A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)

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cg.authorship.typesCGIAR and advanced research instituteen_US
cg.contributor.affiliationBen-Gurion Universityen_US
cg.contributor.affiliationCornell Universityen_US
cg.contributor.affiliationInternational Food Policy Research Instituteen_US
cg.contributor.affiliationColorado State Universityen_US
cg.contributor.donorCornell Universityen_US
cg.contributor.donorUnited States Department of Agricultureen_US
cg.contributor.donorNational Aeronautics and Space Administrationen_US
cg.contributor.donorUnited States Agency for International Developmenten_US
cg.contributor.initiativeClimate Resilienceen_US
cg.contributor.initiativeDigital Innovationen_US
cg.contributor.initiativeForesighten_US
cg.creator.identifierYanyan Liu: 0000-0001-7553-2464en_US
cg.creator.identifierLiangzhi You: 0000-0001-7930-8814en_US
cg.howPublishedFormally Publisheden_US
cg.identifier.doihttps://doi.org/10.1088/1748-9326/ad3142en_US
cg.identifier.projectIFPRI - Foresight and Policy Modeling Uniten_US
cg.identifier.projectIFPRI - Markets, Trade, and Institutions Uniten_US
cg.identifier.projectIFPRI - Feed the Futureen_US
cg.identifier.publicationRankAen_US
cg.isijournalISI Journalen_US
cg.issn1748-9326en_US
cg.issue4en_US
cg.journalEnvironmental Research Lettersen_US
cg.reviewStatusPeer Reviewen_US
cg.subject.actionAreaSystems Transformationen_US
cg.subject.impactAreaClimate adaptation and mitigationen_US
cg.subject.impactAreaPoverty reduction, livelihoods and jobsen_US
cg.volume19en_US
dc.contributor.authorKira, Ozen_US
dc.contributor.authorWen, Jiamingen_US
dc.contributor.authorHan, Jimeien_US
dc.contributor.authorMcDonald, Andrew J.en_US
dc.contributor.authorBarrett, Christopher B.en_US
dc.contributor.authorOrtiz-Bobea, Arielen_US
dc.contributor.authorLiu, Yanyanen_US
dc.contributor.authorYou, Liangzhien_US
dc.contributor.authorMueller, Nathaniel D.en_US
dc.contributor.authorSun, Yingen_US
dc.date.accessioned2025-02-03T19:12:27Zen_US
dc.date.available2025-02-03T19:12:27Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/172756en_US
dc.titleA scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)en_US
dcterms.abstractProjected increases in food demand driven by population growth coupled with heightened agricultural vulnerability to climate change jointly pose severe threats to global food security in the coming decades, especially for developing nations. By providing real-time and low-cost observations, satellite remote sensing has been widely employed to estimate crop yield across various scales. Most such efforts are based on statistical approaches that require large amounts of ground measurements for model training/calibration, which may be challenging to obtain on a large scale in developing countries that are most food-insecure and climate-vulnerable. In this paper, we develop a generalizable framework that is mechanism-guided and practically parsimonious for crop yield estimation. We then apply this framework to estimate crop yield for two crops (corn and wheat) in two contrasting regions, the US Corn Belt US-CB, and India's Indo–Gangetic plain Wheat Belt IGP-WB, respectively. This framework is based on the mechanistic light reactions (MLR) model utilizing remotely sensed solar-induced chlorophyll fluorescence (SIF) as a major input. We compared the performance of MLR to two commonly used machine learning (ML) algorithms: artificial neural network and random forest. We found that MLR-SIF has comparable performance to ML algorithms in US-CB, where abundant and high-quality ground measurements of crop yield are routinely available (for model calibration). In IGP-WB, MLR-SIF significantly outperforms ML algorithms. These results demonstrate the potential advantage of MLR-SIF for yield estimation in developing countries where ground truth data is limited in quantity and quality. In addition, high-resolution and crop-specific satellite SIF is crucial for accurate yield estimation. Therefore, harnessing the mechanism-guided MLR-SIF and rapidly growing satellite SIF measurements (with high resolution and crop-specificity) hold promise to enhance food security in developing countries towards more effective responses to food crises, agricultural policies, and more efficient commodity pricing.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.audienceScientistsen_US
dcterms.available2024-04-12en_US
dcterms.bibliographicCitationKira, Oz; Wen, Jiaming; Han, Jimei; McDonald, Andrew J.; Barrett, Christopher B.; Ortiz-Bobea, Ariel; Liu, Yanyan; et al. 2024. Environmental Research Letters 19(4): 044071. https://doi.org/10.1088/1748-9326/ad3142en_US
dcterms.extent044071en_US
dcterms.issued2024-04en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherIOP Publishingen_US
dcterms.subjectchlorophyll fluorescenceen_US
dcterms.subjectcrop yielden_US
dcterms.subjectdeveloping countriesen_US
dcterms.subjectremote sensingen_US
dcterms.typeJournal Articleen_US

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