23.2.8.11 Vineyard Analysis, Viticulture, Grapes, Production, Detection, Health, Change

Chapter Contents (Back)
Classification. Vineyard Analysis. Grapes.
See also Gross Primary Production, Net Primary Production, GPP, NPP.

Warner, T.A.[Timothy A.], Steinmaus, K.[Karen],
Spatial Classification of Orchards and Vineyards with High Spatial Resolution Panchromatic Imagery,
PhEngRS(71), No. 2, February 2005, pp. 179-188. Spatial auto correlation measures are used to classify land cover types with distinct spatial patterns.
WWW Link. 0509
BibRef

Vineyard monitoring system combines global positioning and NIR imaging,
VisSys(17), No. 1, January 2012.
WWW Link. 1201
BibRef

Tarantino, E., Figorito, B.,
Mapping Rural Areas with Widespread Plastic Covered Vineyards Using True Color Aerial Data,
RS(4), No. 7, July 2012, pp. 1913-1928.
DOI Link 1208

See also Extracting Buildings from True Color Stereo Aerial Images Using a Decision Making Strategy. BibRef

Zorer, R.[Roberto], Rocchini, D.[Duccio], Metz, M., Delucchi, L.[Luca], Zottele, F.[Fabio], Meggio, F.[Franco], Neteler, M.[Markus],
Daily MODIS Land Surface Temperature Data for the Analysis of the Heat Requirements of Grapevine Varieties,
GeoRS(51), No. 4, April 2013, pp. 2128-2135.
IEEE DOI 1304
BibRef
Earlier: A1, A2, A4, A5, A6, A7:
Use of multi-annual MODIS Land Surface Temperature data for the characterization of the heat requirements for grapevine varieties,
MultiTemp11(225-228).
IEEE DOI 1109
BibRef

Mathews, A.J.[Adam J.], Jensen, J.L.R.[Jennifer L. R.],
Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud,
RS(5), No. 5, 2013, pp. 2164-2183.
DOI Link 1307
BibRef

Soliman, A.[Aiman], Heck, R.J.[Richard J.], Brenning, A.[Alexander], Brown, R.[Ralph], Miller, S.[Stephen],
Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data,
RS(5), No. 8, 2013, pp. 3729-3748.
DOI Link 1309
BibRef

Carrasco-Benavides, M.[Marcos], Ortega-Farías, S.[Samuel], Lagos, L.O.[Luis Octavio], Kleissl, J.[Jan], Morales-Salinas, L.[Luis], Kilic, A.[Ayse],
Parameterization of the Satellite-Based Model (METRIC) for the Estimation of Instantaneous Surface Energy Balance Components over a Drip-Irrigated Vineyard,
RS(6), No. 11, 2014, pp. 11342-11371.
DOI Link 1412
BibRef

Matese, A.[Alessandro], Toscano, P.[Piero], di Gennaro, S.F.[Salvatore Filippo], Genesio, L.[Lorenzo], Vaccari, F.P.[Francesco Primo], Primicerio, J.[Jacopo], Belli, C.[Claudio], Zaldei, A.[Alessandro], Bianconi, R.[Roberto], Gioli, B.[Beniamino],
Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture,
RS(7), No. 3, 2015, pp. 2971-2990.
DOI Link 1504
BibRef

Pôças, I.[Isabel], Rodrigues, A.[Arlete], Gonçalves, S.[Sara], Costa, P.M.[Patrícia M.], Gonçalves, I.[Igor], Pereira, L.S.[Luís S.], Cunha, M.[Mário],
Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices,
RS(7), No. 12, 2015, pp. 15835.
DOI Link 1601
BibRef

Vanino, S.[Silvia], Pulighe, G.[Giuseppe], Nino, P.[Pasquale], de Michele, C.[Carlo], Bolognesi, S.F.[Salvatore Falanga], d'Urso, G.[Guido],
Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment,
RS(7), No. 11, 2015, pp. 14708.
DOI Link 1512
BibRef

Rey-Caramés, C.[Clara], Diago, M.P.[María P.], Martín, M.P.[M. Pilar], Lobo, A.[Agustín], Tardaguila, J.[Javier],
Using RPAS Multi-Spectral Imagery to Characterise Vigour, Leaf Development, Yield Components and Berry Composition Variability within a Vineyard,
RS(7), No. 11, 2015, pp. 14458.
DOI Link 1512
BibRef

Karakizi, C.[Christina], Oikonomou, M.[Marios], Karantzalos, K.[Konstantinos],
Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data,
RS(8), No. 3, 2016, pp. 235.
DOI Link 1604
BibRef

