Articles | Volume 17, issue 2
https://doi.org/10.5194/essd-17-661-2025
© 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-17-661-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
EARice10: a 10 m resolution annual rice distribution map of East Asia for 2023
Mingyang Song
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Ji Ge
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Hong Zhang
CORRESPONDING AUTHOR
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Lijun Zuo
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Jingling Jiang
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Yinhaibin Ding
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Yazhe Xie
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Fan Wu
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
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Hong Zhang, Mingyang Song, Yinhaibin Ding, Yazhe Xie, Huadong Guo, Lu Xu, Ji Ge, Yafei Zhu, Shenghan Wang, Zihuan Guo, Zhe Wang, Haoxuan Duan, Lijun Zuo, and Wenjiang Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-24, https://doi.org/10.5194/essd-2026-24, 2026
Preprint under review for ESSD
Short summary
Short summary
Accurate rice farming data is vital for global food security. We created GlobalRice20, a global map of paddy rice at 20-meter resolution for 2015 and 2024. By utilizing satellite imagery and advanced processing to overcome cloud issues, this dataset fills a critical gap in agricultural monitoring. It provides a reliable baseline for analyzing food production trends and helps policymakers track progress toward the Sustainable Development Goal of Zero Hunger.
Jingling Jiang, Hong Zhang, Ji Ge, Lijun Zuo, Lu Xu, Mingyang Song, Yinhaibin Ding, Yazhe Xie, and Wenjiang Huang
Earth Syst. Sci. Data, 17, 1781–1805, https://doi.org/10.5194/essd-17-1781-2025, https://doi.org/10.5194/essd-17-1781-2025, 2025
Short summary
Short summary
This study uses temporal synthetic aperture radar (SAR) data and optical imagery to conduct rice-mapping experiments in 34 African countries with rice-planting areas exceeding 5000 ha in 2022, achieving a 20 m resolution spatial distribution mapping for 2023. The average classification accuracy based on the validation set exceeded 85 %, and the R2; values for linear fitting with existing statistical data all surpassed 0.9, demonstrating the effectiveness of the proposed mapping method.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
Short summary
Short summary
Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Cited articles
Abdali, E., Valadan Zoej, M. J., Taheri Dehkordi, A., and Ghaderpour, E.: A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data, Remote Sens., 16, 127, https://doi.org/10.3390/rs16010127, 2023.
Achanta, R. and Susstrunk, S.: Superpixels and polygons using simple non-iterative clustering, Proceedings of the IEEE conference on computer vision and pattern recognition, 21–26 July 2017, Honolulu, HI, USA, 4651–4660, 2017.
Carrasco, L., Fujita, G., Kito, K., and Miyashita, T.: Historical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine, ISPRS J. Photogramm. Remote, 191, 277–289, https://doi.org/10.1016/j.isprsjprs.2022.07.018, 2022.
Chen, N., Yu, L., Zhang, X., Shen, Y., Zeng, L., Hu, Q., and Niyogi, D.: Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform, Remote Sens., 12, 2992, https://doi.org/10.3390/rs12182992, 2020.
Chen, W. and Zhao, X.: Understanding global rice trade flows: Network evolution and implications, Foods, 12, 3298, https://doi.org/10.3390/foods12173298, 2023.
d'Andrimont, R., Verhegghen, A., Lemoine, G., Kempeneers, P., Meroni, M., and Van Der Velde, M.: From parcel to continental scale–A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations, Remote Sens. Environ., 266, 112708, https://doi.org/10.1016/j.rse.2021.112708, 2021.
Dong, J. and Xiao, X.: Evolution of regional to global paddy rice mapping methods: A review, ISPRS J. Photogramm. Remote, 119, 214–227, https://doi.org/10.1016/j.isprsjprs.2016.05.010, 2016.
Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., Biradar, C., and Moore 3rd, 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.
FAO: World rice production (Crops > Items > Rice, paddy), https://www.fao.org/faostat/en/#data/QCL, last access: 17 June 2024.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L.: The shuttle radar topography mission, Rev. Geophys., 45, https://doi.org/10.1029/2005RG000183, 2007.
