Articles | Volume 16, issue 7
https://doi.org/10.5194/essd-16-3213-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3213-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ChinaSoyArea10m: a dataset of soybean-planting areas with a spatial resolution of 10 m across China from 2017 to 2021
Qinghang Mei
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Zhao Zhang
CORRESPONDING AUTHOR
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Jichong Han
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
School of Systems Science, Beijing Normal University, Beijing 100875, China
Jie Song
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
School of Systems Science, Beijing Normal University, Beijing 100875, China
Jinwei Dong
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Huaqing Wu
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
Fulu Tao
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Cited articles
Ahmed, M., Seraj, R., and Islam, S. M. S.: The k-means Algorithm: A Comprehensive Survey and Performance Evaluation, Electronics, 9, 1295, https://doi.org/10.3390/electronics9081295, 2020.
Arthur, D. and Vassilvitskii, S.: k-means++: The advantages of careful seeding, Stanford InfoLab Technical Report, No. 2006-13, Stanford University, http://ilpubs.stanford.edu:8090/778/ (last access: 3 July 2024), 2006.
Bach, F. and Jordan, M.: Learning Spectral Clustering, in: Advances in Neural Information Processing Systems, Proceedings of the 16th International Conference on Neural Information Processing Systems (NIPS 2003), Vancouver, Canada, 9–12 December 2003, MIT Press, https://proceedings.neurips.cc/paper_files/paper/2003/file/d04863f100d59b3eb688a11f95b0ae60-Paper.pdf (last access: 3 July 2024), 2003.
Chabalala, Y., Adam, E., and Ali, K. A.: Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes, Remote Sens., 14, 2621, https://doi.org/10.3390/rs14112621, 2022.
Chen, D., Huang, J., and Jackson, T. J.: Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands, Remote Sens. Environ., 98, 225–236, https://doi.org/10.1016/j.rse.2005.07.008, 2005.
Chen, H., Li, H., Liu, Z., Zhang, C., Zhang, S., and Atkinson, P. M.: A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images, Remote Sens. Environ., 295, 113679, https://doi.org/10.1016/j.rse.2023.113679, 2023.
Cui, K. and Shoemaker, S. P.: A look at food security in China, npj Sci. Food, 2, 4, https://doi.org/10.1038/s41538-018-0012-x, 2018.
Di Tommaso, S., Wang, S., and Lobell, D. B.: Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops, Environ. Res. Lett., 16, 125002, https://doi.org/10.1088/1748-9326/ac358c, 2021.
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.
Du, J., Han, T., Gai, J., Yong, T., Sun, X., Wang, X., Yang, F., Liu, J., Shu, K., Liu, W., and Yang, W.: Maize-soybean strip intercropping: Achieved a balance between high productivity and sustainability, J. Integr. Agric., 17, 747–754, https://doi.org/10.1016/S2095-3119(17)61789-1, 2018.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise, KDD, Portland, Oregon, USA, 2–4 August 1996, 226–231, https://cdn.aaai.org/KDD/1996/KDD96-037.pdf (last access: 3 July 2024), 1996.
FAOSTAT: Countries by commodity, https://www.fao.org/faostat/en/#rankings/countries_by_commodity, last access: 10 October 2023.
Fensholt, R. and Sandholt, I.: Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment, Remote Sens. Environ., 87, 111–121, https://doi.org/10.1016/j.rse.2003.07.002, 2003.
Gong, L., Tian, B., Li, Y., and Wu, S.: Phenological Changes of Soybean in Response to Climate Conditions in Frigid Region in China over the Past Decades, Int. J. Plant Prod., 15, 363–375, https://doi.org/10.1007/s42106-021-00145-5, 2021.
Graesser, J. and Ramankutty, N.: Detection of cropland field parcels from Landsat imagery, Remote Sens. Environ., 201, 165–180, https://doi.org/10.1016/j.rse.2017.08.027, 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, W., Ren, J., Liu, X., Chen, Z., Wu, S., and Pan, H.: Winter wheat mapping with globally optimized threshold under total quantity constraint of statistical data, J. Remote Sens., 22, 1023–1041, https://doi.org/10.11834/jrs.20187468, 2018.
Hamano, M., Shiozawa, S., Yamamoto, S., Suzuki, N., Kitaki, Y., and Watanabe, O.: Development of a method for detecting the planting and ridge areas in paddy fields using AI, GIS, and precise DEM, Precision Agric, 24, 1862–1888, https://doi.org/10.1007/s11119-023-10021-z, 2023.
