03785naa a2200277 a 450000100080000000500110000800800410001902200230006002400350008310000130011824501330013126000090026450005290027352024030080265300280320565300160323365300150324965300120326465300210327670000150329770000170331270000170332970000150334670000170336177301290337810639382023-02-08       2023    bl uuuu     u00u1 u    #d  a1873-7331 (online)7 a10.1016/j.eja.2022.1267182DOI1 aGASO, D.  aEfficiency of assimilating leaf area index into a soybean model to assess within-field yield variability.h[electronic resource]  c2023  aArticle history: Received 7 March 2022, Revised 17 October 2022, Accepted 5 December 2022, Available online 22 December 2022, Version of Record 22 December 2022. -- Corresponding author: Deborah Gaso, E-mail addresses: deborah.gasomelgar@wur.nl, dgaso@inia.org.uy (D.V. Gaso). Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen 6708 PB, the Netherlands. -- LICENSE: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). --  aABSTRACT.- Methods for accurately estimating within-field yield are essential to improve site-specific crop management and resource use efficiencies, which would be a major step toward sustainable intensification of agricultural systems. We set out to assess the accuracy of within-field soybean yields predicted by two data assimilation methods and to assess these methods? assimilation efficiency (AE). Yields were estimated by assimilating remotely sensed leaf area index (LAI) data from Sentinel-2 into a soybean crop growth model on a pixel basis. The LAI data was integrated into the model by Ensemble Kalman Filtering (EnKF) or by recalibrating with the Subplex algorithm (recalibration-based). An open-loop setting which only integrates information on the soil layers was used as a baseline scenario for quantifying the AE. We assessed both data assimilation techniques on eight fields (3067 pixels) in the Corn Belt region (Nebraska, Kansas and Kentucky) in the United States. The data set encompassed substantial variation in crop growth conditions: three growing seasons (2018, 2019 and 2020), rainfed and irrigated fields, and early and late planting dates. Ground truth yield acquired from combine monitors was used to validate the yield estimations. Agreement between predicted and observed yield at pixel level was two times higher for both data assimilation methods compared to the open-loop. The root mean square error (RMSE) was 476 kg.ha-1 (RRMSE of 10 %) in the recalibration-based method and 573 kg.ha-1 (RRMSE of 12 %) in the EnKFbased method. For both data assimilation methods, assimilating the LAI improved predictions for 68 % of the pixels. For a further 12 % of pixels, there was no accuracy improvement. For the remaining 20 %, AE was positive for one of the two assimilation methods. The high proportion of pixels with positive AE indicates the potential for overcoming the limitations in applying crop models at high spatial resolution by integrating a crop growth indicator. Assimilating an in-season indicator of crop growth (LAI) into a soybean model made it possible to adjust the simulation pathway, thereby greatly improving the accuracy of the yield estimations at the pixel level. This study elucidates the practical applications of data assimilation strategies for fine-scale within-field crop yield mapping. © 2022 The Author(s). Published by Elsevier B.V.  aAssimilation efficiency  aCrop models  aSentinel-2  aSoybean  aYield prediction1 aDE WIT, A.1 aDE BRUIN, S.1 aPUNTEL, L.A.1 aBERGER, A.1 aKOOISTRA, L.  tEuropean Journal of Agronomy, February 2023, Volume 143, 126718. OPEN ACCESS. doi: https://doi.org/10.1016/j.eja.2022.126718