Articles | Volume 13, issue 6
https://doi.org/10.5194/essd-13-2753-2021
© Author(s) 2021. 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-13-2753-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Jun 2021
Data description paper |
| 15 Jun 2021
| 15 Jun 2021
GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery
Xiao Zhang
State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Xidong Chen
State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Yuan Gao
State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
Shuai Xie
State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Jun Mi
State Key Laboratory of Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
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Updated global maps of greenhouse gas emissions and sequestration by forests from 2001 onwards using satellite-derived data show that forests are strong net carbon sinks, capturing about as much CO2 each year on average as the United States emits from fossil fuels. After reclassifying fluxes to countries’ reporting categories for national greenhouse gas inventories, we found that roughly two-thirds of the total net flux from forests is anthropogenic and one-third is non-anthropogenic.
Jingling Jiang, Hong Zhang, Ji Ge, Lijun Zuo, Lu Xu, Minyang Song, Yinhaibin Ding, Yazhe Xie, and Wenjiang Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-402, https://doi.org/10.5194/essd-2024-402, 2024
Revised manuscript accepted for ESSD
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This study employs temporal SAR data and optical imagery to conduct rice extraction experiments in 34 African countries with annual rice planting areas exceeding 5,000 hectares, achieving 20-meter resolution spatial distribution mapping of rice in Africa for 2023. The average classification accuracy on the validation set exceeded 85 %, and the R² values for linear fitting with existing statistical data all surpassed 0.9, demonstrating the effectiveness of the proposed mapping method.
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-266, https://doi.org/10.5194/essd-2024-266, 2024
Revised manuscript accepted for ESSD
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Our study introduces a Landsat-based data cube simplifying access to detailed environmental data across Europe from 2000 to 2022, covering vegetation, water, soil, and crops. Our experiments demonstrate its effectiveness in developing environmental models and maps. Tailored feature selection is crucial for its effective use in environmental modeling. It aims to support comprehensive environmental monitoring and analysis, helping researchers and policymakers in managing environmental resources.
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.
Michael MacFerrin, Christopher Amante, Kelly Carignan, Matthew Love, and Elliot Lim
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-250, https://doi.org/10.5194/essd-2024-250, 2024
Revised manuscript accepted for ESSD
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Here we present Earth TOPOgraphy (ETOPO) 2022, the latest iteration of NOAA’s global, seamless topographic-bathymetric dataset. ETOPO 2022 is a significant upgrade in resolution and accuracy from previous ETOPO releases, freely available in multiple data formats and resolutions for all uses (public or private), excepting navigation.
Shuchao Ye, Peiyu Cao, and Chaoqun Lu
Earth Syst. Sci. Data, 16, 3453–3470, https://doi.org/10.5194/essd-16-3453-2024, https://doi.org/10.5194/essd-16-3453-2024, 2024
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We reconstructed annual cropland density and crop type maps, including nine major crop types (corn, soybean, winter wheat, spring wheat, durum wheat, cotton, sorghum, barley, and rice), from 1850 to 2021 at 1 km × 1 km resolution. We found that the US total crop acreage has increased by 118 × 106 ha (118 Mha), mainly driven by corn (30 Mha) and soybean (35 Mha). Additionally, the US cropping diversity experienced an increase in the 1850s–1960s, followed by a decline over the past 6 decades.
Fang Chen, Lei Wang, Yu Wang, Haiying Zhang, Ning Wang, Pengfei Ma, and Bo Yu
Earth Syst. Sci. Data, 16, 3369–3382, https://doi.org/10.5194/essd-16-3369-2024, https://doi.org/10.5194/essd-16-3369-2024, 2024
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Storage tanks are responsible for approximately 25 % of CH4 emissions in the atmosphere, exacerbating climate warming. Currently there is no publicly accessible storage tank inventory. We generated the first high-spatial-resolution (1–2 m) storage tank dataset (STD) over 92 typical cities in China in 2021, totaling 14 461 storage tanks with the construction year from 2000–2021. It shows significant agreement with CH4 emission spatially and temporally, promoting the CH4 control strategy proposal.
Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian
Earth Syst. Sci. Data, 16, 3307–3332, https://doi.org/10.5194/essd-16-3307-2024, https://doi.org/10.5194/essd-16-3307-2024, 2024
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The Tibetan Plateau, with its diverse vegetation ranging from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the map TP_LC10-2022. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Simon Boitard, Arnaud Mialon, Stéphane Mermoz, Nemesio J. Rodríguez-Fernández, Philippe Richaume, Julio César Salazar-Neira, Stéphane Tarot, and Yann H. Kerr
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-184, https://doi.org/10.5194/essd-2024-184, 2024
Revised manuscript accepted for ESSD
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Above Ground Biomass (AGB) is a critical component of the Earth carbon cycle. The presented dataset aims to help monitoring this essential climate variable with AGB time series from 2011 onward, derived with a carefully calibrated spatial relationship between the measurements of the Soil Moisture and Ocean Salinity (SMOS) mission and pre-existing AGB maps. The produced dataset has been extensively compared with other available AGB time series and can be used in AGB studies.
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.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-193, https://doi.org/10.5194/essd-2024-193, 2024
Revised manuscript accepted for ESSD
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This study developed a new approach to long-time continuous annual vegetation mapping from remote sensing imagery, and mapped the vegetation of the Qinghai-Tibet Plateau (QTP) from 2000 to 2022 through the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 80.9%, with the reference annual 2020 reaching an accuracy of 86.5% and a Kappa coefficient of 0.85. The study supports the use of remote sensing data to mapping a long-time continuous annual vegetation.
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.
Zhenghang Yuan, Zhitong Xiong, Lichao Mou, and Xiao Xiang Zhu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-140, https://doi.org/10.5194/essd-2024-140, 2024
Revised manuscript accepted for ESSD
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ChatEarthNet is an image-text dataset that provides high-quality, detailed natural language descriptions for global-scale satellite data. It consists of 163,488 image-text pairs with captions generated by ChatGPT-3.5, and an additional 10,000 image-text pairs with captions generated by ChatGPT-4V(ision). This dataset has significant potential for training and evaluating vision-language geo-foundation models in remote sensing.
Ewa Grabska-Szwagrzyk, Dirk Tiede, Martin Sudmanns, and Jacek Kozak
Earth Syst. Sci. Data, 16, 2877–2891, https://doi.org/10.5194/essd-16-2877-2024, https://doi.org/10.5194/essd-16-2877-2024, 2024
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We accurately mapped 16 dominant tree species and genera in Poland using Sentinel-2 observations from short periods in spring, summer, and autumn (2018–2021). The classification achieved more than 80% accuracy in country-wide forest species mapping, with variation based on species, region, and observation frequency. Freely accessible resources, including the forest tree species map and training and test data, can be found at https://doi.org/10.5281/zenodo.10180469.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
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NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
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We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Lingcheng Li, Gautam Bisht, Dalei Hao, and L. Ruby Leung
Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, https://doi.org/10.5194/essd-16-2007-2024, 2024
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This study fills a gap to meet the emerging needs of kilometer-scale Earth system modeling by developing global 1 km land surface parameters for land use, vegetation, soil, and topography. Our demonstration simulations highlight the substantial impacts of these parameters on spatial variability and information loss in water and energy simulations. Using advanced explainable machine learning methods, we identified influential factors driving spatial variability and information loss.
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).
Fuyou Tian, Bingfang Wu, Hongwei Zeng, Miao Zhang, Weiwei Zhu, Nana Yan, Yuming Lu, and Yifan Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-536, https://doi.org/10.5194/essd-2023-536, 2024
Revised manuscript accepted for ESSD
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Our team has developed an irrigation map with 100 m resolution, which is more detailed than existing one. We used satellite images and focused on the crop status during the dry conditions. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the US, and Pakistan. We also explored the distribution of central pivot systems, which are commonly used in the US and Saudi Arabia. This new map can better support water management and food security globally.
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
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We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Cameron I. Ludemann, Nathan Wanner, Pauline Chivenge, Achim Dobermann, Rasmus Einarsson, Patricio Grassini, Armelle Gruere, Kevin Jackson, Luis Lassaletta, Federico Maggi, Griffiths Obli-Laryea, Martin K. van Ittersum, Srishti Vishwakarma, Xin Zhang, and Francesco N. Tubiello
Earth Syst. Sci. Data, 16, 525–541, https://doi.org/10.5194/essd-16-525-2024, https://doi.org/10.5194/essd-16-525-2024, 2024
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Nutrient budgets help identify the excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow the calculation of indicators, such as the nutrient balance (surplus or deficit) and nutrient use efficiency, that help to monitor agricultural productivity and sustainability. This article describes a global cropland nutrient budget that provides data on 205 countries and territories from 1961 to 2020 (data available at https://www.fao.org/faostat/en/#data/ESB).
Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III
Earth Syst. Sci. Data, 16, 321–336, https://doi.org/10.5194/essd-16-321-2024, https://doi.org/10.5194/essd-16-321-2024, 2024
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Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
Xiangan Liang, Qiang Liu, Jie Wang, Shuang Chen, and Peng Gong
Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, https://doi.org/10.5194/essd-16-177-2024, 2024
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The state-of-the-art MODIS surface reflectance products suffer from temporal and spatial gaps, which make it difficult to characterize the continuous variation of the terrestrial surface. We proposed a framework for generating the first global 500 m daily seamless data cubes (SDC500), covering the period from 2000 to 2022. We believe that the SDC500 dataset can interest other researchers who study land cover mapping, quantitative remote sensing, and ecological science.
Rémy Ballot, Nicolas Guilpart, and Marie-Hélène Jeuffroy
Earth Syst. Sci. Data, 15, 5651–5666, https://doi.org/10.5194/essd-15-5651-2023, https://doi.org/10.5194/essd-15-5651-2023, 2023
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Assessing the benefits of crop diversification – a key element of agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of Europe. To fill this gap, we used a dataset that provides temporally and spatially incomplete land cover information to create a map of dominant crop sequence types for Europe over 2012–2018. This map is a useful baseline for assessing the benefits of future crop diversification.
Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams, Daniele Zanaga, Marjorie Battude, Alex Grosu, Joost Brombacher, Myroslava Lesiv, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Inbal Becker-Reshef, Belén Franch, Bertran Mollà-Bononad, Hendrik Boogaard, Arun Kumar Pratihast, Benjamin Koetz, and Zoltan Szantoi
Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, https://doi.org/10.5194/essd-15-5491-2023, 2023
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WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.
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.
Cited articles
Azzari, G. and Lobell, D. B.: Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring, Remote Sens. Environ., 202, 64–74, https://doi.org/10.1016/j.rse.2017.05.025, 2017.
Ban, Y., Gong, P., and Giri, C.: Global land cover mapping using Earth observation satellite data: Recent progresses and challenges, ISPRS J. Photogramm., 103, 1–6, https://doi.org/10.1016/j.isprsjprs.2015.01.001, 2015.
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016.
Bontemps, S., Defourny, P., Bogaert, E. V., Arino, O., Kalogirou, V., and Perez, J. R.: GLOBCOVER 2009 Products Description and Validation Report, available at: http://due.esrin.esa.int/files/GLOBCOVER2009_Validation_Report_2.2.pdf (last access: 15 August 2020), 2010.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., and Smets, B.: Copernicus Global Land Cover Layers – Collection 2, Remote Sens., 12, 1044, https://doi.org/10.3390/rs12061044, 2020.
Calderón-Loor, M., Hadjikakou, M., and Bryan, B. A.: High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015, Remote Sens. Environ., 252, 112148, https://doi.org/10.1016/j.rse.2020.112148, 2021.
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Zhang, W., Tong, X., and Mills, J.: Global land cover mapping at 30 m resolution: A POK-based operational approach, ISPRS J. Photogramm., 103, 7–27, https://doi.org/10.1016/j.isprsjprs.2014.09.002, 2015.
Defourny, P., Kirches, G., Brockmann, C., Boettcher, M., Peters, M., Bontemps, S., Lamarche, C., Schlerf, M., and Santoro, M.: Land Cover CCI: Product User Guide Version 2, available at: https://www.esa-landcover-cci.org/?q=webfm_send/84 (last access: 15 August 2020), 2018.
Du, P., Samat, A., Waske, B., Liu, S., and Li, Z.: Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features, ISPRS J. Photogramm., 105, 38–53, https://doi.org/10.1016/j.isprsjprs.2015.03.002, 2015.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005rg000183, 2007.
Feng, M., Huang, C., Channan, S., Vermote, E. F., Masek, J. G., and Townshend, J. R.: Quality assessment of Landsat surface reflectance products using MODIS data, Comput. Geosci., 38, 9–22, https://doi.org/10.1016/j.cageo.2011.04.011, 2012.