Han, J., Zhang, Z., Luo, Y., Cao, J., Zhang, L., Zhang, J., and Li, Z.: The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data, Earth Syst. Sci. Data, 13, 2857–2874, https://doi.org/10.5194/essd-13-2857-2021, 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, Agric. Syst., 200, 103437, https://doi.org/10.1016/j.agsy.2022.103437, 2022.
Hartman, G. L., West, E. D., and Herman, T. K.: Crops that feed the World 2. Soybean – worldwide production, use, and constraints caused by pathogens and pests, Food Secur., 3, 5–17, https://doi.org/10.1007/s12571-010-0108-x, 2011.
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H.: The elements of statistical learning: data mining, inference, and prediction, Springer, ISBN 978-1-4899-0519-2, ISBN 978-0-387-21606-5 (eBook), https://doi.org/10.1007/978-0-387-21606-5, 2009.
Housman, I. W., Chastain, R. A., and Finco, M. V.: An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States, Remote Sens., 10, 1184, https://doi.org/10.3390/rs10081184, 2018.
Huang, Y., Qiu, B., Chen, C., Zhu, X., Wu, W., Jiang, F., Lin, D., and Peng, Y.: Automated soybean mapping based on canopy water content and chlorophyll content using Sentinel-2 images, Int. J. Appl. Earth Obs., 109, 102801, https://doi.org/10.1016/j.jag.2022.102801, 2022.
Kerdprasop, K., Kerdprasop, N., and Sattayatham, P.: Weighted K-means for density-biased clustering, in: International conference on data warehousing and knowledge discovery, Copenhagen, Denmark, 22–26 August 2005, 488–497, https://doi.org/10.1007/11546849_48, 2005.
Konduri, V. S., Kumar, J., Hargrove, W. W., Hoffman, F. M., and Ganguly, A. R.: Mapping crops within the growing season across the United States, Remote Sens. Environ., 251, 112048, https://doi.org/10.1016/j.rse.2020.112048, 2020.
Kumari, M., Murthy, C. S., Pandey, V., and Bairagi, G. D.: Soybean Cropland Mapping Using Multi-Temporal Sentinel-1 Data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W6, 109–114, https://doi.org/10.5194/isprs-archives-XLII-3-W6-109-2019, 2019.
Kwak, G.-H. and Park, N.-W.: Unsupervised Domain Adaptation with Adversarial Self-Training for Crop Classification Using Remote Sensing Images, Remote Sens., 14, 4639, https://doi.org/10.3390/rs14184639, 2022.
Li, B. and Yang, L.: Clustering accuracy analysis of building area in high spatial resolution remote sensing images based on k-means algorithm, in: 2017 2nd International Conference on Frontiers of Sensors Technologies (ICFST), 2017 2nd International Conference on Frontiers of Sensors Technologies (ICFST), Shenzhen, China, 14–16 April 2017, 174–178, https://doi.org/10.1109/ICFST.2017.8210497, 2017.
Li, H., Song, X.-P., Hansen, M. C., Becker-Reshef, I., Adusei, B., Pickering, J., Wang, L., Wang, L., Lin, Z., Zalles, V., Potapov, P., Stehman, S. V., and Justice, C.: Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation, Remote Sens. Environ., 294, 113623, https://doi.org/10.1016/j.rse.2023.113623, 2023.
Li, L., Friedl, M. A., Xin, Q., Gray, J., Pan, Y., and Frolking, S.: Mapping Crop Cycles in China Using MODIS-EVI Time Series, Remote Sens., 6, 2473–2493, https://doi.org/10.3390/rs6032473, 2014.
Li, T., Johansen, K., and McCabe, M. F.: A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data, ISPRS J. Photogramm. Remote Sens., 186, 83–101, https://doi.org/10.1016/j.isprsjprs.2022.02.002, 2022.
Li, Y. and Qu, H.: LSD and Skeleton Extraction Combined with Farmland Ridge Detection, in: Advances in Intelligent, Interactive Systems and Applications, Cham, 446–453, https://doi.org/10.1007/978-3-030-02804-6_59, 2019.
Liu, H., Zhang, J., Pan, Y., Shuai, G., Zhu, X., and Zhu, S.: An Efficient Approach Based on UAV Orthographic Imagery to Map Paddy With Support of Field-Level Canopy Height From Point Cloud Data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 11, 2034–2046, https://doi.org/10.1109/JSTARS.2018.2829218, 2018.
Liu, J., Wang, L., Yang, F., Yao, B., and Yang, L.: Recognition ability of red edge and short wave infrared spectrum on maize and soybean, Chinese Agricultural Science Bulletin, 34, 120–129, 2018 (in Chinese).
