Dr. Jing Wang Profile

Dr. Jing Wang

at Jiangsu Academy of Agricultural Sciences

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 14 February 2020 Paper

Assimilation of HJ-LAI into the WOFOST model for estimating regional rice yield

Jing Wang, Zhiming Wang, Bihui Lu

Proceedings Volume 11432, 114320B (2020) https://doi.org/10.1117/12.2538178

KEYWORDS: Data modeling, Remote sensing, Data conversion, Agriculture

Read Abstract +

Regional crop production prediction is a significant component of national food security assessment. Crop growth models are successfully applicable for yield estimation in simple point scale, however, they are hampered by the deriving of regional crop key input parameters. The World Food Studies (WOFOST) model had been used to express the characteristic of time series LAI in crop growth season in the study area. To solve the system errors of coarse resolution data extracted LAI due to the mixed pixels effect, the corrected LAI was implemented by combining the field measured LAI data and the HJ-LAI temporal trend information. Time-series LAI was assimilated through combined corrected HJ-LAI and WOFOST simulated LAI during the whole growth stage with the ensemble Kalman filter (EnKF) algorithm. The assimilated optimal LAI was used to drive the WOFOST model per-pixel to estimate the regional yield. Scheduling the assimilation of different step length observed quantities, comparing the accuracy and the efficiency of the assimilation at different time scale, we selected the proper time scale of the assimilation. The results indicated that selecting the time scale of the step length between 10 days and 16 days was more appropriate. Compared with the statistical yield, the coefficient of determination was 0.66 and RMSE was 1.61 ton/hm. The results showed that assimilation of the remotely sensed data into crop growth model with EnKF can provide a reliable approach for estimate regional crop yield and had great potential in agricultural applications.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years