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

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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.H.[Jun-Hua], 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
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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

Gavrilovic, M.[Milan], Jovanovic, D.[Dušan], Božovic, 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

Brandmeier, M.[Melanie], Heßdörfer, D.[Daniel], Siebenlist, P.[Philipp], Meyer-Spelbrink, A.[Adrian], Kraus, A.[Anja],
Time Series Analysis of Multisensor Data for Precision Viticulture: Assessing Microscale Variations in Plant Development with Respect to Irrigation and Topography,
RS(16), No. 8, 2024, pp. 1419.
DOI Link 2405
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Bakon, M.[Matus], Teixeira, A.C.[Ana Cláudia], Pádua, L.[Luís], Morais, R.[Raul], Papco, J.[Juraj], Kubica, L.[Lukas], Rovnak, M.[Martin], Perissin, D.[Daniele], Sousa, J.J.[Joaquim J.],
Synthetic Aperture Radar in Vineyard Monitoring: Examples, Demonstrations, and Future Perspectives,
RS(16), No. 12, 2024, pp. 2106.
DOI Link 2406
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Brown, L.A.[Luke A.], Morris, H.[Harry], MacLachlan, A.[Andrew], D'Adamo, F.[Francesco], Adams, J.[Jennifer], Lopez-Baeza, E.[Ernesto], Albero, E.[Erika], Martínez, B.[Beatriz], Sánchez-Ruiz, S.[Sergio], Campos-Taberner, M.[Manuel], Lidón, A.[Antonio], Lull, C.[Cristina], Bautista, I.[Inmaculada], Clewley, D.[Daniel], Llewellyn, G.[Gary], Xie, Q.Y.[Qiao-Yun], Camacho, F.[Fernando], Pastor-Guzman, J.[Julio], Morrone, R.[Rosalinda], Sinclair, M.[Morven], Williams, O.[Owen], Hunt, M.[Merryn], Hueni, A.[Andreas], Boccia, V.[Valentina], Dransfeld, S.[Steffen], Dash, J.[Jadunandan],
Hyperspectral Leaf Area Index and Chlorophyll Retrieval over Forest and Row-Structured Vineyard Canopies,
RS(16), No. 12, 2024, pp. 2066.
DOI Link 2406
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Li, Y.F.[Ya-Feng], Xu, X.G.[Xin-Gang], Wu, W.[Wenbiao], Zhu, Y.H.[Yao-Hui], Yang, G.J.[Gui-Jun], Yang, X.D.[Xiao-Dong], Meng, Y.[Yang], Jiang, X.T.[Xiang-Tai], Xue, H.Y.[Han-Yu],
Hyperspectral Estimation of Chlorophyll Content in Grape Leaves Based on Fractional-Order Differentiation and Random Forest Algorithm,
RS(16), No. 12, 2024, pp. 2174.
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López, A.[Alfonso], Ogayar, C.J.[Carlos J.], Feito, F.R.[Francisco R.], Sousa, J.J.[Joaquim J.],
Classification of Grapevine Varieties Using UAV Hyperspectral Imaging,
RS(16), No. 12, 2024, pp. 2103.
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del Rio, M.S.[Maria S.], Cicuéndez, V.[Victor], Yagüe, C.[Carlos],
Characterisation of Two Vineyards in Mexico Based on Sentinel-2 and Meteorological Data,
RS(16), No. 14, 2024, pp. 2538.
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Sharma, P.[Prakriti], Villegas-Diaz, R.[Roberto], Fennell, A.[Anne],
Predicting Grapevine Physiological Parameters Using Hyperspectral Remote Sensing Integrated with Hybrid Convolutional Neural Network and Ensemble Stacked Regression,
RS(16), No. 14, 2024, pp. 2626.
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Fernandes, A.[António], Kovac, N.[Nataša], Fraga, H.[Hélder], Fonseca, A.[André], Radonjic, S.Š.[Sanja Šucur], Simeunovic, M.[Marko], Ratkovic, K.[Kruna], Menz, C.[Christoph], Costafreda-Aumedes, S.[Sergi], Santos, J.A.[João A.],
Challenges to Viticulture in Montenegro under Climate Change,
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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
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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
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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
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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
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Kandylakis, Z., Karantzalos, K.,
Precision Viticulture From Multitemporal, Multispectral Very High Resolution Satellite Data,
ISPRS16(B8: 919-925).
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d'Urso, M.G.[Maria Grazia], Marino, C.L.[Costantino Luis],
An Application Of Close-up Photogrammetry In Viticulture,
ISPRS16(B8: 1243-1250).
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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).
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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).
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Le Bris, A.,
Extraction Of Vineyards Out Of Aerial Ortho-image Using Texture Information,
AnnalsPRS(I-3), No. 2012, pp. 383-388.
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Le Bris, A., Boldo, D.,
Extraction of Landcover Themes out of Aerial Orthoimages in Mountainous Areas Using External Information,
PIA07(123).
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Cotton, Analysis and Change .

Last update:Sep 28, 2024 at 17:47:54