remote sensing Article Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models Sergii Skakun 1,2,3,* , Eric Vermote 3, Belen Franch 1,3, Jean-Claude Roger 1,3 , Nataliia Kussul 4, Junchang Ju 5,6 and Jeffrey Masek 6 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 * Correspondence:
[email protected]; Tel.: +1-301-405-2179 Received: 9 July 2019; Accepted: 25 July 2019; Published: 27 July 2019 Abstract: 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.