Articles | Volume 17, issue 5
https://doi.org/10.5194/essd-17-2193-2025
© Author(s) 2025. 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-17-2193-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
26 May 2025
Data description paper |
| 26 May 2025
| 26 May 2025
CCD-Rice: a long-term paddy rice distribution dataset in China at 30 m resolution
Ruoque Shen
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Qiongyan Peng
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Xiangqian Li
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Xiuzhi Chen
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Wenping Yuan
CORRESPONDING AUTHOR
Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Related authors
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.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
Short summary
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.
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.
Sylvain Schmitt, Fabian J. Fischer, James G. C. Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy W. Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
Geosci. Model Dev., 18, 5205–5243, https://doi.org/10.5194/gmd-18-5205-2025, https://doi.org/10.5194/gmd-18-5205-2025, 2025
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity, dynamics, and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote sensing products. The model realistically predicts the structure and composition as well as the seasonality of carbon and water fluxes at both sites.
Xueqin Yang, Qingling Sun, Liusheng Han, Jie Tian, Wenping Yuan, Liyang Liu, Wei Zheng, Mei Wang, Yunpeng Wang, and Xiuzhi Chen
Earth Syst. Sci. Data, 17, 3293–3314, https://doi.org/10.5194/essd-17-3293-2025, https://doi.org/10.5194/essd-17-3293-2025, 2025
Short summary
Short summary
Understanding how leaves absorb carbon from the atmosphere is essential for predicting changes in global forests. Young leaves play a key role in this process, but their efficiency has been difficult to measure at large scales. Using satellite data, we developed a new method to track the seasonal patterns of young leaves’ photosynthetic capacity from 2001 to 2018. Our dataset helps scientists better understand forest growth and how ecosystems respond to climate change.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, 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, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets 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 (1750–2024). 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.
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.
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.
Kai Yan, Jingrui Wang, Rui Peng, Kai Yang, Xiuzhi Chen, Gaofei Yin, Jinwei Dong, Marie Weiss, Jiabin Pu, and Ranga B. Myneni
Earth Syst. Sci. Data, 16, 1601–1622, https://doi.org/10.5194/essd-16-1601-2024, https://doi.org/10.5194/essd-16-1601-2024, 2024
Short summary
Short summary
Variations in observational conditions have led to poor spatiotemporal consistency in leaf area index (LAI) time series. Using prior knowledge, we leveraged high-quality observations and spatiotemporal correlation to reprocess MODIS LAI, thereby generating HiQ-LAI, a product that exhibits fewer abnormal fluctuations in time series. Reprocessing was done on Google Earth Engine, providing users with convenient access to this value-added data and facilitating large-scale research and applications.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
Short summary
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.
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.
Yidi Xu, Philippe Ciais, Le Yu, Wei Li, Xiuzhi Chen, Haicheng Zhang, Chao Yue, Kasturi Kanniah, Arthur P. Cracknell, and Peng Gong
Geosci. Model Dev., 14, 4573–4592, https://doi.org/10.5194/gmd-14-4573-2021, https://doi.org/10.5194/gmd-14-4573-2021, 2021
Short summary
Short summary
In this study, we implemented the specific morphology, phenology and harvest process of oil palm in the global land surface model ORCHIDEE-MICT. The improved model generally reproduces the same leaf area index, biomass density and life cycle fruit yield as observations. This explicit representation of oil palm in a global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.
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.
Yuan Zhang, Ana Bastos, Fabienne Maignan, Daniel Goll, Olivier Boucher, Laurent Li, Alessandro Cescatti, Nicolas Vuichard, Xiuzhi Chen, Christof Ammann, M. Altaf Arain, T. Andrew Black, Bogdan Chojnicki, Tomomichi Kato, Ivan Mammarella, Leonardo Montagnani, Olivier Roupsard, Maria J. Sanz, Lukas Siebicke, Marek Urbaniak, Francesco Primo Vaccari, Georg Wohlfahrt, Will Woodgate, and Philippe Ciais
Geosci. Model Dev., 13, 5401–5423, https://doi.org/10.5194/gmd-13-5401-2020, https://doi.org/10.5194/gmd-13-5401-2020, 2020
Short summary
Short summary
We improved the ORCHIDEE LSM by distinguishing diffuse and direct light in canopy and evaluated the new model with observations from 159 sites. Compared with the old model, the new model has better sunny GPP and reproduced the diffuse light fertilization effect observed at flux sites. Our simulations also indicate different mechanisms causing the observed GPP enhancement under cloudy conditions at different times. The new model has the potential to study large-scale impacts of aerosol changes.