Liu, L., Xiao, X., Qin, Y., Wang, J., Xu, X., Hu, Y., and Qiao, Z.: Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine, Remote Sens. Environ., 239, 111624, https://doi.org/10.1016/j.rse.2019.111624, 2020.
Liu, M. and Fan, Q.: Study on the Current Situation and Problems of Soybean Consumption, Production and Import in China, Grain Science And Technology And Economy, 46, 28–35, https://doi.org/10.16465/j.gste.cn431252ts.20210606, 2021 (in Chinese).
Liu, Z., Ying, H., Chen, M., Bai, J., Xue, Y., Yin, Y., Batchelor, W. D., Yang, Y., Bai, Z., Du, M., Guo, Y., Zhang, Q., Cui, Z., Zhang, F., and Dou, Z.: Optimization of China's maize and soy production can ensure feed sufficiency at lower nitrogen and carbon footprints, Nat. Food, 2, 426–433, https://doi.org/10.1038/s43016-021-00300-1, 2021.
Lowder, S. K., Skoet, J., and Raney, T.: The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide, World Dev., 87, 16–29, https://doi.org/10.1016/j.worlddev.2015.10.041, 2016.
Luo, C., Liu, H., Lu, L., Liu, Z., Kong, F., and Zhang, X.: Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine, J. Integr. Agr., 20, 1944–1957, https://doi.org/10.1016/S2095-3119(20)63329-9, 2021.
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.
Luo, Y., Zhang, Z., Zhang, L., Han, J., Cao, J., and Zhang, J.: Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data, Remote Sens., 14, 1809, https://doi.org/10.3390/rs14081809, 2022.
Ma, Z., Liu, Z., Zhao, Y., Zhang, L., Liu, D., Ren, T., Zhang, X., and Li, S.: An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning, ISPRS Int. J. Geo-Inf., 9, 648, https://doi.org/10.3390/ijgi9110648, 2020.
Marshall, M., Belgiu, M., Boschetti, M., Pepe, M., Stein, A., and Nelson, A.: Field-level crop yield estimation with PRISMA and Sentinel-2, ISPRS J. Photogramm. Remote Sens., 187, 191–210, https://doi.org/10.1016/j.isprsjprs.2022.03.008, 2022.
Mei, Q., Zhang, Z., Han, J., Song, J., Dong, J., Wu, H., Xu, J., and Tao, F.: ChinaSoyArea10m: a dataset of soybean planting areas with a spatial resolution of 10 m across China from 2017 to 2021 (V1), Zenodo [data set], https://doi.org/10.5281/zenodo.10071427, 2023.
National Bureau of Statistics of China: National Data, https://data.stats.gov.cn/english/, last access: 6 July 2024.
Olsen, J. L., Stisen, S., Proud, S. R., and Fensholt, R.: Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin, Remote Sens. Environ., 159, 57–69, https://doi.org/10.1016/j.rse.2014.11.029, 2015.
Oreopoulos, L., Wilson, M. J., and Várnai, T.: Implementation on Landsat Data of a Simple Cloud-Mask Algorithm Developed for MODIS Land Bands, IEEE Geosci. Remote. Sens. Lett., 8, 597–601, https://doi.org/10.1109/LGRS.2010.2095409, 2011.
Pan, B. and Yuan, W.: Dataset of 10-meter resolution planting distribution of double-season rice in China from 2016 to 2020, National Ecosystem Research Network Data Center [data set], https://doi.org/10.12199/nesdc.ecodb.rs.2022.012, 2022 (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, 2021.
Potapov, P., Turubanova, S., Hansen, M. C., Tyukavina, A., Zalles, V., Khan, A., Song, X.-P., Pickens, A., Shen, Q., and Cortez, J.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century, Nat. Food, 3, 19–28, https://doi.org/10.1038/s43016-021-00429-z, 2022.
Raykov, Y. P., Boukouvalas, A., Baig, F., and Little, M. A.: What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm, PLOS ONE, 11, e0162259, https://doi.org/10.1371/journal.pone.0162259, 2016.
Rivera, A. J., Pérez-Godoy, M. D., Elizondo, D., Deka, L., and del Jesus, M. J.: Analysis of clustering methods for crop type mapping using satellite imagery, Neurocomputing, 492, 91–106, https://doi.org/10.1016/j.neucom.2022.04.002, 2022.
