Articles | Volume 13, issue 12
https://doi.org/10.5194/essd-13-5951-2021
© 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-13-5951-2021
© 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
| 
23 Dec 2021
Data description paper |
| 23 Dec 2021
| 23 Dec 2021
Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries
Audrey Jolivot
CORRESPONDING AUTHOR
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Valentine Lebourgeois
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Louise Leroux
CIRAD, UPR AIDA, Dakar, Senegal
AIDA, Univ Montpellier, CIRAD, Montpellier, France
Centre de Suivi Ecologique (CSE), Dakar, Senegal
Mael Ameline
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Valérie Andriamanga
CIRAD, UMR TETIS, 34398 Montpellier, France
Centre National de la Recherche Appliquée au Développement
Rural (FOFIFA), Antsirabe, Madagascar
Beatriz Bellón
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Mathieu Castets
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Arthur Crespin-Boucaud
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Pierre Defourny
Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve,
Belgium
Santiana Diaz
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Mohamadou Dieye
Institut Sénégalais de Recherches Agricoles (ISRA), Dakar,
Senegal
Stéphane Dupuy
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Rodrigo Ferraz
Brazilian Agricultural Research Corporation (EMBRAPA), Rio de Janeiro,
Brazil
Raffaele Gaetano
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Marie Gely
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Camille Jahel
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Bertin Kabore
independent consultant: Ouagadougou, Burkina Faso
Camille Lelong
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Guerric le Maire
CIRAD, UMR Eco&Sols, 34398 Montpellier, France
Eco&Sols, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD,
Montpellier, France
Danny Lo Seen
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Martha Muthoni
independent consultant: Nairobi, Kenya
Babacar Ndao
Centre de Suivi Ecologique (CSE), Dakar, Senegal
Institute of Environmental Sciences, Université Cheikh Anta Diop de Dakar (UCAD), Dakar, Senegal
Terry Newby
Agricultural Research Council (ARC), Pretoria, South Africa
Cecília Lira Melo de Oliveira Santos
Interdisciplinary Center on Energy Planning, NIPE, University of
Campinas, UNICAMP, Campinas, São Paulo 13083-896, Brazil
Eloise Rasoamalala
CIRAD, UMR TETIS, 34398 Montpellier, France
Centre National de la Recherche Appliquée au Développement
Rural (FOFIFA), Antsirabe, Madagascar
Margareth Simoes
Brazilian Agricultural Research Corporation (EMBRAPA), Rio de Janeiro,
Brazil
Ibrahima Thiaw
Institute of Environmental Sciences, Université Cheikh Anta Diop de Dakar (UCAD), Dakar, Senegal
Alice Timmermans
Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve,
Belgium
Annelise Tran
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
Agnès Bégué
CIRAD, UMR TETIS, 34398 Montpellier, France
TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE,
Montpellier, France
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Revised manuscript accepted for ESSD
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Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
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, 16, 5267–5285, https://doi.org/10.5194/essd-16-5267-2024, https://doi.org/10.5194/essd-16-5267-2024, 2024
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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.
Jia Zhou, Jin Niu, Ning Wu, and Tao Lu
Earth Syst. Sci. Data, 16, 5171–5189, https://doi.org/10.5194/essd-16-5171-2024, https://doi.org/10.5194/essd-16-5171-2024, 2024
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The study provided an annual 100 m 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.
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data, 16, 5111–5129, https://doi.org/10.5194/essd-16-5111-2024, https://doi.org/10.5194/essd-16-5111-2024, 2024
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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.
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.
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-408, https://doi.org/10.5194/essd-2024-408, 2024
Revised manuscript accepted for ESSD
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The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP), which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, Elodie Salmon, Philippe Ciais, Zhen Zhang, Shushi Peng, and Marielle Saunois
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-466, https://doi.org/10.5194/essd-2024-466, 2024
Revised manuscript accepted for ESSD
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Wetlands are responsible for about a third of global emissions of methane, a potent greenhouse gas. We have developed the GIEMS-MethaneCentric (GIEMS-MC) dataset to represent the dynamics of wetland extent on a global scale (0.25°x0.25° resolution, monthly time step). This updated resource combines satellite data and existing wetland databases, covering 1992 to 2020. Consistent maps of other methane-emitting surface waters (lakes, rivers, reservoirs, rice paddies) are also provided.
