Data description paper
15 Jun 2021
Data description paper
| 15 Jun 2021
GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery
Xiao Zhang et al.
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Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
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Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Xidong Chen, Liangyun Liu, Xiao Zhang, Junsheng Li, Shenglei Wang, Yuan Gao, and Jun Mi
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-630, https://doi.org/10.5194/hess-2021-630, 2022
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A 30 m LAke Water Secchi Depth (LAWSD30) dataset was first developed for 1985–2020, and a national-scale water clarity estimations of lakes in China over the past 35 years were analyzed. The lake clarity in China exhibited a significant downward trend before the 21st century, but improved after 2000. The developed LAWSD30 dataset and the evaluation results can provide effective guidance for the water preservation and restoration.
Xiao Zhang, Liangyun Liu, Changshan Wu, Xidong Chen, Yuan Gao, Shuai Xie, and Bing Zhang
Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, https://doi.org/10.5194/essd-12-1625-2020, 2020
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The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Yuan Gao, Xidong Chen, and Jun Mi
Earth Syst. Sci. Data, 14, 1831–1856, https://doi.org/10.5194/essd-14-1831-2022, https://doi.org/10.5194/essd-14-1831-2022, 2022
Short summary
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Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral-generalization method and time-series Landsat imagery on the Google Earth Engine cloud computing platform.
Xidong Chen, Liangyun Liu, Xiao Zhang, Junsheng Li, Shenglei Wang, Yuan Gao, and Jun Mi
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-630, https://doi.org/10.5194/hess-2021-630, 2022
Revised manuscript accepted for HESS
Short summary
Short summary
A 30 m LAke Water Secchi Depth (LAWSD30) dataset was first developed for 1985–2020, and a national-scale water clarity estimations of lakes in China over the past 35 years were analyzed. The lake clarity in China exhibited a significant downward trend before the 21st century, but improved after 2000. The developed LAWSD30 dataset and the evaluation results can provide effective guidance for the water preservation and restoration.
Xiao Zhang, Liangyun Liu, Changshan Wu, Xidong Chen, Yuan Gao, Shuai Xie, and Bing Zhang
Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, https://doi.org/10.5194/essd-12-1625-2020, 2020
Short summary
Short summary
The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
Xiaojin Qian, Liangyun Liu, Holly Croft, and Jingming Chen
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-228, https://doi.org/10.5194/bg-2019-228, 2019
Preprint withdrawn
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The leaf maximum carboxylation rate (Vcmax) is a key photosynthesis parameter. We attempt to investigate whether a universal and stable relationship exists between leaf Vcmax25 and chlorophyll content across different C3 plant types from a plant physiological perspective and verify it using field experiments. The results confirm that leaf chlorophyll can be a reliable proxy for estimating Vcmax25, providing an operational approach for the global mapping of Vcmax25 across different plant types.
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Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning
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ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products
Yi Zheng, Ana Cláudia dos Santos Luciano, Jie Dong, and Wenping Yuan
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Brazil is the largest sugarcane producer. Sugarcane in Brazil can be harvested all year round. The flexible phenology makes it difficult to identify sugarcane in Brazil at a country scale. We developed a phenology-based method which can identify sugarcane with limited training data. The sugarcane maps for Brazil obtain high accuracy through comparison against field samples and statistical data. The maps can be used to monitor growing conditions and evaluate the feedback to climate of sugarcane.
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Audrey Jolivot, Valentine Lebourgeois, Louise Leroux, Mael Ameline, Valérie Andriamanga, Beatriz Bellón, Mathieu Castets, Arthur Crespin-Boucaud, Pierre Defourny, Santiana Diaz, Mohamadou Dieye, Stéphane Dupuy, Rodrigo Ferraz, Raffaele Gaetano, Marie Gely, Camille Jahel, Bertin Kabore, Camille Lelong, Guerric le Maire, Danny Lo Seen, Martha Muthoni, Babacar Ndao, Terry Newby, Cecília Lira Melo de Oliveira Santos, Eloise Rasoamalala, Margareth Simoes, Ibrahima Thiaw, Alice Timmermans, Annelise Tran, and Agnès Bégué
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This paper presents nine standardized crop type reference datasets collected between 2013 and 2020 in seven tropical countries. It aims at participating in the difficult exercise of mapping agricultural land use through satellite image classification in those complex areas where few ground truth or census data are available. These quality-controlled datasets were collected in the framework of the international JECAM initiative and contain 27 074 polygons documented by detailed keywords.
