DOI: 10.1016/j.jag.2015.04.023
Scopus记录号: 2-s2.0-84943609857
论文题名: An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice
作者: Jin M ; , Liu X ; , Wu L ; , Liu M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 41 起始页码: 118
结束页码: 129
语种: 英语
英文关键词: Assimilation
; Efficiency assessment
; Heavy metal stress
; Improved WOFOST model
; Remote sensing
Scopus关键词: bioaccumulation
; data assimilation
; efficiency measurement
; growth response
; heavy metal
; modeling
; physiological response
; remote sensing
; rice
英文摘要: Heavy metal contamination in crops is a worldwide problem that requires accurate and timely monitoring. This study is aimed at improving the accuracy of monitoring heavy metal stress levels in rice utilizing remote sensing data. An assimilation framework based on remote sensing and improved crop growth model was developed to continuously monitor heavy metal stress levels over the entire period of crop growth based on the growth law of crops and the stress mechanism. Compared with other physiological indices, dry weight of rice roots (WRT) was selected as the best indicator to estimate heavy metal stress levels. The World Food Study (WOFOST) model, widely used for the description of crop growth, was improved by incorporating stress factors with overall consideration for the changes in physiological status under heavy metal stress. Three scenarios were put forward based on the stress factors fDTGA and fCVF, which, respectively, correspond to the daily total gross assimilation of CO2 and carbohydrate-to-dry matter conversion coefficient, and were analyzed for their efficiency of simulating WRT. A method of assimilating the leaf area index (LAI) retrieved from remotely sensed data into the improved WOFOST model was applied to optimize fDTGA and fCVF. The results suggested that the scenario using both factors can simulate WRT under heavy metal stress more accurately, with a relative percent error (RPE) lower than 14%. Based on the RS-WOFOST assimilation framework, continuous spatial-temporal evaluation of heavy metal stress levels based on WRT can be accomplished. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79487
Appears in Collections: 气候变化事实与影响
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作者单位: China University of Geosciences, School of Information Engineering, 29 Xueyuan Road, Beijing, China; Peking University, Institute of Remote Sensing and GIS, 5 Yiheyuan Road, Beijing, China
Recommended Citation:
Jin M,, Liu X,, Wu L,et al. An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,41