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遥感技术与应用  2021, Vol. 36 Issue (5): 1057-1071    DOI: 10.11873/j.issn.1004-0323.2021.5.1057
叶绿素荧光专栏     
基于TROPOMI叶绿素荧光遥感的冬小麦旱情监测
王思远1,2(),李强子1(),王红岩1,张源1,杜鑫1,高亮1,2
1.中国科学院空天信息创新研究院,北京 100101
2.中国科学院大学 资源与环境学院,北京 100049
A Winter Wheat Drought Index based on TROPOMI Solar-Induced Chlorophyll Fluorescence
Siyuan Wang1,2(),Qiangzi Li1(),Hongyan Wang1,Yuan Zhang1,Xin Du1,Liang Gao1,2
1.Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China
2.College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China
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摘要:

针对太阳诱导叶绿素荧光(Solar-Induced chlorophyll Fluorescence, SIF)可以有效指示陆表植被水分胁迫的特点,提出了归一化叶绿素荧光干旱指数(Normalized SIF Drought Index, NSDI)用于黄淮海地区冬小麦旱情监测。该方法首先基于哨兵-5p卫星(Sentinel-5p)对流层观测仪(Tropospheric Monitoring Instrument, TROPOMI)传感器反演得到的SIF原始产品集,通过0.1°等经纬步长栅格化处理为空间连续数据,然后基于时间序列分析进行了缺失值线性插补,再经过S-G滤波重建获得了高时空分辨率荧光数据集。以此数据集为基础,结合研究区冬小麦分布数据构建NSDI指数。通过选取典型旱情事件对比分析,NSDI指数与同期归一化植被指数(Normalized Difference Vegetation Index, NDVI)以及温度植被干旱指数(Temperature Vegetation Drought Index, TVDI)都有良好的相关性,其中与NDVI的R2为0.60,与TVDI的R2为0.41;NSDI指数与野外土壤水分调查结果也高度相关,其中河北样区R2为0.53,山东样区R2为0.54,整体R2为0.51;通过物联网监测数据分析显示,NSDI指数可以在优于2 d的滞后期内响应旱情的变化,其变化趋势与田间土壤水分保持高度相关。实验结果表明:NSDI指数可以在时空尺度上有效指示黄淮海地区冬小麦旱情。

关键词: 太阳诱导叶绿素荧光(SIF)旱情监测NSDI指数Sentinel?5pTROPOMI    
Abstract:

According to the characteristic that Solar-Induced chlorophyll Fluorescence (SIF) can effectively indicate the water stress of land surface vegetation, we proposed a Normalized Solar-Induced Chlorophyll Fluorescence Drought Index (NSDI) for winter wheat drought monitoring in the Huang-Huai-Hai region. First, the original SIF data retrieved by the Sentinel-5p Tropospheric Instrument (TROPOMI) were processed into spatially continuous data with a spatial resolution of 0.1 degree. Missing values were then filled via the linear interpolation based on time series analysis, and S-G filters were applied to reconstruct high spatial and temporal resolution SIF dataset. The NSDI is developed using this reconstructed SIF dataset and winter wheat distribution data. The analysis of typical drought events revealed that the NSDI and the Normalized Difference Vegetation Index (NDVI) are strongly correlated with the R2 of 0.60, the NSDI and the temperature vegetation drought index (TVDI) are also strongly correlated in different mature regions, with the highest R2 of 0.66 in Yanshan region, and the lowest R2 of 0.44 in Huanghuai plain region. The NSDI index is also highly correlated with the in-situ soil moisture data, with an R2 of 0.53 and 0.54 respectively in Hebei and Shandong sample area, and an overall R2 of 0.51. Analysis of monitoring data from the Internet of Things shows that the NSDI index can respond to changes of drought within a lag period of less than 2 days, and its change trend is highly correlated with soil moisture in the field. The experimental results show that the NSDI index can effectively indicate the drought of winter wheat in Huang-Huai-Hai region from the spatiotemporal perspective.

Key words: Solar-Induced chlorophyll Fluorescence    Drought Monitoring    NSDI    Sentinel-5p    TROPOMI
收稿日期: 2019-12-27 出版日期: 2021-12-07
ZTFLH:  S423  
基金资助: 国家重点研发计划项目“主要粮食作物气象灾害监测技术体系研发”(2017YFD0300402);国家重点研发计划项目“三大粮食作物气象灾害预警模型研制”(2017YFD0300404?1)
通讯作者: 李强子     E-mail: wangsy@aircas.ac.cn;liqz@aircas.ac.cn
作者简介: 王思远(1995-),男,山东烟台人,硕士研究生,主要从事农业生态遥感、植被遥感、灾害监测研究。E?mail:wangsy@aircas.ac.cn
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引用本文:

王思远,李强子,王红岩,张源,杜鑫,高亮. 基于TROPOMI叶绿素荧光遥感的冬小麦旱情监测[J]. 遥感技术与应用, 2021, 36(5): 1057-1071.

Siyuan Wang,Qiangzi Li,Hongyan Wang,Yuan Zhang,Xin Du,Liang Gao. A Winter Wheat Drought Index based on TROPOMI Solar-Induced Chlorophyll Fluorescence. Remote Sensing Technology and Application, 2021, 36(5): 1057-1071.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.5.1057        http://www.rsta.ac.cn/CN/Y2021/V36/I5/1057

图1  实验区地理位置及冬小麦分布图审图号:GS(2020)4618
图2  野外调查土壤水分样点分布图 审图号:GS(2020)4618
监测站点序号所处行政地区经度/°E纬度/°N
1河北省衡水市深州市115.70656837.900735
2河南省新乡市原阳县113.68795235.014712
3河南省驻马店市西平县114.0200133.29542
4山东省泰安市岱岳区117.08871335.96824
5河南省商丘市115.7100634.531862
表1  物联网监测站点地理坐标
图3  黄淮海地区冬小麦分布密度图审图号:GS(2020)4618
图4  技术流程图
图5  不同步长等经纬栅格化结果审图号:GS(2020)4618
图6  SIF序列数据的线性插补及S-G滤波重建
图7  滤波重建前后的SIF数据对比图审图号:GS(2020)4618
图8  NSDI指数与同期NDVI相关性分析结果
图9  3月30日NSDI指数同期TVDI监测结果审图号:GS(2020)4618
图10  NSDI指数与同期TVDI相关性分析结果
熟制区划名称R2熟制区划名称R2
燕山地区0.664 2黑龙港地区0.608 2
鲁西北地区0.540 2山东丘陵地区0.525 4
豫西地区0.601 6黄淮平原地区0.444 8
鄂豫皖地区0.485 1江淮平原地区0.514 8
表2  不同熟制区划相关性分析结果
图11  NSDI与土壤水分的相关性分析结果
图12  NSDI指数与物联网监测数据动态变化图
图13  基于NSDI、NDVI、TVDI及物联网数据的动态监测
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