Fisher, J. R. B., Acosta, E. A., Dennedy-Frank, P. J., Kroeger, T., and Boucher, T. M.: Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality, Remote Sensing in Ecology & Conservation, 4, 137–149, https://doi.org/10.1002/rse2.61, 2018.
Foody, G. M.: Sample size determination for image classification accuracy assessment and comparison, Int. J. Remote Sens., 30, 5273–5291, https://doi.org/10.1080/01431160903130937, 2009.
Foody, G. M. and Arora, M. K.: An evaluation of some factors affecting the accuracy of classification by an artificial neural network, Int. J. Remote Sens., 18, 799–810, https://doi.org/10.1080/014311697218764, 2010.
Foody, G. M. and Mathur, A.: Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification, Remote Sens. Environ., 93, 107–117, https://doi.org/10.1016/j.rse.2004.06.017, 2004.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Gómez, C., White, J. C., and Wulder, M. A.: Optical remotely sensed time series data for land cover classification: A review, ISPRS J. Photogramm., 116, 55–72, https://doi.org/10.1016/j.isprsjprs.2016.03.008, 2016.
Giri, C., Zhu, Z. L., and Reed, B.: A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets, Remote Sens. Environ., 94, 123–132, https://doi.org/10.1016/j.rse.2004.09.005, 2005.
Giri, C., Pengra, B., Long, J., and Loveland, T. R.: Next generation of global land cover characterization, mapping, and monitoring, Int. J. Appl. Earth Obs., 25, 30–37, https://doi.org/10.1016/j.jag.2013.03.005, 2013.
Gomariz-Castillo, F., Alonso-Sarría, F., and Cánovas-García, F.: Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015, Remote Sens., 9, 1058, https://doi.org/10.3390/rs9101058, 2017.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng, Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu, X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z., Chen, J., and Chen, J.: Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data, Int. J. Remote Sens., 34, 2607–2654, https://doi.org/10.1080/01431161.2012.748992, 2013.
Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., Clinton, N., Ji, L., Li, W., Bai, Y., Chen, B., Xu, B., Zhu, Z., Yuan, C., Ping Suen, H., Guo, J., Xu, N., Li, W., Zhao, Y., Yang, J., Yu, C., Wang, X., Fu, H., Yu, L., Dronova, I., Hui, F., Cheng, X., Shi, X., Xiao, F., Liu, Q., and Song, L.: Stable classification with limited sample: transferring a 30 m resolution sample set collected in 2015 to mapping 10 m resolution global land cover in 2017, Sci. Bull., 64, 370–373, https://doi.org/10.1016/j.scib.2019.03.002, 2019.
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.
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.
Grekousis, G., Mountrakis, G., and Kavouras, M.: An overview of 21 global and 43 regional land-cover mapping products, Int. J. Remote Sens., 36, 5309–5335, https://doi.org/10.1080/01431161.2015.1093195, 2015.
Griffiths, P., Linden, S. V. D., Kuemmerle, T., and Hostert, P.: A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping, IEEE J. Sel. Top. Appl., 6, 2088–2101, https://doi.org/10.1109/JSTARS.2012.2228167, 2013.
Hansen, M. C., Egorov, A., Potapov, P. V., Stehman, S. V., Tyukavina, A., Turubanova, S. A., Roy, D. P., Goetz, S. J., Loveland, T. R., Ju, J., Kommareddy, A., Kovalskyy, V., Forsyth, C., and Bents, T.: Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD), Remote Sens. Environ., 140, 466–484, https://doi.org/10.1016/j.rse.2013.08.014, 2014.
Herold, M., Woodcock, C., Stehman, S., Nightingale, J., Friedl, M., and Schmullius, C.: The GOFC-GOLD/CEOS land cover harmonization and validation initiative: technical design and implementation, available at: http://articles.adsabs.harvard.edu/pdf/2010ESASP.686E.268H (last access: 15 August 2020), 2010.
Huete, A., Justice, C., and Van Leeuwen, W.: MODIS vegetation index (MOD13), Algorithm theoretical basis document, 3, 213, 1999.
Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D., and Rodes, I.: Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series, Remote Sens., 9, 95, https://doi.org/10.3390/rs9010095, 2017.