Pham, M.T.[Minh-Tan], Mercier, G.[Grégoire], Regniers, O.[Oliver], Michel, J.[Julien],
Texture Retrieval from VHR Optical Remote Sensed Images Using the Local Extrema Descriptor with Application to Vineyard Parcel Detection,
RS(8), No. 5, 2016, pp. 368.
DOI Link 1606
BibRef

Sepúlveda-Reyes, D.[Daniel], Ingram, B.[Benjamin], Bardeen, M.[Matthew], Zúñiga, M.[Mauricio], Ortega-Farías, S.[Samuel], Poblete-Echeverría, C.[Carlos],
Selecting Canopy Zones and Thresholding Approaches to Assess Grapevine Water Status by Using Aerial and Ground-Based Thermal Imaging,
RS(8), No. 10, 2016, pp. 822.
DOI Link 1609
BibRef

Weiss, M.[Marie], Baret, F.[Frédéric],
Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
BibRef

Poblete-Echeverría, C.[Carlos], Olmedo, G.F.[Guillermo Federico], Ingram, B.[Ben], Bardeen, M.[Matthew],
Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Albetis, J.[Johanna], Duthoit, S.[Sylvie], Guttler, F.[Fabio], Jacquin, A.[Anne], Goulard, M.[Michel], Poilvé, H.[Hervé], Féret, J.B.[Jean-Baptiste], Dedieu, G.[Gérard],
Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Sun, L.[Liang], Gao, F.[Feng], Anderson, M.C.[Martha C.], Kustas, W.P.[William P.], Alsina, M.M.[Maria M.], Sanchez, L.[Luis], Sams, B.[Brent], McKee, L.[Lynn], Dulaney, W.[Wayne], White, W.A.[William A.], Alfieri, J.G.[Joseph G.], Prueger, J.H.[John H.], Melton, F.[Forrest], Post, K.[Kirk],
Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Espinoza, C.Z.[Carlos Zúñiga], Khot, L.R.[Lav R.], Sankaran, S.[Sindhuja], Jacoby, P.W.[Pete W.],
High Resolution Multispectral and Thermal Remote Sensing-Based Water Stress Assessment in Subsurface Irrigated Grapevines,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Balbontín, C.[Claudio], Campos, I.[Isidro], Odi-Lara, M.[Magali], Ibacache, A.[Antonio], Calera, A.[Alfonso],
Irrigation Performance Assessment in Table Grape Using the Reflectance-Based Crop Coefficient,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Matese, A.[Alessandro], Baraldi, R.[Rita], Berton, A.[Andrea], Cesaraccio, C.[Carla], di Gennaro, S.F.[Salvatore Filippo], Duce, P.[Pierpaolo], Facini, O.[Osvaldo], Mameli, M.G.[Massimiliano Giuseppe], Piga, A.[Alessandra], Zaldei, A.[Alessandro],
Estimation of Water Stress in Grapevines Using Proximal and Remote Sensing Methods,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Silva, R.[Rui], Gomes, V.[Véronique], Mendes-Faia, A.[Arlete], Melo-Pinto, P.[Pedro],
Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Loggenberg, K.[Kyle], Strever, A.[Albert], Greyling, B.[Berno], Poona, N.[Nitesh],
Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Duarte, L.[Lia], Silva, P.[Pedro], Teodoro, A.C.[Ana Cláudia],
Development of a QGIS Plugin to Obtain Parameters and Elements of Plantation Trees and Vineyards with Aerial Photographs,
IJGI(7), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Al-Saddik, H.[Hania], Laybros, A.[Anthony], Billiot, B.[Bastien], Cointault, F.[Frederic],
Using Image Texture and Spectral Reflectance Analysis to Detect Yellowness and Esca in Grapevines at Leaf-Level,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

