Assimilating MODIS-LAI into Crop Growth Model with EnKF to Predict Regional Crop Yield

被引：0
|
作者：
Wu, Sijie [2 ]
Huang, Jianxi [1 ]
Liu, Xingquan [2 ]
Fan, Jinlong [3 ]
Ma, Guannan [1 ]
Zou, Jinqiu [4 ]
机构：
[1] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
[2] Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Hunan, Peoples R China
[3] China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing 100083, Peoples R China
[4] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China
来源：
COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE V, PT III | 2012年 / 370卷
基金：
美国国家科学基金会;
关键词：
Remote sensing; WOFOST; EnKF; Data Assimilation; ENSEMBLE KALMAN FILTER; REMOTE-SENSING DATA; SOIL;
D O I：
暂无
中图分类号：
S [农业科学];
学科分类号：
09 ;
摘要：
Regional crop yield prediction is a vital component of national food security assessment. Data assimilation method which combines crop growth model and remotely sensed data has been proven the most potential method in regional crop production estimation. This paper takes Hengshui district as study area, WOFOST as crop model, MODIS-LAI as observation data to test and verify the efficiency of EnKF assimilation method. The results show that the precision of crop yield estimation are obviously improved with EnKF assimilation, in the WOFOST potential level the R-2 improved from 0.10 to 0.38 and RMSE was reduced from 2480 kg/ha to 880kg/ha. Our study indicates that EnKF assimilation method has great potential in regional crop production forecasting.
引用
收藏
页码：410 / +
页数：3
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