{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T15:06:00Z","timestamp":1790003160372,"version":"4.0.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 72134008"],"award-info":[{"award-number":["No. 72134008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 42192580"],"award-info":[{"award-number":["No. 42192580"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Food"],"DOI":"10.1038\/s43016-026-01418-w","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T09:02:31Z","timestamp":1788166951000},"page":"878-888","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An integrative framework for early estimation of global crop yields demonstrated under large-scale disruptions"],"prefix":"10.1038","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4103-3792","authenticated-orcid":false,"given":"Ziyue","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanzhao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9327-5756","authenticated-orcid":false,"given":"Xing","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei-po","family":"Kwan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7576-4481","authenticated-orcid":false,"given":"Lin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiqi","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6457-3583","authenticated-orcid":false,"given":"Qiangyi","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaozhong","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingbo","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoqun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuheng","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqiang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manchun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"1418_CR1","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1038\/s44221-023-00135-z","volume":"1","author":"R Chakraborti","year":"2023","unstructured":"Chakraborti, R. et al. Crop switching for water sustainability in India\u2019s food bowl yields co-benefits for food security and farmers\u2019 profits. Nat. Water 1, 864\u2013878 (2023).","journal-title":"Nat. Water"},{"key":"1418_CR2","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1038\/s43016-020-00211-7","volume":"2","author":"T Falkendal","year":"2021","unstructured":"Falkendal, T. et al. Grain export restrictions during COVID-19 risk food insecurity in many low- and middle-income countries. Nat. Food 2, 11\u201314 (2021).","journal-title":"Nat. Food"},{"key":"1418_CR3","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1038\/s43016-022-00559-y","volume":"3","author":"AR Bentley","year":"2022","unstructured":"Bentley, A. R. et al. Near- to long-term measures to stabilize global wheat supplies and food security. Nat. Food 3, 483\u2013486 (2022).","journal-title":"Nat. Food"},{"key":"1418_CR4","doi-asserted-by":"publisher","unstructured":"Espitia, A., Rocha, N. & Ruta, M. Covid-19 and Food Protectionism: The Impact of the Pandemic and Export Restrictions on World Food Markets https:\/\/doi.org\/10.1596\/1813-9450-9253 (Policy Research Working Paper 9253, World Bank, 2020).","DOI":"10.1596\/1813-9450-9253"},{"key":"1418_CR5","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1038\/s43247-024-01915-5","volume":"5","author":"N Jia","year":"2024","unstructured":"Jia, N. et al. The Russia\u2013Ukraine war reduced food production and exports with a disparate geographical impact worldwide. Commun. Earth Environ. 5, 765 (2024).","journal-title":"Commun. Earth Environ."},{"key":"1418_CR6","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/s43016-022-00504-z","volume":"3","author":"DK Ray","year":"2022","unstructured":"Ray, D. K. et al. Crop harvests for direct food use insufficient to meet the UN\u2019s food security goal. Nat. Food 3, 367\u2013374 (2022).","journal-title":"Nat. Food"},{"key":"1418_CR7","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1038\/s43016-023-00729-6","volume":"4","author":"M Brander","year":"2023","unstructured":"Brander, M., Bernauer, T. & Huss, M. Trade policy announcements can increase price volatility in global food commodity markets. Nat. Food 4, 331\u2013340 (2023).","journal-title":"Nat. Food"},{"key":"1418_CR8","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1038\/s43247-023-00992-2","volume":"4","author":"T Tanaka","year":"2023","unstructured":"Tanaka, T., Sun, L., Becker-Reshef, I., Song, X.-P. & Puricelli, E. Satellite forecasting of crop harvest can trigger a cross-hemispheric production response and improve global food security. Commun. Earth Environ. 4, 334 (2023).","journal-title":"Commun. Earth Environ."