de Castro, A.I.[Ana I.], Jiménez-Brenes, F.M.[Francisco M.], Torres-Sánchez, J.[Jorge], Peña, J.M.[José M.], Borra-Serrano, I.[Irene], López-Granados, F.[Francisca],
3-D Characterization of Vineyards Using a Novel UAV Imagery-Based OBIA Procedure for Precision Viticulture Applications,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Helman, D.[David], Bahat, I.[Idan], Netzer, Y.S.[Yi-Shai], Ben-Gal, A.[Alon], Alchanatis, V.[Victor], Peeters, A.[Aviva], Cohen, Y.[Yafit],
Using Time Series of High-Resolution Planet Satellite Images to Monitor Grapevine Stem Water Potential in Commercial Vineyards,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Remke, A.[Alexander], Rodrigo-Comino, J.[Jesús], Gyasi-Agyei, Y.[Yeboah], Cerdà, A.[Artemi], Ries, J.B.[Johannes B.],
Combining the Stock Unearthing Method and Structure-from-Motion Photogrammetry for a Gapless Estimation of Soil Mobilisation in Vineyards,
IJGI(7), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Pádua, L.[Luís], Marques, P.[Pedro], Hruška, J.[Jonáš], Adão, T.[Telmo], Peres, E.[Emanuel], Morais, R.[Raul], Sousa, J.J.[Joaquim J.],
Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Pádua, L.[Luís], Adão, T.[Telmo], Sousa, A.[António], Peres, E.[Emanuel], Sousa, J.J.[Joaquim J.],
Individual Grapevine Analysis in a Multi-Temporal Context Using UAV-Based Multi-Sensor Imagery,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Rançon, F.[Florian], Bombrun, L.[Lionel], Keresztes, B.[Barna], Germain, C.[Christian],
Comparison of SIFT Encoded and Deep Learning Features for the Classification and Detection of Esca Disease in Bordeaux Vineyards,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Albetis, J.[Johanna], Jacquin, A.[Anne], Goulard, M.[Michel], Poilvé, H.[Hervé], Rousseau, J.[Jacques], Clenet, H.[Harold], Dedieu, G.[Gerard], Duthoit, S.[Sylvie],
On the Potentiality of UAV Multispectral Imagery to Detect Flavescence dorée and Grapevine Trunk Diseases,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Magarreiro, C.[Clarisse], Gouveia, C.M.[Célia M.], Barroso, C.M.[Carla M.], Trigo, I.F.[Isabel F.],
Modelling of Wine Production Using Land Surface Temperature and FAPAR: The Case of the Douro Wine Region,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Khaliq, A.[Aleem], Comba, L.[Lorenzo], Biglia, A.[Alessandro], Aimonino, D.R.[Davide Ricauda], Chiaberge, M.[Marcello], Gay, P.[Paolo],
Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Tucci, G.[Grazia], Parisi, E.I.[Erica Isabella], Castelli, G.[Giulio], Errico, A.[Alessandro], Corongiu, M.[Manuela], Sona, G.[Giovanna], Viviani, E.[Enea], Bresci, E.[Elena], Preti, F.[Federico],
Multi-Sensor UAV Application for Thermal Analysis on a Dry-Stone Terraced Vineyard in Rural Tuscany Landscape,
IJGI(8), No. 2, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Maimaitiyiming, M.[Matthew], Sagan, V.[Vasit], Sidike, P.[Paheding], Kwasniewski, M.T.[Misha T.],
Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

Cinat, P.[Paolo], di Gennaro, S.F.[Salvatore Filippo], Berton, A.[Andrea], Matese, A.[Alessandro],
Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Henry, D., Aubert, H., Véronèse, T.,
Proximal Radar Sensors for Precision Viticulture,
GeoRS(57), No. 7, July 2019, pp. 4624-4635.
IEEE DOI 1907
Pipelines, Radar imaging, Estimation, Radar measurements, Optical sensors, Precision viticulture (PV), proximal sensing, radar imagery BibRef

Mayoral, C.Q., González, C.G.[C. García], Galarregui, J.C.I., Marín, D., Gastón, D., Miranda, C., Gonzalo, R., Maestrojuán, I., Santesteban, L.G., Ederra, I.,
Water Content Continuous Monitoring of Grapevine Xylem Tissue Using a Portable Low-Power Cost-Effective FMCW Radar,
GeoRS(57), No. 8, August 2019, pp. 5595-5605.
IEEE DOI 1908
CW radar, FM radar, irrigation, optimisation, radar resolution, signal classification, telecommunication power management, xylem water content BibRef

del-Campo-Sanchez, A.[Ana], Moreno, M.[Miguel], Ballesteros, R.[Rocio], Hernandez-Lopez, D.[David],
Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Gong, C.Z.[Chi-Zhang], Buddenbaum, H.[Henning], Retzlaff, R.[Rebecca], Udelhoven, T.[Thomas],
An Empirical Assessment of Angular Dependency for RedEdge-M in Sloped Terrain Viticulture,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Gennaro, S.F.D.[Salvatore Filippo Di], Dainelli, R.[Riccardo], Palliotti, A.[Alberto], Toscano, P.[Piero], Matese, A.[Alessandro],
Sentinel-2 Validation for Spatial Variability Assessment in Overhead Trellis System Viticulture Versus UAV and Agronomic Data,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Zhao, L.C.[Long-Cai], Li, Q.Z.[Qiang-Zi], Zhang, Y.[Yuan], Wang, H.Y.[Hong-Yan], Du, X.[Xin],
Integrating the Continuous Wavelet Transform and a Convolutional Neural Network to Identify Vineyard Using Time Series Satellite Images,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Cogato, A.[Alessia], Pagay, V.[Vinay], Marinello, F.[Francesco], Meggio, F.[Franco], Grace, P.[Peter], de Antoni Migliorati, M.[Massimiliano],
Assessing the Feasibility of Using Sentinel-2 Imagery to Quantify the Impact of Heatwaves on Irrigated Vineyards,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Mirzaei, M.[Mohsen], Verrelst, J.[Jochem], Marofi, S.[Safar], Abbasi, M.[Mozhgan], Azadi, H.[Hossein],
Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Mesas-Carrascosa, F.J.[Francisco-Javier], de Castro, A.I.[Ana I.], Torres-Sánchez, J.[Jorge], Triviño-Tarradas, P.[Paula], Jiménez-Brenes, F.M.[Francisco M.], García-Ferrer, A.[Alfonso], López-Granados, F.[Francisca],
Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
BibRef

