Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-3203-2023
© Author(s) 2023. 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-15-3203-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
26 Jul 2023
Data description paper |
| 26 Jul 2023
| 26 Jul 2023
High-resolution distribution maps of single-season rice in China from 2017 to 2022
Ruoque Shen
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Baihong Pan
Department of Microbiology and Plant Biology, University of Oklahoma,
Norman, OK 73019, USA
Qiongyan Peng
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Jie Dong
College of Geomatics and Municipal Engineering, Zhejiang University
of Water Resources and Electric Power, Hangzhou 310018, Zhejiang, China
Xuebing Chen
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Xi Zhang
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Key Laboratory of Environmental Change and Natural Disaster, Ministry
of Education, Beijing Normal University, Beijing 100875, China
Jianxi Huang
College of Land Science and Technology, China Agricultural University,
Beijing 100083, China
Wenping Yuan
CORRESPONDING AUTHOR
International Research Center of Big Data for Sustainable Development
Goals, Beijing 100094, China
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Related authors
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 17, 2193–2216, https://doi.org/10.5194/essd-17-2193-2025, https://doi.org/10.5194/essd-17-2193-2025, 2025
Short summary
Short summary
Rice is a vital staple crop that plays a crucial role in food security in China. However, long-term high-resolution rice distribution maps in China are lacking. This study developed a new rice-mapping method, mitigating the impact of cloud contamination and missing data in optical remote sensing observations on rice mapping. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed a strong correlation with statistical data.
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-147, https://doi.org/10.5194/essd-2024-147, 2024
Manuscript not accepted for further review
Short summary
Short summary
Rice is a vital staple crop that plays a crucial role in food security in China. However, long-term high-resolution rice distribution maps in China are lacking. This study developed a new rice mapping method using to address the challenges of cloud contamination and missing data in optical remote sensing observations. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed strong correlation with statistical data.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Rubaya Pervin, Scott Robeson, Mallory Barnes, Stephen Sitch, Anthony Walker, Ben Poulter, Fabienne Maignan, Qing Sun, Thomas Colligan, Sönke Zaehle, Kashif Mahmud, Peter Anthoni, Almut Arneth, Vivek Arora, Vladislav Bastrikov, Liam Bogucki, Bertrand Decharme, Christine Delire, Stefanie Falk, Akihiko Ito, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Michael O’Sullivan, Wenping Yuan, and Natasha MacBean
EGUsphere, https://doi.org/10.5194/egusphere-2025-2841, https://doi.org/10.5194/egusphere-2025-2841, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Drylands contribute more than a third of the global vegetation productivity. Yet, these regions are not well represented in global vegetation models. Here, we tested how well 15 global models capture annual changes in dryland vegetation productivity. Models that didn’t have vegetation change over time or fire have lower variability in vegetation productivity. Models need better representation of grass cover types and their coverage. Our work highlights where and how these models need to improve.
Ning Zhan, Tao Ye, Mario Herrero, Jian Peng, Weihang Liu, and Heng Ma
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-263, https://doi.org/10.5194/essd-2025-263, 2025
Manuscript not accepted for further review
Short summary
Short summary
We separated grazing ruminant livestock (cattle, sheep, and goats) from those raised in non-grazing systems in China and mapped where they graze from 2000 to 2021. Using machine learning methods that combine multiple models, we produced accurate maps showing livestock distribution across seasons. This helps track seasonal changes in grazing and supports better land use, animal health, and climate-related planning.
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 17, 2193–2216, https://doi.org/10.5194/essd-17-2193-2025, https://doi.org/10.5194/essd-17-2193-2025, 2025
Short summary
Short summary
Rice is a vital staple crop that plays a crucial role in food security in China. However, long-term high-resolution rice distribution maps in China are lacking. This study developed a new rice-mapping method, mitigating the impact of cloud contamination and missing data in optical remote sensing observations on rice mapping. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed a strong correlation with statistical data.
Ran Sun, Tao Ye, Yiqing Liu, Weihang Liu, and Shuo Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1393, https://doi.org/10.5194/egusphere-2025-1393, 2025
Short summary
Short summary
Climate change is intensifying extreme weather threats to rice. Our study shows divergent spatial distribution patterns yet increasing temporal trends of concurrent events for rice. Heat stress dominated heat-drought events, while chilling and rain jointly dominated chilling-rainy events. Both event types caused similar yield losses. Ignoring stage-specific risks and local challenges heightens vulnerability. These insights help policymakers prioritize adaptive actions to protect rice crops.