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Severe burning struck Indonesia in 2019. Drawing on new satellite imagery, we present and validate new 2019 burned-area estimates for Indonesia.
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Qiaofeng Xue, Xiaobin Jin, Yinong Cheng, Xuhong Yang, and Yinkang Zhou
Earth Syst. Sci. Data, 13, 5071–5085, https://doi.org/10.5194/essd-13-5071-2021, https://doi.org/10.5194/essd-13-5071-2021, 2021
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Miao Zhang, Bingfang Wu, Hongwei Zeng, Guojin He, Chong Liu, Shiqi Tao, Qi Zhang, Mohsen Nabil, Fuyou Tian, José Bofana, Awetahegn Niguse Beyene, Abdelrazek Elnashar, Nana Yan, Zhengdong Wang, and Yiliang Liu
Earth Syst. Sci. Data, 13, 4799–4817, https://doi.org/10.5194/essd-13-4799-2021, https://doi.org/10.5194/essd-13-4799-2021, 2021
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Cropping intensity (CI) is essential for agricultural land use management, but fine-resolution global CI is not available. We used multiple satellite data on Google Earth Engine to develop a first 30 m resolution global CI (GCI30). GCI30 performed well, with an overall accuracy of 92 %. GCI30 not only exhibited high agreement with existing CI products but also provided many spatial details. GCI30 can facilitate research on sustained cropland intensification to improve food production.
Louise Chini, George Hurtt, Ritvik Sahajpal, Steve Frolking, Kees Klein Goldewijk, Stephen Sitch, Raphael Ganzenmüller, Lei Ma, Lesley Ott, Julia Pongratz, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 4175–4189, https://doi.org/10.5194/essd-13-4175-2021, https://doi.org/10.5194/essd-13-4175-2021, 2021
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Carbon emissions from land-use change are a large and uncertain component of the global carbon cycle. The Land-Use Harmonization 2 (LUH2) dataset was developed as an input to carbon and climate simulations and has been updated annually for the Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2 updates and describe the 2019 version which used new cropland and grazing land data inputs for the globally important region of Brazil.
Zoltan Szantoi, Andreas Brink, and Andrea Lupi
Earth Syst. Sci. Data, 13, 3767–3789, https://doi.org/10.5194/essd-13-3767-2021, https://doi.org/10.5194/essd-13-3767-2021, 2021
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The ever-evolving landscapes in the African, Caribbean and Pacific regions should be monitored for land cover changes. The Global Land Monitoring Service of the Copernicus Programme, and in particular the Hot Spot Monitoring activity, developed a satellite-imagery-based workflow to monitor such areas. Here, we present a total of 852 025 km2 of areas mapped with up to 32 land cover classes. Thematic land cover and land cover change maps, as well as validation datasets, are presented.
Zhen Yu, Xiaobin Jin, Lijuan Miao, and Xuhong Yang
Earth Syst. Sci. Data, 13, 3203–3218, https://doi.org/10.5194/essd-13-3203-2021, https://doi.org/10.5194/essd-13-3203-2021, 2021
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We reconstructed the annual, 5 km × 5 km resolution cropland percentage map that covers mainland China and spans from 1900 to 2016. Our results are advantageous, as they reconcile accuracy, temporal coverage, and spatial resolutions. We further examined the cropland shift pattern and its driving factors in China using the reconstructed maps. This work will greatly contribute to the field of global ecology and land surface modeling.
Xueqiong Wei, Mats Widgren, Beibei Li, Yu Ye, Xiuqi Fang, Chengpeng Zhang, and Tiexi Chen
Earth Syst. Sci. Data, 13, 3035–3056, https://doi.org/10.5194/essd-13-3035-2021, https://doi.org/10.5194/essd-13-3035-2021, 2021
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The cropland area of each administrative unit based on statistics in Scandinavia from 1690 to 1999 is allocated into 1 km grid cells. The cropland area increased from 1690 to 1950 and then decreasd in the following years, especially in southeastern Scandinavia. Comparing global datasets with this study, the spatial patterns show considerable differences. Our dataset is validated using satellite-based cropland cover data and results in previous studies.
Jichong Han, Zhao Zhang, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, and Ziyue Li
Earth Syst. Sci. Data, 13, 2857–2874, https://doi.org/10.5194/essd-13-2857-2021, https://doi.org/10.5194/essd-13-2857-2021, 2021
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Large-scale and high-resolution maps of rapeseed are important for ensuring global energy security. We generated a new database for the rapeseed planting area (2017–2019) at 10 m spatial resolution based on multiple data. Also, we analyzed the rapeseed rotation patterns in 25 representative areas from different countries. The derived rapeseed maps are useful for many purposes including crop growth monitoring and production and optimizing planting structure.