Nassar, A.[Ayman], Torres-Rua, A.[Alfonso], Kustas, W.[William], Nieto, H.[Hector], McKee, M.[Mac], Hipps, L.[Lawrence], Stevens, D.[David], Alfieri, J.[Joseph], Prueger, J.[John], Alsina, M.M.[Maria Mar], McKee, L.[Lynn], Coopmans, C.[Calvin], Sanchez, L.[Luis], Dokoozlian, N.[Nick],
Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Jones, E.G.[Eriita G.], Wong, S.[Sebastien], Milton, A.[Anthony], Sclauzero, J.[Joseph], Whittenbury, H.[Holly], McDonnell, M.D.[Mark D.],
The Impact of Pan-Sharpening and Spectral Resolution on Vineyard Segmentation through Machine Learning,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Meyers, J.M.[James M.], Dokoozlian, N.[Nick], Ryan, C.[Casey], Bioni, C.[Cella], Heuvel, J.E.V.[Justine E. Vanden],
A New, Satellite NDVI-Based Sampling Protocol for Grape Maturation Monitoring,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Zabawa, L.[Laura], Kicherer, A.[Anna], Klingbeil, L.[Lasse], Töpfer, R.[Reinhard], Kuhlmann, H.[Heiner], Roscher, R.[Ribana],
Counting of grapevine berries in images via semantic segmentation using convolutional neural networks,
PandRS(164), 2020, pp. 73-83.
Elsevier DOI 2005
Deep learning, Semantic segmentation, Geoinformation, High-throughput analysis, Plant phenotyping, Vitis BibRef

Bendel, N.[Nele], Kicherer, A.[Anna], Backhaus, A.[Andreas], Köckerling, J.[Janine], Maixner, M.[Michael], Bleser, E.[Elvira], Klück, H.C.[Hans-Christian], Seiffert, U.[Udo], Voegele, R.T.[Ralf T.], Töpfer, R.[Reinhard],
Detection of Grapevine Leafroll-Associated Virus 1 and 3 in White and Red Grapevine Cultivars Using Hyperspectral Imaging,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Cogato, A.[Alessia], Meggio, F.[Franco], Collins, C.[Cassandra], Marinello, F.[Francesco],
Medium-Resolution Multispectral Data from Sentinel-2 to Assess the Damage and the Recovery Time of Late Frost on Vineyards,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Chrobak, K.[Katarzyna], Chrobak, G.[Grzegorz], Kazak, J.K.[Jan K.],
The Use of Common Knowledge in Fuzzy Logic Approach for Vineyard Site Selection,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Bellvert, J.[Joaquim], Jofre-Cekalovic, C.[Christian], Pelechá, A.[Ana], Mata, M.[Mercè], Nieto, H.[Hector],
Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