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.
David A. Gibbs, Melissa Rose, Giacomo Grassi, Joana Melo, Simone Rossi, Viola Heinrich, and Nancy L. Harris
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-397, https://doi.org/10.5194/essd-2024-397, 2024
Revised manuscript accepted for ESSD
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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.
Yifan Cheng, Lei Zhao, Tirthankar Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-416, https://doi.org/10.5194/essd-2024-416, 2024
Revised manuscript accepted for ESSD
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Absence of globally consistent and spatially continuous urban surface properties have long prevented large-scale high-resolution urban climate modeling. We developed the U-Surf data, a 1km-resolution dataset that provides key urban surface properties worldwide. U-Surf enhances urban representation in models, enables city-to-city comparison, and supports kilometer-scale Earth system modeling. Its broader applications can be extended to machine learning and many other non-climatic practices.
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.
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.
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.
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).
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.
Cited articles
Alemohammad, S. H., Ballantyne, A., Bromberg Gaber, Y., Booth, K.,
Nakanuku-Diggs, L., and Miglarese, A. H.: LandCoverNet: A Global Land Cover
Classification Training Dataset, Radiant MLHub [data set], available at: https://radiant-mlhub.s3-us-west-2.amazonaws.com/landcovernet/Documentation.pdf,
last access: 7 September 2020.
Auricht, C., Dixon, J., Boffa, J.-M., and Garrity, D.: Farming Systems of
Africa, in: Atlas of African agriculture research and development: Revealing
agriculture's place in Africa, 14–15, https://doi.org/10.2499/9780896298460_06, 2014.
Bégué, A., Arvor, D., Bellon, B., Betbeder, J., de Abelleyra, D., Ferraz, R. P. D., Lebourgeois, V., Lelong, C., Simões, M., and
Verón, S. R.: Remote Sensing and Cropping Practices: A Review, Remote
Sens., 10, 99, https://doi.org/10.3390/rs10010099, 2018.
Bellón, B., Bégué, A., Lo Seen, D., Lebourgeois, V.,
Evangelista, B. A., Simões, M., and Demonte Ferraz, R. P.: Improved
regional-scale Brazilian cropping systems' mapping based on a semi-automatic
object-based clustering approach, Int. J. Appl. Earth Obs. Geoinf.,
68, 127–138, https://doi.org/10.1016/j.jag.2018.01.019, 2018.
Bontemps, S., Arias, M., Cara, C., Dedieu, G., Guzzonato, E., Hagolle, O.,
Inglada, J., Matton, N., Morin, D., Popescu, R., Rabaute, T., Savinaud, M.,
Sepulcre, G., Valero, S., Ahmad, I., Bégué, A., Wu, B., de
Abelleyra, D., Diarra, A., Dupuy, S., French, A., Akhtar, I. U. H.,
Kussul, N., Lebourgeois, V., Le Page, M., Newby, T., Savin, I., Verón,
S., Koetz, B., and Defourny, P.: Building a Data Set over 12 Globally
Distributed Sites to Support the Development of Agriculture Monitoring
Applications with Sentinel-2, Remote Sens., 7, 16062–16090,
https://doi.org/10.3390/rs71215815, 2015.
Choden, K., Ravon, S., Epstein, J. H., Hoem, T., Furey, N., Gely, M.,
Jolivot, A., Hul, V., Neung, C., Tran, A., and Cappelle, J.: Pteropus lylei
primarily forages in residential areas in Kandal, Cambodia, Ecol. Evol., 9,
4181–4191, https://doi.org/10.1002/ece3.5046, 2019.
Defourny, P., Jarvis, I., and Blaes, X.: JECAM Guidelines for cropland and
crop type definition and field data collection, JECAM, available at:
http://jecam.org/wp-content/uploads/2018/10/JECAM_Guidelines_for_Field_Data_Collection_v1_0.pdf (last access: 17 May 2021), 2014.
