Articles | Volume 14, issue 8
https://doi.org/10.5194/essd-14-3835-2022
© Author(s) 2022. 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-14-3835-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
29 Aug 2022
Data description paper |
| 29 Aug 2022
| 29 Aug 2022
A global map of local climate zones to support earth system modelling and urban-scale environmental science
Matthias Demuzere
CORRESPONDING AUTHOR
Urban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum, Germany
Jonas Kittner
Urban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum, Germany
Alberto Martilli
Environmental Department, CIEMAT, Madrid, Spain
Gerald Mills
School of Geography, University College Dublin, Dublin, Ireland
Christian Moede
Urban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum, Germany
Iain D. Stewart
Global Cities Institute, University of Toronto, Toronto, Ontario, Canada
Jasper van Vliet
Institute for Environmental Studies, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081, HV, Amsterdam, the Netherlands
Benjamin Bechtel
Urban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum, Germany
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Jeroen Claessen, Annalisa Molini, Brecht Martens, Matteo Detto, Matthias Demuzere, and Diego G. Miralles
Biogeosciences, 16, 4851–4874, https://doi.org/10.5194/bg-16-4851-2019, https://doi.org/10.5194/bg-16-4851-2019, 2019
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Zongbo Shi, Tuan Vu, Simone Kotthaus, Roy M. Harrison, Sue Grimmond, Siyao Yue, Tong Zhu, James Lee, Yiqun Han, Matthias Demuzere, Rachel E. Dunmore, Lujie Ren, Di Liu, Yuanlin Wang, Oliver Wild, James Allan, W. Joe Acton, Janet Barlow, Benjamin Barratt, David Beddows, William J. Bloss, Giulia Calzolai, David Carruthers, David C. Carslaw, Queenie Chan, Lia Chatzidiakou, Yang Chen, Leigh Crilley, Hugh Coe, Tie Dai, Ruth Doherty, Fengkui Duan, Pingqing Fu, Baozhu Ge, Maofa Ge, Daobo Guan, Jacqueline F. Hamilton, Kebin He, Mathew Heal, Dwayne Heard, C. Nicholas Hewitt, Michael Hollaway, Min Hu, Dongsheng Ji, Xujiang Jiang, Rod Jones, Markus Kalberer, Frank J. Kelly, Louisa Kramer, Ben Langford, Chun Lin, Alastair C. Lewis, Jie Li, Weijun Li, Huan Liu, Junfeng Liu, Miranda Loh, Keding Lu, Franco Lucarelli, Graham Mann, Gordon McFiggans, Mark R. Miller, Graham Mills, Paul Monk, Eiko Nemitz, Fionna O'Connor, Bin Ouyang, Paul I. Palmer, Carl Percival, Olalekan Popoola, Claire Reeves, Andrew R. Rickard, Longyi Shao, Guangyu Shi, Dominick Spracklen, David Stevenson, Yele Sun, Zhiwei Sun, Shu Tao, Shengrui Tong, Qingqing Wang, Wenhua Wang, Xinming Wang, Xuejun Wang, Zifang Wang, Lianfang Wei, Lisa Whalley, Xuefang Wu, Zhijun Wu, Pinhua Xie, Fumo Yang, Qiang Zhang, Yanli Zhang, Yuanhang Zhang, and Mei Zheng
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Hendrik Wouters, Matthias Demuzere, Ulrich Blahak, Krzysztof Fortuniak, Bino Maiheu, Johan Camps, Daniël Tielemans, and Nicole P. M. van Lipzig
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B. Bechtel, M. Pesaresi, L. See, G. Mills, J. Ching, P. J. Alexander, J. J. Feddema, A. J. Florczyk, and I. Stewart
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B. Bechtel
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 243–250, https://doi.org/10.5194/isprs-archives-XLI-B8-243-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-243-2016, 2016
O. Conrad, B. Bechtel, M. Bock, H. Dietrich, E. Fischer, L. Gerlitz, J. Wehberg, V. Wichmann, and J. Böhner
Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, https://doi.org/10.5194/gmd-8-1991-2015, 2015
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The System for Automated Geoscientific Analyses (SAGA) is a comprehensive and globally established open source geographic information system (GIS) for scientific analysis and modeling. The current version 2.1.4 offers more than 700 tools that represent the broad scopes of SAGA in numerous fields of geoscientific endeavor. In this paper, we inform about the system’s architecture and functionality and highlight the wide spectrum of scientific applications of SAGA in a review of published studies.
H. Wouters, K. De Ridder, M. Demuzere, D. Lauwaet, and N. P. M. van Lipzig
Atmos. Chem. Phys., 13, 8525–8541, https://doi.org/10.5194/acp-13-8525-2013, https://doi.org/10.5194/acp-13-8525-2013, 2013
Related subject area
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Annual high-resolution grazing intensity maps on the Qinghai-Tibet Plateau from 1990 to 2020
Mapping Rangeland Health Indicators in East Africa from 2000 to 2022
A new cropland area database by country circa 2020
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach
SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data
HISDAC-ES: historical settlement data compilation for Spain (1900–2020)
LCM2021 – the UK Land Cover Map 2021
ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
Refined fine-scale mapping of tree cover using time series of Planet-NICFI and Sentinel-1 imagery for Southeast Asia (2016–2021)
High-resolution global map of closed-canopy coconut palm
High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe
Global urban fractional changes at a 1 km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs)
China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery
High-resolution distribution maps of single-season rice in China from 2017 to 2022
Mapping global non-floodplain wetlands
An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach
Annual emissions of carbon from land use, land-use change, and forestry from 1850 to 2020
An open-source automatic survey of green roofs in London using segmentation of aerial imagery
Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data, 16, 4971–4994, https://doi.org/10.5194/essd-16-4971-2024, https://doi.org/10.5194/essd-16-4971-2024, 2024
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We reconstructed a cropland area change dataset in Northeast China over the past millennium by integrating multisource data with a unified standard using the historical and archaeological record, statistical yearbook, and national land survey. Cropland in Northeast China exhibited phases of expansion–reduction–expansion over the past millennium. This dataset can be used for improving the land use and land cover change (LUCC) dataset and assessing LUCC-induced carbon emission and climate change.
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data, 16, 4931–4947, https://doi.org/10.5194/essd-16-4931-2024, https://doi.org/10.5194/essd-16-4931-2024, 2024
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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 10 m global dataset of sugarcane maps spanning 2019–2022. Comparisons with field data, pre-existing maps, and official government statistics all indicate the high precision and high recall of our maps.
Jie Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Geli Zhang, Xuebin Yang, Xiaocui Wu, Chandrashekhar Biradar, and Yang Hu
Earth Syst. Sci. Data, 16, 4619–4639, https://doi.org/10.5194/essd-16-4619-2024, https://doi.org/10.5194/essd-16-4619-2024, 2024
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Existing satellite-based forest maps have large uncertainties due to different forest definitions and mapping algorithms. To effectively manage forest resources, timely and accurate annual forest maps at a high spatial resolution are needed. This study improved forest maps by integrating PALSAR-2 and Landsat images. Annual evergreen and non-evergreen forest-type maps were also generated. This critical information supports the Global Forest Resources Assessment.
Xin Zhao, Kazuya Nishina, Haruka Izumisawa, Yuji Masutomi, Seima Osako, and Shuhei Yamamoto
Earth Syst. Sci. Data, 16, 3893–3911, https://doi.org/10.5194/essd-16-3893-2024, https://doi.org/10.5194/essd-16-3893-2024, 2024
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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.
Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-274, https://doi.org/10.5194/essd-2024-274, 2024
Revised manuscript accepted for ESSD
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The national scale, continuous maps of arithmetic mean height and weighted mean height across China address the challenges of accurately estimating forest stand mean height using a tree-based approach. These maps produced in this study provide critical datasets for forest sustainable management in China, including climate change mitigation (e.g., terrestrial carbon estimation), forest ecosystem assessment, and forest inventory practices.
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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The FPA FOD-Attributes dataset provides > 300 biological, physical, social, and administrative attributes associated with > 2.3×106 wildfire incidents across the US from 1992 to 2020. The dataset can be used to (1) answer numerous questions about the covariates associated with human- and lightning-caused wildfires and (2) support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-217, https://doi.org/10.5194/essd-2024-217, 2024
Revised manuscript accepted for ESSD
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Given the limited coverage or spatial resolution of existing datasets, we develop the first global building height map (3D-GloBFP) at the building footprint scale using Earth observation datasets and advanced machine learning techniques. Our map reveals the complex 3-D morphology of the world's building heights at a finer scale and provides reliable results (i.e., R2: 0.66–0.96, RMSEs: 1.9 m–14.6 m) over global regions 3D-GloBFP has great potential to support both macro- and micro-urban analysis
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
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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.
Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, and Peng Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-178, https://doi.org/10.5194/essd-2024-178, 2024
Revised manuscript accepted for ESSD
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The inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses challenges to large-scale land monitoring. We developed a global, 30-m, 23-year (2000–2022), and daily Seamless Data Cube (SDC) of surface reflectance based on Landsat 5,7,8,9 and MODIS products. The SDC exhibits enhanced capabilities for monitoring land cover changes and robust consistency in both spatial and temporal dimensions, which are important for global environmental monitoring.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
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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.
