Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

dc.contributor.authorSkakun, Sergii
dc.contributor.authorVermote, Eric
dc.contributor.authorFranch, Belen
dc.contributor.authorRoger, Jean-Claude
dc.contributor.authorKussul, Nataliia
dc.contributor.authorJu, Junchang
dc.contributor.authorMasek, Jeffrey
dc.date.accessioned2023-11-14T17:47:21Z
dc.date.available2023-11-14T17:47:21Z
dc.date.issued2019-07-27
dc.description.abstractA combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3–5 days) at moderate spatial resolution (10–30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA’s Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016–2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R2 = 0.73 on cross-validation.
dc.description.urihttps://doi.org/10.3390/rs11151768
dc.identifierhttps://doi.org/10.13016/dspace/7jrk-6nks
dc.identifier.citationSkakun, S.; Vermote, E.; Franch, B.; Roger, J.-C.; Kussul, N.; Ju, J.; Masek, J. Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models. Remote Sens. 2019, 11, 1768.
dc.identifier.urihttp://hdl.handle.net/1903/31396
dc.language.isoen_US
dc.publisherMDPI
dc.relation.isAvailableAtCollege of Behavioral & Social Sciencesen_us
dc.relation.isAvailableAtGeographyen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectagriculture
dc.subjectcrop yield
dc.subjectwheat
dc.subjectLandsat 8
dc.subjectSentinel-2
dc.subjectHLS
dc.subjectphenological fitting
dc.subjectgrowing degree days (GDD)
dc.titleWinter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models
dc.typeArticle
local.equitableAccessSubmissionNo

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