02944naa a2200241 a 450000100080000000500110000800800410001902200140006002400380007410000130011224501730012526000090029850002560030752019010056365300220246465300120248665300200249865300100251865300100252870000150253870000160255377301330256910607352020-01-31       2019    bl uuuu     u00u1 u    #d  a0168-16997 a10.1016/j.compag.2019.02.0262DOI1 aGASO, D.  aPredicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images.h[electronic resource]  c2019  aArticle history: Received 8 February 2018 / Revised 22 February 2019 / Accepted 25 February 2019 / Available online 4 March 2019.. This work was supported by ANII fellowship program and INIA fundings. The authors thank farmers who provided field data.  aABSTRACT. Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale. © 2019 Elsevier B.V.  aCrop growth model  aLandsat  aLeaf area index  aWheat  aYield1 aBERGER, A.1 aCIGANDA, V.  tComputers and Electronics in Agriculture, April 2019, Volume 159, Pages 75-83. Doi: https://doi.org/10.1016/j.compag.2019.02.026