López-Granados, F.[Francisca], Torres-Sánchez, J.[Jorge], Jiménez-Brenes, F.M.[Francisco M.], Oneka, O.[Oihane], Marín, D.[Diana], Loidi, M.[Maite], de Castro, A.I.[Ana I.], Santesteban, L.G.,
Monitoring Vineyard Canopy Management Operations Using UAV-Acquired Photogrammetric Point Clouds,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Ohana-Levi, N.[Noa], Knipper, K.[Kyle], Kustas, W.P.[William P.], Anderson, M.C.[Martha C.], Netzer, Y.S.[Yi-Shai], Gao, F.[Feng], del Mar Alsina, M.[Maria], Sanchez, L.A.[Luis A.], Karnieli, A.[Arnon],
Using Satellite Thermal-Based Evapotranspiration Time Series for Defining Management Zones and Spatial Association to Local Attributes in a Vineyard,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Jurado, J.M.[Juan M.], Pádua, L.[Luís], Feito, F.R.[Francisco R.], Sousa, J.J.[Joaquim J.],
Automatic Grapevine Trunk Detection on UAV-Based Point Cloud,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Maimaitiyiming, M.[Matthew], Sagan, V.[Vasit], Sidike, P.[Paheding], Maimaitijiang, M.[Maitiniyazi], Miller, A.J.[Allison J.], Kwasniewski, M.[Misha],
Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Kerkech, M.[Mohamed], Hafiane, A.[Adel], Canals, R.[Raphael],
VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Moghimi, A.[Ali], Pourreza, A.[Alireza], Zuniga-Ramirez, G.[German], Williams, L.E.[Larry E.], Fidelibus, M.W.[Matthew W.],
A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Bendel, N.[Nele], Backhaus, A.[Andreas], Kicherer, A.[Anna], Köckerling, J.[Janine], Maixner, M.[Michael], Jarausch, B.[Barbara], Biancu, S.[Sandra], Klück, H.C.[Hans-Christian], Seiffert, U.[Udo], Voegele, R.T.[Ralf T.], Töpfer, R.[Reinhard],
Detection of Two Different Grapevine Yellows in Vitis vinifera Using Hyperspectral Imaging,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Kalua, M.[Michael], Rallings, A.M.[Anna M.], Booth, L.[Lorenzo], Medellín-Azuara, J.[Josué], Carpin, S.[Stefano], Viers, J.H.[Joshua H.],
sUAS Remote Sensing of Vineyard Evapotranspiration Quantifies Spatiotemporal Uncertainty in Satellite-Borne ET Estimates,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Laroche-Pinel, E.[Eve], Albughdadi, M.[Mohanad], Duthoit, S.[Sylvie], Chéret, V.[Véronique], Rousseau, J.[Jacques], Clenet, H.[Harold],
Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

García-Gutiérrez, V.[Víctor], Stöckle, C.[Claudio], Gil, P.M.[Pilar Macarena], Meza, F.J.[Francisco Javier],
Evaluation of Penman-Monteith Model Based on Sentinel-2 Data for the Estimation of Actual Evapotranspiration in Vineyards,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Zhou, X.X.[Xi-Xuan], Yang, L.[Liao], Wang, W.S.[Wei-Sheng], Chen, B.[Baili],
UAV Data as an Alternative to Field Sampling to Monitor Vineyards Using Machine Learning Based on UAV/Sentinel-2 Data Fusion,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Puig-Sirera, À.[Àngela], Antichi, D.[Daniele], Raffa, D.W.[Dylan Warren], Rallo, G.[Giovanni],
Application of Remote Sensing Techniques to Discriminate the Effect of Different Soil Management Treatments over Rainfed Vineyards in Chianti Terroir,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Liu, W.[Wei], Zhang, X.D.[Xiao-Dong], He, F.[Fei], Xiong, Q.[Quan], Zan, X.L.[Xu-Li], Liu, Z.[Zhe], Sha, D.X.[De-Xuan], Yang, C.W.[Chao-Wei], Li, S.M.[Shao-Ming], Zhao, Y.Y.[Yuan-Yuan],
Open-air grape classification and its application in parcel-level risk assessment of late frost in the eastern Helan Mountains,
PandRS(174), 2021, pp. 132-150.
Elsevier DOI 2103
Open-air grape parcel, Late frost, Google Earth Engine, Crop classification, Sentinel-2, OpenStreetMap, Eastern Helan Mountains BibRef

