An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice
An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice
复制标题
WOFOST模型中纳入胁迫因素的改进同化方法，用于有效评估水稻重金属胁迫水平
DOI：
10.1016/j.jag.2015.04.023
复制
发表时间：
2015-09-01
影响因子：
7.5
通讯作者：
Liu, Meiling
中科院分区：
文献类型：
--
作者：
Jin, Ming;Liu, Xiangnan;Liu, Meiling
农作物重金属污染是一个世界性的问题，需要准确和及时的监测。本研究旨在提高利用遥感数据监测水稻重金属胁迫水平的准确性。根据作物生长规律和胁迫机理，建立了基于遥感和改进作物生长模型的同化框架，实现了对作物全生育期重金属胁迫水平的连续监测。通过与其他生理指标的比较，确定了水稻根干重（WRT）是衡量水稻重金属胁迫水平的最佳指标。对世界粮食研究（WOFOST）模型进行了改进，引入了胁迫因子，全面考虑了重金属胁迫下作物生理状态的变化。基于胁迫因子fDTGA和fcvF分别对应CO2日同化总量和碳水化合物-干物质转换系数，提出了3种情景，并分析了其模拟WRT的效率。将遥感数据反演的叶面积指数（LAI）同化到改进的WOFOST模型中，对furGA和fc-vF进行优化。结果表明，使用这两个因素的情景可以更准确地模拟重金属胁迫下的WRT，相对百分误差（RPE）低于14%。基于RS-WOFOST同化框架，可以实现基于WRT的重金属胁迫水平时空连续评估。(C)2015 Elsevier B. V.版权所有。
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 furGA and fc-vF. 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. (C) 2015 Elsevier B.V. All rights reserved.