Articles | Volume 16, issue 8
https://doi.org/10.5194/essd-16-3893-2024
© Author(s) 2024. 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-16-3893-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
30 Aug 2024
Data description paper |
| 30 Aug 2024
| 30 Aug 2024
Monsoon Asia Rice Calendar (MARC): a gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images
Xin Zhao
CORRESPONDING AUTHOR
Biogeochemical Cycle Modeling and Analysis Section, Earth System Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
Biogeochemical Cycle Modeling and Analysis Section, Earth System Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
Haruka Izumisawa
Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, Japan
Yuji Masutomi
Asia-Pacific Climate Change Adaptation Research Section, Center for Climate Change Adaption, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
Seima Osako
DATAFLUCT, Inc., 1-19-9 Dogenzaka, Shibuya, Tokyo, 150-0043, Japan
Shuhei Yamamoto
DATAFLUCT, Inc., 1-19-9 Dogenzaka, Shibuya, Tokyo, 150-0043, Japan
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EGUsphere, https://doi.org/10.5194/egusphere-2025-1885, https://doi.org/10.5194/egusphere-2025-1885, 2025
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We developed maize version of process-based crop model coupled with a land surface model (MATCRO). It extends the original MATCRO-Rice by incorporating C4 photosynthesis and maize-specific parameters. The model was validated using field data from four sites and global yield data from FAOSTAT. MATCRO-Maize captured the interannual yield variability in global and county-level yield data, demonstrating its potential for climate impact assessments on maize production.
Astrid Yusara, Tomomichi Kato, Elizabeth A. Ainsworth, Rafael Battisti, Etsushi Kumagai, Satoshi Nakano, Yushan Wu, Yutaka Tsusumi-Morita, Kazuhiko Kobayashi, and Yuji Masutomi
EGUsphere, https://doi.org/10.5194/egusphere-2025-453, https://doi.org/10.5194/egusphere-2025-453, 2025
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We developed a soybean model, an ecosystem model for crop yield (namely MATCRO-Soy), integrating crop response toward climate variable. It offers a detailed yield estimation. Parameter tuning in the model used literature and field experiments. The model shows a moderate correlation with observed yields at the global, national, and grid levels. Development of MATCRO-Soy enhances crop modeling diversity approaches, particularly in climate change impact studies.
Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, https://doi.org/10.5194/essd-14-4551-2022, 2022
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Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
Yuji Masutomi, Toshichika Iizumi, Key Oyoshi, Nobuyuki Kayaba, Wonsik Kim, Takahiro Takimoto, and Yoshimitsu Masaki
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-131, https://doi.org/10.5194/gmd-2021-131, 2021
Revised manuscript not accepted
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The accuracy of seasonal climate forecasts for monthly precipitation of JMA/MRI-CPS2, a dynamical seasonal climate forecast (SCF) system, is higher than that of statistical SCF (St-SCF) system using climate indices around the equator (10° S–10° N) even for six-month lead forecasts. On a global scale, the forecast accuracy of JMA/MRI-CPS2 is higher for one-month lead forecasts; however, St-SCFs were more accurate for forecasts more than two months in advance.
Thomas A. M. Pugh, Tim Rademacher, Sarah L. Shafer, Jörg Steinkamp, Jonathan Barichivich, Brian Beckage, Vanessa Haverd, Anna Harper, Jens Heinke, Kazuya Nishina, Anja Rammig, Hisashi Sato, Almut Arneth, Stijn Hantson, Thomas Hickler, Markus Kautz, Benjamin Quesada, Benjamin Smith, and Kirsten Thonicke
Biogeosciences, 17, 3961–3989, https://doi.org/10.5194/bg-17-3961-2020, https://doi.org/10.5194/bg-17-3961-2020, 2020
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The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle. Estimates from six contemporary models found this time to range from 12.2 to 23.5 years for the global mean for 1985–2014. Future projections do not give consistent results, but 13 model-based hypotheses are identified, along with recommendations for pragmatic steps to test them using existing and novel observations, which would help to reduce large current uncertainty.
