Conference Paper

Assimilation of Field Measured LAI into Crop Growth Model based on SCE-UA Optimization Algorithm.

Key Lab. of Resources Remote-Sensing & Digital Agric., Minist. of Agric., Beijing, China
DOI: 10.1109/IGARSS.2009.5417822 Conference: IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, July 12-17, 2009, University of Cape Town, Cape Town, South Africa, Proceedings
Source: DBLP

ABSTRACT

Assimilating external data into a crop growth model to improve accuracy of crop growth monitoring and yield estimation has been a research focus in recent years. In this paper, the shuffled complex evolution (SCE-UA) global optimization algorithm was used to assimilate field measured LAI into EPIC model to simulate yield, sowing date and nitrogen fertilizer application amount of summer maize in Huanghuaihai Plain in China. The results showed that RMSE between simulated yield and field measured yield of summer maize was 0.84 t ha-1 and the R2 was only 0.033 without external data assimilation. While the performances of EPIC model of simulating yield, sowing date and nitrogen fertilizer application amount of summer maize was better through assimilating field measured LAI into the EPIC model. The RMSE of between simulated yield and field measured yield of summer maize was 0.60 t ha-1 and the R2 was 0.5301. The relative error between simulated sowing date and real sowing date of summer maize was 2.28%. On the simulation of nitrogen fertilizer application rate, the relative error was -6.00% compared with local statistical data. These above accuracy could meet the need of crop growth monitoring and yield estimation at regional scale. It proved that assimilating field measured LAI into crop growth model based on SCE-UA optimization algorithm to monitor crop growth and estimate crop yield was feasible.

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Available from: Yunyan du, May 28, 2015
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