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-157, https://doi.org/10.5194/essd-2024-157, 2024
Revised manuscript accepted for ESSD
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This study provides a 10-m global oil palm extent layer for 2021 and a 30-m oil palm planting year layer from 1990 to 2021. The oil palm extent layer was produced using a convolutional neural network that identified industrial and smallholder plantations using Sentinel-1 data. The oil palm planting year was developed using a methodology specifically designed to detect the early stages of oil palm development in the Landsat time series.
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.
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).
Raphaël d'Andrimont, Momchil Yordanov, Fernando Sedano, Astrid Verhegghen, Peter Strobl, Savvas Zachariadis, Flavia Camilleri, Alessandra Palmieri, Beatrice Eiselt, Jose Miguel Rubio Iglesias, and Marijn van der Velde
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-494, https://doi.org/10.5194/essd-2023-494, 2024
Revised manuscript accepted for ESSD
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LUCAS 2022 Copernicus is a large an systematic in-situ field survey of 137,966 polygons over the EU in 2022. The data holds 82 land cover classes and 40 land use classes.
Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III
Earth Syst. Sci. Data, 16, 321–336, https://doi.org/10.5194/essd-16-321-2024, https://doi.org/10.5194/essd-16-321-2024, 2024
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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.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-432, https://doi.org/10.5194/essd-2023-432, 2023
Revised manuscript accepted for ESSD
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This study proposed the Winter-Triticeae Crops Index (WTCI)，which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 65 countries worldwide, with an overall accuracy of 87.7 %. The first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 were further produced based on the WTCI method. The product can serve as an important basis for agricultural applications.
Jia Zhou, Jin Niu, Ning Wu, and Tao Lu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-403, https://doi.org/10.5194/essd-2023-403, 2023
Revised manuscript accepted for ESSD
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The study provided an annual 100-meter resolution glimpse into the grazing activities across the Qinghai-Tibet Plateau. The newly minted Gridded Dataset of Grazing Intensity (GDGI) not only boasts exceptional accuracy but also acts as a pivotal resource for further research and strategic planning, with the potential to shape sustainable grazing practices, guide informed environmental stewardship, and ensure the longevity of the region’s precious ecosystems.
Gerardo E. Soto, Steven Wilcox, Patrick E. Clark, Francesco P. Fava, Nathan M. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher Barrett
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-217, https://doi.org/10.5194/essd-2023-217, 2023
Revised manuscript accepted for ESSD
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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.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data, 15, 4749–4780, https://doi.org/10.5194/essd-15-4749-2023, https://doi.org/10.5194/essd-15-4749-2023, 2023
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Nowadays, a very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1 m resolution LC map of China, SinoLC-1, was built. The results showed that SinoLC-1 had an overall accuracy of 73.61 % and conformed to the official survey reports. Comparison with other datasets suggests that SinoLC-1 can be a better support for downstream applications and provide more accurate LC information to users.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José A. Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data, 15, 4713–4747, https://doi.org/10.5194/essd-15-4713-2023, https://doi.org/10.5194/essd-15-4713-2023, 2023
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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.
Cited articles
Abascal, A., Rothwell, N., Shonowo, A., Thomson, D. R., Elias, P., Elsey, H.,
Yeboah, G., and Kuffer, M.: “Domains of deprivation framework” for
mapping slums, informal settlements, and other deprived areas in LMICs to
improve urban planning and policy: A scoping review, Comput. Enviro.
Urban, 93, 101770, https://doi.org/10.1016/j.compenvurbsys.2022.101770,
2022. a
Alexander, P., Bechtel, B., Chow, W., Fealy, R., and Mills, G.: Linking urban
climate classification with an urban energy and water budget model:
Multi-site and multi-seasonal evaluation, Urban Climate, 17, 196–215,
https://doi.org/10.1016/j.uclim.2016.08.003, 2016. a
Alexander, P. J., Mills, G., and Fealy, R.: Using LCZ data to run an urban
energy balance model, Urban Climate, 13, 14–37,
https://doi.org/10.1016/j.uclim.2015.05.001, 2015. a
Aminipouri, M., Knudby, A. J., Krayenhoff, E. S., Zickfeld, K., and Middel, A.:
Modelling the impact of increased street tree cover on mean radiant
temperature across Vancouver's local climate zones, Urban For. Urban
Gree., 39, 9–17, https://doi.org/10.1016/j.ufug.2019.01.016, 2019. a
Assarkhaniki, Z., Sabri, S., and Rajabifard, A.: Using open data to detect the
structure and pattern of informal settlements: an outset to support inclusive
SDGs' achievement, Big Earth Data, 5, 497–526,
https://doi.org/10.1080/20964471.2021.1948178, 2021. a
Bai, X., Dawson, R. J., Ürge-Vorsatz, D., Delgado, G. C., Barau, A. S.,
Dhakal, S., Dodman, D., Leonardsen, L., Masson-Delmotte, V., Roberts, D., and
Schultz, S.: Six research priorities for cities, Nature, 555, 23–25,
https://doi.org/10.1038/d41586-018-02409-z, 2018. a, b
Baklanov, A., Grimmond, C., Carlson, D., Terblanche, D., Tang, X., Bouchet, V.,
Lee, B., Langendijk, G., Kolli, R., and Hovsepyan, A.: From urban
meteorology, climate and environment research to integrated city services,
Urban Climate, 23, 330–341, https://doi.org/10.1016/j.uclim.2017.05.004, 2018. a
Bande, L., Manandhar, P., Ghazal, R., and Marpu, P.: Characterization of Local
Climate Zones Using ENVI-met and Site Data in the City of Al-Ain, UAE,
International Journal of Sustainable Development and Planning, 15, 751–760,
https://doi.org/10.18280/ijsdp.150517, 2020. a
Barlow, J. F.: Progress in observing and modelling the urban boundary layer,
Urban Climate, 10, 216–240, https://doi.org/10.1016/j.uclim.2014.03.011, 2014. a
Bauer, P., Stevens, B., and Hazeleger, W.: A digital twin of Earth for the
green transition, Nat. Clim. Change, 11,
https://doi.org/10.1038/s41558-021-00986-y, 2021. a
Bechtel, B. and Daneke, C.: Classification of local climate zones based on
multiple earth observation data, IEEE J. Sel. Top. Appl., 5, 1191–1202,
https://doi.org/10.1109/JSTARS.2012.2189873, 2012. a
Bechtel, B., Alexander, P., Böhner, J., Ching, J., Conrad, O., Feddema,
J., Mills, G., See, L., and Stewart, I.: Mapping Local Climate Zones for a
Worldwide Database of the Form and Function of Cities, ISPRS International
Journal of Geo-Information, 4, 199–219, https://doi.org/10.3390/ijgi4010199, 2015. a, b, c, d, e
Bechtel, B., Demuzere, M., Sismanidis, P., Fenner, D., Brousse, O., Beck, C.,
Van Coillie, F., Conrad, O., Keramitsoglou, I., Middel, A., Mills, G.,
Niyogi, D., Otto, M., See, L., and Verdonck, M.-L.: Quality of Crowdsourced
Data on Urban Morphology—The Human Influence Experiment (HUMINEX), Urban
Science, 1, 15, https://doi.org/10.3390/urbansci1020015, 2017. a, b, c, d
Bechtel, B., Alexander, P. J., Beck, C., Böhner, J., Brousse, O., Ching,
J., Demuzere, M., Fonte, C., Gál, T., Hidalgo, J., Hoffmann, P.,
Middel, A., Mills, G., Ren, C., See, L., Sismanidis, P., Verdonck, M.-L., Xu,
G., and Xu, Y.: Generating WUDAPT Level 0 data – Current status of
production and evaluation, Urban Climate, 27, 24–45,
https://doi.org/10.1016/j.uclim.2018.10.001, 2019a. a
Bechtel, B., Demuzere, M., Mills, G., Zhan, W., Sismanidis, P., Small, C., and
Voogt, J.: SUHI analysis using Local Climate Zones – A comparison of 50
cities, Urban Climate, 28, 100451, https://doi.org/10.1016/j.uclim.2019.01.005,
2019b. a
Bechtel, B., Demuzere, M., and Stewart, I. D.: A Weighted Accuracy Measure for
Land Cover Mapping: Comment on Johnson et al. Local Climate Zone (LCZ) Map
Accuracy Assessments Should Account for Land Cover Physical Characteristics
that Affect the Local Thermal Environment,
Remote Sens., 12, 1769, https://doi.org/10.3390/rs12111769, 2020. a, b
Beck, C., Straub, A., Breitner, S., Cyrys, J., Philipp, A., Rathmann, J.,
Schneider, A., Wolf, K., and Jacobeit, J.: Air temperature characteristics
of local climate zones in the Augsburg urban area (Bavaria, southern Germany)
under varying synoptic conditions, Urban Climate, 25, 152–166,
https://doi.org/10.1016/j.uclim.2018.04.007, 2018. a
Benjamin, K., Luo, Z., and Wang, X.: Crowdsourcing Urban Air Temperature Data
for Estimating Urban Heat Island and Building Heating/Cooling Load in
London, Energies, 14, 5208, https://doi.org/10.3390/en14165208, 2021. a
Bettencourt, L. and West, G.: A unified theory of urban living, Nature, 467,
912–913, https://doi.org/10.1038/467912a, 2010. a
Bettencourt, L. M., Lobo, J., Helbing, D., Kühnert, C., and West, G. B.:
Growth, innovation, scaling, and the pace of life in cities, P. Natl. Acad. Sci. USA, 104,
7301–7306, https://doi.org/10.1073/pnas.0610172104, 2007. a
Bettencourt, L. M., Yang, V. C., Lobo, J., Kempes, C. P., Rybski, D., and
Hamilton, M. J.: The interpretation of urban scaling analysis in time,
J. R. Soc. Interface, 17, 1–9, https://doi.org/10.1098/rsif.2019.0846,
2020. a
Biljecki, F., Chew, L. Z. X., Milojevic-Dupont, N., and Creutzig, F.: Open
government geospatial data on buildings for planning sustainable and
resilient cities, arXiv [preprint], https://doi.org/10.48550/arXiv.2107.04023, 2021. a
Bokwa, A., Geletič, J., Lehnert, M., Žuvela-Aloise, M.,
Hollósi, B., Gál, T., Skarbit, N., Dobrovolný, P., Hajto,
M. J., Kielar, R., Walawender, J. P., Štastný, P., Holec, J.,
Ostapowicz, K., Burianová, J., and Garaj, M.: Heat load assessment in
Central European cities using an urban climate model and observational
monitoring data, Energ. Buildings, 201, 53–69,
https://doi.org/10.1016/j.enbuild.2019.07.023, 2019. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a, b
Brousse, O., Martilli, A., Foley, M., Mills, G., and Bechtel, B.: WUDAPT, an
efficient land use producing data tool for mesoscale models? Integration of
urban LCZ in WRF over Madrid, Urban Climate, 17, 116–134,
https://doi.org/10.1016/j.uclim.2016.04.001, 2016. a
Brousse, O., Georganos, S., Demuzere, M., Vanhuysse, S., Wouters, H., Wolff,
E., Linard, C., and Lipzig, N. P. V.: Urban Climate Using Local Climate
Zones in Sub-Saharan Africa to tackle urban health issues, Urban Climate,
27, 227–242, https://doi.org/10.1016/j.uclim.2018.12.004, 2019. a, b, c
Brousse, O., Georganos, S., Demuzere, M., Dujardin, S., Lennert, M., Linard,
C., Snow, R. W., Thiery, W., and van Lipzig, N. P. M.: Can we use local
climate zones for predicting malaria prevalence across sub-Saharan African
cities?, Environ. Res. Lett., 15, 124051,
https://doi.org/10.1088/1748-9326/abc996, 2020a. a, b, c, d, e
Brousse, O., Wouters, H., Demuzere, M., Thiery, W., Van de Walle, J., and
Lipzig, N. P. M.: The local climate impact of an African city during
clear‐sky conditions – Implications of the recent urbanization in Kampala
(Uganda), Int. J. Climatol., 40, 4586–4608,
https://doi.org/10.1002/joc.6477, 2020b. a, b
Brousse, O., Simpson, C., Walker, N., Fenner, D., Meier, F., Taylor, J., and
Heaviside, C.: Evidence of horizontal urban heat advection in London using
six years of data from a citizen weather station network, Environ.
