Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

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dc.contributor.author Peralta, Nahuel R.
dc.contributor.author Yared, Assefa
dc.contributor.author Du, Juan
dc.contributor.author Barden, Charles J.
dc.contributor.author Ciampitti, Ignacio A.
dc.date.accessioned 2016-11-11T23:06:48Z
dc.date.available 2016-11-11T23:06:48Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/2097/34479
dc.description Citation: Peralta, N.R.; Assefa, Y.; Du, J.; Barden, C.J.; Ciampitti, I.A. Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield. Remote Sens. 2016, 8, 848.
dc.description.abstract This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.eodc.eu). Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value). en_US
dc.language.iso en_US en_US
dc.relation.uri http://dx.doi.org/10.3390/rs8100848 en_US
dc.rights Attribution 4.0 International (CC BY 4.0) en_US
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject High-Resolution Satellite Imagery en_US
dc.subject Forecasting Corn Yields en_US
dc.subject Spatial Econometric en_US
dc.subject Within-Field Variability en_US
dc.title Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield en_US
dc.type Text en_US
dc.date.published 2016 en_US
dc.citation.doi 10.3390/rs8100848 en_US
dc.citation.issn 2072-4292 en_US
dc.citation.issue 11 en_US
dc.citation.jtitle Remote Sensing en_US
dc.citation.volume 8 en_US
dc.citation Peralta, N.R.; Assefa, Y.; Du, J.; Barden, C.J.; Ciampitti, I.A. Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield. Remote Sens. 2016, 8, 848.
dc.contributor.authoreid ciampitti en_US
dc.contributor.authoreid cbarden en_US
dc.description.version Article: Version of Record


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