干旱区生态环境知识资源中心(Arid): Leaf area index estimation using top-of-canopy airborne RGB images

Arid

DOI	10.1016/j.jag.2020.102282
	Leaf area index estimation using top-of-canopy airborne RGB images
	Raj, Rahul; Walker, Jeffrey P.; Pingale, Rohit; Nandan, Rohit; Naik, Balaji; Jagarlapudi, Adinarayana
通讯作者	Raj, R (corresponding author), Indian Inst Technol, Agroinformat Lab, CSRE, Mumbai, Maharashtra, India.
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
EISSN	1872-826X
出版年	2021
卷号	96
英文摘要	Leaf Area Index (LAI) is one of the most important biophysical properties of a crop, used in detecting long-term water stress, estimating biomass, and identifying crop growth stage. Remote sensing based LAI estimation techniques perform well for early growth stages but tend to produce high error during the crop reproductive stage due to canopy closure. Moreover, estimation of the true LAI from individual leaf measurements remains a challenge. Consequently, two alternate methods have been developed and compared for estimating the LAI of a maize crop using top-of-canopy RGB images collected throughout the growing season using a hexacopter. Both methods used the RGB images to estimate the canopy height and the green-canopy cover together with a 'vertical leaf area distribution factor' (VLADF) from allometric relations (using crop height from RBG images and days after sowing). The first method used an empirical approach to estimate the LAI from training a linear function of the above inputs to Licor canopy analyser values of LAI. The method was trialled for a research farm located in a semi-arid area of southern peninsula India and found to have validation results with an R-2 of 0.84 and RMSE of 0.36 for the unused portion of the Rabi (post-monsoon) season data of 2018-19, and R-2 of 0.77 and RMSE of 0.45 for the Rabi 2019-20 season data when compared with Licor LAI values. While seemingly acceptable, the Licor canopy analyser gives a foliage area index and so the accuracy of this model was very low (R-2 of 0.56 and RMSE of 1.34) when evaluated with true LAI from manual measurements of the leaf area. Consequently, a refinement was introduced using only VLADF, green-canopy cover estimates from the RBG images, and a field measured top leaf angle. The output derived from this conceptual model had an R-2 of similar to 0.6 and RMSE of 0.73 when compared with the true LAI values. Importantly, the LAI from this conceptual model was found to be unaffected by canopy closure during the reproductive stage of the crop.
英文关键词	Leaf area index Drone-based imaging Precision agriculture VLADF
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000608483000001
WOS关键词	VEGETATION INDEXES ; GREEN LAI ; UAV ; PHOTOGRAPHY ; LAI-2000
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/347951
作者单位	[Raj, Rahul] IITB Monash Res Acad, Mumbai, Maharashtra, India; [Walker, Jeffrey P.] Monash Univ, Melbourne, Vic, Australia; [Pingale, Rohit; Nandan, Rohit; Jagarlapudi, Adinarayana] Indian Inst Technol, Mumbai, Maharashtra, India; [Naik, Balaji] Jayashankar Telangana State Agr Univ, Hyderabad, Telangana, India
推荐引用方式 GB/T 7714	Raj, Rahul,Walker, Jeffrey P.,Pingale, Rohit,et al. Leaf area index estimation using top-of-canopy airborne RGB images[J],2021,96.
APA	Raj, Rahul,Walker, Jeffrey P.,Pingale, Rohit,Nandan, Rohit,Naik, Balaji,&Jagarlapudi, Adinarayana.(2021).Leaf area index estimation using top-of-canopy airborne RGB images.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,96.
MLA	Raj, Rahul,et al."Leaf area index estimation using top-of-canopy airborne RGB images".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 96(2021).