Cited articles
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.
Bouman, B. A. M., Humphreys, E., Tuong, T. P., and Barker, R.: Rice and Water, in: Advances in Agronomy, vol. 92, Elsevier, 187–237, https://doi.org/10.1016/S0065-2113(04)92004-4, 2007.
Che, X., Zhang, H. K., Li, Z. B., Wang, Y., Sun, Q., Luo, D., and Wang, H.: Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks, ISPRS J. Photogramm., 212, 73–95, https://doi.org/10.1016/j.isprsjprs.2024.04.021, 2024.
Che, Z., Purushotham, S., Cho, K., Sontag, D., and Liu, Y.: Recurrent Neural Networks for Multivariate Time Series with Missing Values, Sci. Rep., 8, 6085, https://doi.org/10.1038/s41598-018-24271-9, 2018.
Clauss, K., Yan, H., and Kuenzer, C.: Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series, Remote Sens., 8, 434, https://doi.org/10.3390/rs8050434, 2016.
Dong, J., Xiao, X., Kou, W., Qin, Y., Zhang, G., Li, L., Jin, C., Zhou, Y., Wang, J., Biradar, C., Liu, J., and Moore, B.: Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms, Remote Sens. Environ., 160, 99–113, https://doi.org/10.1016/j.rse.2015.01.004, 2015.
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.
Dong, J., Pang, Z., Fu, Y., Peng, Q., Li, X., and Yuan, W.: Annual winter wheat mapping dataset in China from 2001 to 2020, Sci Data, 11, 1218, https://doi.org/10.1038/s41597-024-04065-7, 2024.
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 2023, FAO, Rome, Italy, https://doi.org/10.4060/cc8166en, 2023.
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., van der 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.
Fu, Y., Shen, R., Song, C., Dong, J., Han, W., Ye, T., and Yuan, W.: Exploring the effects of training samples on the accuracy of crop mapping with machine learning algorithm, Science of Remote Sensing, 7, 100081, https://doi.org/10.1016/j.srs.2023.100081, 2023.
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.
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.
Han, J., Zhang, Z., Luo, Y., Cao, J., Zhang, L., Zhuang, H., Cheng, F., Zhang, J., and Tao, F.: Annual paddy rice planting area and cropping intensity datasets and their dynamics in the Asian monsoon region from 2000 to 2020, Agr. Syst., 200, 103437, https://doi.org/10.1016/j.agsy.2022.103437, 2022.
Hu, J., Chen, Y., Cai, Z., Wei, H., Zhang, X., Zhou, W., Wang, C., You, L., and Xu, B.: Mapping Diverse Paddy Rice Cropping Patterns in South China Using Harmonized Landsat and Sentinel-2 Data, Remote Sens., 15, 1034, https://doi.org/10.3390/rs15041034, 2023.
Jiang, M., Li, X., Xin, L., and Tan, M.: Paddy rice multiple cropping index changes in Southern China: Impacts on national grain production capacity and policy implications, J. Geogr. Sci., 29, 1773–1787, https://doi.org/10.1007/s11442-019-1689-8, 2019.
Jiang, R., Sanchez-Azofeifa, A., Laakso, K., Xu, Y., Zhou, Z., Luo, X., Huang, J., Chen, X., and Zang, Y.: Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations, Remote Sens., 13, 2961, https://doi.org/10.3390/rs13152961, 2021.
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.
Li, X., Peng, Q., Zheng, Y., Lin, S., He, B., Qiu, Y., Chen, J., Chen, Y., and Yuan, W.: Incorporating environmental variables into spatiotemporal fusion model to reconstruct high-quality vegetation index data, IEEE T. Geosci. Remote, 62, 4401812, https://doi.org/10.1109/TGRS.2024.3349513, 2024.
Liu, Z., Li, Z., Tang, P., Li, Z., Wu, W., Yang, P., You, L., and Tang, H.: Change analysis of rice area and production in China during the past three decades, J. Geogr. Sci., 23, 1005–1018, https://doi.org/10.1007/s11442-013-1059-x, 2013.
Luo, Y., Zhang, Z., Li, Z., Chen, Y., Zhang, L., Cao, J., and Tao, F.: Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources, Environ. Res. Lett., 15, 074003, https://doi.org/10.1088/1748-9326/ab80f0, 2020.