Jin, H., Stehman, S. V., and Mountrakis, G.: Assessing the impact of training sample selection on accuracy of an urban classification: a case study in Denver, Colorado, Int. J. Remote Sens., 35, 2067–2081, https://doi.org/10.1080/01431161.2014.885152, 2014.
Jokar Arsanjani, J., See, L., and Tayyebi, A.: Assessing the suitability of GlobeLand30 for mapping land cover in Germany, Int. J. Digit. Earth, 9, 873–891, https://doi.org/10.1080/17538947.2016.1151956, 2016a.
Jokar Arsanjani, J., Tayyebi, A., and Vaz, E.: GlobeLand30 as an alternative fine-scale global land cover map: Challenges, possibilities, and implications for developing countries, Habitat Int., 55, 25–31, https://doi.org/10.1016/j.habitatint.2016.02.003, 2016b.
Kang, J., Wang, Z., Sui, L., Yang, X., Ma, Y., and Wang, J.: Consistency Analysis of Remote Sensing Land Cover Products in the Tropical Rainforest Climate Region: A Case Study of Indonesia, Remote Sens., 12, 1410, https://doi.org/10.3390/rs12091410, 2020.
Lehner, B. and Döll, P.: Global Lakes and Wetlands Database GLWD, GLWD Docu mentation, available at: https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database (last access: 15 August 2020), 2004.
Li, C., Peng, G., Wang, J., Zhu, Z., Biging, G. S., Yuan, C., Hu, T., Zhang, H., Wang, Q., and Li, X.: The first all-season sample set for mapping global land cover with Landsat-8 data, Sci. Bull., 62, 508–515, https://doi.org/10.1016/j.scib.2017.03.011, 2017.
Li, M., Zang, S., Zhang, B., Li, S., and Wu, C.: A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information, European Journal of Remote Sensing, 47, 389-411, https://doi.org/10.5721/EuJRS20144723, 2017.
Liu, L., Zhang, X., Hu, Y., and Wang, Y.: Automatic land cover mapping for Landsat data based on the time-series spectral image database, Geoscience and Remote Sensing Symposium (IGARSS), 23–28 July 2017, Fort Worth, 17379785, 4282–4285, https://doi.org/10.5281/zenodo.3551995, 2017.
Liu, L., Gao, Y., Zhang, X., Chen, X., and Xie, S.: A Dataset of Global Land Cover Validation Samples, Zenodo [data], https://doi.org/10.5281/zenodo.3551995, 2019.
Liu, L., Zhang, X., Chen, X., Gao, Y., and Mi, J.: GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery, Zenodo [data], https://doi.org/10.5281/zenodo.3986872, 2020.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., and Merchant, J. W.: Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330, https://doi.org/10.1080/014311600210191, 2000.
Ludwig, C., Walli, A., Schleicher, C., Weichselbaum, J., and Riffler, M.: A highly automated algorithm for wetland detection using multi-temporal optical satellite data, Remote Sens. Environ., 224, 333–351, https://doi.org/10.1016/j.rse.2019.01.017, 2019.
Massey, R., Sankey, T. T., Yadav, K., Congalton, R. G., and Tilton, J. C.: Integrating cloud-based workflows in continental-scale cropland extent classification, Remote Sens. Environ., 219, 162–179, https://doi.org/10.1016/j.rse.2018.10.013, 2018.
Mellor, A., Boukir, S., Haywood, A., and Jones, S.: Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin, ISPRS J. Photogramm., 105, 155–168, https://doi.org/10.1016/j.isprsjprs.2015.03.014, 2015.
Miller, J. D. and Thode, A. E.: Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR), Remote Sens. Environ., 109, 66–80, https://doi.org/10.1016/j.rse.2006.12.006, 2007.
Mishra, V. N., Prasad, R., Kumar, P., Gupta, D. K., and Ohri, A.: Evaluating the effects of spatial resolution on land use and land cover classification accuracy, International Conference on Microwave, Optical and Communication Engineering (ICMOCE), 18–20 December 2015, Bhubaneswar, 16072605, 208–211, https://doi.org/10.1109/ICMOCE.2015.7489727, 2015.
Olofsson, P., Foody, G. M., Stehman, S. V., and Woodcock, C. E.: Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation, Remote Sens. Environ., 129, 122–131, https://doi.org/10.1016/j.rse.2012.10.031, 2013.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A.: Good practices for estimating area and assessing accuracy of land change, Remote Sens. Environ., 148, 42–57, https://doi.org/10.1016/j.rse.2014.02.015, 2014.
