Assimilation of Sentinel-2 Estimated LAI into a Crop Model: Influence of Timing and Frequency of Acquisitions on Simulation of Water Stress and Biomass Production of Winter Wheat

Andreas Tewes,Gunther Krauss,Carsten Montzka,Manuel Nolte,Thomas Gaiser,Holger Hoffmann

doi:10.3390/agronomy10111813

Andreas Tewes, Gunther Krauss + Show 4 more

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https://doi.org/10.3390/agronomy10111813

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Abstract

The Sentinel-2 (S2) Toolbox permits for the automated retrieval of leaf area index (LAI). LAI assimilation into crop simulation models could aid to improve the prediction accuracy for biomass at field level. We investigated if the combined effects of assimilation date and corresponding growth stage plus observational frequency have an impact on the crop model-based simulation of water stress and biomass production. We simulated winter wheat growth in nine fields in Germany over two years. S2 LAI estimations for each field were categorized into three phases, depending on the development stage of the crop at acquisition date (tillering, stem elongation, booting to flowering). LAI was assimilated in every possible combinational setup using the ensemble Kalman filter (EnKF). We evaluated the performance of the simulations based on the comparison of measured and simulated aboveground biomass at harvest. The results showed that the effects on water stress remained largely limited, because it mostly occurred after we stopped LAI assimilation. With regard to aboveground biomass, we found that the assimilation of only one LAI estimate from either the tillering or the booting to flowering stage resulted in simulated biomass values similar or closer to measured values than in those where more than one LAI estimate from the stem elongation phase were assimilated. LAI assimilation after the tillering phase might therefore be not necessarily required, as it may not lead to the desired improvement effect.

Highlights

Originally developed for point-based applications that neglect spatial variation of weather, soil and management, dynamic crop simulation models have been increasingly used for field, regional, national and global scale purposes to simulate growth status, yield and soil–plant–atmosphere interactions of a variety of crops [1,2,3,4,5]
We found that the inclusion of soil-related model variables in the data assimilation process is a promising approach for crop modeling at sub-field level under the consideration of simulated water stress [7]
We investigated if the assimilation of Sentinel-2-derived leaf area index (LAI) estimations into the SIMPLACE crop model using the ensemble Kalman filter (EnKF) in different phenophases and with varying timing and frequency had an impact on the simulation results of winter wheat growth in nine fields across Germany with regard to (a) the occurrence and magnitude of water stress and (b) the prediction accuracy of total aboveground biomass at harvest

Summary

Introduction

Originally developed for point-based applications that neglect spatial variation of weather, soil and management, dynamic crop simulation models have been increasingly used for field, regional, national and global scale purposes to simulate growth status, yield and soil–plant–atmosphere interactions of a variety of crops [1,2,3,4,5]. These models could help to guide the farmer in decisions related to site-specific management of crop input in terms of timing and amount [6] as well as the provision of spatially explicit yield forecasts [7]. The assimilation of information on crop status and within-field variability into crop simulation models could help to limit the impact of poor model parameterization [9] Modern remote sensing platforms and instruments enable one to capture biophysical crop canopy variables at spatial scales that allow for the recognition of variability within a field [10,11].

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