Bowen Cao, Le Yu, Victoria Naipal, Philippe Ciais, Wei Li, Yuanyuan Zhao, Wei Wei, Die Chen, Zhuang Liu, and Peng Gong
Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021, https://doi.org/10.5194/essd-13-2437-2021, 2021
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In this study, the first 30 m resolution terrace map of China was developed through supervised pixel-based classification using multisource, multi-temporal data based on the Google Earth Engine platform. The classification performed well with an overall accuracy of 94 %. The terrace mapping algorithm can be used to map large-scale terraces in other regions globally, and the terrace map will be valuable for studies on soil erosion, carbon cycle, and ecosystem service assessments.
Dominik Kaim, Marcin Szwagrzyk, Monika Dobosz, Mateusz Troll, and Krzysztof Ostafin
Earth Syst. Sci. Data, 13, 1693–1709, https://doi.org/10.5194/essd-13-1693-2021, https://doi.org/10.5194/essd-13-1693-2021, 2021
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We present a dataset of mid-19th-century building structure locations in former Galicia and Austrian Silesia (parts of the Habsburg Monarchy), located in present-day Czechia, Poland, and Ukraine. It consists of two kinds of building structures: residential and farm-related buildings. The dataset may serve as an important input in studying long-term socio-economic processes and human–environmental interactions or as a valuable reference for continental settlement reconstructions.
Adrià Descals, Serge Wich, Erik Meijaard, David L. A. Gaveau, Stephen Peedell, and Zoltan Szantoi
Earth Syst. Sci. Data, 13, 1211–1231, https://doi.org/10.5194/essd-13-1211-2021, https://doi.org/10.5194/essd-13-1211-2021, 2021
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Decision-making for sustainable vegetable oil production requires accurate global oil crop maps. We used high-resolution satellite data to train a deep learning model that accurately classified industrial and smallholder oil palm, the main oil-producing crop. Our results outperformed previous studies and proved the suitability of deep learning for land use mapping. The global oil palm area was 21±0.42 Mha for 2019; however, young and sparse plantations were not included in this estimate.
Johannes H. Uhl, Stefan Leyk, Caitlin M. McShane, Anna E. Braswell, Dylan S. Connor, and Deborah Balk
Earth Syst. Sci. Data, 13, 119–153, https://doi.org/10.5194/essd-13-119-2021, https://doi.org/10.5194/essd-13-119-2021, 2021
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Fine-grained geospatial data on the spatial distribution of human settlements are scarce prior to the era of remote-sensing-based Earth observation. In this paper, we present datasets derived from a large, novel building stock database, enabling the spatially explicit analysis of 200 years of land development in the United States at an unprecedented spatial and temporal resolution. These datasets greatly facilitate long-term studies of socio-environmental systems in the conterminous USA.
Wenhui Kuang, Shu Zhang, Xiaoyong Li, and Dengsheng Lu
Earth Syst. Sci. Data, 13, 63–82, https://doi.org/10.5194/essd-13-63-2021, https://doi.org/10.5194/essd-13-63-2021, 2021
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We propose a hierarchical principle for remotely sensed urban land use and land cover change for mapping intra-urban structure and component dynamics. China’s Land Use/cover Dataset (CLUD) is updated, delineating the imperviousness and green surface conditions in cities from 2000 to 2018. The newly developed datasets can be used to enhance our understanding of urbanization impacts on ecological and regional climatic conditions and on urban dwellers' environments.
Qiangyi Yu, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Alison K. B. Joglekar, Steffen Fritz, Wei Xiong, Miao Lu, Wenbin Wu, and Peng Yang
Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, https://doi.org/10.5194/essd-12-3545-2020, 2020
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SPAM makes plausible estimates of crop distribution within disaggregated units. It moves the data from coarser units such as countries and provinces to finer units such as grid cells and creates a global gridscape at the confluence between earth and agricultural-production systems. It improves spatial understanding of crop production systems and allows policymakers to better target agricultural- and rural-development policies for increasing food security with minimal environmental impacts.
Jie Dong, Yangyang Fu, Jingjing Wang, Haifeng Tian, Shan Fu, Zheng Niu, Wei Han, Yi Zheng, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, https://doi.org/10.5194/essd-12-3081-2020, 2020
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For the first time, we produced a 30 m winter wheat distribution map in China for 3 years during 2016–2018. Validated with 33 776 survey samples, the map had perfect performance with an overall accuracy of 89.88 %. Moreover, the method can identify planting areas of winter wheat 3 months prior to harvest; that is valuable information for production predictions and is urgently necessary for policymakers to reduce economic loss and assess food security.