Res. Lett., 17, 044041, https://doi.org/10.1088/1748-9326/ac5c0f, 2022. a
Buchhorn, M., Lesiv, M., Tsendbazar, N. E., Herold, M., Bertels, L., and Smets,
B.: Copernicus global land cover layers-collection 2, Remote Sens., 12,
1–14, https://doi.org/10.3390/rs12061044, 2020a. a, b
Buchhorn, M., Smets, B., Bertels, L., Roo, B. D., Lesiv, M., Tsendbazar, N.-E.,
Herold, M., and Fritz, S.: Copernicus Global Land Service: Land Cover 100m:
collection 3: epoch 2018: Globe, Zenodo [data set], https://doi.org/10.5281/zenodo.3518038,
2020b. a, b
Chen, C., Bagan, H., Xie, X., La, Y., and Yamagata, Y.: Combination of
sentinel-2 and palsar-2 for local climate zone classification: A case study
of nanchang, China, Remote Sens., 13, 1–21, https://doi.org/10.3390/rs13101902,
2021a. a, b
Chen, G., Xie, J., Li, W., Li, X., Hay Chung, L. C., Ren, C., and Liu, X.:
Future “local climate zon” spatial change simulation in Greater Bay
Area under the shared socioeconomic pathways and ecological control line,
Build. Environ., 203, 108077,
https://doi.org/10.1016/j.buildenv.2021.108077, 2021b. a
Chieppa, J., Bush, A., and Mitra, C.: Using `Local Climate Zones” to
detect urban heat island on two small cities in Alabama, Earth Interact.,
22, 1–22, https://doi.org/10.1175/EI-D-17-0020.1, 2018. a
Chinchor, N.: MUC-4 evaluation metrics, in: Proceedings of the 4th conference
on Message understanding – MUC4 '92, Association for Computational
Linguistics, Morristown, NJ, USA, p. 22, https://doi.org/10.3115/1072064.1072067, 1992. a, b
Ching, J., Mills, G., Bechtel, B., See, L., Feddema, J., Wang, X., Ren, C.,
Brousse, O., Martilli, A., Neophytou, M., Mouzourides, P., Stewart, I.,
Hanna, A., Ng, E., Foley, M., Alexander, P., Aliaga, D., Niyogi, D.,
Shreevastava, A., Bhalachandran, P., Masson, V., Hidalgo, J., Fung, J.,
Andrade, M., Baklanov, A., Dai, W., Milcinski, G., Demuzere, M., Brunsell,
N., Pesaresi, M., Miao, S., Mu, Q., Chen, F., and Theeuwes, N.: WUDAPT: An
Urban Weather, Climate, and Environmental Modeling Infrastructure for the
Anthropocene, B. Am. Meteorol. Soc., 99,
1907–1924, https://doi.org/10.1175/BAMS-D-16-0236.1, 2018. a, b, c, d, e, f, g, h
Ching, J., Aliaga, D., Mills, G., Masson, V., See, L., Neophytou, M., Middel,
A., Baklanov, A., Ren, C., Ng, E., Fung, J., Wong, M., Huang, Y., Martilli,
A., Brousse, O., Stewart, I., Zhang, X., Shehata, A., Miao, S., Wang, X.,
Wang, W., Yamagata, Y., Duarte, D., Li, Y., Feddema, J., Bechtel, B.,
Hidalgo, J., Roustan, Y., Kim, Y., Simon, H., Kropp, T., Bruse, M., Lindberg,
F., Grimmond, S., Demuzure, M., Chen, F., Li, C., Gonzales-Cruz, J.,
Bornstein, B., He, Q., Tzu-Ping, Hanna, A., Erell, E., Tapper, N., Mall, R.,
and Niyogi, D.: Pathway using WUDAPT's Digital Synthetic City tool towards
generating urban canopy parameters for multi-scale urban atmospheric
modeling, Urban Climate, 28, 100459, https://doi.org/10.1016/j.uclim.2019.100459,
2019. a, b, c, d
Conners, R. W., Trivedi, M. M., and Harlow, C. A.: Segmentation of a
high-resolution urban scene using texture operators (Sunnyvale,
California), Comput. Vision Graph., 25, 273–310,
https://doi.org/10.1016/0734-189X(84)90197-X, 1984. a
Coops, N. C. and Wulder, M. A.: Breaking the Habit(at), Trend. Ecol.
Evol., 34, 585–587, https://doi.org/10.1016/j.tree.2019.04.013, 2019. a
Corbane, C., Pesaresi, M., Politis, P., Syrris, V., Florczyk, A. J., Soille,
P., Maffenini, L., Burger, A., Vasilev, V., Rodriguez, D., Sabo, F.,
Dijkstra, L., and Kemper, T.: Big earth data analytics on Sentinel-1 and
Landsat imagery in support to global human settlements mapping, Big Earth
Data, 1, 118–144, https://doi.org/10.1080/20964471.2017.1397899, 2017. a
Corbane, C., Pesaresi, M., Kemper, T., Politis, P., Florczyk, A. J., Syrris,
V., Melchiorri, M., Sabo, F., and Soille, P.: Automated global delineation
of human settlements from 40 years of Landsat satellite data archives, Big
Earth Data, 3, 140–169, https://doi.org/10.1080/20964471.2019.1625528, 2019. a
Corbane, C., Politis, P., Kempeneers, P., Simonetti, D., Soille, P., Burger,
A., Pesaresi, M., Sabo, F., Syrris, V., and Kemper, T.: A global cloud free
pixel- based image composite from Sentinel-2 data, Data in Brief, 31,
105737, https://doi.org/10.1016/j.dib.2020.105737, 2020. a
Corbane, C., Syrris, V., Sabo, F., Politis, P., Melchiorri, M., Pesaresi, M.,
Soille, P., and Kemper, T.: Convolutional neural networks for global human
settlements mapping from Sentinel-2 satellite imagery, Neural Comput.
