Articles | Volume 18, issue 3
https://doi.org/10.5194/essd-18-2413-2026
© Author(s) 2026. 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-18-2413-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
02 Apr 2026
Data description article |
| 02 Apr 2026
| 02 Apr 2026
NortheastChinaSoybeanYield20m: an annual soybean yield dataset at 20 m in Northeast China from 2019 to 2023
Jingyuan Xu
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100190, China
Xin Du
CORRESPONDING AUTHOR
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100190, China
Taifeng Dong
National Wildlife Research Centre, Environment and Climate Change Canada, 1125 Colonel By Drive, Ottawa, ON K1A0H3, Canada
Qiangzi Li
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100190, China
Yuan Zhang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100190, China
Hongyan Wang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100190, China
Jing Xiao
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100190, China
Jiashu Zhang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
School of Science, China University of Geosciences (Beijing), Beijing 100083, China
Yunqi Shen
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100190, China
Yong Dong
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100190, China
Cited articles
Açikkar, M.: Fast grid search: A grid search-inspired algorithm for optimizing hyperparameters of support vector regression, Turk. J. Electr. Eng. Co., 32, 68–92, https://doi.org/10.55730/1300-0632.4056, 2024.
Anderson, M. C., Kustas, W. P., Norman, J. M., Diak, G. T., Hain, C. R., Gao, F., Yang, Y., Knipper, K. R., Xue, J., Yang, Y., Crow, W. T., Holmes, T. R. H., Nieto, H., Guzinski, R., Otkin, J. A., Mecikalski, J. R., Cammalleri, C., Torres-Rua, A. T., Zhan, X., Fang, L., Colaizzi, P. D., and Agam, N.: A brief history of the thermal IR-based Two-Source Energy Balance (TSEB) model – diagnosing evapotranspiration from plant to global scales, Agr. Forest Meteorol., 350, 109951, https://doi.org/10.1016/j.agrformet.2024.109951, 2024.
Ang, Y., Shafri, H. Z. M., Lee, Y. P., Abidin, H., Bakar, S. A., Hashim, S. J., Che'Ya, N. N., Hassan, M. R., Lim, H. S., and Abdullah, R.: A novel ensemble machine learning and time series approach for oil palm yield prediction using Landsat time series imagery based on NDVI, Geocarto Int., 37, 9865–9896, https://doi.org/10.1080/10106049.2022.2025920, 2022.
Azzari, G., Jain, M., and Lobell, D. B.: Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries, Remote Sens. Environ., 202, 129–141, https://doi.org/10.1016/j.rse.2017.04.014, 2017.
Baup, F., Fieuzal, R., and Betbeder, J.: Estimation of soybean yield from assimilated optical and radar data into a simplified agrometeorological model, in: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IGARSS 2015 – 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy, 3961–3964, https://doi.org/10.1109/IGARSS.2015.7326692, 2015.
Boote, K. J., Pickering, N. B., and Allen Jr., L. H.: Plant Modeling: Advances and Gaps in Our Capability to Predict Future Crop Growth and Yield in Response to global Climate Change, Advances in Carbon Dioxide Effects Research, 179–228, https://doi.org/10.2134/asaspecpub61.c10, 1997.
Cao, H., Zhao, R., Xia, L., Wu, S., and Yang, P.: Trends in crop yield estimation via data assimilation based on multi-interdisciplinary analysis, Field Crop. Res., 322, 109745, https://doi.org/10.1016/j.fcr.2025.109745, 2025.
Cao, J., Zhang, Z., Tao, F., Zhang, L., Luo, Y., Zhang, J., Han, J., and Xie, J.: Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches, Agr. Forest Meteorol., 297, 108275, https://doi.org/10.1016/j.agrformet.2020.108275, 2021.
Che, Z., Purushotham, S., Cho, K., Sontag, D., and Liu, Y.: Recurrent Neural Networks for Multivariate Time Series with Missing Values, Sci. Rep.-UK, 8, 6085, https://doi.org/10.1038/s41598-018-24271-9, 2018.
Chen, Q., Zheng, B., Chen, T., and Chapman, S. C.: Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning, J. Exp. Bot., 73, 6558–6574, https://doi.org/10.1093/jxb/erac291, 2022a.