Zoltan Szantoi, Andreas Brink, Andrea Lupi, Claudio Mammone, and Gabriel Jaffrain
Earth Syst. Sci. Data, 12, 3001–3019, https://doi.org/10.5194/essd-12-3001-2020, https://doi.org/10.5194/essd-12-3001-2020, 2020
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Larger ecological zones and wildlife corridors in sub-Saharan Africa require monitoring, as social and economic demands put high pressure on them. Copernicus’ Hot-Spot Monitoring service developed a satellite-imagery-based monitoring workflow to map such areas. Here, we present a total of 560 442 km2 from which 153 665 km2 is mapped with eight land cover classes while 406 776 km2 is mapped with up to 32 classes. Besides presenting the thematic products, we also present our validation datasets.
David M. Theobald, Christina Kennedy, Bin Chen, James Oakleaf, Sharon Baruch-Mordo, and Joe Kiesecker
Earth Syst. Sci. Data, 12, 1953–1972, https://doi.org/10.5194/essd-12-1953-2020, https://doi.org/10.5194/essd-12-1953-2020, 2020
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We developed a global, high-resolution dataset and quantified recent rates of land transformation and current patterns of human modification for 2017, globally. Briefly, we found that increased human activities and land use modification have caused 1.6 × 106 km2 of natural land to be lost between 1990 and 2015 and the rate of loss has increased over that time. While troubling, we believe these findings are invaluable to underpinning global and national discussions of conservation priorities.
Miao Lu, Wenbin Wu, Liangzhi You, Linda See, Steffen Fritz, Qiangyi Yu, Yanbing Wei, Di Chen, Peng Yang, and Bing Xue
Earth Syst. Sci. Data, 12, 1913–1928, https://doi.org/10.5194/essd-12-1913-2020, https://doi.org/10.5194/essd-12-1913-2020, 2020
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Global cropland distribution is critical for agricultural monitoring and food security. We propose a new Self-adapting Statistics Allocation Model (SASAM) to develop the global map of cropland distribution. SASAM is based on the fusion of multiple existing cropland maps and multilevel statistics of cropland area, which is independent of training samples. The synergy map has higher accuracy than the input datasets and better consistency with the cropland statistics.
Xiao Zhang, Liangyun Liu, Changshan Wu, Xidong Chen, Yuan Gao, Shuai Xie, and Bing Zhang
Earth Syst. Sci. Data, 12, 1625–1648, https://doi.org/10.5194/essd-12-1625-2020, https://doi.org/10.5194/essd-12-1625-2020, 2020
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The amount of impervious surface is an important indicator in the monitoring of the intensity of human activity and environmental change. In this study, a global 30 m impervious surface map was developed by using multisource, multitemporal remote sensing data based on the Google Earth Engine platform. The accuracy assessment indicated that the generated map had more optimal measurement accuracy compared with other state-of-art impervious surface products.
Yidi Xu, Le Yu, Wei Li, Philippe Ciais, Yuqi Cheng, and Peng Gong
Earth Syst. Sci. Data, 12, 847–867, https://doi.org/10.5194/essd-12-847-2020, https://doi.org/10.5194/essd-12-847-2020, 2020
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The first annual oil palm area dataset (AOPD) for Malaysia and Indonesia from 2001 to 2016 was produced by integrating multiple satellite datasets and a change-detection algorithm (BFAST). This dataset reveals that oil palm plantations have expanded from 5.69 to 19.05 M ha in the two countries during the past 16 years. The AOPD is useful in understanding the deforestation process in Southeast Asia and may serve as land-use change inputs in dynamic global vegetation models.
Yuchuan Luo, Zhao Zhang, Yi Chen, Ziyue Li, and Fulu Tao
Earth Syst. Sci. Data, 12, 197–214, https://doi.org/10.5194/essd-12-197-2020, https://doi.org/10.5194/essd-12-197-2020, 2020
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For the first time, we generated a 1 km gridded-phenology product for three staple crops in China during 2000–2015, called ChinaCropPhen1km. Compared with the phenological observations from the agricultural meteorological stations, the dataset had high accuracy, with errors of retrieved phenological date of less than 10 d. The well-validated dataset is sufficiently reliable for many applications, including improving the agricultural-system or earth-system modeling over a large area.
Cited articles
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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...