Kazuya Nishina, Akihiko Ito, Naota Hanasaki, and Seiji Hayashi
Earth Syst. Sci. Data, 9, 149–162, https://doi.org/10.5194/essd-9-149-2017, https://doi.org/10.5194/essd-9-149-2017, 2017
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Available historical global N fertilizer map as an input data to global biogeochemical model is still limited and existing maps were not considered NH4+ and NO3− in the fertilizer application rates. In our products, by utilizing national fertilizer species consumption data in FAOSTAT database, we succeeded to estimate the ratio of NH4+ to NO3− in the N fertilizer map. The products could be widely utilized for global N cycling studies.
S. Hashimoto, N. Carvalhais, A. Ito, M. Migliavacca, K. Nishina, and M. Reichstein
Biogeosciences, 12, 4121–4132, https://doi.org/10.5194/bg-12-4121-2015, https://doi.org/10.5194/bg-12-4121-2015, 2015
Y. Kim, K. Nishina, N. Chae, S. J. Park, Y. J. Yoon, and B. Y. Lee
Biogeosciences, 11, 5567–5579, https://doi.org/10.5194/bg-11-5567-2014, https://doi.org/10.5194/bg-11-5567-2014, 2014
K. Nishina, A. Ito, D. J. Beerling, P. Cadule, P. Ciais, D. B. Clark, P. Falloon, A. D. Friend, R. Kahana, E. Kato, R. Keribin, W. Lucht, M. Lomas, T. T. Rademacher, R. Pavlick, S. Schaphoff, N. Vuichard, L. Warszawaski, and T. Yokohata
Earth Syst. Dynam., 5, 197–209, https://doi.org/10.5194/esd-5-197-2014, https://doi.org/10.5194/esd-5-197-2014, 2014
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This study created the first detailed annual maps of Africa's cropland extent from 2000 to 2022 in 30 m resolution to support global efforts against hunger and sustainable farming. Our findings show Africa's cropland grew by 8.5 % over two decade, while 11.5 % of cropland was abandoned by 2018 – revealing hidden challenges in agricultural sustainability. These yearly, field-sized maps help governments track where farming grows or shrinks, plan food supplies, and protect vital cropland.
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Earth Syst. Sci. Data, 17, 1245–1263, https://doi.org/10.5194/essd-17-1245-2025, https://doi.org/10.5194/essd-17-1245-2025, 2025
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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.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-73, https://doi.org/10.5194/essd-2025-73, 2025
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-59, https://doi.org/10.5194/essd-2025-59, 2025
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Eurostat and JRC developed a new methodology to make geospatial data from agricultural census available to users, while ensuring that no confidential information from individuals is disclosed. The geospatial data presented in the article correspond to the contextual indicators of the monitoring and evaluation framework of the Common Agricultural Policy. Our exploratory analysis reveals several interesting patterns which contribute to the broader debate on the future of European agriculture.
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Earth Syst. Sci. Data, 17, 855–880, https://doi.org/10.5194/essd-17-855-2025, https://doi.org/10.5194/essd-17-855-2025, 2025
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Our study introduces GMIE, a high-resolution global map of irrigated cropland at 100 m resolution, covering 403.17 Mha and utilizing irrigation performance under drought stress. We found that 23.4 % of global cropland is irrigated, with the most extensive areas in India, China, the United States, and Pakistan. We identified the distribution of central pivot systems commonly used in the United States and Saudi Arabia. This new map can better support water management and food security globally.
Guangsheng Zhou, Hongrui Ren, Lei Zhang, Xiaomin Lv, and Mengzi Zhou
Earth Syst. Sci. Data, 17, 773–797, https://doi.org/10.5194/essd-17-773-2025, https://doi.org/10.5194/essd-17-773-2025, 2025
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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 using the MOD09A1 product. The overall accuracy of continuous annual QTP vegetation mapping reached 83.3%, with the reference annual 2020 data reaching an accuracy of 83.3% and a kappa coefficient of 0.82. The study supports the use of remote sensing data to mapping long-time continuous annual vegetation.
Xuemeng Tian, Davide Consoli, Martijn Witjes, Florian Schneider, Leandro Parente, Murat Şahin, Yu-Feng Ho, Robert Minařík, and Tomislav Hengl
Earth Syst. Sci. Data, 17, 741–772, https://doi.org/10.5194/essd-17-741-2025, https://doi.org/10.5194/essd-17-741-2025, 2025
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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 policy-makers in managing environmental resources.