Defourny, P., Bontemps, S., Bellemans, N., Cara, C., Dedieu, G., Guzzonato, E., Hagolle, O., Inglada, J., Nicola, L., Rabaute, T., Savinaud, M., Udroiu, C., Valero, S., Begue, A., Dejoux, J.-F., El Harti, A., Ezzahar, J., Kussul, N., Labbassi, K., Lebourgeois, V., Miao, Z., Newby, T., Nyamugama, A., Salh, N., Shelestov, A., Simonneaux, V., Traore, P. S., Traore, S. S., and Koetz, B.: Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world, Remote Sens. Environ., 221, 551–568, https://doi.org/10.1016/j.rse.2018.11.007, 2019.
de Oliveira Santos, C. L. M., Lamparelli, R. A. C., Dantas Araújo
Figueiredo, G. K., Dupuy, S., Boury, J., dos Santos Luciano, A. C., da Silva Torres, R., and le Maire, G.: Classification of Crops, Pastures, and Tree
Plantations along the Season with Multi-Sensor Image Time Series in a
Subtropical Agricultural Region, Remote Sens., 11, 334,
https://doi.org/10.3390/rs11030334, 2019.
Dixon, J., Gulliver, A., and Gibbon, D.: Farming Systems and Poverty:
Improving Farmers' Livelihoods in a Changing World, FAO, World Bank, Rome,
Washington DC, USA, 49 pp., 2001.
FAO: LLand use, irrigation and agricultural practices – Definitions,
available at: http://www.fao.org/fileadmin/templates/ess/ess_test_folder/Definitions/Land_Use_Definitions_FAOSTAT.xlsx, last access: 9 September 2020.
Fourie, A.: Better Crop Estimates in South Africa Integrating GIS with other
business systems, in: GIS Best Practices – GIS for Agriculture, 9–13, available at: https://www.esri.com/content/dam/esrisites/sitecore-archive/Files/Pdfs/library/bestpractices/gis-for-agriculture.pdf (last access: 15 December 2021), 2009.
Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E.,
Duerauer, M., Albrecht, F., Schill, C., Perger, C., Havlik, P., Mosnier, A.,
Thornton, P., Wood-Sichra, U., Herrero, M., Becker-Reshef, I., Justice, C.,
Hansen, M., Gong, P., Abdel Aziz, S., Cipriani, A., Cumani, R., Cecchi, G.,
Conchedda, G., Ferreira, S., Gomez, A., Haffani, M., Kayitakire, F.,
Malanding, J., Mueller, R., Newby, T., Nonguierma, A., Olusegun, A., Ortner,
S., Rajak, D. R., Rocha, J., Schepaschenko, D., Schepaschenko, M., Terekhov,
A., Tiangwa, A., Vancutsem, C., Vintrou, E., Wenbin, W., Velde, M.,
Dunwoody, A., Kraxner, F., and Obersteiner, M.: Mapping global cropland and
field size, Glob. Change Biol., 21, 1980–1992,
https://doi.org/10.1111/gcb.12838, 2015.
Fritz, S., See, L., Perger, C., McCallum, I., Schill, C., Schepaschenko, D.,
Duerauer, M., Karner, M., Dresel, C., Laso-Bayas, J.-C., Lesiv, M., Moorthy,
I., Salk, C. F., Danylo, O., Sturn, T., Albrecht, F., You, L., Kraxner, F.,
and Obersteiner, M.: A global dataset of crowdsourced land cover and land
use reference data, Sci. Data, 4, 170075, https://doi.org/10.1038/sdata.2017.75,
2017.
Gbodjo, Y. J. E., Ienco, D., Leroux, L., Interdonato, R., Gaetano, R., and
Ndao, B.: Object-Based Multi-Temporal and Multi-Source Land Cover Mapping
Leveraging Hierarchical Class Relationships, Remote Sens., 12, 2814,
https://doi.org/10.3390/rs12172814, 2020.