Shangguan, Y., Li, X., Lin, Y., Deng, J., and Yu, L.: Mapping spatial-temporal nationwide soybean planting area in Argentina using Google Earth Engine, Int. J. Remote Sens., 43, 1724–1748, https://doi.org/10.1080/01431161.2022.2049913, 2022.
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, 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.
Sobejano-Paz, V., Mikkelsen, T. N., Baum, A., Mo, X., Liu, S., Köppl, C. J., Johnson, M. S., Gulyas, L., and García, M.: Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought, Remote Sens., 12, 3182, https://doi.org/10.3390/rs12193182, 2020.
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, https://doi.org/10.1016/j.rse.2017.01.008, 2017.
Tan, P.-N., Steinbach, M., and Kumar, V.: Introduction to data mining, Pearson Education India, ISBN 978-93-325-8605-5, 2016.
Wang, S., Azzari, G., and Lobell, D. B.: Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques, Remote Sens. Environ., 222, 303–317, https://doi.org/10.1016/j.rse.2018.12.026, 2019.
Wang, S., Di Tommaso, S., Deines, J. M., and Lobell, D. B.: Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive, Sci. Data, 7, 307, https://doi.org/10.1038/s41597-020-00646-4, 2020.
Wang, Y. and Gai, J.: Study on the ecological regions of soybean in China II – Ecological environment and representative varieties, Chinese Journal of Applied Ecology, 71–75, 2002 (in Chinese).
Wang, Y., Feng, L., Sun, W., Zhang, Z., Zhang, H., Yang, G., and Meng, X.: Exploring the potential of multi-source unsupervised domain adaptation in crop mapping using Sentinel-2 images, Gisci. Remote Sens., 59, 2247–2265, https://doi.org/10.1080/15481603.2022.2156123, 2022.
Wang, Y., Ling, X., Ma, C., Liu, C., Zhang, W., Huang, J., Peng, S., and Deng, N.: Can China get out of soy dilemma? A yield gap analysis of soybean in China, Agron. Sustain. Dev., 43, 47, https://doi.org/10.1007/s13593-023-00897-6, 2023.
Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., and Thau, D.: Automated cropland mapping of continental Africa using Google Earth Engine cloud computing, ISPRS J. Photogramm. Remote Sens., 126, 225–244, https://doi.org/10.1016/j.isprsjprs.2017.01.019, 2017.
Yang, F., Huang, S., Gao, R., Liu, W., Yong, T., Wang, X., Wu, X., and Yang, W.: Growth of soybean seedlings in relay strip intercropping systems in relation to light quantity and red:far-red ratio, Field Crops Res., 155, 245–253, https://doi.org/10.1016/j.fcr.2013.08.011, 2014.
You, N. and Dong, J.: Examining earliest identifiable timing of crops using all available Sentinel imagery and Google Earth Engine, ISPRS J. Photogramm. Remote Sens., 161, 109–123, https://doi.org/10.1016/j.isprsjprs.2020.01.001, 2020.
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.
You, N., Dong, J., Li, J., Huang, J., and Jin, Z.: Rapid early-season maize mapping without crop labels, Remote Sens. Environ., 290, 113496, https://doi.org/10.1016/j.rse.2023.113496, 2023.
Zhang, C., Dong, J., and Ge, Q.: Quantifying the accuracies of six 30 m cropland datasets over China: A comparison and evaluation analysis, Comput. Electron. Agr., 197, 106946, https://doi.org/10.1016/j.compag.2022.106946, 2022.
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.
Zhong, L., Hu, L., Yu, L., Gong, P., and Biging, G. S.: Automated mapping of soybean and corn using phenology, ISPRS J. Photogramm. Remote Sens., 119, 151–164, https://doi.org/10.1016/j.isprsjprs.2016.05.014, 2016.
Zhou, W., Wei, H., Chen, Y., Zhang, X., Hu, J., Cai, Z., Yang, J., Hu, Q., Xiong, H., Yin, G., and Xu, B.: Monitoring intra-annual and interannual variability in spatial distribution of plastic-mulched citrus in cloudy and rainy areas using multisource remote sensing data, Eur. J. Agron., 151, 126981, https://doi.org/10.1016/j.eja.2023.126981, 2023.
Short summary
In order to make up for the lack of long-term soybean planting area maps in China, we firstly generated a dataset of soybean planting area with a spatial resolution of 10 m for major producing areas in China from 2017 to 2021 (ChinaSoyArea10m). Compared with existing datasets, ChinaSoyArea10m has higher consistency with census data and further improvement in spatial details. The dataset can provide reliable support for subsequent studies on yield monitoring and food security.
In order to make up for the lack of long-term soybean planting area maps in China, we firstly...
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