Mingyang Song, Lu Xu, Ji Ge, Hong Zhang, Lijun Zuo, Jingling Jiang, Yinhaibin Ding, Yazhe Xie, and Fan Wu
Earth Syst. Sci. Data, 17, 661–683, https://doi.org/10.5194/essd-17-661-2025, https://doi.org/10.5194/essd-17-661-2025, 2025
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We created a 10 m resolution rice distribution map for East Asia in 2023 (EARice10), achieving an overall accuracy (OA) of 90.48 % on validation samples. EARice10 shows strong consistency with statistical data (coefficient of determination, R2: 0.94–0.98) and existing datasets (R2: 0.79–0.98). It is the most up-to-date map, covering the four major rice-producing countries in East Asia at 10 m resolution.
Maximilian Freudenberg, Sebastian Schnell, and Paul Magdon
Earth Syst. Sci. Data, 17, 351–367, https://doi.org/10.5194/essd-17-351-2025, https://doi.org/10.5194/essd-17-351-2025, 2025
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Classifying tree species in satellite images is an important task for environmental monitoring and forest management. Here we present a dataset containing Sentinel-2 satellite pixel time series of individual trees intended for training machine learning models. The dataset was created by merging information from the German National Forest Inventory in 2012 with satellite data. It sparsely covers the whole of Germany for the years 2015 to 2022 and comprises 48 species and 3 species groups.
Mauro Marty, Livia Piermattei, Lars T. Waser, and Christian Ginzler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-428, https://doi.org/10.5194/essd-2024-428, 2025
Preprint under review for ESSD
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Millions of aerial photographs represent an enormous, resource for geoscientists. In this study, we used freely available historical stereo images covering Switzerland, allowing us to derive four countrywide DSMs at a 1 m spatial resolution across four epochs. Our DSMs achieved sub-metric accuracy compared to reference data and high image matching completeness, demonstrating the feasibility of capturing continuous surface change at a high spatial resolution over different land cover classes.
Yangyang Fu, Xiuzhi Chen, Chaoqing Song, Xiaojuan Huang, Jie Dong, Qiongyan Peng, and Wenping Yuan
Earth Syst. Sci. Data, 17, 95–115, https://doi.org/10.5194/essd-17-95-2025, https://doi.org/10.5194/essd-17-95-2025, 2025
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This study proposed the Winter-Triticeae Crops Index (WTCI), which had great performance and stable spatiotemporal transferability in identifying winter-triticeae crops in 66 countries worldwide, with an overall accuracy of 87.7 %. The first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022 were further produced based on the WTCI method. The product can serve as an important basis for agricultural applications.
Jiahao Shi, Hua Yuan, Wanyi Lin, Wenzong Dong, Hongbin Liang, Zhuo Liu, Jianxin Zeng, Haolin Zhang, Nan Wei, Zhongwang Wei, Shupeng Zhang, Shaofeng Liu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 17, 117–134, https://doi.org/10.5194/essd-17-117-2025, https://doi.org/10.5194/essd-17-117-2025, 2025
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Flux tower data are widely recognized as benchmarking data for land surface models, but insufficient emphasis on and deficiency in site attribute data limits their true value. We collect site-observed vegetation, soil, and topography data from various sources. The final dataset encompasses 90 sites globally, with relatively complete site attribute data and high-quality flux validation data. This work has provided more reliable site attribute data, benefiting land surface model development.
Yanghai Yu, Yang Lei, Paul Siqueira, Xiaotong Liu, Denuo Gu, Anmin Fu, Yong Pang, Wenli Huang, and Jiancheng Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-596, https://doi.org/10.5194/essd-2024-596, 2025
Revised manuscript accepted for ESSD
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This paper presents a global-to-local method to improve forest height estimates by fusing InSAR and GEDI data. The large-scale ability was tested on open-access ALOS-1 data, where a two-fold solution is used to address temporal gap between GEDI and ALOS data. Produced products of 30 m gridded forest height mosaics for the northeastern U.S. and China show improved accuracy at 3–4 m/ha and 20 % enhancement over interpolated GEDI maps. The prototype is promising to fuse GEDI and future NISAR data.