Hao Jiang, Mengjun Ku, Xia Zhou, Qiong Zheng, Yangxiaoyue Liu, Jianhui Xu, Dan Li, Chongyang Wang, Jiayi Wei, Jing Zhang, Shuisen Chen, and Jianxi Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-44, https://doi.org/10.5194/essd-2025-44, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
Existing cropland datasets in China show significant discrepancies. We created a high-resolution cropland map of China for 2020, using imagery from Mapbox and Google. By combining image quality assessments, active learning for semantic segmentation, and results integration. The accuracy achieved to 88.73 %, with 30 out of 32 provincial units reporting area estimates within ±10 % of official statistics. In contrast, only 9 to 1 provinces from 7 existing datasets meet the same accuracy standard.
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
Short summary
Short summary
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.
Kaiqi Du, Guilong Xiao, Jianxi Huang, Xiaoyan Kang, Xuecao Li, Yelu Zeng, Quandi Niu, Haixiang Guan, and Jianjian Song
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-432, https://doi.org/10.5194/essd-2024-432, 2025
Manuscript not accepted for further review
Short summary
Short summary
In this manuscript, we developed a 500-m spatial resolution monthly SIF dataset for the China region (CNSIF) from 2003 to 2022 based on high-resolution apparent reflectance and thermal infrared data. The comparison of CNSIF with tower-based SIF observations, tower-based GPP observations, MODIS GPP products, and other SIF datasets has validated CNSIF's ability to capture photosynthetic activity across different vegetation types and its potential for estimating carbon fluxes.
Ning Zhan, Tao Ye, Mario Herrero, Jian Peng, Weihang Liu, and Heng Ma
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-534, https://doi.org/10.5194/essd-2024-534, 2024
Manuscript not accepted for further review
Short summary
Short summary
The CLRD-GLPS dataset, mapping China’s annual distribution of ruminant livestock in grazing systems from 2000 to 2021, includes key advancements: (1) segmentation of grazing ruminant livestock from total livestock, (2) categorizing grazing pastures into warm-season, cold-season, and year-round types, and (3) analyze and explain spatial-temporal patterns. These innovations improve understanding of livestock seasonal dynamics and provide data for effective livestock management and policy-making.
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-147, https://doi.org/10.5194/essd-2024-147, 2024
Manuscript not accepted for further review
Short summary
Short summary
Rice is a vital staple crop that plays a crucial role in food security in China. However, long-term high-resolution rice distribution maps in China are lacking. This study developed a new rice mapping method using to address the challenges of cloud contamination and missing data in optical remote sensing observations. The resulting dataset, CCD-Rice (China Crop Dataset-Rice), achieved high accuracy and showed strong correlation with statistical data.
Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 16, 2465–2481, https://doi.org/10.5194/essd-16-2465-2024, https://doi.org/10.5194/essd-16-2465-2024, 2024
Short summary
Short summary
This study generated a high-precision dataset, locating forest harvested carbon and quantifying post-harvest wood emissions for various uses. It enhances our understanding of forest harvesting and post-harvest carbon dynamics in China, providing essential data for estimating the forest ecosystem carbon budget and emphasizing wood utilization's impact on carbon emissions.
