Modeling Carbon Uptake of Dryland Maize Using High Resolution Satellite Imagery

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dc.contributor.authorMenefee, Dorothyen
dc.contributor.authorRajan, Nithyaen
dc.contributor.authorShafian, Sanazen
dc.contributor.authorCui, Songen
dc.date.accessioned2023-01-17T20:27:52Zen
dc.date.available2023-01-17T20:27:52Zen
dc.date.issued2022-03en
dc.date.updated2023-01-16T15:59:13Zen
dc.description.abstractQuantifying carbon uptake or gross primary production (GPP) from agroecosystems is important for understanding the spatial and temporal dynamics of carbon fixation by crops. The availability of high-resolution remote sensing data can significantly improve GPP estimation of small-scale agricultural fields. Multispectral satellite data with 3-m spatial resolution and frequent global coverage are available from the PlanetScope network of satellites. However, this data remains largely unexplored for studying the carbon dynamics of agroecosystems. The overarching goal of this study was to develop a simple empirical method for quantifying the GPP of dryland maize (<jats:italic>Zea mays L.</jats:italic>) using remotely sensed vegetation indices along with <jats:italic>in-situ</jats:italic> measurements of photosynthetically active radiation and leaf area index by linking it with carbon uptake data from an eddy covariance flux tower. Four vegetation indices were investigated: the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), the weighted difference vegetation index (WDVI), and the two-band enhanced vegetation index (EVI2). This study was conducted over a three-year period from 2017 to 2019 in East-Central Texas. A total of 12 GPP prediction models were developed using individual yearly data and were used for predicting GPP of the other 2 years. Predicted maize GPP values were then compared against tower-based GPP. The NDVI models were the least successful in predicting GPP and had the highest root mean square error (average: 10.1 3 gC m<jats:sup>−2</jats:sup>; maximum: 26.3 gC m<jats:sup>−2</jats:sup>). Models based on SAVI performed especially well with error ranging from 0.05 to 0.94 gC m<jats:sup>−2</jats:sup>. The slope of the regression between SAVI-based estimated GPP and measured GPP was not different from 1.0 in all combinations of years. The success of the SAVI-based GPP models for predicting dryland maize carbon uptake indicates that it was the least affected vegetation index by changing soil background condition in this row cropping system.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/frsen.2022.810030en
dc.identifier.eissn2673-6187en
dc.identifier.urihttp://hdl.handle.net/10919/113206en
dc.identifier.volume3en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleModeling Carbon Uptake of Dryland Maize Using High Resolution Satellite Imageryen
dc.title.serialFrontiers in Remote Sensingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/CALS T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/School of Plant and Environmental Sciencesen

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