globalchange  > 气候变化事实与影响
DOI: 10.1016/j.jag.2017.02.013
Scopus记录号: 2-s2.0-85019948318
论文题名:
Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region
作者: Pôças I; , Gonçalves J; , Costa P; M; , Gonçalves I; , Pereira L; S; , Cunha M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 58
起始页码: 177
结束页码: 190
语种: 英语
英文关键词: Crop water deficit ; Handheld spectroradiometer ; Reflectance data ; Statistical and machine learning techniques ; Vegetation indices ; Vineyard
Scopus关键词: machine learning ; modeling ; NDVI ; prediction ; spectral analysis ; statistical analysis ; vegetation index ; vineyard ; water use ; Portugal ; Vitis
英文摘要: In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Ψpd) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Ψpd, with an average determination coefficient (R2) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R2 = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Ψpd observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support irrigation scheduling in vineyard. © 2017 Elsevier B.V.
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79926
Appears in Collections:气候变化事实与影响

Files in This Item:

There are no files associated with this item.


作者单位: Linking Landscape, Environment, Agriculture and Food, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa, Portugal; Geo-Space Sciences Research CentreGeo-Space Sciences Research Centre, (CICGE), Rua do Campo Alegre, Porto, Portugal; InBIO/CIBIO, Research Centre in Biodiversity and Genetic Resources, University of Porto, Campus Agrário de Vairão, Rua Padre Armando Quintas, nr. 7, Vairão, Portugal; Faculdade de Ciências da Universidade do Porto, Rua do Campo Alegre, Porto, Portugal; Associação para o Desenvolvimento da Viticultura DurienseAssociação para o Desenvolvimento da Viticultura Duriense, Edifício Centro de Excelência da Vinha e do Vinho Parque de Ciência e Tecnologia de Vila RealEdifício Centro de Excelência da Vinha e do Vinho Parque de Ciência e Tecnologia de Vila Real, Régia Douro ParkRégia Douro Park, Portugal

Recommended Citation:
Pôças I,, Gonçalves J,, Costa P,et al. Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,58
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Pôças I]'s Articles
[, Gonçalves J]'s Articles
[, Costa P]'s Articles
百度学术
Similar articles in Baidu Scholar
[Pôças I]'s Articles
[, Gonçalves J]'s Articles
[, Costa P]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Pôças I]‘s Articles
[, Gonçalves J]‘s Articles
[, Costa P]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.