Appl., 33, 6697–6720, https://doi.org/10.1007/s00521-020-05449-7, 2021. a, b, c, d, e, f, g
Costello, A., Abbas, M., Allen, A., Ball, S., Bell, S., Bellamy, R., Friel, S.,
Groce, N., Johnson, A., Kett, M., Lee, M., Levy, C., Maslin, M., McCoy, D.,
McGuire, B., Montgomery, H., Napier, D., Pagel, C., Patel, J., de Oliveira,
J. A. P., Redclift, N., Rees, H., Rogger, D., Scott, J., Stephenson, J.,
Twigg, J., Wolff, J., and Patterson, C.: Managing the health effects of
climate change, Lancet, 373, 1693–1733, https://doi.org/10.1016/s0140-6736(09)60935-1, 2009. a
Creutzig, F., Baiocchi, G., Bierkandt, R., Pichler, P.-P., and Seto, K. C.:
Global typology of urban energy use and potentials for an urbanization
mitigation wedge, P. Natl. Acad. Sci. USA, 112, 6283–6288,
201315545, https://doi.org/10.1073/pnas.1315545112, 2015. a
Creutzig, F., Agoston, P., Minx, J. C., Canadell, J. G., Andrew, R. M.,
Quéré, C. L., Peters, G. P., Sharifi, A., Yamagata, Y., and
Dhakal, S.: Urban infrastructure choices structure climate solutions,
Nat. Clim. Change, 6, 1054, https://doi.org/10.1038/nclimate3169, 2016. a
Creutzig, F., Lohrey, S., Bai, X., Baklanov, A., Dawson, R., Dhakal, S., Lamb,
W. F., McPhearson, T., Minx, J., Munoz, E., and Walsh, B.: Upscaling urban
data science for global climate solutions, Global Sustain., 2, 1–25,
https://doi.org/10.1017/sus.2018.16, 2019. a
D'Amour, C. B., Reitsma, F., Baiocchi, G., Barthel, S., Güneralp, B.,
Erb, K. H., Haberl, H., Creutzig, F., and Seto, K. C.: Future urban land
expansion and implications for global croplands, P. Natl. Acad. Sci. USA, 114, 8939–8944,
https://doi.org/10.1073/pnas.1606036114, 2017. a
Demuzere, M., Orru, K., Heidrich, O., Olazabal, E., Geneletti, D., Orru, H.,
Bhave, A. G., Mittal, N., Feliu, E., and Faehnle, M.: Mitigating and
adapting to climate change: Multi-functional and multi-scale assessment of
green urban infrastructure, J. Environ. Manage., 146,
107–115, https://doi.org/10.1016/j.jenvman.2014.07.025, 2014. a
Demuzere, M., Hankey, S., Mills, G., Zhang, W., Lu, T., and Bechtel, B.:
Combining expert and crowd-sourced training data to map urban form and
functions for the continental US, Nature Scientific Data, 7, 1–13,
https://doi.org/10.1038/s41597-020-00605-z, 2020a. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Demuzere, M., Mihara, T., Redivo, C. P., Feddema, J., and Setton, E.:
Multi-temporal LCZ maps for Canadian functional urban areas, OSF Preprints,
https://doi.org/10.31219/osf.io/h5tm6, 2020b. a
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D.,
van Vliet, J., and Bechtel, B.: Global map of Local Climate Zones, Zenodo [data set],
https://doi.org/10.5281/zenodo.6364594, 2022a. a, b
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D.,
van Vliet, J., and Bechtel, B.: Teaser Local Climate Zone maps extracted
from the global map of Local Climate Zones, Zenodo [data set], https://doi.org/10.5281/zenodo.6364705,
2022b. a
Demuzere, M., Argüeso, D., Zonato, A., and Kittner, J.: W2W: A Python package that injects WUDAPT's Local Climate Zone information in WRF (v0.4.1), Zenodo [code], https://doi.org/10.5281/zenodo.7016607, 2022c. a
Di Gregorio, A. and Leonardi, U.: Land cover classification system – user manual software version 3, https://www.fao.org/3/i5428e/i5428e.pdf (last access: 23 August 2022), 2016. a
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John,
J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E.,
Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K. A., Dupuis, C.,
Durachta, J., Dussin, R., Gauthier, P. P., Griffies, S. M., Guo, H.,
Hallberg, R. W., Harrison, M., He, J., Hurlin, W., McHugh, C., Menzel, R.,
Milly, P. C., Nikonov, S., Paynter, D. J., Ploshay, J., Radhakrishnan, A.,
Rand, K., Reichl, B. G., Robinson, T., Schwarzkopf, D. M., Sentman, L. T.,
Underwood, S., Vahlenkamp, H., Winton, M., Wittenberg, A. T., Wyman, B.,
Zeng, Y., and Zhao, M.: The GFDL Earth System Model Version 4.1 (GFDL-ESM
4.1): Overall Coupled Model Description and Simulation Characteristics,
J. Adv. Model. Earth Sy., 12, 1–56,
https://doi.org/10.1029/2019MS002015, 2020. a
Eldesoky, A. H., Gil, J., and Pont, M. B.: The suitability of the urban local
climate zone classification scheme for surface temperature studies in
distinct macroclimate regions, Urban Climate, 37, 100823,
https://doi.org/10.1016/j.uclim.2021.100823, 2021. a
ESA: Land Cover CCI Product User Guide Version 2.0, Tech. rep., European
Space Agency, http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 23 August 2022), 2017. a
Esch, T., Heldens, W., Hirne, A., Keil, M., Marconcini, M., Roth, A., Zeidler,
J., Dech, S., and Strano, E.: Breaking new ground in mapping human
settlements from space – The Global Urban Footprint, ISPRS J.
Photogramm., 134, 30–42,
https://doi.org/10.1016/j.isprsjprs.2017.10.012, 2017. a
Esch, T., Brzoska, E., Dech, S., Leutner, B., Palacios-Lopez, D.,
Metz-Marconcini, A., Marconcini, M., Roth, A., and Zeidler, J.: World
Settlement Footprint 3D – A first three-dimensional survey of the global
building stock, Remote Sens. Environ., 270, 112877,
https://doi.org/10.1016/j.rse.2021.112877, 2022. a
Fenner, D., Meier, F., Bechtel, B., Otto, M., and Scherer, D.: Intra and inter
“local climate zone” variability of air temperature as observed by
crowdsourced citizen weather stations in Berlin, Germany, Meteorol.
Z., 26, 525–547, https://doi.org/10.1127/metz/2017/0861, 2017. a
Fenner, D., Bechtel, B., Demuzere, M., Kittner, J., and Meier, F.:
CrowdQC+ – A Quality-Control for Crowdsourced Air-Temperature Observations
Enabling World-Wide Urban Climate Applications, Front. Environ.
Sci., 9, 1–21, https://doi.org/10.3389/fenvs.2021.720747, 2021. a
Florczyk, A., Melchiorri, M., Corban, C., Schiavina, M., Maffenini, L.,
Pesaresi, M., Politis, P., Sabo, F., Carneiro Freire, S., Ehrlich, D.,
Kemper, T., Tommasi, P., Airaghi, D., and Zanchetta, L.: Description of the
GHS Urban Centre Database 2015, public release 2019, version 1.0, Publications Office, https://doi.org/10.2760/037310, 2019. a, b
Forget, Y., Shimoni, M., and Gilbert, M.: Complementarity Between Sentinel-1
and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa, Preprints 2018, 2018100695,
https://doi.org/10.20944/preprints201810.0695.v1, 2018. a
Fuhrer, O., Chadha, T., Hoefler, T., Kwasniewski, G., Lapillonne, X., Leutwyler, D., Lüthi, D., Osuna, C., Schär, C., Schulthess, T. C., and Vogt, H.: Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0, Geosci. Model Dev., 11, 1665–1681, https://doi.org/10.5194/gmd-11-1665-2018, 2018. a
Gál, T., Mahó, S. I., Skarbit, N., and Unger, J.: Numerical
modelling for analysis of the effect of different urban green spaces on urban
heat load patterns in the present and in the future, Comput. Environ.
Urban, 87, 101600, https://doi.org/10.1016/j.compenvurbsys.2021.101600, 2021. a
Georgescu, M., Chow, W. T. L., Wang, Z. H., Brazel, A., Trapido-Lurie, B., Roth, M., and Benson-Lira, V.: Prioritizing urban sustainability solutions:
coordinated approaches must incorporate scale-dependent built environment
induced effects, Environ. Res. Lett., 10, 061001,
https://doi.org/10.1088/1748-9326/10/6/061001, 2015. a
Gilabert, J., Deluca, A., Lauwaet, D., Ballester, J., Corbera, J., and Llasat, M. C.: Assessing heat exposure to extreme temperatures in urban areas using the Local Climate Zone classification, Nat. Hazards Earth Syst. Sci., 21, 375–391, https://doi.org/10.5194/nhess-21-375-2021, 2021. a
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang,
J., Zhang, W., and Zhou, Y.: Annual maps of global artificial impervious
area (GAIA) between 1985 and 2018, Remote Sens. Environ., 236,
111510, https://doi.org/10.1016/j.rse.2019.111510, 2020. a, b
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. a
Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X.,
and Briggs, J. M.: Global change and the ecology of cities, Science, 319, 756–760, https://doi.org/10.1126/science.1150195, 2008. a
Grimmond, S., Bouchet, V., Molina, L. T., Baklanov, A., Tan, J.,
Schlünzen, K. H., Mills, G., Golding, B., Masson, V., Ren, C., Voogt,
J., Miao, S., Lean, H., Heusinkveld, B., Hovespyan, A., Teruggi, G., Parrish,
P., and Joe, P.: Integrated urban hydrometeorological, climate and
environmental services: Concept, methodology and key messages, Urban
Climate, 33, 100623, https://doi.org/10.1016/j.uclim.2020.100623, 2020. a
Güneralp, B., Zhou, Y., Ürge-Vorsatz, D., Gupta, M., Yu, S., Patel,
P. L., Fragkias, M., Li, X., and Seto, K. C.: Global scenarios of urban
density and its impacts on building energy use through 2050, P.
