干旱区生态环境知识资源中心(Arid): Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle

Arid

DOI	10.3390/rs13132639
	Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle
	Gautam, Deepak; Ostendorf, Bertram; Pagay, Vinay
通讯作者	Pagay, V (corresponding author), Univ Adelaide, Sch Agr Food & Wine, PMB 1, Glen Osmond, SA 5064, Australia.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:13
英文摘要	Crop water status and irrigation requirements are of great importance to the horticultural industry due to changing climatic conditions leading to high evaporative demands, drought and water scarcity in semi-arid and arid regions worldwide. Irrigation scheduling strategies based on evapotranspiration (ET), such as regulated deficit irrigation, requires the estimation of seasonal crop coefficients (k(c)). The ET-driven irrigation decisions for grapevines rely on the sampling of several k(c) values from each irrigation zone. Here, we present an unmanned aerial vehicle (UAV)-based technique to estimate k(c) at the single vine level in order to capture the spatial variability of water requirements in a commercial vineyard located in South Australia. A UAV carrying a multispectral sensor is used to extract the spectral, as well as the structural, information of Cabernet Sauvignon grapevines. The spectral and structural information, acquired at the various phenological stages of the vine through two seasons, is used to model k(c) using univariate (simple linear), multivariate (generalised linear and additive) and machine learning (convolution neural network and random forest) model frameworks. The structural information (e.g., canopy top view area) had the strongest correlation with k(c) throughout the season (p <= 0.001; Pearson R = 0.56), while the spectral indices (e.g., normalised indices) turned less-sensitive post veraison-the onset of ripening in grapes. Combining structural and spectral information improved the model's performance. Among the investigated predictive models, the random forest predicted k(c) with the highest accuracy (R-2: 0.675, root mean square error: 0.062, and mean absolute error: 0.047). This UAV-based approach improves the precision of irrigation by capturing the spatial variability of k(c) within a vineyard. Combined with an energy balance model, the water needs of a vineyard can be computed on a weekly or sub-weekly basis for precision irrigation. The UAV-based characterisation of k(c) can further enhance the water management and irrigation zoning by matching the infrastructure with the spatial variability of the irrigation demand.
英文关键词	UAV UAS drone precision irrigation remote sensing spatial variability random forest
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:000671032700001
WOS关键词	EMPIRICAL LINE METHOD ; WATER STATUS ; EVAPOTRANSPIRATION ESTIMATION ; VEGETATION INDEXES ; IRRIGATION ; VARIABILITY ; MODEL ; AREA ; EVAPORATION ; MANAGEMENT
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351541
作者单位	[Gautam, Deepak; Pagay, Vinay] Univ Adelaide, Sch Agr Food & Wine, PMB 1, Glen Osmond, SA 5064, Australia; [Gautam, Deepak] Charles Darwin Univ, Res Inst Environm & Livelihoods, Casuarina, NT 0810, Australia; [Ostendorf, Bertram] Univ Adelaide, Sch Biol Sci, Oliphant Bldg,North Terrace Campus, Adelaide, SA 5005, Australia
推荐引用方式 GB/T 7714	Gautam, Deepak,Ostendorf, Bertram,Pagay, Vinay. Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle[J],2021,13(13).
APA	Gautam, Deepak,Ostendorf, Bertram,&Pagay, Vinay.(2021).Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle.REMOTE SENSING,13(13).
MLA	Gautam, Deepak,et al."Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle".REMOTE SENSING 13.13(2021).