Bahat, I.[Idan], Netzer, Y.S.[Yi-Shai], Grünzweig, J.M.[José M.], Alchanatis, V.[Victor], Peeters, A.[Aviva], Goldshtein, E.[Eitan], Ohana-Levi, N.[Noa], Ben-Gal, A.[Alon], Cohen, Y.[Yafit],
In-Season Interactions between Vine Vigor, Water Status and Wine Quality in Terrain-Based Management-Zones in a 'Cabernet Sauvignon' Vineyard,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Squeri, C.[Cecilia], Poni, S.[Stefano], di Gennaro, S.F.[Salvatore Filippo], Matese, A.[Alessandro], Gatti, M.[Matteo],
Comparison and Ground Truthing of Different Remote and Proximal Sensing Platforms to Characterize Variability in a Hedgerow-Trained Vineyard,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Gautam, D.[Deepak], Ostendorf, B.[Bertram], Pagay, V.[Vinay],
Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Calamita, F.[Federico], Imran, H.A.[Hafiz Ali], Vescovo, L.[Loris], Mekhalfi, M.L.[Mohamed Lamine], La Porta, N.[Nicola],
Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Fernández-Novales, J.[Juan], Saiz-Rubio, V.[Verónica], Barrio, I.[Ignacio], Rovira-Más, F.[Francisco], Cuenca-Cuenca, A.[Andrés], Alves, F.S.[Fernando Santos], Valente, J.[Joana], Tardaguila, J.[Javier], Diago, M.P.[María Paz],
Monitoring and Mapping Vineyard Water Status Using Non-Invasive Technologies by a Ground Robot,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Hajjar, C.[Chantal], Ghattas, G.[Ghassan], Sarkis, M.K.[Maya Kharrat], Chamoun, Y.G.[Yolla Ghorra],
Vine Identification and Characterization in Goblet-Trained Vineyards Using Remotely Sensed Images,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Wei, H.E.[Hsiang-En], Grafton, M.[Miles], Bretherton, M.[Michael], Irwin, M.[Matthew], Sandoval, E.[Eduardo],
Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Li, W.J.[Wen-Jie], Huang, J.F.[Jing-Feng], Yang, L.B.[Ling-Bo], Chen, Y.[Yan], Fang, Y.[Yahua], Jin, H.W.[Hong-Wei], Sun, H.[Han], Huang, R.[Ran],
A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Debnath, S.[Sourabhi], Paul, M.[Manoranjan], Rahaman, D.M.M.[D. M. Motiur], Debnath, T.[Tanmoy], Zheng, L.H.[Li-Hong], Baby, T.[Tintu], Schmidtke, L.M.[Leigh M.], Rogiers, S.Y.[Suzy Y.],
Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Tyc, J.[Jakub], Hensel, D.S.[Defne Sunguroglu], Parisi, E.I.[Erica Isabella], Tucci, G.[Grazia], Hensel, M.U.[Michael Ulrich],
Integration of Remote Sensing Data into a Composite Voxel Model for Environmental Performance Analysis of Terraced Vineyards in Tuscany, Italy,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