Chen, Y., Liu, S., Li, H., Li, X. F., Song, C. Y., Cruse, R. M., and Zhang, X. Y.: Effects of conservation tillage on corn and soybean yield in the humid continental climate region of Northeast China, Soil Till. Res., 115–116, 56–61, https://doi.org/10.1016/j.still.2011.06.007, 2011.
Chen, Y.-P., Huang, C.-H., Lo, Y.-H., Chen, Y.-Y., and Lai, F.: Combining attention with spectrum to handle missing values on time series data without imputation, Inform. Sciences, 609, 1271–1287, https://doi.org/10.1016/j.ins.2022.07.124, 2022b.
Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y.: Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 1724–1734, https://doi.org/10.3115/v1/D14-1179, 2014.
Choi, D.-H., Ban, H.-Y., Seo, B.-S., Lee, K.-J., and Lee, B.-W.: Phenology and Seed Yield Performance of Determinate Soybean Cultivars Grown at Elevated Temperatures in a Temperate Region, PLoS ONE, 11, e0165977, https://doi.org/10.1371/journal.pone.0165977, 2016.
Diepen, C. A., Wolf, J., Keulen, H., and Rappoldt, C.: WOFOST: a simulation model of crop production, Soil Use Manage., 5, 16–24, https://doi.org/10.1111/j.1475-2743.1989.tb00755.x, 1989.
Dokoohaki, H., Kivi, M. S., Martinez-Feria, R., Miguez, F. E., and Hoogenboom, G.: A comprehensive uncertainty quantification of large-scale process-based crop modeling frameworks, Environ. Res. Lett., 16, 084010, https://doi.org/10.1088/1748-9326/ac0f26, 2021.
Dong, T., Liu, J., Shang, J., Qian, B., Ma, B., Kovacs, J. M., Walters, D., Jiao, X., Geng, X., and Shi, Y.: Assessment of red-edge vegetation indices for crop leaf area index estimation, Remote Sens. Environ., 222, 133–143, https://doi.org/10.1016/j.rse.2018.12.032, 2019.
Dong, T., Liu, J., Qian, B., He, L., Liu, J., Wang, R., Jing, Q., Champagne, C., McNairn, H., Powers, J., Shi, Y., Chen, J. M., and Shang, J.: Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data, ISPRS J. Photogramm., 168, 236–250, https://doi.org/10.1016/j.isprsjprs.2020.08.003, 2020.
Du, X., Song, F., Wang, H., Huanxuezhang, Meng, J., Li, Q., Liu, J., Ding, L., and Lu, Y.: Soybean yield estimation using HJ-1 CCD data in Northeast China, in: 2014 The Third International Conference on Agro-Geoinformatics, 2014 Third International Conference on Agro-Geoinformatics, Beijing, China, 1–4, https://doi.org/10.1109/Agro-Geoinformatics.2014.6910627, 2014.
Du, X., Zhu, J., Xu, J., Li, Q., Tao, Z., Zhang, Y., Wang, H., and Hu, H.: Remote sensing-based winter wheat yield estimation integrating machine learning and crop growth multi-scenario simulations, Int. J. Digit. Earth, 18, 2443470, https://doi.org/10.1080/17538947.2024.2443470, 2025.
Duchemin, B., Maisongrande, P., Boulet, G., and Benhadj, I.: A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index, Environ. Modell. Softw., 23, 876–892, https://doi.org/10.1016/j.envsoft.2007.10.003, 2008.
Falcon, W. P., Naylor, R. L., and Shankar, N. D.: Rethinking Global Food Demand for 2050, Popul. Dev. Rev., 48, 921–957, https://doi.org/10.1111/padr.12508, 2022.
Fan, R., Zhang, X., Liang, A., Shi, X., Chen, X., Bao, K., Yang, X., and Jia, S.: Tillage and rotation effects on crop yield and profitability on a Black soil in northeast China, Can. J. Soil Sci., 92, 463–470, https://doi.org/10.4141/cjss2010-020, 2012.
FAOSTAT: Food and Agriculture Organization of the United Nations, FAO Statistical Databases, http://www.fao.org/faostat/en/ (last access: 30 March 2024), 2022.
Feng, P., Wang, B., Liu, D. L., Waters, C., Xiao, D., Shi, L., and Yu, Q.: Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique, Agr. Forest Meteorol., 285–286, 107922, https://doi.org/10.1016/j.agrformet.2020.107922, 2020.
