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

doi:10.3390/rs12244170

	New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle
	Chen, Pengfei 1,3; Wang, Fangyong 2
刊名	REMOTE SENSING
	2020-12-01
卷号	12 期号:24 页码:19
关键词	textural index biomass cotton unmanned aerial vehicle optical image
DOI	10.3390/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.
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关键词	HYPERSPECTRAL VEGETATION INDEXES ; NITROGEN ; CORN ; REFLECTANCE ; PREDICTION ; MODELS ; SYSTEM ; WATER
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000603192700001
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
内容类型	期刊论文
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.Xinjiang Acad Agr & Reclamat Sci, Cotton Inst, Shihezi 832000, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, 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.