Raphaël d'Andrimont, Momchil Yordanov, Fernando Sedano, Astrid Verhegghen, Peter Strobl, Savvas Zachariadis, Flavia Camilleri, Alessandra Palmieri, Beatrice Eiselt, Jose Miguel Rubio Iglesias, and Marijn van der Velde
Earth Syst. Sci. Data, 16, 5723–5735, https://doi.org/10.5194/essd-16-5723-2024, https://doi.org/10.5194/essd-16-5723-2024, 2024
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The Land Use/Cover Area frame Survey (LUCAS) Copernicus 2022 is a large and systematic in situ field survey of 137 966 polygons over the European Union in 2022. The data contain 82 land cover classes and 40 land use classes.
Shuang Chen, Jie Wang, Qiang Liu, Xiangan Liang, Rui Liu, Peng Qin, Jincheng Yuan, Junbo Wei, Shuai Yuan, Huabing Huang, and Peng Gong
Earth Syst. Sci. Data, 16, 5449–5475, https://doi.org/10.5194/essd-16-5449-2024, https://doi.org/10.5194/essd-16-5449-2024, 2024
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The inconsistent coverage of Landsat data due to its long revisit intervals and frequent cloud cover poses challenges to large-scale land monitoring. We developed a global 30 m 23-year (2000–2022) daily seamless data cube (SDC) of surface reflectance based on Landsat 5, 7, 8, and 9 and MODIS products. The SDC exhibits enhanced capabilities for monitoring land cover changes and robust consistency in both spatial and temporal dimensions, which are important for global environmental monitoring.
Gerardo E. Soto, Steven W. Wilcox, Patrick E. Clark, Francesco P. Fava, Nathaniel D. Jensen, Njoki Kahiu, Chuan Liao, Benjamin Porter, Ying Sun, and Christopher B. Barrett
Earth Syst. Sci. Data, 16, 5375–5404, https://doi.org/10.5194/essd-16-5375-2024, https://doi.org/10.5194/essd-16-5375-2024, 2024
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This paper uses machine learning and linear unmixing to produce rangeland health indicators: Landsat time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground in arid and semi-arid Kenya, Ethiopia, and Somalia. This represents the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangeland health in the arid and semi-arid rangelands of this portion of eastern Africa.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
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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.
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.
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.
Dina Jahanianfard, Joana Parente, Oscar Gonzalez-Pelayo, and Akli Benali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-305, https://doi.org/10.5194/essd-2024-305, 2024
Revised manuscript accepted for ESSD
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This data description paper provides details on the development of the first Portuguese burn severity atlas in Portugal from 1984 to 2022 derived from satellite imagery via Google Earth Engine platform. Moreover, a semi-automated code was also developed, which can be used to create burn severity atlas of any other region in the world. The maps of this atlas can be used not only in fields related to fire ecology and management, but also within research areas related to air, water, and soil.
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.
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).
Cited articles
Abrol, I. P.: Sustaining rice-wheat cropping system productivity in the Indo-Gangetic Plains, in: The 4th JIRCAS International Symposium, Sustainable Agricultural Development Compatible with Environmental Conservation in Asia, 26 August 1997, 155–1651, 1997.
Agostinelli, C. and Lund, U.: R package “circular”: Circular Statistics (version 0.50), https://CRAN.R-project.org/package=_circular (last access: 8 July 2023), 2023.
Ahmad, I. and Iram, S.: Rice-wheat cropping pattern and resource conservation technologies: http://www.pakissan.com/english/agri.overview/rice.wheat.cropping.pattern.shtml, last access: 6 June 2023.
Berens, P.: CircStat: A MATLAB Toolbox for circular statistics, J. Stat. Softw., 31, 1–21, https://doi.org/10.18637/jss.v031.i10, 2009.
Best, D. and Fisher, N.: The BIAS of the maximum likelihood estimators of the von mises-fisher concentration parameters: The BIAS of the maximum likelihood estimators, Commun. Stat.-Simul. Comput., 10, 493–502, https://doi.org/10.1080/03610918108812225, 1981.
Chen, C., Groenigen, K., Yang, H., Hungate, B., Yang, B., Tian, Y., Chen, J., Dong, W., Huang, S., Deng, A., Jiang, Y., and Zhang, W.: Global warming and shifts in cropping systems together reduce China's rice production, Glob. Food Secur., 24, 100359, https://doi.org/10.1016/j.gfs.2020.100359, 2020.
Clauss, K., Ottinger, M., Leinenkugel, P., and Kuenzer, C.: Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data, Int. J. Appl. Earth Obs., 73, 574–585, https://doi.org/10.1016/j.jag.2018.07.022, 2018.
