Paper Info
Title
Integrating Remotely Sensed Lai With Epic Model Based On Global Optimization Algorithm For Regional Crop Yield Assessment
Abstract
Assimilating external data into crop growth model to improve accuracy of crop growth monitoring and yield estimation has been being a research hotspot in recent years. In this paper, the global optimization algorithm SCE-UA (Shuffled Complex Evolution method - University of Arizona) was used to integrate remotely sensed leaf area index (LAI) with crop growth model EPIC to simulate regional yield, sowing date, plant density and net nitrogen fertilizer application rate of summer maize in Huanghuaihai Plain. The final results showed that average relative error of estimated summer maize yield was 4.37% and RMSE was 0.44t/ha. Meanwhile, compared with actual observation and investigation data, average relative error of simulated sowing date, plant density and net N fertilization application rate was 1.85%, -7.78% and -10.60% respectively. These above accuracy of simulated results could meet the need of crop monitoring at regional scale. It was proved that integrating remotely sensed LAI with EPIC model based on global optimization algorithm SCE-UA for simulation of crop growth condition and crop yield was feasible.
Year
DOI
Venue
2010
10.1109/IGARSS.2010.5654060
2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
Keywords
Field
DocType
Crop growth model, EPIC, data assimilation, remote sensing, global optimization algorithm, yield estimation, LAI
Leaf area index,Data modeling,Crop yield,Crop,Computer science,Remote sensing,Mean squared error,Fertilizer,Sowing,Approximation error,Agricultural engineering
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
2
5
Name
Order
Citations
PageRank
Jianqiang Ren1499.27
Fushui Yu210.82
Jun Qin300.34
Zhongxin Chen46718.05
Huajun Tang500.34