利用作物生长模型和时序信号甄别水稻镉胁迫  

Identifying heavy metal(Cd)stress in rice using time-series signals and crop growth model

作　　者：孔丽 刘美玲[1] 刘湘南[1] 邹信裕 Kong Li;Liu Meiling;Liu Xiangnan;Zou Xinyu(School of Information Engineering,China University of Geosciences,Beijing 100083,China)

机构地区：[1]中国地质大学(北京)信息工程学院,北京100083

出　　处：《农业工程学报》2021年第4期249-256,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(42071391,41601473);中央高校基本科研业务费优秀教师基金项目(2-9-2018-080)。

摘　　要：在自然农田生态系统中,农作物的生长通常受到各类环境胁迫(如重金属胁迫、病虫害、水分、营养)的影响,如何区分重金属胁迫与其他胁迫有待进一步研究。该研究选取了湖南省株洲为试验区,收集2017—2019年的Sentinel-2卫星影像数据,结合野外实测数据,开展水稻重金属镉(Cd)胁迫识别研究。首先,利用作物生长模型World Food Studies(WOFOST)同化时序遥感数据获取每年的叶面积指数(Leaf Area Index,LAI)时间序列曲线;然后运用集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)方法对LAI时间序列进行多尺度分解,得到不同的时序信号分量(Intrinsic Mode Function,IMF);最后使用动态时间规整(Dynamic Time Warping,DTW)方法计算受胁迫水稻分解后的时间序列与健康水稻分解后的时间序列之间的DTW距离,即归一化胁迫指数。结果表明:归一化胁迫指数是水稻重金属胁迫敏感的参数,与土壤重金属含量的相关系数为0.851,水稻受到的胁迫程度越高,归一化胁迫指数值越大,反之越低;在试验区中,水稻重度重金属胁迫的分布面积比例相对较低,且主要集中在西部、东北部以及偏东南地区。融合集合经验模态分解和动态时间规整方法能有效地甄别并定量分析水稻重金属胁迫状况,从而为作物重金属污染胁迫监测提供重要参考。Rice is one of the most important food crops around the world.In recent years,the accumulation of heavy metals in rice crops has posed a great threat to rice growth and quality,even to human bodies through food chains.Traditional field surveys cannot map the extent of heavy metal contamination for large regions,although they can detect heavy metal stress in rice.Remote sensing can real-time monitor the heavy metal stress in crops with wider coverage.However,most previous studies focused mainly on the physiological and biochemical characteristics of rice,or spectral response similar to other remote sensing monitoring of environmental stress,such as diseases and pests,or water and nutrition stress.Moreover,the growth of rice depends usually on various types of environmental stresses with complex conditions in a large-scale eco-environment system,such as heavy metal stresses,disease,drought,or flood.Specifically,there are different stress types in various spaces,where multiple stresses covered one or several growth stages in the same space.It is also difficult to distinguish heavy metal stress from others in the farmland,due to the similarity of canopy spectral variation induced by multiple stresses.Particularly,heavy metal stress in rice is characterized by the stability in space and time,leading to a difference from other transient stress.Sentinel-2 satellite can be used to monitor crops under multiple stresses.Sentinel-2 data includes three bands in the"red edge",providing more important information on the vegetation state.Taking Zhuzhou,Hunan Province of China as a study area,Sentinel-2 satellite images were collected for the field data from 2017 to 2019.First,the leaf area index(LAI)time series of the year was simulated using the World Food Studies(WOFOST)model.Second,an ensemble empirical mode decomposition(EEMD)was used to disassemble a series of LAI in rice growth stages,and thereby to obtain different intrinsic mode functions(IMF).The time series that may contain heavy metal stress can be achieved through a

关 键 词：遥感 模型 重金属 镉胁迫 时序信号分解 WOFOST 

分 类 号：S127[农业科学—农业基础科学]