Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

Abstract

This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling.-- Trabajo desarrollado bajo la financiación del proyecto "Soil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping Systems" (773903), coordinado por José Alfonso Gómez Calero, investigador del Instituto de Agricultura Sostenible (IAS). ; Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data. ; This research was funded by the European Union's Horizon 2020 research and innovation programme, grant numbers 773903, 774234, and 633945. The APC was funded by the European Union's Horizon 2020 research and innovation programme, grant number 773903. ; Peer reviewed