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Open AccessArticle

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

1
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
2
College of Information Studies (iSchool), University of Maryland, College Park, MD 20742, USA
3
NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
4
Space Research Institute NAS Ukraine & SSA Ukraine, 03680 Kyiv, Ukraine
5
Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD 20742, USA
6
NASA Goddard Space Flight Center Code 618, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1768; https://doi.org/10.3390/rs11151768
Received: 9 July 2019 / Revised: 24 July 2019 / Accepted: 25 July 2019 / Published: 27 July 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
A 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. View Full-Text
Keywords: agriculture; crop yield; wheat; Landsat 8; Sentinel-2; HLS; phenological fitting; growing degree days (GDD) agriculture; crop yield; wheat; Landsat 8; Sentinel-2; HLS; phenological fitting; growing degree days (GDD)
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MDPI and ACS Style

Skakun, 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.

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