Dhanda, S., Yadav, A., Yadav, D. B., and Chauhan, B. S.: Emerging issues and potential opportunities in the rice-wheat cropping system of North-Western India, Front. Plant Sci., 13, 832683, https://doi.org/10.3389/fpls.2022.832683, 2022.
Diem, P. K., Diem, N. K., and Hung, H. V.: Assessment of the efficiency of using MODIS MCD43A4 in mapping of rice transplanting calendar in the Mekong Delta, IOP Conf. Ser.-Earth Environ Sci., 652, 012015, https://doi.org/10.1088/1755-1315/652/1/012015, 2021.
Elliott, J., Müller, C., Deryng, D., Chryssanthacopoulos, J., Boote, K. J., Büchner, M., Foster, I., Glotter, M., Heinke, J., Iizumi, T., Izaurralde, R. C., Mueller, N. D., Ray, D. K., Rosenzweig, C., Ruane, A. C., and Sheffield, J.: The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0), Geosci. Model Dev., 8, 261–277, https://doi.org/10.5194/gmd-8-261-2015, 2015.
FAOSTAT: https://www.fao.org/faostat/en/#data (last access: 2 May 2023), 2022.
Fatchurrachman, Rudiyanto, Soh, N. C., Shah, R. M., Giap, S. G. E., Setiawan, B. I., and Minasny, B.: High-resolution mapping of paddy rice extent and growth stages across Peninsular Malaysia using a fusion of Sentinel-1 and 2 time series data in Google Earth Engine, Remote Sens., 14, 1875, https://doi.org/10.3390/rs14081875, 2022.
Franch, B., Cintas, J., Becker-Reshef, I., Sanchez-Torres, M. J., Roger, J., Skakun, S., Sobrino, J. A., Van Trichet, K., Degerickx, J., Gilliams, S., Koetz, B., Szantoi, Z., and Whitcraft, A.: Global crop calendars of maize and wheat in the framework of the WorldCreal project, GISci. Remote Sens., 59, 885–913, https://doi.org/10.1080/15481603.2022.2079273, 2022.
Iizumi, T., Kim, W., and Nishimori, M.: Modeling the global sowing and harvesting windows of major crops around the year 2000, J. Adv. Model. Earth Sy., 11, 99–112, https://doi.org/10.1029/2018MS001477, 2019.
Inoue, S., Ito, A., and Yonezawa, C.: Mapping paddy fields in Japan by using a Sentinel-1 SAR time series supplemented by Sentinel-2 images on Google Earth Engine, Remote Sens., 12, 1622, https://doi.org/10.3390/rs12101622, 2020.
Ito, A., Inoue, S., and Inatomi, M.: Model-based evaluation of methane emissions from paddy fields in East Asia, J. Agr. Meteorol., 78, 56–65, https://doi.org/10.2480/agrmet.D-21-00037, 2022.
Jiang, Y., Carrijo, D., Huang, S., Chen, J., Balaine, N., Zhang, W., van Groenigen K. J., and Linquist, B.: Water management to mitigate the global warming potential of rice systems: A global meta-analysis, Field Crop Res., 234, 47–54, https://doi.org/10.1016/j.fcr.2019.02.010, 2019.
Kotsuki, S. and Tanaka, K.: SACRA – a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI, Hydrol. Earth Syst. Sci., 19, 4441–4461, https://doi.org/10.5194/hess-19-4441-2015, 2015.
Laborte, A. G., Bie, K. D., Smaling, E. M. A., Moya, P. F., Boling, A. A., and Ittersum, M. K. V.: Rice yields and yield gaps in southeast Asia: past trends and future outlook, Eur. J. Agron., 36, 9–20, https://doi.org/10.1016/j.eja.2011.08.005, 2012.
Laborte, A. G., Gutierrez, M. A., Balanza, J. G., Saito, K., Zwart, S. J., Boschetti, M., Murty, M. V. R., Villano, L., Aunario, J. K., Reinke, R., Koo, J., Hijmans, R. J., and Nelson, A.: RiceAtlas, a spatial database of global rice calendars and production, Sci. Data, 4, 170074, https://doi.org/10.1038/sdata.2017.74, 2017.