Natl. Acad. Sci. USA, 114,
8945–8950, https://doi.org/10.1073/pnas.1606035114, 2017. a
Gutowski, W. J., Ullrich, P. A., Hall, A., Leung, L. R., O'Brien, T. A.,
Patricola, C. M., Arritt, R. W., Bukovsky, M. S., Calvin, K. V., Feng, Z.,
Jones, A. D., Kooperman, G. J., Monier, E., Pritchard, M. S., Pryor, S. C.,
Qian, Y., Rhoades, A. M., Roberts, A. F., Sakaguchi, K., Urban, N., and
Zarzycki, C.: The Ongoing Need for High-Resolution Regional Climate Models:
Process Understanding and Stakeholder Information, B. Am.
Meteorol. Soc., 101, E664–E683, https://doi.org/10.1175/BAMS-D-19-0113.1,
2020. a
Hammerberg, K., Brousse, O., Martilli, A., and Mahdavi, A.: Implications of
employing detailed urban canopy parameters for mesoscale climate modelling: a
comparison between WUDAPT and GIS databases over Vienna, Austria,
Int. J. Climatol., 38, e1241–e1257,
https://doi.org/10.1002/joc.5447, 2018. a
Haralick, R. M., Dinstein, I., and Shanmugam, K.: Textural Features for Image
Classification, IEEE T. Syst. Man Cyb., SMC-3, 610–621,
https://doi.org/10.1109/TSMC.1973.4309314, 1973. a
Hay Chung, L. C., Xie, J., and Ren, C.: Improved machine-learning mapping of
local climate zones in metropolitan areas using composite Earth observation
data in Google Earth Engine, Build. Environ., 199, 107879,
https://doi.org/10.1016/j.buildenv.2021.107879, 2021. a, b, c
Hertwig, D., Ng, M., Grimmond, S., Vidale, P. L., and McGuire, P. C.:
High‐resolution global climate simulations: Representation of cities,
Int. J. Climatol., 41, 3266–3285, https://doi.org/10.1002/joc.7018,
2021. a, b
Hidalgo, J., Lemonsu, A., and Masson, V.: Between progress and obstacles in
urban climate interdisciplinary studies and knowledge transfer to society,
Ann. NY Acad. Sci., 1436, 5–18,
https://doi.org/10.1111/nyas.13986, 2018. a
Hirsch, A. L., Evans, J. P., Thomas, C., Conroy, B., Hart, M. A., Lipson, M.,
and Ertler, W.: Resolving the influence of local flows on urban heat
amplification during heatwaves, Environ. Res. Lett., 16,
064066, https://doi.org/10.1088/1748-9326/ac0377, 2021. a
Hu, J., Ghamisi, P., and Zhu, X.: Feature Extraction and Selection of
Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification,
ISPRS Int. Geo-Inf., 7, 379,
https://doi.org/10.3390/ijgi7090379, 2018. a
IPCC: Climate Change 2022: Impacts, Adaptation, and Vulnerability, Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press, in Press, 2022. a
Jiang, S., Huang, F., Zhan, W., Bechtel, B., Liu, Z., Demuzere, M., Huang, Y.,
Xu, Y., Xia, W., Quan, J., Hong, F., Jiang, L., Lai, J., Wang, C., Kong, F.,
Du, H., Miao, S., Chen, Y., Zhang, X., Planning, R., and Development, U.:
Mapping Local Climate Zones : A Bibliometric Meta- Analysis and Systematic
Review, OSF preprints, 1–106, https://doi.org/10.31219/osf.io/c2bez, 2021. a, b
Jung, M., Dahal, P. R., Butchart, S. H., Donald, P. F., De Lamo, X., Lesiv,
M., Kapos, V., Rondinini, C., and Visconti, P.: A global map of terrestrial
habitat types, Sci. Data, 7, 1–8, https://doi.org/10.1038/s41597-020-00599-8,
2020. a
Kabano, P., Lindley, S., and Harris, A.: Evidence of urban heat island impacts
on the vegetation growing season length in a tropical city, Landscape
Urban Plan., 206, 103989, https://doi.org/10.1016/j.landurbplan.2020.103989, 2021. a
Kamath, H. G., Singh, M., Magruder, L. A., Yang, Z.-l., and Niyogi, D.:
GLOBUS: GLObal Building heights for Urban Studies, ArXiv [preprint],
https://doi.org/10.48550/arXiv.2205.12224, 2022. a
Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., Oleson, K. W.,
Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban
energy balance and wintertime stability simulated using three land-surface
models in the high-latitude city Helsinki, Q. J. Roy.
Meteor. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016. a
Kotharkar, R., Ghosh, A., Kapoor, S., and Reddy, D. G. K.: Approach to local
climate zone based energy consumption assessment in an Indian city, Energ.
Buildings, 259, 111835, https://doi.org/10.1016/j.enbuild.2022.111835, 2022. a
Kuffer, M., Thomson, D. R., Boo, G., Mahabir, R., Grippa, T., Vanhuysse, S.,
Engstrom, R., Ndugwa, R., Makau, J., Darin, E., de Albuquerque, J. P., and
Kabaria, C.: The role of earth observation in an integrated deprived area
mapping “system” for low-to-middle income countries, Remote Sensing, 12, 982,
https://doi.org/10.3390/rs12060982, 2020. a
Lai, D., Liu, W., Gan, T., Liu, K., and Chen, Q.: A review of mitigating
strategies to improve the thermal environment and thermal comfort in urban
outdoor spaces, Sci. Total Environ., 661, 337–353,
https://doi.org/10.1016/j.scitotenv.2019.01.062, 2019. a
Leconte, F., Bouyer, J., and Claverie, R.: Nocturnal cooling in Local Climate
Zone: Statistical approach using mobile measurements, Urban Climate, 33,
100629, https://doi.org/10.1016/j.uclim.2020.100629, 2020. a
Lehnert, M., Savić, S., Milošević, D., Dunjić, J., and
Geletič, J.: Mapping Local Climate Zones and Their Applications in
European Urban Environments: A Systematic Literature Review and Future
Development Trends, ISPRS Int. Geo-Inf., 10, 260,
https://doi.org/10.3390/ijgi10040260, 2021. a
Li, M., Koks, E., Taubenböck, H., and van Vliet, J.: Continental-scale
mapping and analysis of 3D building structure, Remote Sens.
Environ., 245, 111859, https://doi.org/10.1016/j.rse.2020.111859,
2020. a, b
Li, X., Gong, P., Zhou, Y., Wang, J., Bai, Y., Chen, B., Hu, T., Xiao, Y., Xu,
B., Yang, J., Liu, X., Cai, W., Huang, H., Wu, T., Wang, X., Lin, P., Li, X.,
Chen, J., He, C., Li, X., Yu, L., Clinton, N., and Zhu, Z.: Mapping global
urban boundaries from the global artificial impervious area (GAIA) data,
Environ. Res. Lett., 15, 094044, https://doi.org/10.1088/1748-9326/ab9be3,
2020. a
Lindberg, F., Grimmond, C., Gabey, A., Huang, B., Kent, C. W., Sun, T.,
Theeuwes, N. E., Järvi, L., Ward, H. C., Capel-Timms, I., Chang, Y.,
Jonsson, P., Krave, N., Liu, D., Meyer, D., Olofson, K. F. G., Tan, J.,
Wästberg, D., Xue, L., and Zhang, Z.: Urban Multi-scale Environmental
Predictor (UMEP): An integrated tool for city-based climate services,
Environ. Model. Softw., 99, 70–87,
https://doi.org/10.1016/j.envsoft.2017.09.020, 2018. a
Liu, S., Qi, Z., Li, X., and Yeh, A. G.-O.: Integration of Convolutional
Neural Networks and Object-Based Post-Classification Refinement for Land Use
and Land Cover Mapping with Optical and SAR Data, Remote Sensing, 11, 690,
https://doi.org/10.3390/rs11060690, 2019. a
Lu, T., Marshall, J. D., Zhang, W., Hystad, P., Kim, S.-Y., Bechle, M. J.,
Demuzere, M., and Hankey, S.: National Empirical Models of Air Pollution
Using Microscale Measures of the Urban Environment, Environ. Sci.