Fisette, T., Rollin, P., Aly, Z., Campbell, L., Daneshfar, B., Filyer, P., Smith, A., Davidson, A., Shang, J., and Jarvis, I.: AAFC annual crop inventory, 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 12–16 August 2013, Fairfax, VA, USA, 270–274, 2013.
Gao, X., Chi, H., Huang, J., Han, Y., Li, Y., and Ling, F.: Comparison of Cloud-Mask Algorithms and Machine-Learning Methods Using Sentinel-2 Imagery for Mapping Paddy Rice in Jianghan Plain, Remote Sens., 16, 1305, https://doi.org/10.3390/rs16071305, 2024.
Gao, Y., Pan, Y., Zhu, X., Li, L., Ren, S., Zhao, C., and Zheng, X.: FARM: A fully automated rice mapping framework combining Sentinel-1 SAR and Sentinel-2 multi-temporal imagery, Computers and Electronics in Agriculture, 213, 108262, https://doi.org/10.1016/j.compag.2023.108262, 2023.
Gitelson, A. A., Gritz, Y., and Merzlyak, M. N.: Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves, J. Plant Physiol,, 160, 271–282, 2003.
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, 2017.
Griffiths, P., Nendel, C., and Hostert, P.: Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping, Remote Sens. Environ., 220, 135–151, 2019.
Han, J., Zhang, Z., Luo, Y., Cao, J., Zhang, L., Cheng, F., Zhuang, H., and Zhang, J.: APRA500: a 500 m annual paddy rice dataset for monsoon Asia using multisource remote sensing data, Zenodo [data set], https://doi.org/10.5281/zenodo.5555721, 2021a.
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, 2021b.
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, Zenodo [data set], https://doi.org/10.5281/zenodo.5645344, 2021c.
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, Agric. Syst., 200, 103437, https://doi.org/10.1016/j.agsy.2022.103437, 2022.
Hao, P., Di, L., Zhang, C., and Guo, L.: Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples, Sci. Total Environ., 733, 138869, https://doi.org/10.1016/j.scitotenv.2020.138869, 2020.
He, Y., Dong, J., Liao, X., Sun, L., Wang, Z., You, N., Li, Z., and Fu, P.: Examining rice distribution and cropping intensity in a mixed single-and double-cropping region in South China using all available Sentinel 1/2 images, Int. J. Appl. Earth Obs., 101, 102351, https://doi.org/10.1016/j.jag.2021.102351, 2021.
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.
Huang, C., You, S., Liu, A., Li, P., Zhang, J., and Deng, J.: High-Resolution National-Scale Mapping of Paddy Rice Based on Sentinel-1/2 Data, Remote Sens., 15, 4055, https://doi.org/10.3390/rs15164055, 2023.
Huete, A., Justice, C., and Liu, H.: Development of vegetation and soil indices for MODIS-EOS, Remote Sens. Environ., 49, 224–234, 1994.
Huete, A., Liu, H., Batchily, K., and Van Leeuwen, W.: A comparison of vegetation indices over a global set of TM images for EOS-MODIS, Remote Sens. Environ., 59, 440–451, 1997.
Jo, H.-W. and Lee, W.-K.: Paddy Rice Maps South Korea (2017–2021), Zenodo [data set], https://doi.org/10.5281/zenodo.5845896, 2022.
Jo, H.-W., Park, E., Sitokonstantinou, V., Kim, J., Lee, S., Koukos, A., and Lee, W.-K.: Recurrent U-Net based dynamic paddy rice mapping in South Korea with enhanced data compatibility to support agricultural decision making, GIScience Remote Sens., 60, 2206539, https://doi.org/10.1080/15481603.2023.2206539, 2023.
Johnson, D. M. and Mueller, R.: The 2009 cropland data layer, Photogramm. Eng. Remote Sens, 76, 1201–1205, 2010.
Johnson, D. M. and Mueller, R.: Pre-and within-season crop type classification trained with archival land cover information, Remote Sens. Environ., 264, 112576, https://doi.org/10.1016/j.rse.2021.112576, 2021.
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, 2017a.
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, Harvard Dataverse, V4 [data set], https://doi.org/10.7910/DVN/JE6R2R, 2017b.
