Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model

dc.contributor.author Lewis-Beck, Colin
dc.contributor.author Walker, Victoria
dc.contributor.author Niemi, Jarad
dc.contributor.author Caragea, Petrutza
dc.contributor.author Hornbuckle, Brian
dc.contributor.department Statistics
dc.contributor.department Agronomy
dc.date 2020-03-11T14:22:26.000
dc.date.accessioned 2020-07-02T06:57:43Z
dc.date.available 2020-07-02T06:57:43Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.issued 2020-01-01
dc.description.abstract <p>Remote sensing observations that vary in response to plant growth and senescence can be used to monitor crop development within and across growing seasons. Identifying when crops reach specific growth stages can improve harvest yield prediction and quantify climate change. Using the Level 2 vegetation optical depth (VOD) product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite, we retrospectively estimate the timing of a key crop development stage in the United States Corn Belt. We employ nonlinear curves nested within a hierarchical modeling framework to extract the timing of the third reproductive development stage of corn (R3) as well as other new agronomic signals from SMOS VOD. We compare our estimates of the timing of R3 to United States Department of Agriculture (USDA) survey data for the years 2011, 2012, and 2013. We find that 87%, 70%, and 37%, respectively, of our model estimates of R3 timing agree with USDA district-level observations. We postulate that since the satellite estimates can be directly linked to a physiological state (the maximum amount of plant water, or water contained within plant tissue per ground area) it is more accurate than the USDA data which is based upon visual observations from roadways. Consequently, SMOS VOD could be used to replace, at a finer resolution than the district-level USDA reports, the R3 data that has not been reported by the USDA since 2013. We hypothesize the other model parameters contain new information about soil and crop management and crop productivity that are not routinely collected by any federal or state agency in the Corn Belt.</p>
dc.description.comments <p>This article is published as Lewis-Beck, Colin, Victoria A. Walker, Jarad Niemi, Petruţa Caragea, and Brian K. Hornbuckle. "Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model." <em>Remote Sensing</em> 12, no. 5 (2020): 827. doi: <a href="https://doi.org/10.3390/rs12050827">10.3390/rs12050827</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/294/
dc.identifier.articleid 1296
dc.identifier.contextkey 16766658
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/294
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90614
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/294/2020_Niemi_ExtractingAgronomic.pdf|||Fri Jan 14 23:14:56 UTC 2022
dc.source.uri 10.3390/rs12050827
dc.subject.disciplines Agricultural Science
dc.subject.disciplines Agriculture
dc.subject.disciplines Remote Sensing
dc.subject.disciplines Statistical Models
dc.subject.keywords SMOS
dc.subject.keywords VOD
dc.subject.keywords crop development
dc.subject.keywords Bayesian estimation
dc.subject.keywords asymmetric Gaussian
dc.title Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model
dc.type article
dc.type.genre article
dspace.entity.type Publication
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