Particle swarm optimization for assimilation of remote sensing data in dynamic crop models

Matthias P. Wagner; Alireza Taravat; Natascha Oppelt

doi:10.1117/12.2532531

21 October 2019 Particle swarm optimization for assimilation of remote sensing data in dynamic crop models

Matthias P. Wagner, Alireza Taravat, Natascha Oppelt

Proceedings Volume 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI; 111490L (2019) https://doi.org/10.1117/12.2532531
Event: SPIE Remote Sensing, 2019, Strasbourg, France

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.

Conference Presentation

Citation Download Citation

Matthias P. Wagner, Alireza Taravat, and Natascha Oppelt "Particle swarm optimization for assimilation of remote sensing data in dynamic crop models", Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111490L (21 October 2019); https://doi.org/10.1117/12.2532531

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available