d'Urso, G.[Guido], Bolognesi, S.F.[Salvatore Falanga], Kustas, W.P.[William P.], Knipper, K.R.[Kyle R.], Anderson, M.C.[Martha C.], Alsina, M.M.[Maria M.], Hain, C.R.[Christopher R.], Alfieri, J.G.[Joseph G.], Prueger, J.H.[John H.], Gao, F.[Feng], McKee, L.G.[Lynn G.], de Michele, C.[Carlo], McElrone, A.J.[Andrew J.], Bambach, N.[Nicolas], Sanchez, L.[Luis], Belfiore, O.R.[Oscar Rosario],
Determining Evapotranspiration by Using Combination Equation Models with Sentinel-2 Data and Comparison with Thermal-Based Energy Balance in a California Irrigated Vineyard,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Paciolla, N.[Nicola], Corbari, C.[Chiara], Maltese, A.[Antonino], Ciraolo, G.[Giuseppe], Mancini, M.[Marco],
Proximal-Sensing-Powered Modelling of Energy-Water Fluxes in a Vineyard: A Spatial Resolution Analysis,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Chancia, R.[Robert], Bates, T.[Terry], Heuvel, J.V.[Justine Vanden], van Aardt, J.[Jan],
Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Fredes, S.N.[Sandra N.], Ruiz, L.Á.[Luis Á.], Recio, J.A.[Jorge A.],
Modeling Phenols, Anthocyanins and Color Intensity of Wine Using Pre-Harvest Sentinel-2 Images,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Yang, R.[Rui], Lu, X.Y.[Xiang-Yu], Huang, J.[Jing], Zhou, J.[Jun], Jiao, J.[Jie], Liu, Y.F.[Yu-Fei], Liu, F.[Fei], Su, B.F.[Bao-Feng], Gu, P.[Peiwen],
A Multi-Source Data Fusion Decision-Making Method for Disease and Pest Detection of Grape Foliage Based on ShuffleNet V2,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Sassu, A.[Alberto], Ghiani, L.[Luca], Salvati, L.[Luca], Mercenaro, L.[Luca], Deidda, A.[Alessandro], Gambella, F.[Filippo],
Integrating UAVs and Canopy Height Models in Vineyard Management: A Time-Space Approach,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Ilniyaz, O.[Osman], Kurban, A.[Alishir], Du, Q.Y.[Qing-Yun],
Leaf Area Index Estimation of Pergola-Trained Vineyards in Arid Regions Based on UAV RGB and Multispectral Data Using Machine Learning Methods,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Rodríguez-Febereiro, M.[Marta], Dafonte, J.[Jorge], Fandiño, M.[María], Cancela, J.J.[Javier J.], Rodríguez-Pérez, J.R.[José Ramón],
Evaluation of Spectroscopy and Methodological Pre-Treatments to Estimate Soil Nutrients in the Vineyard,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Peng, X.L.[Xue-Lian], Chen, D.[Dianyu], Zhou, Z.J.[Zhen-Jiang], Zhang, Z.[Zhitao], Xu, C.[Can], Zha, Q.[Qing], Wang, F.[Fang], Hu, X.T.[Xiao-Tao],
Prediction of the Nitrogen, Phosphorus and Potassium Contents in Grape Leaves at Different Growth Stages Based on UAV Multispectral Remote Sensing,
RS(14), No. 11, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Bai, H.Q.[Hui-Qing], Sun, Z.X.[Zhong-Xiang], Yao, X.[Xuenan], Kong, J.[Junhua], Wang, Y.J.[Yong-Jian], Zhang, X.Y.[Xiao-Yu], Chen, W.P.[Wei-Ping], Fan, P.[Peige], Li, S.H.[Shao-Hua], Liang, Z.C.[Zhen-Chang], Dai, Z.W.[Zhan-Wu],
Viticultural Suitability Analysis Based on Multi-Source Data Highlights Climate-Change-Induced Decrease in Potential Suitable Areas: A Case Analysis in Ningxia, China,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Morellos, A.[Antonios], Pantazi, X.E.[Xanthoula Eirini], Paraskevas, C.[Charalampos], Moshou, D.[Dimitrios],
Comparison of Deep Neural Networks in Detecting Field Grapevine Diseases Using Transfer Learning,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Arab, S.T.[Sara Tokhi], Ahamed, T.[Tofael],
Land Suitability Analysis for Potential Vineyards Extension in Afghanistan at Regional Scale Using Remote Sensing Datasets,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Petrovic, I.[Igor], Secnik, M.[Matej], Hocevar, M.[Marko], Berk, P.[Peter],
Vine Canopy Reconstruction and Assessment with Terrestrial Lidar and Aerial Imaging,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Wei, H.E.[Hsiang-En], Grafton, M.[Miles], Bretherton, M.[Mike], Irwin, M.[Matthew], Sandoval, E.[Eduardo],
Evaluation of the Use of UAV-Derived Vegetation Indices and Environmental Variables for Grapevine Water Status Monitoring Based on Machine Learning Algorithms and SHAP Analysis,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Lu, S.H.[Sai-Hong], Xuan, J.J.[Jun-Jie], Zhang, T.[Tong], Bai, X.[Xueer], Tian, F.[Fei], Ortega-Farias, S.[Samuel],
Effect of the Shadow Pixels on Evapotranspiration Inversion of Vineyard: A High-Resolution UAV-Based and Ground-Based Remote Sensing Measurements,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Palazzi, F.[Francesco], Biddoccu, M.[Marcella], Mondino, E.C.B.[Enrico Corrado Borgogno], Cavallo, E.[Eugenio],
Use of Remotely Sensed Data for the Evaluation of Inter-Row Cover Intensity in Vineyards,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Yao, Z.Y.[Zhi-Ying], Zhao, Y.Y.[Yuan-Yuan], Wang, H.B.[Heng-Bin], Li, H.D.[Hong-Dong], Yuan, X.[Xinqun], Ren, T.W.[Tian-Wei], Yu, L.[Le], Liu, Z.[Zhe], Zhang, X.D.[Xiao-Dong], Li, S.[Shaoming],
Comparison and Assessment of Data Sources with Different Spatial and Temporal Resolution for Efficiency Orchard Mapping: Case Studies in Five Grape-Growing Regions,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Gao, R.[Rui], Torres-Rua, A.F.[Alfonso F.], Nieto, H.[Hector], Zahn, E.[Einara], Hipps, L.[Lawrence], Kustas, W.P.[William P.], Alsina, M.M.[Maria Mar], Bambach, N.[Nicolas], Castro, S.J.[Sebastian J.], Prueger, J.H.[John H.], Alfieri, J.[Joseph], McKee, L.G.[Lynn G.], White, W.A.[William A.], Gao, F.[Feng], McElrone, A.J.[Andrew J.], Anderson, M.[Martha], Knipper, K.[Kyle], Coopmans, C.[Calvin], Gowing, I.[Ian], Agam, N.[Nurit], Sanchez, L.[Luis], Dokoozlian, N.[Nick],
ET Partitioning Assessment Using the TSEB Model and sUAS Information across California Central Valley Vineyards,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Lyu, H.Y.[Hong-Yi], Grafton, M.[Miles], Ramilan, T.[Thiagarajah], Irwin, M.[Matthew], Sandoval, E.[Eduardo],
Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models,
RS(15), No. 6, 2023, pp. 1497.
DOI Link 2304
BibRef

García-Gutiérrez, V.[Víctor], Meza, F.[Francisco],
Modeling Phenology Combining Data Assimilation Techniques and Bioclimatic Indices in a Cabernet Sauvignon Vineyard (Vitis vinifera L.) in Central Chile,
RS(15), No. 14, 2023, pp. 3537.
DOI Link 2307
BibRef

