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Proceedings Paper

Particle swarm optimization for assimilation of remote sensing data in dynamic crop models
Author(s): Matthias P. Wagner; Alireza Taravat; Natascha Oppelt
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Paper Abstract

Agricultural monitoring is of growing importance due to an increasing world population, slowing growth of agricultural output and concerns regarding food security. Remote sensing and dynamic crop modeling are powerful tools for yield prediction and frequently applied in literature. A large question arising in this context is the assimilation of remote sensing data into the model process. We present a novel technique employing Particle Swarm Optimization in an updating scheme flexibly incorporating different sources of uncertainty in both the model simulation and remote sensing observations. We tested the technique with the AquaCrop-OS model for winter wheat yield prediction by updating canopy cover obtained from remote sensing datasets. Preliminary results showed that the new method can outperform both a simple replacement update and a Kalman filter approach. It succeeded in removing the bias from field-level yield predictions and reducing the RMSE from 1.32 t/ha to 0.89 t/ha.

Paper Details

Date Published: 21 October 2019
PDF: 10 pages
Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111490L (21 October 2019); doi: 10.1117/12.2532531
Show Author Affiliations
Matthias P. Wagner, Christian-Albrechts-Univ. zu Kiel (Germany)
Alireza Taravat, Christian-Albrechts-Univ. zu Kiel (Germany)
Natascha Oppelt, Christian-Albrechts-Univ. zu Kiel (Germany)

Published in SPIE Proceedings Vol. 11149:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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