Technol., 55, 15519–15530, https://doi.org/10.1021/acs.est.1c04047, 2021. a
Lu, Y., Yang, J., and Ma, S.: Dynamic Changes of Local Climate Zones in the
Guangdong–Hong Kong–Macao Greater Bay Area and Their Spatio-Temporal
Impacts on the Surface Urban Heat Island Effect between 2005 and 2015,
Sustainability, 13, 6374, https://doi.org/10.3390/su13116374, 2021. a
Lyu, T., Buccolieri, R., and Gao, Z.: A numerical study on the correlation
between sky view factor and summer microclimate of local climate zones,
Atmosphere, 10, 438, https://doi.org/10.3390/atmos10080438, 2019. a
MacKenzie, A. R., Whyatt, J. D., Barnes, M. J., Davies, G., and Hewitt, C. N.:
Urban form strongly mediates the allometric scaling of airshed pollution
concentrations, Environ. Res. Lett., 14, 124078,
https://doi.org/10.1088/1748-9326/ab50e3, 2019. a
Maharoof, N., Emmanuel, R., and Thomson, C.: Compatibility of local climate
zone parameters for climate sensitive street design: Influence of openness
and surface properties on local climate, Urban Climate, 33, 100642,
https://doi.org/10.1016/j.uclim.2020.100642, 2020. a
Manoli, G., Fatichi, S., Schläpfer, M., Yu, K., Crowther, T. W., Meili,
N., Burlando, P., Katul, G. G., and Bou-Zeid, E.: Magnitude of urban heat
islands largely explained by climate and population, Nature, 573, 55–60,
https://doi.org/10.1038/s41586-019-1512-9, 2019. a, b
Martilli, A.: An idealized study of city structure, urban climate, energy
consumption, and air quality, Urban Climate, 10, 430–446,
https://doi.org/10.1016/j.uclim.2014.03.003, 2014. a
Martilli, A., Roth, M., Winston, T. L., Demuzere, M., Lipson, M., Krayenhoff,
S., Sailor, D., Nazarian, N., Voogt, J., Wouters, H., Middel, A., Stewart,
I. D., Bechtel, B., Christen, A., and Hart, M.: Summer average urban-rural
surface temperature differences do not indicate the need for urban heat
reduction, OSF preprints, https://doi.org/10.31219/osf.io/8gnbf, 2020. a, b
Masson, V., Heldens, W., Bocher, E., Bonhomme, M., Bucher, B., Burmeister, C.,
de Munck, C., Esch, T., Hidalgo, J., Kanani-Sühring, F., Kwok, Y. T.,
Lemonsu, A., Lévy, J. P., Maronga, B., Pavlik, D., Petit, G., See, L.,
Schoetter, R., Tornay, N., Votsis, A., and Zeidler, J.: City-descriptive
input data for urban climate models: Model requirements, data sources and
challenges, Urban Climate, 31, 100536, https://doi.org/10.1016/j.uclim.2019.100536,
2020. a
Matsaba, E. O., Langer, I., Adimo, A. O., Mukundi, J. B., and Wesonga, J. M.:
Spatio-Temporal Variability of Simulated 2 m Air Temperature for Nairobi
City, Kenya, Current Urban Studies, 8, 205–221,
https://doi.org/10.4236/cus.2020.82011, 2020. a
McDonough, L. K., Santos, I. R., Andersen, M. S., O'Carroll, D. M., Rutlidge,
H., Meredith, K., Oudone, P., Bridgeman, J., Gooddy, D. C., Sorensen, J. P.,
Lapworth, D. J., MacDonald, A. M., Ward, J., and Baker, A.: Changes in
global groundwater organic carbon driven by climate change and urbanization,
Nat. Commun., 11, 1–10, https://doi.org/10.1038/s41467-020-14946-1, 2020. a, b
Middel, A., Häb, K., Brazel, A. J., Martin, C. a., and Guhathakurta, S.:
Impact of urban form and design on mid-afternoon microclimate in Phoenix
Local Climate Zones, Landscape Urban Plan., 122, 16–28,
https://doi.org/10.1016/j.landurbplan.2013.11.004, 2014. a
Mills, S., Weiss, S., and Liang, C.: VIIRS day/night band (DNB) stray light
characterization and correction, in: Earth Observing Systems XVIII, edited
by: Butler, J. J., Xiong, X. J., and Gu, X.,
International Society for Optics and Photonics, SPIE, 8866, 549–566,
https://doi.org/10.1117/12.2023107, 2013. a
Milošević, D., Savić, S., Kresoja, M., Lužanin, Z.,
Šećerov, I., Arsenović, D., Dunjić, J., and
Matzarakis, A.: Analysis of air temperature dynamics in the “local climate
zones” of Novi Sad (Serbia) based on long-term database from an urban
meteorological network, Int. J. Biometeorol., 66, 371–384,
https://doi.org/10.1007/s00484-020-02058-w, 2021. a
Molnár, G., Gyöngyösi, A. Z., and Gál, T.: Integration
of an LCZ-based classification into WRF to assess the intra-urban temperature
pattern under a heatwave period in Szeged, Hungary, Theor. Appl.
Climatol., 138, 1139–1158, https://doi.org/10.1007/s00704-019-02881-1, 2019. a
Moradi, M., Krayenhoff, E. S., and Aliabadi, A. A.: A comprehensive
indoor–outdoor urban climate model with hydrology: The Vertical City
Weather Generator (VCWG v2.0.0), Build. Environ., 207, 108406,
https://doi.org/10.1016/j.buildenv.2021.108406, 2022. a
Mu, Q., Miao, S., Wang, Y., Li, Y., He, X., and Yan, C.: Evaluation of
employing local climate zone classification for mesoscale modelling over
Beijing metropolitan area, Meteorol. Atmos. Phys., 132,
315–326, https://doi.org/10.1007/s00703-019-00692-7, 2020. a
Nagendra, H., Bai, X., Brondizio, E. S., and Lwasa, S.: The urban south and
the predicament of global sustainability, Nature Sustainability, 1,
341–349, https://doi.org/10.1038/s41893-018-0101-5, 2018. a
Oke, T. R., Mills, G., Christen, A., and Voogt, J. A.: Urban Climates,
Cambridge University Press, Cambridge, https://doi.org/10.1017/9781139016476, 2017. a, b, c
Oleson, K. W. and Feddema, J.: Parameterization and Surface Data Improvements
and New Capabilities for the Community Land Model Urban (CLMU), J.
Adv. Model. Earth Sy., 12, 1–30, https://doi.org/10.1029/2018ms001586,
2020. a, b
Owusu, M., Kuffer, M., Belgiu, M., Grippa, T., Lennert, M., Georganos, S., and
Vanhuysse, S.: Towards user-driven earth observation-based slum mapping,
Comput. Environ. Urban, 89, 101681,
https://doi.org/10.1016/j.compenvurbsys.2021.101681, 2021. a
Pan, Z.: WUDAPT Level 0 training data for HUAICHU (China, People's Republic of), LCZ Generator, submitted,
https://lcz-generator.rub.de/factsheets/f81f240993b6ca50180232c878755c18736e308a/f81f240993b6ca50180232c878755c18736e308a_factsheet.html (last access: 22 August 2022), 2021. a
Patel, P., Karmakar, S., Ghosh, S., and Niyogi, D.: Improved simulation of
very heavy rainfall events by incorporating WUDAPT urban land use/land cover
in WRF, Urban Climate, 32, 100616, https://doi.org/10.1016/j.uclim.2020.100616,
2020. a
Patel, P., Jamshidi, S., Nadimpalli, R., Aliaga, D. G., Mills, G., Chen, F.,
Demuzere, M., and Niyogi, D.: Modeling Large‐Scale Heatwave by
Incorporating Enhanced Urban Representation, J. Geophys.
Res.-Atmos., 127, 1–33, https://doi.org/10.1029/2021JD035316, 2022. a, b
Patella, V., Florio, G., Magliacane, D., Giuliano, A., Crivellaro, M. A.,
Bartolomeo, D., Genovese, A., Palmieri, M., Postiglione, A., Ridolo, E.,
Scaletti, C., Ventura, M. T., and Zollo, A.: Urban air pollution and climate
change: “The Decalogue: Allergy Safe Tree” for allergic and respiratory
diseases care, Clinical and Molecular Allergy, 16, 20,
https://doi.org/10.1186/s12948-018-0098-3, 2018. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in {P}ython, J. Mach.
Learn. Res., 12, 2825–2830, 2011. a, b
Pellegatti Franco, D. M., Andrade, M. d. F., Ynoue, R. Y., and Ching, J.:
Effect of Local Climate Zone (LCZ) classification on ozone chemical
transport model simulations in Sao Paulo, Brazil, Urban Climate, 27,
293–313, https://doi.org/10.1016/j.uclim.2018.12.007, 2019. a
Perera, N. and Emmanuel, R.: A “Local Climate Zone” based approach to
urban planning in Colombo, Sri Lanka, Urban Climate, 23, 188–203,
https://doi.org/10.1016/j.uclim.2016.11.006, 2018. a
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C.,
Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston,
J., Dubayah, R., Blair, J. B., and Hofton, M.: Mapping global forest canopy
height through integration of GEDI and Landsat data, Remote Sens.
Environ., 253, 112165, https://doi.org/10.1016/j.rse.2020.112165, 2021. a
Potgieter, J., Nazarian, N., Lipson, M. J., Hart, M. A., Ulpiani, G., Morrison,
W., and Benjamin, K.: Combining High-Resolution Land Use Data With
Crowdsourced Air Temperature to Investigate Intra-Urban Microclimate,
Front. Environ. Sci., 9, 1–19,
https://doi.org/10.3389/fenvs.2021.720323, 2021. a
Quah, A. K. and Roth, M.: Diurnal and weekly variation of anthropogenic heat
emissions in a tropical city, Singapore, Atmos. Environ., 46,
92–103, https://doi.org/10.1016/j.atmosenv.2011.10.015, 2012. a
Raven, J., Stone, B., Mills, G., Towers, J., Katzschner, L., Leone, M. F.,
Gaborit, P., Georgescu, M., Hariri, M., Lee, J., LeJava, J., Sharifi, A.,
Visconti, C., and Rudd, A.: Urban Planning and Urban Design, in: Climate Change
and Cities, Cambridge University Press, 139–172, https://doi.org/10.1017/9781316563878.012, 2018. a
Reba, M. and Seto, K. C.: A systematic review and assessment of algorithms to
detect, characterize, and monitor urban land change, Remote Sens.