Gao, F. and Anderson, M.: Evaluating Yield Variability of Corn and Soybean Using Landsat-8, Sentinel-2 and Modis in Google Earth Engine, in: IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 7286–7289, https://doi.org/10.1109/IGARSS.2019.8897990, 2019.
Gaso, D. V., Paudel, D., De Wit, A., Puntel, L. A., Mullissa, A., and Kooistra, L.: Beyond assimilation of leaf area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction, Agr. Forest Meteorol., 351, 110022, https://doi.org/10.1016/j.agrformet.2024.110022, 2024.
Gevaert, C. M.: Explainable AI for earth observation: A review including societal and regulatory perspectives, Int. J. Appl. Earth Obs., 112, 102869, https://doi.org/10.1016/j.jag.2022.102869, 2022.
Gitelson, A. and Merzlyak, M. N.: Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation, J. Plant Physiol., 143, 286–292, https://doi.org/10.1016/S0176-1617(11)81633-0, 1994.
Gopi, P. S. S. and Karthikeyan, M.: Red fox optimization with ensemble recurrent neural network for crop recommendation and yield prediction model, Multimed. Tools Appl., 83, 13159–13179, https://doi.org/10.1007/s11042-023-16113-2, 2023.
Graham, P. H. and Vance, C. P.: Legumes: Importance and constraints to greater use, Plant Physiol., 131, 872–877, https://doi.org/10.1104/pp.017004, 2003.
Guo, S., Guo, E., Zhang, Z., Dong, M., Wang, X., Fu, Z., Guan, K., Zhang, W., Zhang, W., Zhao, J., Liu, Z., Zhao, C., and Yang, X.: Impacts of mean climate and extreme climate indices on soybean yield and yield components in Northeast China, Sci. Total Environ., 838, 156284, https://doi.org/10.1016/j.scitotenv.2022.156284, 2022.
He, M., Kimball, J., Maneta, M., Maxwell, B., Moreno, A., Beguería, S., and Wu, X.: Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data, Remote Sens.-Basel, 10, 372, https://doi.org/10.3390/rs10030372, 2018.
Hou, M., Tian, F., Zhang, L., Li, S., Du, T., Huang, M., and Yuan, Y.: Estimating Crop Transpiration of Soybean under Different Irrigation Treatments Using Thermal Infrared Remote Sensing Imagery, Agronomy, 9, 8, https://doi.org/10.3390/agronomy9010008, 2018.
Hu, P., Zheng, B., Chen, Q., Grunefeld, S., Choudhury, M. R., Fernandez, J., Potgieter, A., and Chapman, S. C.: Estimating aboveground biomass dynamics of wheat at small spatial scale by integrating crop growth and radiative transfer models with satellite remote sensing data, Remote Sens. Environ., 311, 114277, https://doi.org/10.1016/j.rse.2024.114277, 2024.
Huang, H., Huang, J., Wu, Y., Zhuo, W., Song, J., Li, X., Li, L., Su, W., Ma, H., and Liang, S.: The Improved Winter Wheat Yield Estimation by Assimilating GLASS LAI Into a Crop Growth Model With the Proposed Bayesian Posterior-Based Ensemble Kalman Filter, IEEE T. Geosci. Remote, 61, 1–18, https://doi.org/10.1109/TGRS.2023.3259742, 2023.
Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., Su, W., Zhang, X., Zhu, D., and Wu, W.: Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model, Agr. Forest Meteorol., 204, 106–121, https://doi.org/10.1016/j.agrformet.2015.02.001, 2015.
Huang, J., Gómez-Dans, J. L., Huang, H., Ma, H., Wu, Q., Lewis, P. E., Liang, S., Chen, Z., Xue, J.-H., Wu, Y., Zhao, F., Wang, J., and Xie, X.: Assimilation of remote sensing into crop growth models: Current status and perspectives, Agr. Forest Meteorol., 276–277, 107609, https://doi.org/10.1016/j.agrformet.2019.06.008, 2019.
Huang, J., Song, J., Huang, H., Zhuo, W., Niu, Q., Wu, S., Ma, H., and Liang, S.: Progress and perspectives in data assimilation algorithms for remote sensing and crop growth model, Science of Remote Sensing, 10, 100146, https://doi.org/10.1016/j.srs.2024.100146, 2024.
