Assimilation of remote sensing data into crop growth model to improve the estimation of regional winter wheat yield

Chaoshun Liu; Wei Gao; Pudong Liu; Zhibin Sun

doi:10.1117/12.2058931

2 October 2014 Assimilation of remote sensing data into crop growth model to improve the estimation of regional winter wheat yield

Chaoshun Liu, Wei Gao, Pudong Liu, Zhibin Sun

Author Affiliations +

Proceedings Volume 9221, Remote Sensing and Modeling of Ecosystems for Sustainability XI; 922104 (2014) https://doi.org/10.1117/12.2058931
Event: SPIE Optical Engineering + Applications, 2014, San Diego, California, United States

Abstract

Accurate regional crop growth monitoring and yield prediction is very critical for the national food security assessment and sustainable development of agriculture, especially for China, which has the largest population in the world. Remote sensing data and crop growth model have been successfully used in the crop production prediction. However, both of them have inherent limitation and uncertainty. The data assimilation method which combines crop growth model and remotely sensed data has been proven to be the most effective method in regional yield estimation. The aim of this paper is to improve the estimation of regional winter wheat yield of crop growth model by using data assimilation schemes with Ensemble Kalman Filter (EnKF) algorithm. WOrld FOod STudies (WOFOST) crop growth model was chosen as the crop growth model which was calibrated and validated by the field measured data. MODIS Leaf Area Index (LAI) values were used as remote sensing observations to adjust the LAI simulated by the WOFOST model based on EnKF. The results illustrate that the EnKF algorithm has significantly improved the regional winter wheat yield estimates over the WOFOST simulation without assimilation in both potential and water-limited modes. Although this study clearly implies that the assimilation of the remotely sensed data into crop growth model with EnKF algorithm has the potential to improve the prediction of regional crop yield and has great potential in agricultural applications, high resolution meteorological data and detailed crop field management are necessary to reach a high accuracy of regional crop yield estimation.

Citation Download Citation

Chaoshun Liu, Wei Gao, Pudong Liu, and Zhibin Sun "Assimilation of remote sensing data into crop growth model to improve the estimation of regional winter wheat yield", Proc. SPIE 9221, Remote Sensing and Modeling of Ecosystems for Sustainability XI, 922104 (2 October 2014); https://doi.org/10.1117/12.2058931

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Remote sensing

Yield improvement

Atmospheric modeling

Statistical modeling

MODIS

Agriculture

Show All Keywords

Keywords/Phrases

Search In:

Publication Years