Environ., 242, 111739, https://doi.org/10.1016/j.rse.2020.111739, 2020. a, b, c
Ribeiro, H. V., Rybski, D., and Kropp, J. P.: Effects of changing population
or density on urban carbon dioxide emissions, Nat. Commun., 10,
1–9, https://doi.org/10.1038/s41467-019-11184-y, 2019. a
Rosentreter, J., Hagensieker, R., and Waske, B.: Towards large-scale mapping
of local climate zones using multitemporal Sentinel 2 data and convolutional
neural networks, Remote Sens. Environ., 237, 111472,
https://doi.org/10.1016/j.rse.2019.111472, 2020. a
Rosenzweig, C., Solecki, W., Hammer, S. A., and Mehrotra, S.: Cities lead the
way in climate-change action, Nature, 467, 909–911, https://doi.org/10.1038/467909a,
2010. a
Santos, L. G. R., Singh, V. K., Mughal, M. O., Nevat, I., Norford, L. K., and
Fonseca, J. A.: Estimating building's anthropogenic heat: a joint local
climate zone and land use classification method, eSIM 2021 conference,
12–19, https://doi.org/10.3929/ethz-b-000445814, 2020. a
Sapena, M., Wurm, M., Taubenböck, H., Tuia, D., and Ruiz, L. A.:
Estimating quality of life dimensions from urban spatial pattern metrics,
Comput. Environ. Urban, 85, 101549,
https://doi.org/10.1016/j.compenvurbsys.2020.101549, 2021. a
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di
Girolamo, S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D.,
Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L.,
Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.:
Kilometer-Scale Climate Models: Prospects and Challenges, B.
Am. Meteorol. Soc., 101, E567–E587,
https://doi.org/10.1175/bams-d-18-0167.1, 2020. a
Schiavina, M., Moreno-Monroy, A., Maffenini, L., and Veneri, P.: GHSL-OECD
Functional Urban Areas 2019, https://doi.org/10.2760/67415, 2019. a
Schläpfer, M., Lee, J., and Bettencourt, L. M. A.: Urban Skylines:
building heights and shapes as measures of city size, ArXiv [preprint], 1–17,
https://doi.org/10.48550/arXiv.1512.00946, 2015. a, b
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. a, b, c
Seto, K. C., Güneralp, B., and Hutyra, L. R.: Global forecasts of urban
expansion to 2030 and direct impacts on biodiversity and carbon pools.,
P. Natl. Acad. Sci. USA, 109, 16 083–8, https://doi.org/10.1073/pnas.1211658109, 2012. a
Sharma, R., Hooyberghs, H., Lauwaet, D., and De Ridder, K.: Urban Heat
Island and Future Climate Change – Implications for Delhi's Heat, J.
Urban Health, 96, 235–251, https://doi.org/10.1007/s11524-018-0322-y, 2019. a
Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Shiraishi, T., Thapa, R., and
Lucas, R.: New global forest/non-forest maps from ALOS PALSAR data
(2007–2010), Remote Sens. Environm., 155, 13–31,
https://doi.org/10.1016/j.rse.2014.04.014, 2014. a
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy
height globally with spaceborne lidar, J. Geophys. Res., 116,
G04021, https://doi.org/10.1029/2011JG001708, 2011. a
Skarbit, N., Stewart, I. D., Unger, J., and Gál, T.: Employing an urban
meteorological network to monitor air temperature conditions in the “local
climate zones” of Szeged, Hungary, Int. J. Climatol., 37,
582–596, https://doi.org/10.1002/joc.5023, 2017. a
Steeneveld, G.-J., Klompmaker, J. O., Groen, R. J., and Holtslag, A. A.: An
urban climate assessment and management tool for combined heat and air
quality judgements at neighbourhood scales, Resour. Conserv.
Recy., 132, 204–217, https://doi.org/10.1016/j.resconrec.2016.12.002, 2018. a
Stewart, I. D.: A systematic review and scientific critique of methodology in
modern urban heat island literature, Int. J. Climatol.,
31, 200–217, https://doi.org/10.1002/joc.2141, 2011a. a
Stewart, I. D.: Redefining the Urban Heat Island, Electronic Theses and Dissertations (ETDs),
University of British Columbia, 1–352,
https://circle.ubc.ca/handle/2429/38069, 2011b. a
Stewart, I. D.: Developing a field guide to identify “local climate zones” in
cities, in: 10th International Conference on Urban Climate/14th Symposium
on the Urban Environment, New York City, USA, https://ams.confex.com/ams/ICUC10/meetingapp.cgi/Paper/342085
(last access: 22 August 2022), 2018. a
Stewart, I. D. and Mills, G.: The Urban Heat Island – A Guidebook, Elsevier,
https://doi.org/10.1016/C2017-0-02872-0, 2021. a
Stewart, I. D., Krayenhoff, E. S., Voogt, J. A., Lachapelle, J. A., Allen,
M. A., and Broadbent, A. M.: Time Evolution of the Surface Urban Heat
Island, Earth's Future, 9, e2021EF002178, https://doi.org/10.1029/2021EF002178, 2021. a
Stone, B., Mednick, A. C., Holloway, T., and Spak, S. N.: Is Compact Growth
Good for Air Quality?, J. Am. Plann. Assoc., 73,
404–418, https://doi.org/10.1080/01944360708978521, 2007. a
Tadono, T., Nagai, H., Ishida, H., Oda, F., Naito, S., Minakawa, K., and
Iwamoto, H.: Generation of the 30 M-MESH global digital surface model by
alos prism, in: International Archives of the Photogrammetry, Remote Sensing
and Spatial Information Sciences – ISPRS Archives, 157–162,
https://doi.org/10.5194/isprsarchives-XLI-B4-157-2016, 2016. a
Takane, Y., Kikegawa, Y., Hara, M., and Grimmond, C. S. B.: Urban warming and
future air-conditioning use in an Asian megacity: importance of positive
feedback, npj Clim. Atmos. Sci., 2, 1–11,
https://doi.org/10.1038/s41612-019-0096-2, 2019. a
Taubenböck, H., Debray, H., Qiu, C., Schmitt, M., Wang, Y., and Zhu,
X. X.: Seven city types representing morphologic configurations of cities
across the globe, Cities, 105, 102814, https://doi.org/10.1016/j.cities.2020.102814,
2020. a
UN: World Urbanization Prospects: The 2018 Revision, in: Population Division of
the United Nations Department of Economic and Social Affairs, 123 pp.,
https://doi.org/10.18356/b9e995fe-en, 2019. a
Van de Walle, J., Brousse, O., Arnalsteen, L., Byarugaba, D., Ddumba, D. S.,
Demuzere, M., Lwasa, S., Nsangi, G., Sseviiri, H., Thiery, W., Vanhaeren, R.,
Wouters, H., and P.M. van Lipzig, N.: Can local fieldwork help to
represent intra-urban variability of canopy parameters relevant for tropical
African climate studies?, Theor. Appl. Climatol., 146, 457–474,
https://doi.org/10.1007/s00704-021-03733-7, 2021. a, b
Van de Walle, J., Brousse, O., Arnalsteen, L., Brimicombe, C., Byarugaba, D.,
Demuzere, M., Jjemba, E., Lwasa, S., Misiani, H., Nsangi, G., Soetewey, F.,
Sseviiri, H., Thiery, W., Vanhaeren, R., Zaitchik, B. F., and van Lipzig, N.
P. M.: Lack of vegetation exacerbates exposure to dangerous heat in dense
settlements in a tropical African city, Environ. Res. Lett., 17,
024004, https://doi.org/10.1088/1748-9326/ac47c3, 2022. a, b
van Vliet, J.: Direct and indirect loss of natural area from urban expansion,
Nature Sustainability, 2, 755–763, https://doi.org/10.1038/s41893-019-0340-0, 2019. a
Vandamme, S., Demuzere, M., Verdonck, M.-L., Zhang, Z., and Coillie, F. V.:
Revealing Kunming's (China) Historical Urban Planning Policies Through Local
Climate Zones, Remote Sensing, 11, 1731, https://doi.org/10.3390/rs11141731, 2019. a, b, c
Varentsov, M., Samsonov, T., and Demuzere, M.: Impact of Urban Canopy
Parameters on a Megacity's Modelled Thermal Environment, Atmosphere, 11,
1349, https://doi.org/10.3390/atmos11121349, 2020. a, b, c
Varentsov, M., Fenner, D., Meier, F., Samsonov, T., and Demuzere, M.:
Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow
with Reference and Crowdsourced Observations, Front. Environ.
Sci., 9, 1–21, https://doi.org/10.3389/fenvs.2021.716968, 2021. a, b
Varquez, A. C., Shota, K., Khanh, D. N., and Kanda, M.: Global 1-km present
and future hourly anthropogenic heat flux, Figshare [data set],
https://doi.org/10.6084/m9.figshare.12612458.v6, 2020. a
Varquez, A. C. G., Kiyomoto, S., Khanh, D. N., and Kanda, M.: Global 1-km
present and future hourly anthropogenic heat flux, Sci. Data, 8, 64,
https://doi.org/10.1038/s41597-021-00850-w, 2021. a, b
Verdonck, M.-L., Okujeni, A., van der Linden, S., Demuzere, M., De Wulf, R.,
and Van Coillie, F.: Influence of neighbourhood information on “Local
Climate Zone” mapping in heterogeneous cities, Int. J.