Huang, Y. and Liu, Z.: Improving Northeast China's soybean and maize planting structure through subsidy optimization considering climate change and comparative economic benefit, Land Use Policy, 146, 107319, https://doi.org/10.1016/j.landusepol.2024.107319, 2024.
Hunt, M. L., Blackburn, G. A., Carrasco, L., Redhead, J. W., and Rowland, C. S.: High resolution wheat yield mapping using Sentinel-2, Remote Sens. Environ., 233, 111410, https://doi.org/10.1016/j.rse.2019.111410, 2019.
Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P. J., Asner, G. P., François, C., and Ustin, S. L.: PROSPECT+SAIL models: A review of use for vegetation characterization, Remote Sens. Environ., 113, S56–S66, https://doi.org/10.1016/j.rse.2008.01.026, 2009.
Jain, A. K. and Dubes, R. C.: Algorithms for clustering data, Technometrics, 32, 227–229, 1988.
Kaur, S. and Singh, M.: Modeling the crop growth – A review, MAUSAM, 71, 103–114, 2020.
Ko, J., Shin, T., Kang, J., Baek, J., and Sang, W.-G.: Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation, Front. Plant Sci., 15, 1320969, https://doi.org/10.3389/fpls.2024.1320969, 2024.
Kodadinne Narayana, N., Wijewardana, C., Alsajri, F. A., Reddy, K. R., Stetina, S. R., and Bheemanahalli, R.: Resilience of soybean genotypes to drought stress during the early vegetative stage, Sci. Rep.-UK, 14, https://doi.org/10.1038/s41598-024-67930-w, 2024.
Li, C., Ma, C., Cui, Y., Lu, G., and Wei, F.: UAV Hyperspectral Remote Sensing Estimation of Soybean Yield Based on Physiological and Ecological Parameter and Meteorological Factor in China, J. Indian Soc. Remot., 49, 873–886, https://doi.org/10.1007/s12524-020-01269-3, 2021.
Li, X., Chen, M., He, S., Xu, X., He, L., Wang, L., Gao, Y., Tang, F., Gong, T., Wang, W., Xu, M., Liu, C., Yu, L., Liu, W., and Yang, W.: Estimation of soybean yield based on high-throughput phenotyping and machine learning, Front. Plant Sci., 15, 1395760, https://doi.org/10.3389/fpls.2024.1395760, 2024.
Liu, X. and Herbert, S. J.: Fifteen years of research examining cultivation of continuous soybean in northeast China: A review, Field Crop. Res., 79, 1–7, https://doi.org/10.1016/S0378-4290(02)00042-4, 2002.
Liu, X., Jin, J., Herbert, S. J., Zhang, Q., and Wang, G.: Yield components, dry matter, LAI and LAD of soybeans in Northeast China, Field Crop. Res., 93, 85–93, https://doi.org/10.1016/j.fcr.2004.09.005, 2005.
Liu, X., Jin, J., Wang, G., and Herbert, S. J.: Soybean yield physiology and development of high-yielding practices in Northeast China, Field Crop. Res., 105, 157–171, https://doi.org/10.1016/j.fcr.2007.09.003, 2008.
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, Earth Syst. Sci. Data, 16, 3213–3231, https://doi.org/10.5194/essd-16-3213-2024, 2024.
Misaal, M. A., Zahra, S. M., Rasul, F., Imran, M., Noor, R., and Fahad, M.: Influence of Climate Change on Crop Yield and Sustainable Agriculture, in: Climate Change Impacts on Natural Resources, Ecosystems and Agricultural Systems, edited by: Pande, C. B., Moharir, K. N., Singh, S. K., Pham, Q. B., and Elbeltagi, A., Springer International Publishing, Cham, 209–223, https://doi.org/10.1007/978-3-031-19059-9_7, 2023.
Muhuri, A., Goïta, K., Magagi, R., and Wang, H.: Soil Moisture Retrieval During Crop Growth Cycle Using Satellite SAR Time Series, IEEE J. Sel. Top. Appl., 16, 9302–9319, https://doi.org/10.1109/JSTARS.2023.3280181, 2023.
Mulvaney, M. J. and Devkota, P. J.: Adjusting Crop Yield to a Standard Moisture Content, EDIS, https://doi.org/10.32473/edis-ag442-2020, 2020.