Liu, S., Chen, Y., Ma, Y., Kong, X., Zhang, X., and Zhang, D.: Mapping ratoon rice transplanting area in Central China using Sentinel-2 time stacks and the phenology-based algorithm, Remote Sens., 12, 3400, https://doi.org/10.3390/rs12203400, 2020.
Luo, Y., Zhang, Z., Chen, Y., Li, Z., and Tao, F.: ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products, Earth Syst. Sci. Data, 12, 197–214, https://doi.org/10.5194/essd-12-197-2020, 2020.
Maciel-Nájera, J. F., Hernández-Velasco, J., Gonzalez-Elizondo M. S., Hernandez-Díaz, J. C., López-Sánchez, C. A., Antúnez, P., Bailón-Soto, C. E., and Wehenkel, C.: Unexpected spatial patterns of natural regeneration in typical uneven-aged mixed pine-oak forests in the Sierra Madre Occidental, Mexico, Glob. Ecol. Conserv., 23, e01074, https://doi.org/10.1016/j.gecco.2020.e01074, 2020.
Mahan, R.: Circular statistical methods: Applications in spatial and temporal performance analysis, Special Research Report, U.S. Army Research Inst. for Behavioral and Social Sciences, Final Rep., https://apps.dtic.mil/sti/tr/pdf/ADA240751.pdf (last access: 16 July 2023), 1991.
Mathison, C., Deva, C., Falloon, P., and Challinor, A. J.: Estimating sowing and harvest dates based on the Asian summer monsoon, Earth Syst. Dynam., 9, 563–592, https://doi.org/10.5194/esd-9-563-2018, 2018.
Mishra, B., Busetto, L., Boschetti, M., Laborte, A., and Nelson, A.: RICA: A rice crop calendar for Asia based on MODIS multi year data, Int. J. Appl. Earth Obs., 103, 102471, https://doi.org/10.1016/j.jag.2021.102471, 2021.
More, R. S., Manjunath, K. R., Jain, N. K., Panigraphy, S., and Parihar, J. S.: Derivation of rice crop calendar and evaluation of crop phenometrics and latitudinal relationship for major south and south-east Asian countries: A remote sensing approach, Comput. Electron. Agr., 127, 336–350, https://doi.org/10.1016/j.compag.2016.06.026, 2016.
Muñoz-Salazar, T., LeQuesne, C., Rozas, V., Christie, D. A., and Rojas-Badilla, M.: Examining the potential of Austrocedrus chilensis tree rings as indicators of past late-spring forest events in central Chile, Dendrochronologia, 74, 125962, https://doi.org/10.1016/j.dendro.2022.125962, 2022.
Nguyen, T. T. H., De Bie, C. A. J. M., Ali, A., Smaling, E. M. A., and Chu, T. H.: Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis, Int. J. Remote Sens., 33, 415–434, https://doi.org/10.1080/01431161.2010.532826, 2012.
Portmann, F. T., Siebert, S., and Döll, P.: MIRCA2000-Global monthly irrigated and rainfed crop areas around year 2000: A new high-resolution data set for agricultural and hydrological modelling, Global Biogeochem. Cy., 24, GB1011, https://doi.org/10.1029/2008GB003435, 2010.
Ramadhani, F., Pullanagari, R., Kereszturi, G., and Procter, J.: Automatic mapping of rice growth stages using the integration of Sentinel-2, MOD13Q1, and Sentinel-1, Remote Sens., 12, 3613, https://doi.org/10.3390/rs12213613, 2020.
Rolinski, S., Horn, H., Petzoldt, T., and Paul, L.: Identifying cardinal dates in phytoplankton time series to enable the analysis of long-term trends, Oecologia, 153, 997–1008, https://doi.org/10.1007/s00442-007-0783-2, 2007.
Sacks, W. J., Deryng, D., Foley, J. A., and Ramankutty, N.: Crop transplanting dates: An analysis of global patterns, Global Ecol. Biogeogr., 19, 607–620, https://doi.org/10.1111/j.1466-8238.2010.00551.x, 2010.
Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., and Ohno, H.: A crop phenology detection method using time-series MODIS data, Remote Sens. Environ., 96, 366–374, https://doi.org/10.1016/j.rse.2005.03.008, 2005.
Singha, M., Dong, J., Zhang, G., and Xiao, X.: High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data, Sci. Data, 6, 1–10, https://doi.org/10.1038/s41597-019-0036-3, 2019.