Mansaray, L. R., Wang, F., Huang, J., Yang, L., and Kanu, A. S.: Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets, Geocarto Int., 35, 1088–1108, https://doi.org/10.1080/10106049.2019.1568586, 2020.
Meng, L., Li, Y., Shen, R., Zheng, Y., Pan, B., Yuan, W., Li, J., and Zhuo, L.: Large-scale and high-resolution paddy rice intensity mapping using downscaling and phenology-based algorithms on Google Earth Engine, Int. J. Appl. Earth Obs., 128, 103725, https://doi.org/10.1016/j.jag.2024.103725, 2024.
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 2023, China Statistics Press, ISBN 978-7-5230-0190-5, 2023.
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., ISBN 978-1-891121-31-9, 2004.
Ouyang, Z., Jackson, R. B., McNicol, G., Fluet-Chouinard, E., Runkle, B. R. K., Papale, D., Knox, S. H., Cooley, S., Delwiche, K. B., Feron, S., Irvin, J. A., Malhotra, A., Muddasir, M., Sabbatini, S., Alberto, Ma. C. R., Cescatti, A., Chen, C.-L., Dong, J., Fong, B. N., Guo, H., Hao, L., Iwata, H., Jia, Q., Ju, W., Kang, M., Li, H., Kim, J., Reba, M. L., Nayak, A. K., Roberti, D. R., Ryu, Y., Swain, C. K., Tsuang, B., Xiao, X., Yuan, W., Zhang, G., and Zhang, Y.: Paddy rice methane emissions across Monsoon Asia, Remote Sens. Environ., 284, 113335, https://doi.org/10.1016/j.rse.2022.113335, 2023.
Pan, B., Zheng, Y., Shen, R., Ye, T., Zhao, W., Dong, J., Ma, H., and Yuan, W.: A 10 m Resolution Distribution Dataset of Double-Season Paddy Rice in China from 2016 to 2020, National Ecosystem Science Data Center [data set], https://doi.org/10.12199/nesdc.ecodb.rs.2022.012, 2021a (in Chinese).
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, 2021b.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Peng, Q., Shen, R., Li, X., Ye, T., Dong, J., Fu, Y., and Yuan, W.: A twenty-year dataset of high-resolution maize distribution in China, Sci. Data, 10, 658, https://doi.org/10.1038/s41597-023-02573-6, 2023.
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.
Rahimi, E. and Jung, C.: Evaluating the applicability of landsat 8 data for global time series analysis, Front. Remote Sens., 5, https://doi.org/10.3389/frsen.2024.1492534, 2024.
Savitzky, A. and Golay, M. J. E.: Smoothing and Differentiation of Data by Simplified Least Squares Procedures, Anal. Chem., 36, 1627–1639, https://doi.org/10.1021/ac60214a047, 1964.
Shen, R.: Codes of a rice mapping method to generate CCD-Rice product, Zenodo [code], https://doi.org/10.5281/zenodo.15468566, 2025.
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, Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, 2023a.
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, 2023b.
Shen, R., Peng, Q., Li, X., Chen, X., and Yuan, W.: CCD-Rice: A paddy rice distribution dataset in China from 1990 to 2016 at 30 m resolution, Science Data Bank [data set], https://doi.org/10.57760/sciencedb.15865, 2024a.
Shen, R., Peng, Q., and Yuan, W.: Several samples visually interpreted from the very high-resolution images from Google Earth for rice mapping research in China, figshare [data set], https://doi.org/10.6084/m9.figshare.25515019.v3, 2024b.
Shen, R., Peng, Q., Li, X., Chen, X., and Yuan, W.: CCD-Rice: A paddy rice distribution dataset in China from 1990 to 2016 at 30 m resolution, https://ee-shenrq.projects.earthengine.app/view/ccd-rice, last access: 20 May 2025.
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.
Sun, C., Zhang, H., Xu, L., Ge, J., Jiang, J., Zuo, L., and Wang, C.: Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data, Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, 2023.
Tan, S., Heerink, N., and Qu, F.: Land fragmentation and its driving forces in China, Land Use Policy, 23, 272–285, https://doi.org/10.1016/j.landusepol.2004.12.001, 2006.
Tian, X., Bai, Y., Li, G., Yang, X., Huang, J., and Chen, Z.: An Adaptive Feature Fusion Network with Superpixel Optimization for Crop Classification Using Sentinel-2 Imagery, Remote Sens., 15, 1990, https://doi.org/10.3390/rs15081990, 2023.