},{"key":"1418_CR9","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1038\/s44168-025-00316-4","volume":"4","author":"FKS Chan","year":"2025","unstructured":"Chan, F. K. S. et al. Food security under climatic extremes in the Asia\u2013Pacific region. npj Clim. Action 4, 111 (2025).","journal-title":"npj Clim. Action"},{"key":"1418_CR10","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1038\/s43247-024-01488-3","volume":"5","author":"B Chen","year":"2024","unstructured":"Chen, B. et al. Quantification of losses in agriculture production in eastern Ukraine due to the Russia\u2013Ukraine war. Commun. Earth Environ. 5, 336 (2024).","journal-title":"Commun. Earth Environ."},{"key":"1418_CR11","doi-asserted-by":"publisher","first-page":"9326","DOI":"10.1073\/pnas.1701762114","volume":"114","author":"C Zhao","year":"2017","unstructured":"Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326\u20139331 (2017).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"1418_CR12","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.econmod.2020.05.006","volume":"90","author":"L Arata","year":"2020","unstructured":"Arata, L., Fabrizi, E. & Sckokai, P. A worldwide analysis of trend in crop yields and yield variability: Evidence from FAO data. Econ. Model. 90, 190\u2013208 (2020).","journal-title":"Econ. Model."},{"key":"1418_CR13","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.agsy.2018.05.010","volume":"168","author":"S Fritz","year":"2019","unstructured":"Fritz, S. et al. A comparison of global agricultural monitoring systems and current gaps. Agric. Syst. 168, 258\u2013272 (2019).","journal-title":"Agric. Syst."},{"key":"1418_CR14","doi-asserted-by":"publisher","first-page":"112514","DOI":"10.1016\/j.rse.2021.112514","volume":"262","author":"L Feng","year":"2021","unstructured":"Feng, L., Wang, Y., Zhang, Z. & Du, Q. Geographically and temporally weighted neural network for winter wheat yield prediction. Remote Sens. Environ. 262, 112514 (2021).","journal-title":"Remote Sens. Environ."},{"key":"1418_CR15","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.rse.2017.06.043","volume":"199","author":"K Guan","year":"2017","unstructured":"Guan, K. et al. The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields. Remote Sens. Environ. 199, 333\u2013349 (2017).","journal-title":"Remote Sens. Environ."},{"key":"1418_CR16","doi-asserted-by":"publisher","first-page":"112408","DOI":"10.1016\/j.rse.2021.112408","volume":"259","author":"Y Ma","year":"2021","unstructured":"Ma, Y., Zhang, Z., Kang, Y. & \u00d6zdo\u011fan, M. Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach. Remote Sens. Environ. 259, 112408 (2021).","journal-title":"Remote Sens. Environ."},{"key":"1418_CR17","doi-asserted-by":"publisher","first-page":"114658","DOI":"10.1016\/j.rse.2025.114658","volume":"320","author":"Y Wang","year":"2025","unstructured":"Wang, Y., Yu, Q., Liu, Z., Ren, W. & Lu, X. A practical SIF-based crop model for predicting crop yields by quantifying the fraction of open PSII reaction centers (qL). Remote Sens. Environ. 320, 114658 (2025).","journal-title":"Remote Sens. Environ."},{"key":"1418_CR18","unstructured":"Dunmore, J. & Karlsson, J. Independent Evaluation of FAO\u2019s Role and Work in Statistics PC 100\/3a (Food and Agriculture Organization of the United Nations, 2008)."},{"key":"1418_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-51555-8","volume":"15","author":"W Anderson","year":"2024","unstructured":"Anderson, W. et al. Preseason maize and wheat yield forecasts for early warning of crop failure. Nat. Commun. 15, 7262 (2024).","journal-title":"Nat. Commun."},{"key":"1418_CR20","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. Random forests. Mach. Learn. 45, 5\u201332 (2001).","journal-title":"Mach. Learn."},{"key":"1418_CR21","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1126\/science.abf5413","volume":"372","author":"TS Jayne","year":"2021","unstructured":"Jayne, T. S. & Sanchez, P. A. Agricultural productivity must improve in Sub-Saharan Africa. Science 372, 1045\u20131047 (2021).","journal-title":"Science"},{"key":"1418_CR22","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1038\/s41893-024-01411-w","volume":"7","author":"P Wollburg","year":"2024","unstructured":"Wollburg, P., Markhof, Y., Bentze, T. & Ponzini, G. Substantial impacts of climate shocks in African smallholder agriculture. Nat. Sustain. 7, 1525\u20131534 (2024).","journal-title":"Nat. Sustain."},{"key":"1418_CR23","doi-asserted-by":"publisher","first-page":"100873","DOI":"10.1016\/j.cois.2022.100873","volume":"50","author":"HE Tonnang","year":"2022","unstructured":"Tonnang, H. E., Sokame, B. M., Abdel-Rahman, E. M. & Dubois, T. Measuring and modelling crop yield losses due to invasive insect pests under climate change. Curr. Opin. Insect Sci. 50, 100873 (2022).","journal-title":"Curr. Opin. Insect Sci."},{"key":"1418_CR24","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1007\/s11676-022-01475-4","volume":"33","author":"AA Agbeshie","year":"2022","unstructured":"Agbeshie, A. A., Abugre, S., Atta-Darkwa, T. & Awuah, R. A review of the effects of forest fire on soil properties. J. For. Res. 33, 1419\u20131441 (2022).","journal-title":"J. For. Res."