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I. N., Deghaye, P., Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L'Abbate, M., Croci, R., Pietropaolo, A., Huchler, M., and Rostan, F.: GMES Sentinel-1 mission, Remote Sens. Environ., 120, 9–24, https://doi.org/10.1016/j.rse.2011.05.028, 2012.
Waha, K., van Bussel, G. J., Müller, C., and Bondeau, A.: Climate-drive simulation of global crop sowing dates, Glob. Ecol. Biogeogr., 21, 247–259, https://doi.org/10.1111/j.1466-8238.2011.00678.x, 2012.
Wang, X., Wang, S., Li, X., Chen, B., Wang, J., Huang, M., and Rahman, A.: Modelling rice yield with temperature optima of rice productivity derived from satellite NIRv in tropical monsoon area, Agr. Forest Meteorol., 294, 108135, https://doi.org/10.1016/j.agrformet.2020.108135, 2020.
Xiao, W., Xu, S., and He, T.: Mapping paddy rice with Sentinel-1/2 phenology-, object-based algorithm-a implementation in Hangjiahu in China using GEE platform, Remote Sens., 13, 990, https://doi.org/10.3390/rs13050990, 2021.
Xin, Q., Li, J., Li, Z., Li, Y., and Zhou, X.: Evaluations and comparisons of rule-based and machine-learning-based methods to retrieve satellite-based vegetation phenology using MODIS and USA National Phenology Network data, Int. J. Appl. Earth Obs., 93, 102189, https://doi.org/10.1016/j.jag.2020.102189, 2020.
Yan, H., Liu, F., Qin, Y., Doughty, R., and Xiao, X.: Tracking the spatio-temporal change of cropping intensity in China during 2000-2015, Environ. Res. Lett., 14, 035008, https://doi.org/10.1088/1748-9326/aaf9c7, 2019.
Yang, Q., Shi, L., Han, J., Yu, J., and Huang, K.: A near real-time deep learning approach for detecting rice phenology based on UAV images, Agr. Forest Meteorol., 287, 107938, https://doi.org/10.1016/j.agrformet.2020.107938, 2020.
Zhang, G., Xiao, X., Dong, J., Xin, F., Zhang, Y., Qin, Y., Doughty, R. B., and Moore, B.: Fingerprint of rice paddies in spatial–temporal dynamics of atmospheric methane concentration in monsoon Asia, Nat. Commun., 11, 554, https://doi.org/10.1038/s41467-019-14155-5, 2020.
Zhang, J., Wu, H., Zhang, Z., Zhang, L., Luo, Y., Han, J., and Tao, F.: Asian rice calendar dynamics detected by remote sensing and their climate drivers, Remote Sens., 14, 4189, https://doi.org/10.3390/rs14174189, 2022.
Zhang, M., Wu, B., Zeng, H., He, G., Liu, C., Tao, S., Zhang, Q., Nabil, M., Tian, F., Bofana, J., Beyene, A. N., Elnashar, A., Yan, N., Wang, Z., and Liu, Y.: GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery, Earth Syst. Sci. Data, 13, 4799–4817, https://doi.org/10.5194/essd-13-4799-2021, 2021.
Zhang, W., Peng, K., Cui, F., Wang, D., Zhao, J., Zhang, J., Yu, N., Wang, Y., Zeng, D., Wang, Y., Cheng, Z., and Zhang, K.: Cytokinin oxidase/dehydrogenase OsCKX11 coordinates source and sink relationship in rice by simultaneous regulation of leaf senescence and grain number, Plant Biotechnol., J., 19, 335–350, https://doi.org/10.1111/pbi.13467, 2021.
Zhao, X. and Nishina, K.: Monsoon Asia Rice Calendar (MARC): a gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images, NIES [data set], https://doi.org/10.17595/20230728.001, 2023.
Zhao, X., Nishina, K., Akitsu, T. K., Jiang, L., Masutomi, Y., and Nasahara, K. N.: Feature-based algorithm for large-scale rice phenology detection based on satellite images, Agr. Forest Meteorol., 329, 109283, https://doi.org/10.1016/j.agrformet.2022.109283, 2023.
Short summary
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.
Mapping a rice calendar in a spatially explicit manner with a consistent framework remains...
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