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遥感技术与应用  2021, Vol. 36 Issue (2): 391-399    DOI: 10.11873/j.issn.1004-0323.2021.2.0391
农业遥感专栏     
基于植被指数融合的冬小麦生物量反演研究
孙奇1,2(),关琳琳2,焦全军2(),刘新杰2,戴华阳1
1.中国矿业大学(北京)地球科学与测绘工程学院,北京 100083
2.中国科学院空天信息创新研究院数字地球重点实验室,北京 100094
Research on Retrieving Biomass of Winter Wheat based on Fusing Vegetation Index
Qi Sun1,2(),Linlin Guan2,Quanjun Jiao2(),Xinjie Liu2,Huayang Dai1
1.College of Geoscience and Surveying Engineering,China University of Mining and Technology (Beijing),Beijing 100083,China
2.Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
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摘要:

作物群体生物量是形成产量的物质基础,遥感技术是高效、客观监测作物地上生物量的重要手段,对农业生产管理具有重要意义。以安徽省龙亢农场为研究区,通过PROSAIL模拟光谱分析了4个LAI相关的可见光-近红外植被指数、2个叶片干物质相关的短波红外植被指数和8个融合植被指数与冬小麦地上生物量的关系,并建立反演模型。模拟结果显示,干物质植被指数与作物生物量的相关性高于LAI相关的植被指数,两者融合的植被指数增强了常用植被指数冬小麦生物量的探测能力。利用实测冬小麦数据对生物量反演模型进行验证,结果显示:融合植被指数普遍提高了单一植被指数的地上生物量反演精度,其中MTVI2×NDMI精度最高(RMSE=606.8 kg/hm2),并为作物地上生物量的高精度反演提供新的技术途径。

关键词: 生物量反演冬小麦PROSAIL模型植被指数融合    
Abstract:

Crop biomass is a vital fundamental substance for predicting yield. Remote sensing is an important technology to monitor crop above-ground biomass efficiently and objectively, which is of great significance for agricultural production and management. Taking Longkang farm in Anhui province as the research area, this paper analyzes the relationship between aboveground biomass of winter wheat and 4 LAI-VIs, 2 DMIs and 8 combined vegetation indices by PROSAIL simulation spectrum, and builds retrieval models. The results show that the correlation between DMIs and crop biomass is higher than LAI-VIs, and the combined vegetation index enhances the crop biomass detection ability of commonly used vegetation indices. The biomass retrieval models are validated with the measured biomass of winter wheat, and the results show that the combined vegetation index generally improves the above-ground biomass retrieval accuracy of single vegetation index, among which MTVI2×NDMI has the highest accuracy (RMSE=606.8 kg/hm2).This paper provides a new technique for high precision retrieval of crop above-ground biomass.

Key words: Biomass retrieval    Winter wheat    PROSAIL model    Vegetation index fusion
收稿日期: 2019-11-18 出版日期: 2021-05-24
ZTFLH:  S127  
基金资助: 国家重点研发项目课题(2016YFD0300601)
通讯作者: 焦全军     E-mail: sunqicumtb@163.com;jiaoqj@radi.ac.cn
作者简介: 孙奇(1991-),男,河南商丘人,博士研究生,主要从事植被定量遥感研究。E?mail:sunqicumtb@163.com
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引用本文:

孙奇,关琳琳,焦全军,刘新杰,戴华阳. 基于植被指数融合的冬小麦生物量反演研究[J]. 遥感技术与应用, 2021, 36(2): 391-399.

Qi Sun,Linlin Guan,Quanjun Jiao,Xinjie Liu,Huayang Dai. Research on Retrieving Biomass of Winter Wheat based on Fusing Vegetation Index. Remote Sensing Technology and Application, 2021, 36(2): 391-399.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.2.0391        http://www.rsta.ac.cn/CN/Y2021/V36/I2/391

图1  研究区地理位置及样方分布
输入参数全称取值范围
LAI叶面积指数1~6;步长1
EWT等效水厚度/cm0.004~0.024;步长0.004
Cm干物质含量/(g/cm2)0.002~0.012;步长0.002
N结构参数1.4~1.8;步长0.2
Cab叶绿素含量/(μg/cm2)20~60;步长20
ALA平均叶倾角/°63.24
θs, φs太阳天顶角和方位角/°0~30
θv, φv观测天顶角和方位角/°0~0
表1  PROSAIL模型输入参数
指数类型植被指数计算公式参考文献
LAI-VIsNDVI(R800-R670)/(R800+R670)[19]
EVI2.5(R800-R670)/(R800+6R670-7.5R450+1)[20]
GNDVI(R800-R550)/(R800-R550)[21]
MTVI21.5[1.2(R800-R500)-2.5(R670-R550)]/{[(2R800+1)2-(6R800-5(R670)1/2]1/2-0.5}[22]
DMIsNDBleaf(R1540-R2160)/(R1540+R2160)[23]
NDMI(R1649-R1722)/(R1649+R1722)[24]
表2  植被指数及公式
图2  叶片干物质、叶片水分和叶面积指数差异对冠层光谱反射率的影响
图3  基于PROSAIL模型的单一植被指数与生物量的关系实线是不同光谱指数与生物量间的回归线
图4  基于PROSAIL模型的融合植被指数与生物量的关系实线是不同融合植被指数与生物量间的回归线
图5  基于单一植被指数的反演模型与冬小麦实测数据验证结果实线是验证结果的拟合线,虚线是1∶1线
图6  基于融合植被指数的反演模型与冬小麦实测数据验证结果实线是验证结果的拟合线,虚线是1∶1线
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