},{"key":"1418_CR25","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1038\/s41564-022-01203-y","volume":"7","author":"AR Nelson","year":"2022","unstructured":"Nelson, A. R. et al. Wildfire-dependent changes in soil microbiome diversity and function. Nat. Microbiol. 7, 1419\u20131430 (2022).","journal-title":"Nat. Microbiol."},{"key":"1418_CR26","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/s40641-019-00128-9","volume":"5","author":"G Lasslop","year":"2019","unstructured":"Lasslop, G., Coppola, A. I., Voulgarakis, A., Yue, C. & Veraverbeke, S. Influence of fire on the carbon cycle and climate. Curr. Clim. Change Rep. 5, 112\u2013123 (2019).","journal-title":"Curr. Clim. Change Rep."},{"key":"1418_CR27","doi-asserted-by":"publisher","unstructured":"Food and Agriculture Organization of the United Nations (FAO). Ukraine: Note on the Impact of the War on Food Security in Ukraine https:\/\/doi.org\/10.4060\/cc1025en (FAO, 2022).","DOI":"10.4060\/cc1025en"},{"key":"1418_CR28","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.3390\/rs12111744","volume":"12","author":"X Wang","year":"2020","unstructured":"Wang, X., Huang, J., Feng, Q. & Yin, D. Winter wheat yield prediction at county level and uncertainty analysis in main wheat-producing regions of china with deep learning approaches. Remote Sens. 12, 1744 (2020).","journal-title":"Remote Sens."},{"key":"1418_CR29","doi-asserted-by":"publisher","first-page":"108407","DOI":"10.1016\/j.fcr.2021.108407","volume":"277","author":"AT Tefera","year":"2022","unstructured":"Tefera, A. T. et al. Estimating early season growth and biomass of field pea for selection of divergent ideotypes using proximal sensing. Field Crops Res. 277, 108407 (2022).","journal-title":"Field Crops Res."},{"key":"1418_CR30","doi-asserted-by":"publisher","first-page":"108551","DOI":"10.1016\/j.fcr.2022.108551","volume":"283","author":"Y Li","year":"2022","unstructured":"Li, Y. et al. Quantifying contributions of leaf area and longevity to leaf area duration under increased planting density and nitrogen input regimens during maize yield improvement. Field Crops Res. 283, 108551 (2022).","journal-title":"Field Crops Res."},{"key":"1418_CR31","doi-asserted-by":"publisher","first-page":"177181","DOI":"10.1016\/j.scitotenv.2024.177181","volume":"955","author":"C Knight","year":"2024","unstructured":"Knight, C., Khouakhi, A. & Waine, T. W. The impact of weather patterns on inter-annual crop yield variability. Sci. Total Environ. 955, 177181 (2024).","journal-title":"Sci. Total Environ."},{"key":"1418_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2024.123267","volume":"201","author":"D Qiao","year":"2024","unstructured":"Qiao, D. et al. Early-season estimation of winter wheat yield: a hybrid machine learning-enabled approach. Technol. Forecast. Soc. Change 201, 123267 (2024).","journal-title":"Technol. Forecast. Soc. Change"},{"key":"1418_CR33","doi-asserted-by":"publisher","first-page":"197","DOI":"10.5194\/essd-12-197-2020","volume":"12","author":"Y Luo","year":"2020","unstructured":"Luo, Y., Zhang, Z., Chen, Y., Li, Z. & Tao, F. ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000\u20132015 based on leaf area index (LAI) products. Earth Syst. Sci. Data 12, 197\u2013214 (2020).","journal-title":"Earth Syst. Sci. Data"},{"key":"1418_CR34","unstructured":"FAOSTAT. FAO https:\/\/www.fao.org\/faostat\/en\/#data (2024)."},{"key":"1418_CR35","doi-asserted-by":"publisher","unstructured":"International Food Policy Research Institute (IFPRI). Global Spatially-Disaggregated Crop Production Statistics Data for 2020, Version 1.0. Harvard Dataverse https:\/\/doi.org\/10.7910\/DVN\/SWPENT (2024).","DOI":"10.7910\/DVN\/SWPENT"},{"key":"1418_CR36","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1038\/s43016-025-01135-w","volume":"6","author":"S Heikonen","year":"2025","unstructured":"Heikonen, S. et al. Climate change threatens crop diversity at low latitudes. Nat. Food 6, 331\u2013342 (2025).","journal-title":"Nat. Food"},{"key":"1418_CR37","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1111\/j.1466-8238.2010.00551.x","volume":"19","author":"WJ Sacks","year":"2010","unstructured":"Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607\u2013620 (2010).","journal-title":"Glob. Ecol. Biogeogr."},{"key":"1418_CR38","doi-asserted-by":"publisher","first-page":"2342","DOI":"10.3390\/rs16132342","volume":"16","author":"Z Zhang","year":"2024","unstructured":"Zhang, Z., Luo, Y., Han, J., Xu, J. & Tao, F. Estimating global wheat yields at 4 km resolution during 1982\u20132020 by a spatiotemporal transferable method. Remote Sens. 16, 2342 (2024).","journal-title":"Remote Sens."