Lin, C., Zhong, L., Song, X.-P., Dong, J., Lobell, D. B., and Jin, Z.: Early-and in-season crop type mapping without current-year ground truth: Generating labels from historical information via a topology-based approach, Remote Sens. Environ., 274, 112994, https://doi.org/10.1016/j.rse.2022.112994, 2022.
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.
Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., and Rakitin, V. Y.: Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening, Physiol. Plant., 106, 135–141, 1999.
Mullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y., Gorelick, N., and Reiche, J.: Sentinel-1 sar backscatter analysis ready data preparation in google earth engine, Remote Sens., 13, 1954, https://doi.org/10.3390/rs13101954, 2021.
Ni, R., Tian, J., Li, X., Yin, D., Li, J., Gong, H., Zhang, J., Zhu, L., and Wu, D.: An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine, ISPRS J. Photogramm. Remote, 178, 282–296, https://doi.org/10.1016/j.isprsjprs.2021.06.018, 2021.
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.
Pandžić, M., Pavlović, D., Matavulj, P., Brdar, S., Marko, O., Crnojević, V., and Kilibarda, M.: Interseasonal transfer learning for crop mapping using Sentinel-1 data, Int. J. Appl. Earth Obs., 128, 103718, https://doi.org/10.1016/j.jag.2024.103718, 2024.
Pasquarella, V. J., Brown, C. F., Czerwinski, W., and Rucklidge, W. J.: Comprehensive quality assessment of optical satellite imagery using weakly supervised video learning, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 17–24 June 2023, Vancouver, BC, Canada, https://doi.org/10.1109/cvprw59228.2023.00206, 2023.
Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., and Skakun, S.: Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping, Front. Earth Sci., 5, 232994, https://doi.org/10.3389/feart.2017.00017, 2017.
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, 2023.
Song, M., Xu, L., Ge, J., Zhang, H., Zuo, L., Jiang, J., Ding, Y., Xie, Y., and Wu, F.: EARice10: A 10 m Resolution Annual Rice Distribution Map of East Asia for 2023, Zenodo [data set], https://doi.org/10.5281/zenodo.13118409, 2024.
Song, X.-P., Potapov, P. V., Krylov, A., King, L., Di Bella, C. M., Hudson, A., Khan, A., Adusei, B., Stehman, S. V., and Hansen, M. C.: National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey, Remote Sens. Environ., 190, 383–395, 2017.
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.
Sun, L., Lou, Y., and Zhang, L.: Spatial domain transfer: Cross-regional paddy rice mapping with a few samples based on Sentinel-1 and Sentinel-2 data on GEE, Int. J. Appl. Earth Obs., 128, 103762, https://doi.org/10.1016/j.jag.2024.103762, 2024.
Tian, G., Li, H., Jiang, Q., Qiao, B., Li, N., Guo, Z., Zhao, J., and Yang, H.: An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images, Remote Sens., 15, 2785, https://doi.org/10.3390/rs15112785, 2023.
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150, 1979.
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, 2017.
Wang, G., Meng, D., Chen, R., Yang, G., Wang, L., Jin, H., Ge, X., and Feng, H.: Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images, Remote Sens., 16, 277, https://doi.org/10.3390/rs16020277, 2024.
Wei, J., Cui, Y., Luo, W., and Luo, Y.: Mapping Paddy Rice Distribution and Cropping Intensity in China from 2014 to 2019 with Landsat Images, Effective Flood Signals, and Google Earth Engine, Remote Sens., 14, 759, https://doi.org/10.3390/rs14030759, 2022.
Wei, S., Zhang, H., Wang, C., Wang, Y., and Xu, L.: Multi-temporal SAR data large-scale crop mapping based on U-Net model, Remote Sens., 11, 68, https://doi.org/10.3390/rs11010068, 2019.
Wen, Y., Li, X., Mu, H., Zhong, L., Chen, H., Zeng, Y., Miao, S., Su, W., Gong, P., and Li, B.: Mapping corn dynamics using limited but representative samples with adaptive strategies, ISPRS J. Photogramm. Remote, 190, 252–266, 2022.
Wu, Q.: geemap: A Python package for interactive mapping with Google Earth Engine, J. Open Source Softw., 5, 2305, https://doi.org/10.21105/joss.02305, 2020.