Appl. Earth Obs., 62, 102–113,
https://doi.org/10.1016/j.jag.2017.05.017, 2017. a, b
Verdonck, M.-l., Demuzere, M., Hooyberghs, H., Beck, C., Cyrys, J., Schneider,
A., Dewulf, R., and Van Coillie, F.: The potential of local climate zones
maps as a heat stress assessment tool, supported by simulated air temperature
data, Landscape Urban Plan., 178, 183–197,
https://doi.org/10.1016/j.landurbplan.2018.06.004, 2018. a, b
Verdonck, M.-l., Demuzere, M., Bechtel, B., Beck, C., Brousse, O., Droste, A.,
Fenner, D., Leconte, F., and Van Coillie, F.: The Human Influence
Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones
Using a Supervised Classification, Urban Science, 3, 27,
https://doi.org/10.3390/urbansci3010027, 2019a. a
Verdonck, M.-L., Demuzere, M., Hooyberghs, H., Priem, F., and Van Coillie,
F.: Heat risk assessment for the Brussels capital region under different
urban planning and greenhouse gas emission scenarios, J.
Environ. Manage., 249, 109210, https://doi.org/10.1016/j.jenvman.2019.06.111,
2019b. a, b
Wang, J. and Ouyang, W.: Attenuating the surface Urban Heat Island within the
Local Thermal Zones through land surface modification, J.
Environ. Manage., 187, 239–252, https://doi.org/10.1016/j.jenvman.2016.11.059,
2017. a
Wang, J., Chen, Y., Liao, W., He, G., Tett, S. F. B., Yan, Z., Zhai, P., Feng,
J., Ma, W., Huang, C., and Hu, Y.: Anthropogenic emissions and urbanization
increase risk of compound hot extremes in cities, Nat. Clim. Change, 11,
1084–1089, https://doi.org/10.1038/s41558-021-01196-2, 2021. a
Wang, R., Cai, M., Ren, C., Bechtel, B., Xu, Y., and Ng, E.: Detecting
multi-temporal land cover change and land surface temperature in Pearl River
Delta by adopting local climate zone, Urban Climate, 28, 100455,
https://doi.org/10.1016/j.uclim.2019.100455, 2019. a
Williams, K., Joynt, J. L., and Hopkins, D.: Adapting to Climate Change in the
Compact City: The Suburban Challenge, Built Environ., 36, 105–115,
https://doi.org/10.2148/benv.36.1.105, 2010. a
Wong, M. M. F., Fung, J. C. H., Ching, J., Yeung, P. P. S., Tse, J. W. P., Ren,
C., Wang, R., and Cai, M.: Evaluation of uWRF performance and modeling
guidance based on WUDAPT and NUDAPT UCP datasets for Hong Kong, Urban
Climate, 28, 100460, https://doi.org/10.1016/j.uclim.2019.100460, 2019. a
Wouters, H., Demuzere, M., Blahak, U., Fortuniak, K., Maiheu, B., Camps, J., Tielemans, D., and van Lipzig, N. P. M.: The efficient urban canopy dependency parametrization (SURY) v1.0 for atmospheric modelling: description and application with the COSMO-CLM model for a Belgian summer, Geosci. Model Dev., 9, 3027–3054, https://doi.org/10.5194/gmd-9-3027-2016, 2016. a
Wu, Y., Sharifi, A., Yang, P., Borjigin, H., Murakami, D., and Yamagata, Y.:
Mapping building carbon emissions within local climate zones in Shanghai,
Enrgy. Proced., 152, 815–822, https://doi.org/10.1016/j.egypro.2018.09.195, 2018. a
Xu, C., Hystad, P., Chen, R., Van Den Hoek, J., Hutchinson, R. A., Hankey,
S., and Kennedy, R.: Application of training data affects success in
broad-scale local climate zone mapping, Int. J. Appl.
Earth Obs., 103, 102482,
https://doi.org/10.1016/j.jag.2021.102482, 2021. a, b, c, d
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal,
J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of
global terrain elevations, Geophysical Research Letters, 44, 5844–5853,
https://doi.org/10.1002/2017GL072874, 2017. a
Yang, X., Yao, L., Jin, T., Peng, L. L., Jiang, Z., Hu, Z., and Ye, Y.:
Assessing the thermal behavior of different local climate zones in the
Nanjing metropolis, China, Build. Environ., 137, 171–184,
https://doi.org/10.1016/j.buildenv.2018.04.009, 2018. a
Yang, X., Peng, L. L., Jiang, Z., Chen, Y., Yao, L., He, Y., and Xu, T.:
Impact of urban heat island on energy demand in buildings: Local climate
zones in Nanjing, Appl. Energ., 260, 114279,
https://doi.org/10.1016/j.apenergy.2019.114279, 2020. a
Yokoya, N., Ghamisi, P., Xia, J., Sukhanov, S., Heremans, R., Tankoyeu, I.,
Bechtel, B., Le Saux, B., Moser, G., and Tuia, D.: Open Data for Global
Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion
Contest, IEEE J. Sel. Top. Appl., 11, 1363–1377, https://doi.org/10.1109/JSTARS.2018.2799698, 2018. a
Yoo, C., Lee, Y., Cho, D., Im, J., and Han, D.: Improving local climate zone
classification using incomplete building data and sentinel 2 images based on
convolutional neural networks, Remote Sensing, 12, 1–22,
https://doi.org/10.3390/rs12213552, 2020. a
Zhang, X., Liu, L., Wu, C., Chen, X., Gao, Y., Xie, S., and Zhang, B.: Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform, Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, 2020. a, b, c, d
Zhang, Y., Kong, D., Gan, R., Chiew, F. H., Mcvicar, T. R., Zhang, Q., and
Yang, Y.: Coupled estimation of 500 m and 8-day resolution global
evapotranspiration and gross primary production in 2002–2017, Remote
Sens. Environ., 222, 165–182, https://doi.org/S003442571830590X, 2019. a
Zhang, Y., Zhang, J., Zhang, X., Zhou, D., and Gu, Z.: Analyzing the
Characteristics of UHI (Urban Heat Island) in Summer Daytime Based on
Observations on 50 Sites in 11 LCZ (Local Climate Zone) Types in Xi'an,
China, Sustainability, 13, 83, https://doi.org/10.3390/su13010083, 2020. a
Zhao, C., Weng, Q., and Hersperger, A. M.: Characterizing the 3-D urban
morphology transformation to understand urban-form dynamics: A case study of
Austin, Texas, USA, Landscape Urban Plan., 203, 103881,
https://doi.org/10.1016/j.landurbplan.2020.103881, 2020. a
Zhao, C., Weng, Q., Wang, Y., Hu, Z., and Wu, C.: Use of local climate zones
to assess the spatiotemporal variations of urban vegetation phenology in
Austin, Texas, USA, GIScience Remote Sens., 59, 393–409,
https://doi.org/10.1080/15481603.2022.2033485, 2022. a, b
Zhao, L., Oleson, K., Bou-Zeid, E., Krayenhoff, E. S., Bray, A., Zhu, Q.,
Zheng, Z., Chen, C., and Oppenheimer, M.: Global multi-model projections of
local urban climates, Nat. Clim. Change, 11, 152–157,
https://doi.org/10.1038/s41558-020-00958-8, 2021. a, b, c, d
Zhao, Z., Shen, L., Li, L., Wang, H., and He, B.-J.: Local Climate Zone
Classification Scheme Can Also Indicate Local-Scale Urban Ventilation
Performance: An Evidence-Based Study, Atmosphere, 11, 776,
https://doi.org/10.3390/atmos11080776, 2020.
a
Zhi, C., Tang, Y., Chang, L., and Demuzere, M.: The evolution of
three-dimensional urban spatial form and its planning response to the surface
heat island effect: Taking Beijing as an example, International Urban
Planning, 36, 61–68, https://doi.org/10.19830/j.upi.2021.407, 2021. a, b
Zhou, Y., Smith, S. J., Zhao, K., Imhoff, M., Thomson, A., Bond-Lamberty, B.,
Asrar, G. R., Zhang, X., He, C., and Elvidge, C. D.: A global map of urban
extent from nightlights, Environ. Res. Lett., 10, 054011,
https://doi.org/10.1088/1748-9326/10/5/054011, 2015. a
Zhu, X. X., Qiu, C., Hu, J., Shi, Y., Wang, Y., Schmitt, M., and
Taubenböck, H.: The urban morphology on our planet – Global
perspectives from space, Remote Sens. Environ., 269, 112794,
https://doi.org/10.1016/j.rse.2021.112794, 2022. a
Zhu, Z., Zhou, Y., Seto, K. C., Stokes, E. C., Deng, C., Pickett, S. T., and
Taubenböck, H.: Understanding an urbanizing planet: Strategic
directions for remote sensing, Remote Sens. Environ., 228, 164–182,
https://doi.org/10.1016/j.rse.2019.04.020, 2019. a
Zhuo, L., Zheng, J., Zhang, X., Li, J., and Liu, L.: An improved method of
night-time light saturation reduction based on EVI, Int. J.
Remote Sens., 36, 4114–4130, https://doi.org/10.1080/01431161.2015.1073861, 2015. a, b
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. a, b
Zonato, A., Martilli, A., Di Sabatino, S., Zardi, D., and Giovannini, L.:
Evaluating the performance of a novel WUDAPT averaging technique to define
urban morphology with mesoscale models, Urban Climate, 31, 100584,
https://doi.org/10.1016/j.uclim.2020.100584, 2020. a
Zong, L., Liu, S., Yang, Y., Ren, G., Yu, M., Zhang, Y., and Li, Y.:
Synergistic Influence of Local Climate Zones and Wind Speeds on the Urban
Heat Island and Heat Waves in the Megacity of Beijing, China, Front.
Earth Sci., 9, 673786, https://doi.org/10.3389/feart.2021.673786, 2021. a
Short summary
Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Because urban areas are key contributors to climate change but are also susceptible to multiple...
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