Ran Sun, Tao Ye, Yiqing Liu, Weihang Liu, and Shuo Chen
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2024-8, https://doi.org/10.5194/esd-2024-8, 2024
Manuscript not accepted for further review
Short summary
Short summary
We found increasing trend of compound hot-dry events for single-rice but decreasing trend of compound chilling-rainy events for late-rice. We also found that the occurrence and duration of concurrent CEs were negatively correlated with local temperature-moisture coupling. Temperature was the dominant factor influencing the change of compound hot-dry events. While for the change in compound chilling-rainy events, the effect of temperature is only slightly larger than that of moisture.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
Short summary
Short summary
Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
Short summary
Short summary
We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Yuchan Chen, Xiuzhi Chen, Meimei Xue, Chuanxun Yang, Wei Zheng, Jun Cao, Wenting Yan, and Wenping Yuan
Hydrol. Earth Syst. Sci., 27, 1929–1943, https://doi.org/10.5194/hess-27-1929-2023, https://doi.org/10.5194/hess-27-1929-2023, 2023
Short summary
Short summary
This study addresses the quantification and estimation of the watershed-characteristic-related parameter (Pw) in the Budyko framework with the principle of hydrologically similar groups. The results show that Pw is closely related to soil moisture and fractional vegetation cover, and the relationship varies across specific hydrologic similarity groups. The overall satisfactory performance of the Pw estimation model improves the applicability of the Budyko framework for global runoff estimation.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
Short summary
Short summary
Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Haicheng Zhang, Ronny Lauerwald, Pierre Regnier, Philippe Ciais, Kristof Van Oost, Victoria Naipal, Bertrand Guenet, and Wenping Yuan
Earth Syst. Dynam., 13, 1119–1144, https://doi.org/10.5194/esd-13-1119-2022, https://doi.org/10.5194/esd-13-1119-2022, 2022
Short summary
Short summary
We present a land surface model which can simulate the complete lateral transfer of sediment and carbon from land to ocean through rivers. Our model captures the water, sediment, and organic carbon discharges in European rivers well. Application of our model in Europe indicates that lateral carbon transfer can strongly change regional land carbon budgets by affecting organic carbon distribution and soil moisture.
Quandi Niu, Xuecao Li, Jianxi Huang, Hai Huang, Xianda Huang, Wei Su, and Wenping Yuan
Earth Syst. Sci. Data, 14, 2851–2864, https://doi.org/10.5194/essd-14-2851-2022, https://doi.org/10.5194/essd-14-2851-2022, 2022
Short summary
Short summary
In this paper we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using Landsat images. The derived phenological indicators agree with in situ observations and provide more spatial details than moderate resolution phenology products. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the response of agroecosystem to global warming in the future.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
Short summary
Short summary
The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Jie Dong, Yangyang Fu, Jingjing Wang, Haifeng Tian, Shan Fu, Zheng Niu, Wei Han, Yi Zheng, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, https://doi.org/10.5194/essd-12-3081-2020, 2020
Short summary
Short summary
For the first time, we produced a 30 m winter wheat distribution map in China for 3 years during 2016–2018. Validated with 33 776 survey samples, the map had perfect performance with an overall accuracy of 89.88 %. Moreover, the method can identify planting areas of winter wheat 3 months prior to harvest; that is valuable information for production predictions and is urgently necessary for policymakers to reduce economic loss and assess food security.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Cited articles
Beaudoin, A., Stussi, N., Troufleau, D., Desbois, N., Piet, L., and
Deshayes, M.: On the use of ERS-1 SAR data over hilly terrain: necessity of
radiometric corrections for thematic applications, in: 1995 International
Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote
Sensing for Science and Applications, 1995 International Geoscience and
Remote Sensing Symposium, 10–14 July 1995, Firenze, Italy, 2179–2182,
https://doi.org/10.1109/IGARSS.1995.524141, 1995.
Belgiu, M. and Csillik, O.: Sentinel-2 cropland mapping using pixel-based
and object-based time-weighted dynamic time warping analysis, Remote Sens. Environ., 204, 509–523, https://doi.org/10.1016/j.rse.2017.10.005,
2018.
Boryan, C., Yang, Z., Mueller, R., and Craig, M.: Monitoring US agriculture:
the US Department of Agriculture, National Agricultural Statistics Service,
Cropland Data Layer Program, Geocarto International, 26, 341–358,
https://doi.org/10.1080/10106049.2011.562309, 2011.
Cao, B., Yu, L., Naipal, V., Ciais, P., Li, W., Zhao, Y., Wei, W., Chen, D., Liu, Z., and Gong, P.: A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine, Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021, 2021.
Chakraborty, D., Ladha, J. K., Rana, D. S., Jat, M. L., Gathala, M. K.,
Yadav, S., Rao, A. N., Ramesha, M. S., and Raman, A.: A global analysis of
alternative tillage and crop establishment practices for economically and
environmentally efficient rice production, Sci. Rep.-UK, 7, 9342,
https://doi.org/10.1038/s41598-017-09742-9, 2017.
Chen, J., Jönsson, Per., Tamura, M., Gu, Z., Matsushita, B., and Eklundh, L.: A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter, Remote Sens. Environ., 91, 332–344, https://doi.org/10.1016/j.rse.2004.03.014, 2004.
Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar,
C., and Moore, B.: Mapping paddy rice planting area in northeastern Asia
with Landsat 8 images, phenology-based algorithm and Google Earth Engine,
Remote Sens. Environ., 185, 142–154,
https://doi.org/10.1016/j.rse.2016.02.016, 2016.
Dong, J., Fu, Y., Wang, J., Tian, H., Fu, S., Niu, Z., Han, W., Zheng, Y., Huang, J., and Yuan, W.: Early-season mapping of winter wheat in China based on Landsat and Sentinel images, Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, 2020.
Elert, E.: Rice by the numbers: A good grain, Nature, 514, S50–S51,
https://doi.org/10.1038/514S50a, 2014.
FAO: World Food and Agriculture – Statistical Yearbook 2021, FAO,
https://doi.org/10.4060/cb4477en, 2021.
Farooq, M., Siddique, K. H. M., Rehman, H., Aziz, T., Lee, D.-J., and Wahid,
A.: Rice direct seeding: Experiences, challenges and opportunities, Soil
Till. Res., 111, 87–98, https://doi.org/10.1016/j.still.2010.10.008,
2011.
Fiorillo, E., Di Giuseppe, E., Fontanelli, G., and Maselli, F.: Lowland Rice
Mapping in Sédhiou Region (Senegal) Using Sentinel 1 and Sentinel 2 Data
and Random Forest, Remote Sens., 12, 3403,
https://doi.org/10.3390/rs12203403, 2020.
Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., Clinton, N., Ji, L., Li, W., Bai, Y., Chen, B., Xu, B., Zhu, Z., Yuan, C., Ping Suen, H., Guo, J., Xu, N., Li, W., Zhao, Y., Yang, J., Yu, C., Wang, X., Fu, H., Yu, L., Dronova, I., Hui, F., Cheng, X., Shi, X., Xiao, F., Liu, Q., and Song, L.: Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017, Sci. B., 64, 370–373, https://doi.org/10.1016/j.scib.2019.03.002, 2019.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q.: Mapping Rice Cropping
Systems in Vietnam Using an NDVI-Based Time-Series Similarity Measurement
Based on DTW Distance, Remote Sens., 8, 19,
https://doi.org/10.3390/rs8010019, 2016.
Guo, Y., Jia, X., Paull, D., and Benediktsson, J. A.: Nomination-favoured
opinion pool for optical-SAR-synergistic rice mapping in face of weakened
flooding signals, ISPRS J. Photogramm. Remote, 155,
187–205, https://doi.org/10.1016/j.isprsjprs.2019.07.008, 2019.
Han, J. and Moraga, C.: The influence of the sigmoid function parameters on
the speed of backpropagation learning, in: From Natural to Artificial Neural
Computation, Berlin, Heidelberg, 195–201,
https://doi.org/10.1007/3-540-59497-3_175, 1995.
Han, J., Zhang, Z., Luo, Y., Cao, J., Zhang, L., Cheng, F., Zhuang, H., Zhang, J., and Tao, F.: NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019, Earth Syst. Sci. Data, 13, 5969–5986, https://doi.org/10.5194/essd-13-5969-2021, 2021.
Huang, X., Fu, Y., Wang, J., Dong, J., Zheng, Y., Pan, B., Skakun, S., and
Yuan, W.: High-Resolution Mapping of Winter Cereals in Europe by Time Series
Landsat and Sentinel Images for 2016–2020, Remote Sens., 14, 2120,
https://doi.org/10.3390/rs14092120, 2022.
IPCC: Climate Change 2022 – Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st edn., edited by: Shukla, P. R. and Skea, J., Cambridge University Press, https://doi.org/10.1017/9781009157926, 2023.
Kuenzer, C. and Knauer, K.: Remote sensing of rice crop areas, Int.
J. Remote Sens., 34, 2101–2139,
https://doi.org/10.1080/01431161.2012.738946, 2013.
Li, J. and Chen, B.: Global Revisit Interval Analysis of Landsat-8 -9 and
Sentinel-2A -2B Data for Terrestrial Monitoring, Sensors, 20, 6631,
https://doi.org/10.3390/s20226631, 2020.
Maus, V., Camara, G., Cartaxo, R., Sanchez, A., Ramos, F. M., and de
Queiroz, G. R.: A Time-Weighted Dynamic Time Warping Method for Land-Use and
Land-Cover Mapping, IEEE J. Sel. Top. Appl. Earth
Obs., 9, 3729–3739,
https://doi.org/10.1109/JSTARS.2016.2517118, 2016.
