Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model.
Deriving the Characteristic Scale for Effectively Monitoring Heavy Metal Stress in Rice by Assimilation of GF-1 Data with the WOFOST Model.
复制标题
通过 GF-1 数据与 WOFOST 模型同化，推导有效监测水稻重金属胁迫的特征量表
DOI：
10.3390/s16030340
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发表时间：
2016-03-07
期刊：
影响因子：
--
通讯作者：
Wu L
中科院分区：
文献类型：
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作者：
Huang Z;Liu X;Jin M;Ding C;Jiang J;Wu L
农作物重金属胁迫的准确监测对保证农业生产和粮食安全具有重要意义，遥感技术是解决这一问题的有效手段。然而，鉴于地球观测仪器提供多种尺度的数据，选择用于此类监测的尺度具有挑战性。以根干重（WRT）为代表特征量，通过对GF-1数据与世界粮食研究（WOFOST）模型的同化，确定了有效监测水稻重金属胁迫的特征尺度。探讨并量化了不同空间尺度下重要状态变量叶面积指数（LAI）对模拟水稻WRT的影响，利用统计特征寻找重金属胁迫监测的临界尺度。此外，基于不同的重金属胁迫水平的比率分析进行识别的特征尺度。结果表明，在重金属胁迫监测研究中，研究水稻WRT的临界阈值应大于64 m，小于256 m。这一发现为选择最合适的图像提供了有用的指导。
Accurate monitoring of heavy metal stress in crops is of great importance to assure agricultural productivity and food security, and remote sensing is an effective tool to address this problem. However, given that Earth observation instruments provide data at multiple scales, the choice of scale for use in such monitoring is challenging. This study focused on identifying the characteristic scale for effectively monitoring heavy metal stress in rice using the dry weight of roots (WRT) as the representative characteristic, which was obtained by assimilation of GF-1 data with the World Food Studies (WOFOST) model. We explored and quantified the effect of the important state variable LAI (leaf area index) at various spatial scales on the simulated rice WRT to find the critical scale for heavy metal stress monitoring using the statistical characteristics. Furthermore, a ratio analysis based on the varied heavy metal stress levels was conducted to identify the characteristic scale. Results indicated that the critical threshold for investigating the rice WRT in monitoring studies of heavy metal stress was larger than 64 m but smaller than 256 m. This finding represents a useful guideline for choosing the most appropriate imagery.