Xiao, W., Xu, S., and He, T.: Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm – A Implementation in Hangjiahu Plain in China Using GEE Platform, Remote Sens., 13, 990, https://doi.org/10.3390/rs13050990, 2021.
Xiao, X., Hollinger, D., Aber, J., Goltz, M., Davidson, E. A., Zhang, Q., and Moore III, B.: Satellite-based modeling of gross primary production in an evergreen needleleaf forest, Remote Sens. Environ., 89, 519–534, 2004.
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, 2005a.
Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., Salas, W., and Moore III, 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, 2005b.
Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J. Y., Salas, W., and Moore III, 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.
Xu, J., Zhu, Y., Zhong, R., Lin, Z., Xu, J., Jiang, H., Huang, J., Li, H., and Lin, T.: DeepCropMapping: A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping, Remote Sens. Environ., 247, 111946, https://doi.org/10.1016/j.rse.2020.111946, 2020.
Xu, L., Zhang, H., Wang, C., Wei, S., Zhang, B., Wu, F., and Tang, Y.: Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model, Remote Sens., 13, 3994, https://doi.org/10.3390/rs13193994, 2021.
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.
Yang, L., Huang, R., Zhang, J., Huang, J., Wang, L., Dong, J., and Shao, J.: Inter-Continental Transfer of Pre-Trained Deep Learning Rice Mapping Model and Its Generalization Ability, Remote Sens., 15, 2443, https://doi.org/10.3390/rs15092443, 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.
Yu, Z., Di, L., Shrestha, S., Zhang, C., Guo, L., Qamar, F., and Mayer, T. J.: RiceMapEngine: A Google Earth Engine-Based Web Application for Fast Paddy Rice Mapping, IEEE J. Sel. Top. Appl. Earth Obs., 16, 7264–7275, https://doi.org/10.1109/jstars.2023.3290677, 2023.
Zhan, P., Zhu, W., and Li, N.: An automated rice mapping method based on flooding signals in synthetic aperture radar time series, Remote Sens. Environ., 252, 112112, https://doi.org/10.1016/j.rse.2020.112112, 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, Computers and Electronics in Agriculture, 212, 108105, https://doi.org/10.1016/j.compag.2023.108105, 2023a.
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, Computers and Electronics in Agriculture, 212, 108105, https://doi.org/10.1016/j.compag.2023.108105, 2023b.
Zhang, H., He, B., and Xing, J.: Mapping Paddy Rice in Complex Landscapes with Landsat Time Series Data and Superpixel-Based Deep Learning Method, Remote Sens., 14, 3721, https://doi.org/10.3390/rs14153721, 2022a.
Zhang, K., Chen, Y., Zhang, B., Hu, J., and Wang, W.: A Multitemporal Mountain Rice Identification and Extraction Method Based on the Optimal Feature Combination and Machine Learning, Remote Sens., 14, 5096, https://doi.org/10.3390/rs14205096, 2022b.
Zhang, X., Wu, B., Ponce-Campos, G. E., Zhang, M., Chang, S., and Tian, F.: Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images, Remote Sens., 10, 1200, https://doi.org/10.3390/rs10081200, 2018.
Zhao, R., Li, Y., Chen, J., Ma, M., Fan, L., and Lu, W.: Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm, Remote Sens., 13, 4400, https://doi.org/10.3390/rs13214400, 2021.
Zhi, F., Dong, Z., Guga, S., Bao, Y., Han, A., Zhang, J., and Bao, Y.: Rapid and Automated Mapping of Crop Type in Jilin Province Using Historical Crop Labels and the Google Earth Engine, Remote Sens., 14, 4028, https://doi.org/10.3390/rs14164028, 2022.
Zhu, W., Peng, X., Ding, M., Li, L., Liu, Y., Liu, W., Yang, M., Chen, X., Cai, J., Huang, H., Dong, Y., and Lu, J.: Decline in Planting Areas of Double-Season Rice by Half in Southern China over the Last Two Decades, Remote Sens., 16, 440, https://doi.org/10.3390/rs16030440, 2024.
Short summary
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.
We created a 10 m resolution rice distribution map for East Asia in 2023 (EARice10), achieving...
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