Payares, L.K.A.[Luz K. Atencia], Tarquis, A.M.[Ana M.], Peralo, R.H.[Roberto Hermoso], Cano, J.[Jesús], Cámara, J.[Joaquín], Nowack, J.[Juan], Gómez-del Campo, M.[María],
Multispectral and Thermal Sensors Onboard UAVs for Heterogeneity in Merlot Vineyard Detection: Contribution to Zoning Maps,
RS(15), No. 16, 2023, pp. 4024.
DOI Link 2309
BibRef

Lyu, H.Y.[Hong-Yi], Grafton, M.[Miles], Ramilan, T.[Thiagarajah], Irwin, M.[Matthew], Wei, H.E.[Hsiang-En], Sandoval, E.[Eduardo],
Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality,
RS(15), No. 22, 2023, pp. 5412.
DOI Link 2311
BibRef

Gavrilovi?, M.[Milan], Jovanovi?, D.[Dušan], Božovi?, P.[Predrag], Benka, P.[Pavel], Govedarica, M.[Miro],
Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery,
RS(16), No. 3, 2024, pp. 584.
DOI Link 2402
BibRef

Ortuani, B.[Bianca], Mayer, A.[Alice], Bianchi, D.[Davide], Sona, G.[Giovanna], Crema, A.[Alberto], Modina, D.[Davide], Bolognini, M.[Martino], Brancadoro, L.[Lucio], Boschetti, M.[Mirco], Facchi, A.[Arianna],
Effectiveness of Management Zones Delineated from UAV and Sentinel-2 Data for Precision Viticulture Applications,
RS(16), No. 4, 2024, pp. 635.
DOI Link 2402
BibRef


Gentilhomme, T.[Theophile], Villamizar, M.[Michael], Corre, J.[Jerome], Odobez, J.M.[Jean-Marc],
Efficient Grapevine Structure Estimation in Vineyards Conditions,
CVPPA23(712-720)
IEEE DOI 2401
BibRef

Musci, M.A., Persello, C., Lingua, A.M.,
UAV Images and Deep-learning Algorithms for Detecting Flavescence Doree Disease In Grapevine Orchards,
ISPRS20(B3:1483-1489).
DOI Link 2012
BibRef

Kerkech, M.[Mohamed], Hafiane, A.[Adel], Canals, R.[Raphael], Ros, F.[Frederic],
Vine Disease Detection by Deep Learning Method Combined with 3d Depth Information,
ICISP20(82-90).
Springer DOI 2009
BibRef

Hruška, J., Adão, T., Pádua, L., Guimarães, N., Peres, E., Morais, R., Sousa, J.J.,
Evaluation of Machine Learning Techniques in Vine Leaves Disease Detection: a Preliminary Case Study On Flavescence DorÉe,
Gi4DM19(151-156).
DOI Link 1912
BibRef

Kandylakis, Z., Karantzalos, K.,
Precision Viticulture From Multitemporal, Multispectral Very High Resolution Satellite Data,
ISPRS16(B8: 919-925).
DOI Link 1610
BibRef

d'Urso, M.G.[Maria Grazia], Marino, C.L.[Costantino Luis],
An Application Of Close-up Photogrammetry In Viticulture,
ISPRS16(B8: 1243-1250).
DOI Link 1610
BibRef

Liu, S.[Scarlett], Whitty, M.[Mark], Cossell, S.[Stephen],
Automatic grape bunch detection in vineyards for precise yield estimation,
MVA15(238-241)
IEEE DOI 1507
Accuracy BibRef

Burgos, S., Mota, M., Noll, D., Cannelle, B.,
Use of Very High-Resolution Airborne Images to Analyse 3D Canopy Architecture of a Vineyard,
GeoUAV15(399-403).
DOI Link 1602
BibRef

Kalisperakis, I., Stentoumis, C., Grammatikopoulos, L., Karantzalos, K.,
Leaf Area Index Estimation in Vineyards from UAV Hyperspectral Data, 2D Image Mosaics and 3D Canopy Surface Models,
UAV-g15(299-303).
DOI Link 1512
BibRef

Le Bris, A.,
Extraction Of Vineyards Out Of Aerial Ortho-image Using Texture Information,
AnnalsPRS(I-3), No. 2012, pp. 383-388.
DOI Link 1209
BibRef

Le Bris, A., Boldo, D.,
Extraction of Landcover Themes out of Aerial Orthoimages in Mountainous Areas Using External Information,
PIA07(123).
PDF File. 0711
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Cotton, Analysis and Change .


Last update:Mar 16, 2024 at 20:36:19