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New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

文献类型：期刊论文

作者Chen, Pengfei1,2; Wang, Fangyong3
刊名REMOTE SENSING
出版日期2020-12-01
卷号12期号:24页码:19
关键词textural index biomass cotton unmanned aerial vehicle optical image
DOI10.3390/rs12244170
通讯作者Chen, Pengfei(pengfeichen@igsnrr.ac.cn)
英文摘要Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)(g)) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)(g)), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.
WOS关键词HYPERSPECTRAL VEGETATION INDEXES ; NITROGEN ; CORN ; REFLECTANCE ; PREDICTION ; MODELS ; SYSTEM ; WATER
资助项目National Research and Development Plan of China[2016YFD0200603] ; National Natural Science Foundation of China[41871344] ; National Natural Science Foundation of China[31560342] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23100101]
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing
语种英语
WOS记录号WOS:000603192700001
出版者MDPI
资助机构National Research and Development Plan of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/137531]  
专题中国科学院地理科学与资源研究所
通讯作者Chen, Pengfei
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
3.Xinjiang Acad Agr & Reclamat Sci, Cotton Inst, Shihezi 832000, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Pengfei,Wang, Fangyong. New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle[J]. REMOTE SENSING,2020,12(24):19.
APA Chen, Pengfei,&Wang, Fangyong.(2020).New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle.REMOTE SENSING,12(24),19.
MLA Chen, Pengfei,et al."New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle".REMOTE SENSING 12.24(2020):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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