Myneni, R., Knyazikhin, Y., and Park, T.: MODIS/Terra+Aqua Leaf Area Index/FPAR 4-Day L4 Global 500m SIN Grid V061, EarthData [data set], https://doi.org/10.5067/MODIS/MCD15A3H.061, 2021.
National Bureau of Statistics of China: National statistical yearbook, China Statistics Press, https://www.stats.gov.cn/sj/ndsj/2023/indexch.htm (last access: 6 March 2024), 2023.
National Soil Survey Office: Soil Species of China, China Agriculture Press, Beijing, 924 pp., https://www.resdc.cn/data.aspx?DATAID=145 (last access: 1 April 2026), 1995.
Nguy-Robertson, A. L., Peng, Y., Gitelson, A. A., Arkebauer, T. J., Pimstein, A., Herrmann, I., Karnieli, A., Rundquist, D. C., and Bonfil, D. J.: Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm, Agr. Forest Meteorol., 192–193, 140–148, https://doi.org/10.1016/j.agrformet.2014.03.004, 2014.
Ntakos, G., Prikaziuk, E., Ten Den, T., Reidsma, P., Vilfan, N., Van Der Wal, T., and Van Der Tol, C.: Coupled WOFOST and SCOPE model for remote sensing-based crop growth simulations, Comput. Electron. Agr., 225, 109238, https://doi.org/10.1016/j.compag.2024.109238, 2024.
Pang, A., Chang, M. W. L., and Chen, Y.: Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia, Sensors-Basel, 22, 717, https://doi.org/10.3390/s22030717, 2022.
Pasqualotto, N., Delegido, J., Van Wittenberghe, S., Rinaldi, M., and Moreno, J.: Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI), Sensors-Basel, 19, 904, https://doi.org/10.3390/s19040904, 2019.
Peng, G. and Yili, Z.: Research on Forest Phenology Prediction Based on LSTM and GRU Model, Journal of Resources and Ecology, 14, 1674-764x, https://doi.org/10.5814/j.issn.1674-764x.2023.01.003, 2023.
Pinke, Z. and Lövei, G. L.: Increasing temperature cuts back crop yields in Hungary over the last 90 years, Glob. Change Biol., 23, 5426–5435, https://doi.org/10.1111/gcb.13808, 2017.
Pu, L., Zhang, S., Yang, J., Chang, L., and Bai, S.: Spatio-Temporal Dynamics of Maize Potential Yield and Yield Gaps in Northeast China from 1990 to 2015, Int. J. Env. Res. Pub. He., 16, 1211, https://doi.org/10.3390/ijerph16071211, 2019.
Qiao, C., Cheng, C., and Ali, T.: How climate change and international trade will shape the future global soybean security pattern, J. Clean. Prod., 422, 138603, https://doi.org/10.1016/j.jclepro.2023.138603, 2023.
Qu, H., Li, X., Zhu, H., Wang, L., Qu, B., Wang, Q., Lv, J., Ji, Y., and Jiang, L.: Effects of combination of low temperature and excessive precipitation at seedling stage on soybean yield in high-latitude cold region, Chinese Journal of Ecology, 43, 3040–3046, https://doi.org/10.13292/j.1000-4890.202410.003, 2024.
Radočaj, D., Plaščak, I., and Jurišić, M.: Phenology-Based Maize and Soybean Yield Potential Prediction Using Machine Learning and Sentinel-2 Imagery Time-Series, Appl. Sci.-Basel, 15, 7216, https://doi.org/10.3390/app15137216, 2025.
Ren, P., Li, H., Han, S., Chen, R., Yang, G., Yang, H., Feng, H., and Zhao, C.: Estimation of Soybean Yield by Combining Maturity Group Information and Unmanned Aerial Vehicle Multi-Sensor Data Using Machine Learning, Remote Sens.-Basel, 15, 4286, https://doi.org/10.3390/rs15174286, 2023a.
Ren, Y., Li, Q., Du, X., Zhang, Y., Wang, H., Shi, G., and Wei, M.: Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning, Plants, 12, 446, https://doi.org/10.3390/plants12030446, 2023b.
Shi, B., Guo, L., and Yu, L.: Accurate LAI estimation of soybean plants in the field using deep learning and clustering algorithms, Front. Plant Sci., 15, https://doi.org/10.3389/fpls.2024.1501612, 2025.
