{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:25:57Z","timestamp":1777573557985,"version":"3.51.4"},"reference-count":99,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vine&Wine Portugal Project","award":["C644866286-00000011"],"award-info":[{"award-number":["C644866286-00000011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate estimation of grapevine biophysical parameters is important for decision support in precision viticulture. This study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield, across mixed-variety vineyards in the Douro Region of Portugal. Data were collected at three phenological stages, from veraison to maturation and two modeling approaches were tested: one using only spectral features, and another combining spectral and geometric features derived from photogrammetric elevation data. Multiple linear regression (MLR) and five ML algorithms were applied, with feature selection performed using both forward and backward selection procedures. Logarithmic transformations were used to mitigate data skewness. Overall, ML algorithms provided better predictive performance than MLR, particularly when geometric features were included. At harvest-ready, Random Forest achieved the highest accuracy for LAI (R2 = 0.83) and yield (R2 = 0.75), while MLR produced the most accurate estimates for pruning wood biomass (R2 = 0.83). Among geometric variables, canopy area was the most informative. For spectral data, the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Soil-Adjusted Vegetation Index (SAVI) were the most relevant. The models performed well across grapevine varieties, indicating that UAV-based monitoring can serve as a practical, non-invasive, and scalable approach for vineyard management in heterogeneous vineyards.<\/jats:p>","DOI":"10.3390\/rs17233915","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T11:29:30Z","timestamp":1764761370000},"page":"3915","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integrating UAV Multi-Temporal Imagery and Machine Learning to Assess Biophysical Parameters of Douro Grapevines"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0240-5469","authenticated-orcid":false,"given":"Pedro","family":"Marques","sequence":"first","affiliation":[{"name":"Agronomy Department, School of Agrarian and Veterinary Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5845-2593","authenticated-orcid":false,"given":"Leilson","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Agronomy Department, School of Agrarian and Veterinary Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2727-0014","authenticated-orcid":false,"given":"Telmo","family":"Ad\u00e3o","sequence":"additional","affiliation":[{"name":"ALGORITMI Research Centre, University of Minho, 4710-057 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4533-930X","authenticated-orcid":false,"given":"Joaquim J.","family":"Sousa","sequence":"additional","affiliation":[{"name":"Engineering Department, School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Centre for Robotics in Industry and Intelligent Systems (CRIIS), Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2440-9153","authenticated-orcid":false,"given":"Raul","family":"Morais","sequence":"additional","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Engineering Department, School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5669-7976","authenticated-orcid":false,"given":"Emanuel","family":"Peres","sequence":"additional","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Engineering Department, School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7570-9773","authenticated-orcid":false,"given":"Lu\u00eds","family":"P\u00e1dua","sequence":"additional","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Engineering Department, School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"135839","DOI":"10.1016\/j.scitotenv.2019.135839","article-title":"Exploring the potential of vineyards for biodiversity conservation and delivery of biodiversity-mediated ecosystem services: A global-scale systematic review","volume":"706","author":"Paiola","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"02009","DOI":"10.1051\/e3sconf\/20185002009","article-title":"Modelling the terroir of the Douro demarcated region, Portugal","volume":"Volume 50","author":"Fraga","year":"2018","journal-title":"Proceedings of the E3S Web of Conferences"},{"key":"ref_3","unstructured":"Duarte, J.B., Brinca, P., and Gon\u00e7alves, M.J. (2022). Setor do Vinho-Avalia\u00e7\u00e3o de Impacto Socioecon\u00f3mico em Portugal, Nova School of Business & Economics. Technical Report."},{"key":"ref_4","first-page":"31","article-title":"Portuguese vines and wines: Heritage, quality symbol, tourism asset","volume":"33","year":"2018","journal-title":"Cienc. E Tec. Vitivinic."