Pelletier, C., Valero, S., Inglada, J., Champion, N., and Dedieu, G.: Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas, Remote Sens. Environ., 187, 156–168, https://doi.org/10.1016/j.rse.2016.10.010, 2016.
Pelletier, C., Valero, S., Inglada, J., Champion, N., Marais Sicre, C., and Dedieu, G.: Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series, Remote Sensing, 9, 173, https://doi.org/10.3390/rs9020173, 2017.
Phalke, A. R., Özdoğan, M., Thenkabail, P. S., Erickson, T., Gorelick, N., Yadav, K., and Congalton, R. G.: Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine, ISPRS J. Photogramm., 167, 104–122, https://doi.org/10.1016/j.isprsjprs.2020.06.022, 2020.
Radoux, J., Lamarche, C., Van Bogaert, E., Bontemps, S., Brockmann, C., and Defourny, P.: Automated Training Sample Extraction for Global Land Cover Mapping, Remote Sens., 6, 3965–3987, https://doi.org/10.3390/rs6053965, 2014.
Rodriguez-Galiano, V. F., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P. M., and Jeganathan, C.: Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture, Remote Sens. Environ., 121, 93–107, https://doi.org/10.1016/j.rse.2011.12.003, 2012.
Roy, D. P. and Kumar, S. S.: Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome, Int. J. Digit. Earth, 10, 54–84, https://doi.org/10.1080/17538947.2016.1208686, 2016.
Roy, D. P., Qin, Y., Kovalskyy, V., Vermote, E. F., Ju, J., Egorov, A., Hansen, M. C., Kommareddy, I., and Yan, L.: Conterminous United States demonstration and characterization of MODIS-based Landsat ETM+ atmospheric correction, Remote Sens. Environ., 140, 433–449, https://doi.org/10.1016/j.rse.2013.09.012, 2014.
Tachikawa, T., Hato, M., Kaku, M., and Iwasaki, A.: Characteristics of ASTER GDEM Version 2, Geoscience and Remote Sensing Symposium (IGARSS), 24–29 July 2011, Vancouver, 12477285, 3657–3660, https://doi.org/10.1109/IGARSS.2011.6050017, 2011.
Tateishi, R., Uriyangqai, B., Al-Bilbisi, H., Ghar, M. A., Tsend-Ayush, J., Kobayashi, T., Kasimu, A., Hoan, N. T., Shalaby, A., Alsaaideh, B., Enkhzaya, T., Gegentana, and Sato, H. P.: Production of global land cover data – GLCNMO, Int. J. Digit. Earth, 4, 22–49, https://doi.org/10.1080/17538941003777521, 2011.
Tateishi, R., Hoan, N. T., Kobayashi, T., Alsaaideh, B., Tana, G., and Phong, D. X.: Production of Global Land Cover Data – GLCNMO2008, Journal of Geography and Geology, 6, 99, https://doi.org/10.5539/jgg.v6n3p99, 2014.
Teluguntla, P., Thenkabail, P. S., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., Yadav, K., and Huete, A.: A 30 m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform, ISPRS J. Photogramm., 144, 325–340, https://doi.org/10.1016/j.isprsjprs.2018.07.017, 2018.
Tootchi, A., Jost, A., and Ducharne, A.: Multi-source global wetland maps combining surface water imagery and groundwater constraints, Earth Syst. Sci. Data, 11, 189–220, https://doi.org/10.5194/essd-11-189-2019, 2019.
Tsendbazar, N. E., de Bruin, S., and Herold, M.: Assessing global land cover reference datasets for different user communities, ISPRS J. Photogramm., 103, 93–114, https://doi.org/10.1016/j.isprsjprs.2014.02.008, 2015.
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150, https://doi.org/10.1016/0034-4257(79)90013-0, 1979.
Vermote, E., Justice, C., Claverie, M., and Franch, B.: Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product, Remote Sens. Environ., 185, 46–56, https://doi.org/10.1016/j.rse.2016.04.008, 2016.
Wang, J., Zhao, Y., Li, C., Yu, L., Liu, D., and Gong, P.: Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution, ISPRS J. Photogramm., 103, 38–47, https://doi.org/10.1016/j.isprsjprs.2014.03.007, 2015.