Shi, X. Z., Yu, D. S., Warner, E. D., Pan, X. Z., Petersen, G. W., Gong, Z. G., and Weindorf, D. C.: Soil Database of 1:1 000 000 Digital Soil Survey and Reference System of the Chinese Genetic Soil Classification System, Soil Horizons, 45, 129, https://doi.org/10.2136/sh2004.4.0129, 2004.
Song, X.-P., Li, H., Potapov, P., and Hansen, M. C.: Annual 30 m soybean yield mapping in Brazil using long-term satellite observations, climate data and machine learning, Agr. Forest Meteorol., 326, 109186, https://doi.org/10.1016/j.agrformet.2022.109186, 2022.
Steduto, P., Hsiao, T. C., Raes, D., and Fereres, E.: AquaCrop – The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles, Agron. J., 101, 426–437, https://doi.org/10.2134/agronj2008.0139s, 2009.
Sun, X., Li, Q., Qiao, Y., Hu, Z., Zhang, X., and Liu, Y.: Warming and Drought in Hailun of Heilongjiang: Effects on Growth and Development of Soybean, Chinese Agricultural Science Bulletin, 38, 27–33, https://doi.org/10.11924/j.issn.1000-6850.casb2021-0788, 2022.
Tan, J., Yang, P., Liu, Z., Wu, W., Zhang, L., Li, Z., You, L., Tang, H., and Li, Z.: Spatio-temporal dynamics of maize cropping system in Northeast China between 1980 and 2010 by using spatial production allocation model, J. Geogr. Sci., 24, 397–410, https://doi.org/10.1007/s11442-014-1096-0, 2014.
Tian, H., Wang, P., Tansey, K., Zhang, J., Zhang, S., and Li, H.: An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China, Agr. Forest Meteorol., 310, 108629, https://doi.org/10.1016/j.agrformet.2021.108629, 2021.
Urda, C., Rezi, R., Varga, A. G., Negrea, A., Muntean, E., Sopterean, L., and Duda, M. M.: Exploring the impact of sowing dates on soybean yield, seed quality and trypsin inhibitor activity, Agrolife Scientific Journal, 13, 223–230, 2024.
Viña, A., Gitelson, A. A., Nguy-Robertson, A. L., and Peng, Y.: Comparison of different vegetation indices for the remote assessment of green leaf area index of crops, Remote Sens. Environ., 115, 3468–3478, https://doi.org/10.1016/j.rse.2011.08.010, 2011.
Von Bloh, M., Nóia Júnior, R. D. S., Wangerpohl, X., Saltýk, A. O., Haller, V., Kaiser, L., and Asseng, S.: Machine learning for soybean yield forecasting in Brazil, Agr. Forest Meteorol., 341, 109670, https://doi.org/10.1016/j.agrformet.2023.109670, 2023.
Wang, B., Chen, C., Liu, D., Asseng, S., Yu, Q., and Yang, X.: Effects of climate trends and variability on wheat yield variability in eastern Australia, Clim. Res., 64, 173–186, https://doi.org/10.3354/cr01307, 2015.
Wang, C., Linderholm, H. W., Song, Y., Wang, F., Liu, Y., Tian, J., Xu, J., Song, Y., and Ren, G.: Impacts of Drought on Maize and Soybean Production in Northeast China During the Past Five Decades, Int. J. Env. Res. Pub. He., 17, 2459, https://doi.org/10.3390/ijerph17072459, 2020.
Wang, H., Liu, D., Chen, P., Li, Y., Han, X., and Hao, X.: Distribution of maturity types of maize based on accumulated temperature rezone in Northeast China, Chinese Journal of Agricultural Resources and Regional Planning, 43, 102–112, 2022.
Wang, X., Zhu, L., Hao, Y., Wang, Z., Xue, L., Ding, K., and Huang, X.: Impacts of aerosol meteorological feedback on China's yield potential of soybean, Meteorol. Appl., 31, e2198, https://doi.org/10.1002/met.2198, 2024.
Xie, Q., Dash, J., Huete, A., Jiang, A., Yin, G., Ding, Y., Peng, D., Hall, C. C., Brown, L., Shi, Y., Ye, H., Dong, Y., and Huang, W.: Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery, Int. J. Appl. Earth Obs., 80, 187–195, https://doi.org/10.1016/j.jag.2019.04.019, 2019.
