Paper Info
Title
Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation.
Abstract
Precise simulation of crop growth is crucial to yield estimation, agricultural field management, and climate change. Although assimilation of crop model and remote sensing data has been applied in crop growth simulation, few studies have considered optimizing the crop model with respect to phenology. In this study, we assimilated phenological information obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) time series data into the World Food Study (WOFOST) model to improve the accuracy of rice growth simulation at the regional scale. The particle swarm optimization (PSO) algorithm was implemented to optimize the initial phenology development stage (IDVS) and transplanting date (TD) in the WOFOST model by minimizing the difference between simulated and observed phenology, including heading and maturity date. Assimilating phenology improved the accuracy of the rice growth simulation, with correlation coefficients (R) equal to 0.793, 0822, and 0.813 at three fieldwork dates. The performance of the proposed strategy is comparable with that of the enhanced vegetation index (EVI) time series assimilation strategy, with less computation time. Additionally, the result confirms that the proposed strategy could be applied with different spatial resolution images and the difference of simulated LAI(mean) is less than 0.35 in three experimental areas. This study offers a novel assimilation strategy with regard to the phenology development process, which is efficient and scalable for crop growth simulation.
Year
DOI
Venue
2019
10.3390/rs11030268
REMOTE SENSING
Keywords
Field
DocType
data assimilation,WOFOST model,remote sensing penology,rice growth simulation
Remote sensing,Geology,Phenology
Journal
Volume
Issue
Citations 
11
3
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Gaoxiang Zhou121.06
Xiangnan Liu27920.45
Ming Liu327650.00