urban land cover classification using Landsat and SAR data, Remote Sens. Environ., 117, 72–82,
https://doi.org/10.1016/j.rse.2011.07.020, 2012.
Zhuo, L., Shi, Q., Tao, H., Zheng, J., and Li, Q.: An improved temporal
mixture analysis unmixing method for estimating impervious surface area
based on MODIS and DMSP-OLS data, ISPRS J. Photogramm., 142, 64–77, https://doi.org/10.1016/j.isprsjprs.2018.05.016, 2018.
Zhou, T., Zhao, M., Sun, C., and Pan, J.: Exploring the Impact of
Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and
GF-1 WFV Images in a Subtropical Monsoon-Climate Region, ISPRS Int. Geo-Inf., 7, 3, https://doi.org/10.3390/ijgi7010003, 2017.
Short summary
The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
The amount of impervious surface is an important indicator in the monitoring of the intensity of...
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