Xie, Y. and Huang, J.: Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China, Remote Sens.-Basel, 13, 4372, https://doi.org/10.3390/rs13214372, 2021.
Xin, M., Zhang, Z., Han, Y., Feng, L., Lei, Y., Li, X., Wu, F., Wang, J., Wang, Z., and Li, Y.: Soybean phenological changes in response to climate warming in three northeastern provinces of China, Field Crop. Res., 302, 109082, https://doi.org/10.1016/j.fcr.2023.109082, 2023.
Xu, J., Du, X., Dong, T., Li, Q., Zhang, Y., Wang, H., Xiao, J., Zhang, J., Shen, Y., and Dong, Y.: NortheastChinaSoybeanYield20m: an annual soybean yield dataset at 20 m in Northeast China from 2019 to 2023, Zenodo [data set], https://doi.org/10.5281/zenodo.14263103, 2024.
Yildirim, T., Moriasi, D. N., Starks, P. J., and Chakraborty, D.: Using Artificial Neural Network (ANN) for Short-Range Prediction of Cotton Yield in Data-Scarce Regions, Agronomy, 12, 828, https://doi.org/10.3390/agronomy12040828, 2022.
Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, 2020.
Zhang, Y., Liu, M., Kong, L., Peng, T., Xie, D., Zhang, L., Tian, L., and Zou, X.: Temporal Characteristics of Stress Signals Using GRU Algorithm for Heavy Metal Detection in Rice Based on Sentinel-2 Images, Int. J. Env. Res. Pub. He., 19, 2567, https://doi.org/10.3390/ijerph19052567, 2022.
Zhao, G., Wang, J., Fan, W., and Ying, T.: Vegetation net primary productivity in Northeast China in 2000–2008: Simulation and seasonal change, J. Appl. Ecol., 22, 621–30, 2011.
Zhao, J., Wang, C., Shi, X., Bo, X., Li, S., Shang, M., Chen, F., and Chu, Q.: Modeling climatically suitable areas for soybean and their shifts across China, Agr. Syst., 192, 103205, https://doi.org/10.1016/j.agsy.2021.103205, 2021.
Zhao, J., Wang, Y., Zhao, M., Wang, K., Li, S., Gao, Z., Shi, X., and Chu, Q.: Prospects for soybean production increase by closing yield gaps in the Northeast Farming Region, China, Field Crop. Res., 293, 108843, https://doi.org/10.1016/j.fcr.2023.108843, 2023a.
Zhao, L., Li, Q., Chang, Q., Shang, J., Du, X., Liu, J., and Dong, T.: In-season crop type identification using optimal feature knowledge graph, ISPRS J. Photogramm., 194, 250–266, https://doi.org/10.1016/j.isprsjprs.2022.10.017, 2022.
Zhao, Y., Han, S., Zheng, J., Xue, H., Li, Z., Meng, Y., Li, X., Yang, X., Li, Z., Cai, S., and Yang, G.: ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China, Earth Syst. Sci. Data, 15, 4047–4063, https://doi.org/10.5194/essd-15-4047-2023, 2023b.
Zheng, L. and Zhang, X.: Harvest time monitoring data of Shengyang Station in Liaoning Province from 1998 to 2008, National Ecosystem Science Data Center, https://doi.org/10.12199/nesdc.ecodb.mon.2020.dp2011.sya.004, 2021.
Zhuo, W., Fang, S., Gao, X., Wang, L., Wu, D., Fu, S., Wu, Q., and Huang, J.: Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009–2013, Int. J. Appl. Earth Obs., 106, 102668, https://doi.org/10.1016/j.jag.2021.102668, 2022.
Short summary
This study proposed a 20 m soybean yield dataset in Northeast China (NortheastChinaSoybeanYield20m) from 2019 to 2023 using a hybrid framework coupling crop growth model with deep learning algorithm. Stable results were achieved through the years. The overall accuracy of the dataset was 287.44 and 272.36 kg ha–1 in the root mean squared error for field and regional scale, respectively. The study satisfied the urgent demands for precise control of crop yield information.
This study proposed a 20 m soybean yield dataset in Northeast China...
Altmetrics
Final-revised paper
Preprint