Grillo, J. and Holt, J.: Application of the Livelihood Zone Maps and Profiles for Food Security Analysis and
Early Warning – Guidance for Famine Early Warning Systems Network (FEWS NET) Representatives
and Partners, USAID FEWS NET, available at: https://fews.net/fews-data/335 (last access: 15 December 2021), 2009.
Hoskins, A. J., Bush, A., Gilmore, J., Harwood, T., Hudson, L. N., Ware, C.,
Williams, K. J., and Ferrier, S.: Downscaling land-use data to provide
global 30” estimates of five land-use classes, Ecol. Evol., 6, 3040–3055,
https://doi.org/10.1002/ece3.2104, 2016.
Ienco, D., Gbodjo, Y. J. E., Gaetano, R., and Interdonato, R.: Weakly
Supervised Learning for Land Cover Mapping of Satellite Image Time Series
via Attention-Based CNN, IEEE Access, 8, 179547–179560,
https://doi.org/10.1109/ACCESS.2020.3024133, 2020.
Inglada, J., Arias, M., Tardy, B., Hagolle, O., Valero, S., Morin, D.,
Dedieu, G., Sepulcre, G., Bontemps, S., Defourny, P., and Koetz, B.:
Assessment of an Operational System for Crop Type Map Production Using High
Temporal and Spatial Resolution Satellite Optical Imagery, Remote Sens., 7,
12356–12379, https://doi.org/10.3390/rs70912356, 2015.
Jolivot, A., Lebourgeois, V., Ameline, M., Andriamanga, V., Bellon, B.,
Castets, M., Crespin-Boucaud, A., Defourny, P., Diaz, S., Dieye, M., Dupuy,
S., Ferraz, R., Gaetano, R., Gely, M., Jahel, C., Kabore, B., Lelong, C., Le Maire, G.,
Leroux, L., Lo Seen, D., Muthoni, M., Ndao, B., Newby, T., De Oliveira
Santos, C. L. M., Rasoamalala, E., Simoes, M., Thiaw, I., Timmermans, A.,
Tran, A., and Begue, A.: Harmonized in situ JECAM datasets for agricultural
land use mapping and monitoring in tropical countries, Cirad [data set],
https://doi.org/10.18167/DVN1/P7OLAP, 2021.
Laso Bayas, J. C., See, L., Perger, C., Justice, C., Nakalembe, C., Dempewolf,
J., and Fritz, S.: Validation of Automatically Generated Global and Regional
Cropland Data Sets: The Case of Tanzania, Remote Sens., 9, 815,
https://doi.org/10.3390/rs9080815, 2017a.
Laso Bayas, J. C., Lesiv, M., Waldner, F., Schucknecht, A., Duerauer, M.,
See, L., Fritz, S., Fraisl, D., Moorthy, I., McCallum, I., Perger, C.,
Danylo, O., Defourny, P., Gallego, J., Gilliams, S., Akhtar, I. ul H.,
Baishya, S. J., Baruah, M., Bungnamei, K., Campos, A., Changkakati, T.,
Cipriani, A., Das, K., Das, K., Das, I., Davis, K. F., Hazarika, P.,
Johnson, B. A., Malek, Z., Molinari, M. E., Panging, K., Pawe, C. K.,
Pérez-Hoyos, A., Sahariah, P. K., Sahariah, D., Saikia, A., Saikia, M.,
Schlesinger, P., Seidacaru, E., Singha, K., and Wilson, J. W.: A global
reference database of crowdsourced cropland data collected using the
Geo-Wiki platform, Sci. Data, 4, 170136,
https://doi.org/10.1038/sdata.2017.136, 2017b.
Lebourgeois, V., Dupuy, S., Vintrou, É., Ameline, M., Butler, S., and
Bégué, A.: A Combined Random Forest and OBIA Classification Scheme
for Mapping Smallholder Agriculture at Different Nomenclature Levels Using
Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM), Remote
Sens., 9, 259, https://doi.org/10.3390/rs9030259, 2017.
Leroux, L., Castets, M., Baron, C., Escorihuela, M.-J., Bégué, A.,
and Lo Seen, D.: Maize yield estimation in West Africa from crop
process-induced combinations of multi-domain remote sensing indices, Eur. J.