Millard, K. and Richardson, M.: On the Importance of Training Data Sample
Selection in Random Forest Image Classification: A Case Study in Peatland
Ecosystem Mapping, Remote Sens., 7, 8489–8515,
https://doi.org/10.3390/rs70708489, 2015.
Mohammadi, A., Khoshnevisan, B., Venkatesh, G., and Eskandari, S.: A
Critical Review on Advancement and Challenges of Biochar Application in
Paddy Fields: Environmental and Life Cycle Cost Analysis, Processes, 8,
1275, https://doi.org/10.3390/pr8101275, 2020.
National Bureau of Statistics of China: China Statistical Yearbook, MARY
MARTIN, edited by: Liu, A. and Ye, Z., China Statistics Press,
ISBN 978-7-5037-9225-0, 2020.
Nguyen, D. B., Gruber, A., and Wagner, W.: Mapping rice extent and cropping
scheme in the Mekong Delta using Sentinel-1A data, Remote Sens. Lett.,
7, 1209–1218, https://doi.org/10.1080/2150704X.2016.1225172, 2016.
Oguro, Y., Suga, Y., Takeuchi, S., Ogawa, M., Konishi, T., and Tsuchiya, K.:
Comparison of SAR and optical sensor data for monitoring of rice plant
around Hiroshima, Adv. Space Res., 28, 195–200,
https://doi.org/10.1016/S0273-1177(01)00345-3, 2001.
Oliver, C. and Quegan, S. (Eds.): Understanding synthetic aperture radar
images, SciTech Publishing, Raleigh, NC, 479 pp., 2004.
Pan, B., Zheng, Y., Shen, R., Ye, T., Zhao, W., Dong, J., Ma, H., and Yuan,
W.: High Resolution Distribution Dataset of Double-Season Paddy Rice in
China, Remote Sens., 13, 4609, https://doi.org/10.3390/rs13224609, 2021.
Petitjean, F., Inglada, J., and Gancarski, P.: Satellite Image Time Series
Analysis Under Time Warping, IEEE T. Geosci. Remote, 50,
3081–3095, https://doi.org/10.1109/TGRS.2011.2179050, 2012.
Phan, H., Le Toan, T., Bouvet, A., Nguyen, L., Pham Duy, T., and Zribi, M.:
Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data, Sensors,
18, 316, https://doi.org/10.3390/s18010316, 2018.
Qiu, B., Luo, Y., Tang, Z., Chen, C., Lu, D., Huang, H., Chen, Y., Chen, N.,
and Xu, W.: Winter wheat mapping combining variations before and after
estimated heading dates, ISPRS J. Photogramm. Remote,
123, 35–46, https://doi.org/10.1016/j.isprsjprs.2016.09.016, 2017.
Shen, R., Dong, J., Yuan, W., Han, W., Ye, T., and Zhao, W.: A 30 m
Resolution Distribution Map of Maize for China Based on Landsat and Sentinel
Images, J. Remote Sens., 2022, 9846712,
https://doi.org/10.34133/2022/9846712, 2022.
Shen, R., Pan, B., Peng, Q., Dong, J., Chen, X., Zhang, X., Ye, T., Huang, J., and Yuan, W.: High-resolution distribution maps of single-season rice in China from 2017 to 2022, Science Data Bank [data set], https://doi.org/10.57760/sciencedb.06963, 2023.
Skakun, S., Vermote, E., Roger, J.-C., and Franch, B.: Combined Use of Landsat-8 and Sentinel-2A Images
for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale,
AIMS Geosciences, 3, 163–186, https://doi.org/10.3934/geosci.2017.2.163,
2017.
Sudmanns, M., Tiede, D., Augustin, H., and Lang, S.: Assessing global
Sentinel-2 coverage dynamics and data availability for operational Earth
observation (EO) applications using the EO-Compass, Int. J.
Digit. Earth, 13, 768–784, https://doi.org/10.1080/17538947.2019.1572799,
2020.
Thorp, K. R. and Drajat, D.: Deep machine learning with Sentinel satellite
data to map paddy rice production stages across West Java, Indonesia, Remote Sens. Environ., 265, 112679,
https://doi.org/10.1016/j.rse.2021.112679, 2021.
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.