Wang, Y., Liu, L., Hu, Y., Li, D., and Li, Z.: Development and validation of the Landsat-8 surface reflectance products using a MODIS-based per-pixel atmospheric correction method, Int. J. Remote Sens., 37, 1291–1314, https://doi.org/10.1080/01431161.2015.1104742, 2016.
Wang, Z. and Liu, L.: Assessment of Coarse-Resolution Land Cover Products Using CASI Hyperspectral Data in an Arid Zone in Northwestern China, Remote Sens., 6, 2864–2883, https://doi.org/10.3390/rs6042864, 2014.
Wessels, K., van den Bergh, F., Roy, D., Salmon, B., Steenkamp, K., MacAlister, B., Swanepoel, D., and Jewitt, D.: Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers, Remote Sens., 8, 888, https://doi.org/10.3390/rs8110888, 2016.
Woodcock, C. E., Allen, R. G., and Anderson, M. C.: Free access to Landsat imagery, Science, 320, 1011, https://doi.org/10.1126/science.320.5879.1011a, 2008.
Xian, G., Homer, C., and Fry, J.: Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods, Remote Sens. Environ., 113, 1133–1147, https://doi.org/10.1016/j.rse.2009.02.004, 2009.
Xie, S., Liu, L., Zhang, X., and Chen, X.: Annual land-cover mapping based on multi-temporal cloud-contaminated landsat images, Int. J. Remote Sens., 40, 1–23, https://doi.org/10.1080/01431161.2018.1553320, 2018.
Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., and Thau, D.: Automated cropland mapping of continental Africa using Google Earth Engine cloud computing, ISPRS J. Photogramm., 126, 225–244, https://doi.org/10.1016/j.isprsjprs.2017.01.019, 2017.
Xu, H.: Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, Int. J. Remote Sens., 27, 3025–3033, https://doi.org/10.1080/01431160600589179, 2006.
Yang, L., Jia, K., Liang, S., Wei, X., Yao, Y., and Zhang, X.: A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data, Remote Sens., 9, 857, https://doi.org/10.3390/rs9080857, 2017.
Yang, Y., Xiao, P., Feng, X., and Li, H.: Accuracy assessment of seven global land cover datasets over China, ISPRS J. Photogramm., 125, 156–173, https://doi.org/10.1016/j.isprsjprs.2017.01.016, 2017.
Zhang, H. K. and Roy, D. P.: Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification, Remote Sens. Environ., 197, 15–34, https://doi.org/10.1016/j.rse.2017.05.024, 2017.
Zhang, X. and Liu, L.: Development of a global 30 m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform, Zenodo [data], https://doi.org/10.5281/zenodo.3505079, 2019.
Zhang, X., Liu, L., Wang, Y., Hu, Y., and Zhang, B.: A SPECLib-based operational classification approach: A preliminary test on China land cover mapping at 30 m, Int. J. Appl. Earth Obs., 71, 83–94, https://doi.org/10.1016/j.jag.2018.05.006, 2018.
Zhang, X., Liu, L., Chen, X., Xie, S., and Gao, Y.: Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach, Remote Sens., 11, 1056, https://doi.org/10.3390/rs11091056, 2019.
Zhang, 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.
Zhu, Z. and Woodcock, C. E.: Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sens. Environ., 118, 83–94, https://doi.org/10.1016/j.rse.2011.10.028, 2012.
Zhu, Z., Woodcock, C. E., Rogan, J., and Kellndorfer, J.: Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data, Remote Sens. Environ., 117, 72–82, https://doi.org/10.1016/j.rse.2011.07.020, 2012.
Zhu, Z., Wang, S. X., and Woodcock, C. E.: Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images, Remote Sens. Environ., 159, 269–277, https://doi.org/10.1016/j.rse.2014.12.014, 2015.
Zhu, Z., Gallant, A. L., Woodcock, C. E., Pengra, B., Olofsson, P., Loveland, T. R., Jin, S., Dahal, D., Yang, L., and Auch, R. F.: Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative, ISPRS J. Photogramm., 122, 206–221, https://doi.org/10.1016/j.isprsjprs.2016.11.004, 2016.
Short summary
Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m landcover classification with a fine classification system for the year 2015 (GLC_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib on the Google Earth Engine computing platform.
Over past decades, a lot of global land-cover products have been released; however, these still...
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