Agron., 108, 11–26, https://doi.org/10.1016/j.eja.2019.04.007, 2019.
Leroux, L., Falconnier, G. N., Diouf, A. A., Ndao, B., Gbodjo, J. E., Tall,
L., Balde, A. A., Clermont-Dauphin, C., Bégué, A., Affholder, F.,
and Roupsard, O.: Using remote sensing to assess the effect of trees on
millet yield in complex parklands of Central Senegal, Agric. Syst., 184,
102918, https://doi.org/10.1016/j.agsy.2020.102918, 2020.
Leroux, L., Faye, N. F., Jahel, C., Falconnier, G. N., Diouf, A. A., Ndao, B., Tiaw, I., Senghor, Y., Kanfany, G., Balde, A., Dieye, M., Sirdey, N., Alobo Loison, S., Corbeels, M., Baudron, F., and Bouquet, E.: Exploring the agricultural landscape diversity-food security nexus: an analysis in two contrasted parklands of Central Senegal, Agr. Syst., 196, 103312, https://doi.org/10.1016/j.agsy.2021.103312, 2022.
Long, Y., Xia, G., Li, S.,Yang, W., Yang, M. Y., Zhu, X., Zhang, L., and Li, D.:
DiRS: On Creating Benchmark Datasets for Remote Sensing Image
Interpretation, available at: https://www.researchgate.net/publication/342377115_DiRS_On_Creating_Benchmark_Datasets_for_Remote_Sensing_Image_Interpretation, last access: 7 September 2020.
Ndao, B., Leroux, L., Gaetano, R., Diouf, A. A., Soti, V., Mbow, C.,
Bégué, A., and Sambou, B.: Landscape heterogeneity analysis using
geospatial techniques and a priori knowledge in Sahelian agroforestry
systems of Senegal, Ecol. Indic., 125, 107481, https://doi.org/10.1016/j.ecolind.2021.107481, 2021.
Tsendbazar, N. E., de Bruin, S., and Herold, M.: Assessing global land cover
reference datasets for different user communities, ISPRS J. Photogramm.
Remote Sens., 103, 93–114, https://doi.org/10.1016/j.isprsjprs.2014.02.008,
2015.
Valero, S., Morin, D., Inglada, J., Sepulcre, G., Arias, M., Hagolle, O.,
Dedieu, G., Bontemps, S., Defourny, P., and Koetz, B.: Production of a
Dynamic Cropland Mask by Processing Remote Sensing Image Series at High
Temporal and Spatial Resolutions, Remote Sens., 8, 55,
https://doi.org/10.3390/rs8010055, 2016.
Waldner, F., Fritz, S., Di Gregorio, A., and Defourny, P.: Mapping
Priorities to Focus Cropland Mapping Activities: Fitness Assessment of
Existing Global, Regional and National Cropland Maps, Remote Sens., 7,
7959–7986, https://doi.org/10.3390/rs70607959, 2015.
Waldner, F., De Abelleyra, D., Verón, S. R., Zhang, M., Wu, B.,
Plotnikov, D., Bartalev, S., Lavreniuk, M., Skakun, S., Kussul, N., Le
Maire, G., Dupuy, S., Jarvis, I., and Defourny, P.: Towards a set of
agrosystem-specific cropland mapping methods to address the global cropland
diversity, Int. J. Remote Sens., 37, 3196–3231,
https://doi.org/10.1080/01431161.2016.1194545, 2016.
Waldner, F., Bellemans, N., Hochman, Z., Newby, T., de Abelleyra, D.,
Verón, S. R., Bartalev, S., Lavreniuk, M., Kussul, N., Maire, G. L.,
Simoes, M., Skakun, S., and Defourny, P.: Roadside collection of training
data for cropland mapping is viable when environmental and management
gradients are surveyed, Int. J. Appl. Earth Obs. Geoinformation, 80, 82–93,
https://doi.org/10.1016/j.jag.2019.01.002, 2019.
Short summary
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.
This paper presents nine standardized crop type reference datasets collected between 2013 and...
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