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.
Waleed, M., Mubeen, M., Ahmad, A., Habib-ur-Rahman, M., Amin, A., Farid, H. U., Hussain, S., Ali, M., Qaisrani, S. A., Nasim, W., Javeed, H. M. R., Masood, N., Aziz, T., Mansour, F., and El Sabagh, A.: Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation, Sci. Rep., 12, 13210, https://doi.org/10.1038/s41598-022-17454-y, 2022.
Wen, Y., Li, X., Mu, H., Zhong, L., Chen, H., Zeng, Y., Miao, S., Su, W., Gong, P., Li, B., and Huang, J.: Mapping corn dynamics using limited but representative samples with adaptive strategies, ISPRS J. Photogramm., 190, 252–266, https://doi.org/10.1016/j.isprsjprs.2022.06.012, 2022.
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.
Xin, F., Xiao, X., Dong, J., Zhang, G., Zhang, Y., Wu, X., Li, X., Zou, Z., Ma, J., Du, G., Doughty, R. B., Zhao, B., and Li, B.: Large increases of paddy rice area, gross primary production, and grain production in Northeast China during 2000–2017, Sci. Total Environ., 711, 135183, https://doi.org/10.1016/j.scitotenv.2019.135183, 2020.
Xu, S., Zhu, X., Chen, J., Zhu, X., Duan, M., Qiu, B., Wan, L., Tan, X., Xu, Y. N., and Cao, R.: A robust index to extract paddy fields in cloudy regions from SAR time series, Remote Sens. Environ., 285, 113374, https://doi.org/10.1016/j.rse.2022.113374, 2023.
Xuan, F., Dong, Y., Li, J., Li, X., Su, W., Huang, X., Huang, J., Xie, Z., Li, Z., Liu, H., Tao, W., Wen, Y., and Zhang, Y.: Mapping crop type in Northeast China during 2013–2021 using automatic sampling and tile-based image classification, Int. J. Appl. Earth Obs., 117, 103178, https://doi.org/10.1016/j.jag.2022.103178, 2023.
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.
Yang, J. and Huang, X.: The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019, Earth Syst. Sci. Data, 13, 3907–3925, https://doi.org/10.5194/essd-13-3907-2021, 2021.
Yang, J. and Huang, X.: The 30 m annual land cover datasets and its dynamics in China from 1985 to 2022, Zenodo [data set], https://doi.org/10.5281/zenodo.8176941, 2023.
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.
Zhang, C., Zhang, H., and Tian, S.: Phenology-assisted supervised paddy rice mapping with the Landsat imagery on Google Earth Engine: Experiments in Heilongjiang Province of China from 1990 to 2020, Comput. Electron. Agr., 212, 108105, https://doi.org/10.1016/j.compag.2023.108105, 2023a.
Zhang, G., Xiao, X., Biradar, C. M., Dong, J., Qin, Y., Menarguez, M. A., Zhou, Y., Zhang, Y., Jin, C., Wang, J., Doughty, R. B., Ding, M., and Moore, B.: Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015, Sci. Total Environ., 579, 82–92, https://doi.org/10.1016/j.scitotenv.2016.10.223, 2017.
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, H. K. and Roy, D. P.: Using the 500m MODIS land cover product to derive a consistent continental scale 30m Landsat land cover classification, Remote Sens. Environ., 197, 15–34, https://doi.org/10.1016/j.rse.2017.05.024, 2017.
Zhang, X., Shen, R., Zhu, X., Pan, B., Fu, Y., Zheng, Y., Chen, X., Peng, Q., and Yuan, W.: Sample-free automated mapping of double-season rice in China using Sentinel-1 SAR imagery, Front. Environ. Sci., 11, https://doi.org/10.3389/fenvs.2023.1207882, 2023b.
Zhao, Q., Ding, X., Zhu, C., Zhao, W., Fan, S., Zhao, L., and Yu, D.: Healthy Diets in China, in: 2023 China and Global Food Policy Report, http://agfep.cau.edu.cn/art/2023/5/23/art_39584_960277.html (last access: 20 May 2025), 2023.
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 Sens., 11, 1808, https://doi.org/10.3390/rs11151808, 2019.
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.
Rice is a vital staple crop that plays a crucial role in food security in China. However,...
Altmetrics
Final-revised paper
Preprint