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
cg.contributor.affiliationBen-Gurion Universityen
cg.contributor.affiliationCornell Universityen
cg.contributor.affiliationInternational Food Policy Research Instituteen
cg.contributor.affiliationColorado State Universityen
cg.contributor.donorCornell Universityen
cg.contributor.donorUnited States Department of Agricultureen
cg.contributor.donorNational Aeronautics and Space Administrationen
cg.contributor.donorUnited States Agency for International Developmenten
cg.contributor.initiativeClimate Resilience
cg.contributor.initiativeDigital Innovation
cg.contributor.initiativeForesight
cg.creator.identifierYanyan Liu: 0000-0001-7553-2464en
cg.creator.identifierLiangzhi You: 0000-0001-7930-8814en
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1088/1748-9326/ad3142en
cg.identifier.projectIFPRI - Foresight and Policy Modeling Uniten
cg.identifier.projectIFPRI - Markets, Trade, and Institutions Uniten
cg.identifier.projectIFPRI - Feed the Futureen
cg.identifier.publicationRankAen
cg.isijournalISI Journalen
cg.issn1748-9326en
cg.issue4en
cg.journalEnvironmental Research Lettersen
cg.reviewStatusPeer Reviewen
cg.subject.actionAreaSystems Transformation
cg.subject.impactAreaClimate adaptation and mitigation
cg.subject.impactAreaPoverty reduction, livelihoods and jobs
cg.volume19en
dc.contributor.authorKira, Ozen
dc.contributor.authorWen, Jiamingen
dc.contributor.authorHan, Jimeien
dc.contributor.authorMcDonald, Andrew J.en
dc.contributor.authorBarrett, Christopher B.en
dc.contributor.authorOrtiz-Bobea, Arielen
dc.contributor.authorLiu, Yanyanen
dc.contributor.authorYou, Liangzhien
dc.contributor.authorMueller, Nathaniel D.en
dc.contributor.authorSun, Yingen
dc.date.accessioned2025-02-03T19:12:27Zen
dc.date.available2025-02-03T19:12:27Zen
dc.identifier.urihttps://hdl.handle.net/10568/172756
dc.titleA scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF)en
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
dcterms.accessRightsOpen Access
dcterms.audienceScientistsen
dcterms.available2024-04-12en
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
dcterms.extent044071en
dcterms.issued2024-04en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherIOP Publishingen
dcterms.subjectchlorophyll fluorescenceen
dcterms.subjectcrop yielden
dcterms.subjectdeveloping countriesen
dcterms.subjectremote sensingen
dcterms.typeJournal Article

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