Veloso, A., Mermoz, S., Bouvet, A., Le Toan, T., Planells, M., Dejoux,
J.-F., and Ceschia, E.: Understanding the temporal behavior of crops using
Sentinel-1 and Sentinel-2-like data for agricultural applications, Remote Sens. Environ., 199, 415–426,
https://doi.org/10.1016/j.rse.2017.07.015, 2017.
Wakabayashi, H., Motohashi, K., Kitagami, T., Tjahjono, B., Dewayani, S.,
Hidayat, D., and Hongo, C.: FLOODED AREA EXTRACTION OF RICE PADDY FIELD IN
INDONESIA USING SENTINEL-1 SAR DATA, Int. Arch. Photogramm. Remote Sens.
Spatial Inf. Sci., XLII-3/W7, 73–76,
https://doi.org/10.5194/isprs-archives-XLII-3-W7-73-2019, 2019.
Xiao, X., He, L., Salas, W., Li, C., Moore, B., Zhao, R., Frolking, S., and
Boles, S.: Quantitative relationships between field-measured leaf area index
and vegetation index derived from VEGETATION images for paddy rice fields,
Int. J. Remote Sens., 23, 3595–3604,
https://doi.org/10.1080/01431160110115799, 2002.
Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., Salas, W.,
and Moore, B.: Mapping paddy rice agriculture in southern China using
multi-temporal MODIS images, Remote Sens. Environ., 95, 480–492,
https://doi.org/10.1016/j.rse.2004.12.009, 2005.
Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J. Y., Salas, W., and
Moore, B.: Mapping paddy rice agriculture in South and Southeast Asia using
multi-temporal MODIS images, Remote Sens. Environ., 100, 95–113,
https://doi.org/10.1016/j.rse.2005.10.004, 2006.
Yan, J., Yang, Z., Li, Z., Li, X., Xin, L., and Sun, L.: Drivers of cropland
abandonment in mountainous areas: A household decision model on farming
scale in Southwest China, Land Use Policy, 57, 459–469,
https://doi.org/10.1016/j.landusepol.2016.06.014, 2016.
You, N., Dong, J., Huang, J., Du, G., Zhang, G., He, Y., Yang, T., Di, Y.,
and Xiao, X.: The 10-m crop type maps in Northeast China during 2017–2019,
Sci Data, 8, 41, https://doi.org/10.1038/s41597-021-00827-9, 2021.
Zhao, H., Chen, Z., Jiang, H., Jing, W., Sun, L., and Feng, M.: Evaluation
of Three Deep Learning Models for Early Crop Classification Using
Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China, Remote
Sens., 11, 2673, https://doi.org/10.3390/rs11222673, 2019.
Zheng, B., Myint, S. W., Thenkabail, P. S., and Aggarwal, R. M.: A support
vector machine to identify irrigated crop types using time-series Landsat
NDVI data, Int. J. Appl. Earth Obs., 34, 103–112, https://doi.org/10.1016/j.jag.2014.07.002,
2015.
Zheng, Y., dos Santos Luciano, A. C., Dong, J., and Yuan, W.: High-resolution map of sugarcane cultivation in Brazil using a phenology-based method, Earth Syst. Sci. Data, 14, 2065–2080, https://doi.org/10.5194/essd-14-2065-2022, 2022a.
Zheng, Y., Li, Z., Pan, B., Lin, S., Dong, J., Li, X., and Yuan, W.:
Development of a Phenology-Based Method for Identifying Sugarcane Plantation
Areas in China Using High-Resolution Satellite Datasets, Remote Sens., 14,
1274, https://doi.org/10.3390/rs14051274, 2022b.
Zhong, L., Hu, L., Zhou, H., and Tao, X.: Deep learning based winter wheat
mapping using statistical data as ground references in Kansas and northern
Texas, US, Remote Sens. Environ., 233, 111411,
https://doi.org/10.1016/j.rse.2019.111411, 2019.
Zhou, Y., Dong, J., Liu, J., Metternicht, G., Shen, W., You, N., Zhao, G.,
and Xiao, X.: Are There Sufficient Landsat Observations for Retrospective
and Continuous Monitoring of Land Cover Changes in China?, Remote Sensing,
11, 1808, https://doi.org/10.3390/rs11151808, 2019.
Short summary
Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Paddy rice is the second-largest grain crop in China and plays an important role in ensuring...
Altmetrics
Final-revised paper
Preprint