},{"key":"1418_CR39","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-025-04650-4","volume":"12","author":"J Cao","year":"2025","unstructured":"Cao, J. et al. Mapping global yields of four major crops at 5-minute resolution from 1982 to 2015 using multi-source data and machine learning. Sci. Data 12, 357 (2025).","journal-title":"Sci. Data"},{"key":"1418_CR40","unstructured":"Global Administrative Unit Layers (GAUL) (Food and Agriculture Organization of the United Nations, 2015)."},{"key":"1418_CR41","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1007\/s11442-023-2113-y","volume":"33","author":"D Ma","year":"2023","unstructured":"Ma, D., Wu, X., Wang, J. & Mu, C. The spatiotemporal scale effect on vegetation interannual trend estimates based on satellite products over Qinghai\u2013Tibet Plateau. J. Geogr. Sci. 33, 924\u2013944 (2023).","journal-title":"J. Geogr. Sci."},{"key":"1418_CR42","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.rse.2013.08.027","volume":"140","author":"Z Wan","year":"2014","unstructured":"Wan, Z. New refinements and validation of the collection-6 MODIS land-surface temperature\/emissivity product. Remote Sens. Environ. 140, 36\u201345 (2014).","journal-title":"Remote Sens. Environ."},{"key":"1418_CR43","doi-asserted-by":"publisher","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","volume":"13","author":"J Mu\u00f1oz-Sabater","year":"2021","unstructured":"Mu\u00f1oz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349\u20134383 (2021).","journal-title":"Earth Syst. Sci. Data"},{"key":"1418_CR44","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01307-4","volume":"9","author":"CF Brown","year":"2022","unstructured":"Brown, C. F. et al. Dynamic World, near real-time global 10 m land use land cover mapping. Sci. Data 9, 251 (2022).","journal-title":"Sci. Data"},{"key":"1418_CR45","doi-asserted-by":"publisher","first-page":"109380","DOI":"10.1016\/j.fcr.2024.109380","volume":"311","author":"A Adak","year":"2024","unstructured":"Adak, A. et al. Photoperiod associated late flowering reaction norm: dissecting loci and genomic-enviromic associated prediction in maize. Field Crops Res. 311, 109380 (2024).","journal-title":"Field Crops Res."},{"key":"1418_CR46","doi-asserted-by":"publisher","first-page":"8219","DOI":"10.1007\/s10462-022-10366-3","volume":"56","author":"X Ran","year":"2023","unstructured":"Ran, X., Xi, Y., Lu, Y., Wang, X. & Lu, Z. Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artif. Intell. Rev. 56, 8219\u20138264 (2023).","journal-title":"Artif. Intell. Rev."},{"key":"1418_CR47","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/S0169-2070(03)00068-2","volume":"20","author":"MH Pesaran","year":"2004","unstructured":"Pesaran, M. H. & Timmermann, A. How costly is it to ignore breaks when forecasting the direction of a time series?. Int. J. Forecast. 20, 411\u2013425 (2004).","journal-title":"Int. J. Forecast."},{"key":"1418_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.wace.2022.100494","volume":"38","author":"X Chen","year":"2022","unstructured":"Chen, X. et al. Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models. Weather Clim. Extremes 38, 100494 (2022).","journal-title":"Weather Clim. Extremes"},{"key":"1418_CR49","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/s13753-020-00305-7","volume":"12","author":"MS Rahman","year":"2021","unstructured":"Rahman, M. S., Di, L., Yu, E., Lin, L. & Yu, Z. Remote sensing based rapid assessment of flood crop damage using novel disaster vegetation damage index (DVDI). Int. J. Disaster Risk Sci. 12, 90\u2013110 (2021).","journal-title":"Int. J. Disaster Risk Sci."},{"key":"1418_CR50","doi-asserted-by":"publisher","first-page":"115016","DOI":"10.1016\/j.rse.2025.115016","volume":"331","author":"Y Liu","year":"2025","unstructured":"Liu, Y., Diao, C., Yang, Z., Mei, W. & Guo, T. A novel normalized harvest phenology index (NHPI) for corn and soybean harvesting date detection using Landsat and Sentinel-2 imagery on Google Earth Engine. Remote Sens. Environ. 331, 115016 (2025).","journal-title":"Remote Sens. Environ."}],"container-title":["Nature Food"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43016-026-01418-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43016-026-01418-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43016-026-01418-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T03:02:26Z","timestamp":1789959746000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43016-026-01418-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":50,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["1418"],"URL":"https:\/\/doi.org\/10.1038\/s43016-026-01418-w","relation":{},"ISSN":["2662-1355"],"issn-type":[{"value":"2662-1355","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]},"assertion":[{"value":"26 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}]}}