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| Dynamic Monitoring of Heavy Metal Stresses in Rice by a Remote Sensing Data-Assimilated WOFOST Model |
| Received:September 23, 2014 |
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| KeyWord:heavy metal stress;remote sensing;weight of root;WOFOST model;data assimilation |
| Author Name | Affiliation | E-mail | | ZHAO Li-ting | School of Information Engineering, China University of Geosciences, Beijing 100083, China | | | LIU Xiang-nan | School of Information Engineering, China University of Geosciences, Beijing 100083, China | liuxncugb@163.com | | DING Chao | School of Information Engineering, China University of Geosciences, Beijing 100083, China | | | LIU Feng | School of Information Engineering, China University of Geosciences, Beijing 100083, China | | | PEI Song-wei | School of Energy, China University of Geosciences, Beijing 100083, China | | | XIA Xiao-peng | School of Information Engineering, China University of Geosciences, Beijing 100083, China | |
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| Abstract: |
| Heavy metal contamination of soil would affect crop growth via influencing eco-physiological parameters of crops such as chlorophyll, LAI (Leaf Area Index) and cell structure, especially the roots. This study explored the feasibility of monitoring heavy metal stresses in rice by using WRT (Weight of Root) changes obtained from remote sensing data and crop growth model. Usually it is difficult to get rice WRT directly through remote sensing method. However the WRT can be well simulated by assimilating remote sensing data into the WOFOST(World Food Studies) model. The LAI is one of output parameters of the WOFOST model and it can be used as a connection between the WOFOST model and remote sensing data. The assimilation process was conducted through the LAI by PSO(Particle Swarm Optimization). In this research, two cultivation areas in Changchun, Jilin Province were selected as the experimental sites (A and B) with different heavy metal stress levels. Grey correlation analysis was performed to select the crop parameter that is sensitive to the WRT. The results showed that CVR(Efficiency of dry matter conversion to root weight) is highly correlated with the WRT with the correlation coefficient (RWRT) of 0.801 6. Hence the CVR was chosen as the parameter to be optimized in the WOFSOT model. The CVR values of the site A and B were 0.527 and 0.806 respectively. The WRT ratio of the site A to site B ranged from 0.894 to 0.972 during the whole rice growth period with an average of 0.922. The lowest WRT ratio of 0.894 occurred at the tillering stage, whereas the significant effect of heavy metal stress on LAI started at the jointing-booting stage. The experimental results showed that the heavy metal stress can be detected by the WRT at the early growth stage compared with the LAI. In conclusion, assimilating remote sensing data into the WOFOST model can directly get the root growth information, which is impossible to obtain directly from remote sensing technique only. Additionally, assimilating remote sensing data into the WOFOST model can also monitor heavy metal stresses in rice on spatial scale dynamically and continuously. |
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