Satellite based remote sensing for estimating crop yield: examining the use of various functional forms and vegetation indices

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dc.contributor.authorMiralda, Shania
dc.contributor.examiningcommitteeCoyle, Barry (Agribusiness and Agricultural Economics)en_US
dc.contributor.examiningcommitteePorth, Lysa (Warren Centre for Actuarial Studies and Research)en_US
dc.contributor.supervisorBoyd, Milton
dc.date.accessioned2022-09-13T16:59:44Z
dc.date.available2022-09-13T16:59:44Z
dc.date.copyright2022-08-25
dc.date.issued2022-08-25
dc.date.submitted2022-08-25T23:33:17Zen_US
dc.degree.disciplineAgribusiness and Agricultural Economicsen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractThis research includes two studies on estimating crop yield using satellite-based vegetation indices. For the first study, the objective is to compare eight different functional forms for estimating U.S crop yield using satellite based NDVI. For the second study, the objective is to examine 10 satellite-based vegetation indices for estimating U.S crop yield. For both studies, corn, soybeans, spring wheat, and winter wheat data for crop yield (bushel/acre) were obtained from the USDA NASS from 2008 to 2019 for a total of 12 years covering all 48 states in the United States except Hawaii and Alaska (though different states are included, based on where the crops are grown). Data for the vegetation indices were obtained from the MODIS satellite using 250m resolution level and selecting for maximum Vegetation Index values. The methodology used regression with crop yield as the dependent variable. The main independent variable is the selected vegetation index (e.g NDVI, GOSAVI, etc). A time trend variable is also included, and dummy variables for U.S States. Results for the first study indicated that relationship between NDVI and crop yield was mostly nonlinear, and piecewise regression was generally found to be the most suitable functional form. Results for the second study showed that for all 10 indices analyzed, that RDVI, GOSAVI, and GSAVI provided better estimates of crop yield than the commonly used NDVI. These results should be useful in providing a better understanding of various functional forms and various satellite based vegetation indices for improving crop yield estimation.en_US
dc.description.noteOctober 2022en_US
dc.identifier.urihttp://hdl.handle.net/1993/36907
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectPiecewise Regressionen_US
dc.subjectSatellite Dataen_US
dc.subjectRemote Sensingen_US
dc.subjectCrop Yield Estimationen_US
dc.subjectNDVIen_US
dc.subjectNormalized Difference Vegetation Indexen_US
dc.subjectRDVIen_US
dc.subjectRenormalized Difference Vegetation Indexen_US
dc.subjectGOSAVIen_US
dc.subjectGreen Optimized Soil Adjusted Vegetation Indexen_US
dc.subjectGSAVIen_US
dc.subjectPolynomial Regressionen_US
dc.subjectGAMen_US
dc.titleSatellite based remote sensing for estimating crop yield: examining the use of various functional forms and vegetation indicesen_US
dc.typemaster thesisen_US
local.subject.manitobanoen_US
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