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Special Issue "Remote Sensing in Viticulture"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editors

Guest Editor
Dr. Emmanuelle Vaudour

Associate Professor, AgroParisTech/UMR ECOSYS, AgroParisTech, INRA, Université Paris Saclay, 78850, Thiverval-Grignon, France
Website | E-Mail
Phone: (+33)1 30 81 52 75
Interests: remote sensing of agroecosystems, viticultural zoning, terroir, remote sensing of agricultural soils, sentinel time series, soil carbon storage
Guest Editor
Dr. Alessandro Matese

IBIMET CNR–Istituto di Biometeorologia, Consiglio Nazionale delle Ricerche, via G. Caproni 8, 50145 Firenze, Italy
Website | E-Mail
Interests: remote sensing of agroecosystems, precision agriculture, precision forestry, UAV and Satellite remote sensing, wireless sensors network, biogeochemical cycles, data fusion, machine learning, computer vision and geostatistics
Guest Editor
Dr. Jose M. Peña

Institute of Agricultural Sciences, CSIC, Plant Protection Department 28006 Madrid, Spain
Website | E-Mail
Interests: precision agriculture; UAV and satellite remote sensing; object-based image analysis (OBIA); digitization and sensors in agriculture; crop protection; weed mapping; sustainable agriculture
Guest Editor
Dr. Konstantinos Karantzalos

Remote Sensing Laboratory, National Technical University of Athens, Heroon Polytechniou 9, 15780, Athens, Greece
Website | E-Mail
Phone: +302107721673
Interests: crop type classification; precision agriculture; hyperspectral imaging; UAVs; earth observation; data fusion; machine learning; computer vision

Special Issue Information

Dear Colleagues,

In conjunction with the development of geospatial technologies and the emergence of open spatial data in several parts of the world, remote sensing applications in viticulture have experienced a considerable rise since the end of the 1990s. In particular, remote sensing provides a powerful means for generating and updating valuable spatial information regarding grapevines, their canopy state, vineyard fertility, viticultural soils, their ecosystem and environment. Applications encompass the mapping of grape and wine quality, the enological potential of terroirs, as well as their change through time at several spatial scales: wine produced at a regional level, wine locally produced and managed by winegrowers, wine produced at field or within-field scales with precision viticulture practices.

During the last decade, remote sensing techniques in viticulture have combined and fused gradually more and more data from proximal field sensors and in situ canopy, grape, and soil observations. Given their widespread availability, high- and very high resolution satellite data, along with dense temporal time series observations, are opening new areas of research in viticulture, especially in the domain of viticultural zoning, which requires the integration or fusion with ancillary data. The use of microsatellites with daily revisits and high spatial resolution capabilities is emerging for vine vegetation monitoring. Moreover, applications relying on multispectral, hyperspectral, infrared, etc., data with ultra-high-resolution data from unmanned aerial vehicles (UAVs) are concentrating significant research effort towards assessing vineyard vegetation status, detecting plant diseases, weed control, etc.

This Special Issue is dedicated, but not limited to, the recent advances in remote sensing for viticulture and invites submissions on the following topics:

  • vine vegetation monitoring from UAVs, airborne, and satellite multitemporal data;
  • management zones delineation at several spatial scales;
  • actual and retrospective terroir spatial characterization;
  • assessment and mapping of viticultural soil properties;
  • remote sensing of viticultural practices and agroforestry viticultural systems;
  • computer vision and machine learning techniques for viticulture;
  • precision viticulture and precision harvesting methods;
  • advances in tractors, machinery, and geospatial information exploitation in viticulture;
  • water stress, nutrition deficiency, weed estimation and mapping.

Dr. Emmanuelle Vaudour
Dr. Alessandro Matese
Dr. Jose M. Peña
Dr. Konstantinos Karantzalos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • satellite time series
  • UAV
  • proximal sensing
  • vineyard environment
  • terroir zoning
  • viticultural practices
  • management zones
  • vine canopy
  • soil

Published Papers (1 paper)

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Research

Open AccessArticle
Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality
Remote Sens. 2019, 11(7), 740; https://doi.org/10.3390/rs11070740
Received: 3 March 2019 / Revised: 22 March 2019 / Accepted: 23 March 2019 / Published: 27 March 2019
PDF Full-text (2764 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Reliable assessment of grapevine productivity is a destructive and time-consuming process. In addition, the mixed effects of grapevine water status and scion-rootstock interactions on grapevine productivity are not always linear. Despite the potential opportunity of applying remote sensing and machine learning techniques to [...] Read more.
Reliable assessment of grapevine productivity is a destructive and time-consuming process. In addition, the mixed effects of grapevine water status and scion-rootstock interactions on grapevine productivity are not always linear. Despite the potential opportunity of applying remote sensing and machine learning techniques to predict plant traits, there are still limitations to previously studied techniques for vine productivity due to the complexity of the system not being adequately modeled. During the 2014 and 2015 growing seasons, hyperspectral reflectance spectra were collected using a handheld spectroradiometer in a vineyard designed to investigate the effects of irrigation level (0%, 50%, and 100%) and rootstocks (1103 Paulsen, 3309 Couderc, SO4 and Chambourcin) on vine productivity. To assess vine productivity, it is necessary to measure factors related to fruit ripeness and not just yield, as an over cropped vine may produce high-yield but poor-quality fruit. Therefore, yield, Total Soluble Solids (TSS), Titratable Acidity (TA) and the ratio TSS/TA (maturation index, IMAD) were measured. A total of 20 vegetation indices were calculated from hyperspectral data and used as input for predictive model calibration. Prediction performance of linear/nonlinear multiple regression methods and Weighted Regularized Extreme Learning Machine (WRELM) were compared with our newly developed WRELM-TanhRe. The developed method is based on two activation functions: hyperbolic tangent (Tanh) and rectified linear unit (ReLU). The results revealed that WRELM and WRELM-TanhRe outperformed the widely used multiple regression methods when model performance was tested with an independent validation dataset. WRELM-TanhRe produced the highest prediction accuracy for all the berry yield and quality parameters (R2 of 0.522–0.682 and RMSE of 2–15%), except for TA, which was predicted best with WRELM (R2 of 0.545 and RMSE of 6%). The results demonstrate the value of combining hyperspectral remote sensing and machine learning methods for improving of berry yield and quality prediction. Full article
(This article belongs to the Special Issue Remote Sensing in Viticulture)
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