Title :
Estimation of maize LAI by assimilating remote sensing data into crop model
Author :
Xiaohua Zhu ; Lingling Ma ; Chuanrong Li ; Lingli Tang ; Bo Zhu
Author_Institution :
Acad. of Opto-Electron., Beijing, China
Abstract :
In this paper, a methodology for maize LAI estimating is proposed by assimilating remotely sensed data into crop model based on temporal and spatial knowledge. Firstly, the spatial knowledge is extracted from MOD09A1 based on its multi-scale feature, and then the spatial knowledge is used for correcting the bias of inversion results. Secondly, the phenology information is extracted from MOD13A1 and used as prior temporal knowledge for building a cost function, and then, based on the cost function the sensitive parameters of WOFOST (WOrld FOod STudies) are calibrated. At last, the calibrated WOFOST model used as forecast operator and remote sensing inversion results used as observation operator, the Kalman Filter (KF) algorithm is used to realize the assimilation of MODIS data into crop model. The experiment results indicate that the methodology proposed in this paper is reasonable and accurate for estimating maize LAI.
Keywords :
Kalman filters; crops; data assimilation; inverse problems; knowledge acquisition; vegetation mapping; KF algorithm; Kalman filter; MOD09A1; MOD13A1; MODIS data assimilation; WOFOST model; WOrld FOod STudies; crop model; forecast operator; inversion result bias; maize LAI estimation; multiscale feature; observation operator; phenology information extraction; remote sensing data assimilation; remote sensing inversion result; spatial knowledge extraction; temporal knowledge; Agriculture; Analytical models; Atmospheric modeling; Data mining; Data models; MODIS; Remote sensing; Assimilation; Crop model; Kalman filter (KF); LAI; temporal and spatial knowledge;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-1114-1
DOI :
10.1109/IGARSS.2013.6721197