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.1108\/BFJ-12-2016-0609","article-title":"The global competitiveness of European wine producers","volume":"119","author":"Balogh","year":"2017","journal-title":"Br. Food J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cunha, J., Teixeira-Santos, M., Braza\u00e3o, J., Fevereiro, P., and Eiras-Dias, J.E. (2013). Portuguese Vitis vinifera L. germplasm: Accessing its diversity and strategies for conservation. The Mediterranean Genetic Code-Grapevine and Olive, Intechopen.","DOI":"10.5772\/52639"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"61","DOI":"10.20870\/oeno-one.2017.51.2.1621","article-title":"Viticulture in Portugal: A review of recent trends and climate change projections","volume":"51","author":"Fraga","year":"2017","journal-title":"OENO One"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1017\/jwe.2015.21","article-title":"The impact of climate change on viticulture and wine quality","volume":"11","author":"Darriet","year":"2016","journal-title":"J. Wine Econ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.3390\/rs70302971","article-title":"Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture","volume":"7","author":"Matese","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"139204","DOI":"10.1016\/j.scitotenv.2020.139204","article-title":"Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes\u2014A systematic review","volume":"732","author":"Klaus","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1093\/jxb\/erg263","article-title":"Ground-based measurements of leaf area index: A review of methods, instruments and current controversies","volume":"54","year":"2003","journal-title":"J. Exp. Bot."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.agrformet.2003.08.027","article-title":"Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography","volume":"121","author":"Jonckheere","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.agrformet.2017.12.250","article-title":"Improving crop yield estimation by assimilating LAI and inputting satellite-based surface incoming solar radiation into SWAP model","volume":"250","author":"Mokhtari","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"083674","DOI":"10.1117\/1.JRS.8.083674","article-title":"Integrating remotely sensed leaf area index and leaf nitrogen accumulation with RiceGrow model based on particle swarm optimization algorithm for rice grain yield assessment","volume":"8","author":"Wang","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"148177","DOI":"10.1016\/j.scitotenv.2021.148177","article-title":"Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling","volume":"791","author":"Paul","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"120","DOI":"10.5344\/ajev.2014.14070","article-title":"Characterization of Vitis vinifera L. canopy using unmanned aerial vehicle-based remote sensing and photogrammetry techniques","volume":"66","author":"Ballesteros","year":"2015","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.agwat.2019.03.051","article-title":"Water consumption, crop coefficient and leaf area relations of a Vitis vinifera cv.\u2018Cabernet Sauvignon\u2019vineyard","volume":"219","author":"Munitz","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_18","first-page":"63","article-title":"Estimating biophysical and geometrical parameters of grapevine canopies (\u2018Sangiovese\u2019) by an unmanned aerial vehicle (UAV) and VIS-NIR cameras","volume":"56","author":"Caruso","year":"2017","journal-title":"Vitis"},{"key":"ref_19","unstructured":"Turner, D., Lucieer, A., and Watson, C. (2011, January 10\u201315). Development of an Unmanned Aerial Vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery. Proceedings of the 34th International Symposium on Remote Sensing of Environment, Sydney, NSW, Australia."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"02007","DOI":"10.1051\/e3sconf\/20185002007","article-title":"How can remote sensing techniques help monitoring the vine and maximize the terroir potential?","volume":"Volume 50","author":"Tondriaux","year":"2018","journal-title":"Proceedings of the E3S Web of Conferences"},{"key":"ref_21","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS, NASA Special Publication."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Puig-Sirera, \u00c0., Antichi, D., Warren Raffa, D., and Rallo, G. (2021). Application of remote sensing techniques to discriminate the effect of different soil management treatments over rainfed vineyards in chianti terroir. Remote Sens., 13.","DOI":"10.3390\/rs13040716"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112398","DOI":"10.1016\/j.scienta.2023.112398","article-title":"The role of LAI and leaf chlorophyll on NDVI estimated by UAV in grapevine canopies","volume":"322","author":"Caruso","year":"2023","journal-title":"Sci. Hortic."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1007\/s00271-022-00776-0","article-title":"LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning","volume":"40","author":"Gao","year":"2022","journal-title":"Irrig. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"299","DOI":"10.5194\/isprsarchives-XL-1-W4-299-2015","article-title":"Leaf area index estimation in vineyards from UAV hyperspectral data, 2D image mosaics and 3D canopy surface models","volume":"40","author":"Kalisperakis","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107723","DOI":"10.1016\/j.compag.2023.107723","article-title":"Leaf area index estimation of pergola-trained vineyards in arid regions using classical and deep learning methods based on UAV-based RGB images","volume":"207","author":"Ilniyaz","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2447","DOI":"10.1007\/s11119-024-10179-0","article-title":"Evaluating the utility of combining high resolution thermal, multispectral and 3D imagery from unmanned aerial vehicles to monitor water stress in vineyards","volume":"25","author":"Guerra","year":"2024","journal-title":"Precis. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Aboutalebi, M., Torres-Rua, A.F., McKee, M., Kustas, W.P., Nieto, H., Alsina, M.M., White, A., Prueger, J.H., McKee, L., and Alfieri, J. (2019). Incorporation of unmanned aerial vehicle (UAV) point cloud products into remote sensing evapotranspiration models. Remote Sens., 12.","DOI":"10.3390\/rs12010050"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"159","DOI":"10.20870\/oeno-one.2021.55.4.4639","article-title":"Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery","volume":"55","author":"Rubio","year":"2021","journal-title":"OENO One"},{"key":"ref_30","first-page":"165","article-title":"Estimation of vineyard vegetative growth: Analysis of 3D point cloud from unmanned aerial vehicle imagery","volume":"118","year":"2022","journal-title":"Asoc. Interprofesional Para El Desarro. Agrar."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1007\/s11119-022-09970-8","article-title":"Use of remote sensing-derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub-field level","volume":"24","author":"Leolini","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Fern\u00e1ndez, M., Sanz-Ablanedo, E., Pereira-Obaya, D., and Rodr\u00edguez-P\u00e9rez, J.R. (2021). Vineyard pruning weight prediction using 3D point clouds generated from UAV imagery and structure from motion photogrammetry. Agronomy, 11.","DOI":"10.3390\/agronomy11122489"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.biosystemseng.2023.06.001","article-title":"Assessment of vineyard vigour and yield spatio-temporal variability based on UAV high resolution multispectral images","volume":"231","author":"Ferro","year":"2023","journal-title":"Biosyst. Eng."},{"key":"ref_34","unstructured":"Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T. (2000, January 16\u201319). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"919","DOI":"10.20870\/oeno-one.2020.54.4.4028","article-title":"Comparison between satellite and ground data with UAV-based information to analyse vineyard spatio-temporal variability: This article is published in cooperation with the XIIIth International Terroir Congress November 17\u201318 2020, Adelaide, Australia. Guest editors: Cassandra Collins and Roberta De Bei","volume":"54","author":"Pastonchi","year":"2020","journal-title":"OENO One"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1007\/s11119-022-09984-2","article-title":"Investigation of the similarities between NDVI maps from different proximal and remote sensing platforms in explaining vineyard variability","volume":"24","author":"Kasimati","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Orlandi, G., Matese, A., Ulrici, A., Calvini, R., Berton, A., and Di Gennaro, S.F. (2025). Automated yield prediction in vineyard using RGB images acquired by a UAV prototype platform. OENO One, 59.","DOI":"10.20870\/oeno-one.2025.59.1.8133"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Torres-S\u00e1nchez, J., Mesas-Carrascosa, F.J., Santesteban, L.G., Jim\u00e9nez-Brenes, F.M., Oneka, O., Villa-Llop, A., Loidi, M., and L\u00f3pez-Granados, F. (2021). Grape cluster detection using UAV photogrammetric point clouds as a low-cost tool for yield forecasting in vineyards. Sensors, 21.","DOI":"10.3390\/s21093083"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Codes-Alcaraz, A.M., Furnitto, N., Sottosanti, G., Failla, S., Puerto, H., Rocamora-Osorio, C., Freire-Garc\u00eda, P., and Ram\u00edrez-Cuesta, J.M. (2025). Automatic Grape Cluster Detection Combining YOLO Model and Remote Sensing Imagery. Remote Sens., 17.","DOI":"10.3390\/rs17020243"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.biosystemseng.2022.10.015","article-title":"Yield estimations in a vineyard based on high-resolution spatial imagery acquired by a UAV","volume":"224","author":"Ortega","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"\u0160up\u010d\u00edk, A., Milics, G., and Mate\u010dn\u1ef3, I. (2024). Predicting Grape Yield with Vine Canopy Morphology Analysis from 3D Point Clouds Generated by UAV Imagery. Drones, 8.","DOI":"10.3390\/drones8060216"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Williams, M., Burnside, N.G., Brolly, M., and Joyce, C.B. (2024). Investigating the Role of Cover-Crop Spectra for Vineyard Monitoring from Airborne and Spaceborne Remote Sensing. Remote Sens., 16.","DOI":"10.3390\/rs16213942"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Gavrilovi\u0107, M., Jovanovi\u0107, D., Bo\u017eovi\u0107, P., Benka, P., and Govedarica, M. (2024). Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery. Remote Sens., 16.","DOI":"10.3390\/rs16030584"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s00271-022-00787-x","article-title":"Application of a remote-sensing three-source energy balance model to improve evapotranspiration partitioning in vineyards","volume":"40","author":"Nieto","year":"2022","journal-title":"Irrig. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Magarreiro, C., Gouveia, C.M., Barroso, C.M., and Trigo, I.F. (2019). Modelling of wine production using land surface temperature and FAPAR\u2014The case of the Douro Wine Region. Remote Sens., 11.","DOI":"10.3390\/rs11060604"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhou, X., Yang, L., Wang, W., and Chen, B. (2021). Uav data as an alternative to field sampling to monitor vineyards using machine learning based on uav\/sentinel-2 data fusion. Remote Sens., 13.","DOI":"10.3390\/rs13030457"},{"key":"ref_48","first-page":"150","article-title":"Radiative transfer models (RTMs) for field phenotyping inversion of rice based on UAV hyperspectral remote sensing","volume":"10","author":"Fenghua","year":"2017","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, L., Li, S., Yang, W., Wang, X., Luo, X., Ran, P., and Zhang, H. (2023). Forest canopy water content monitoring using radiative transfer models and machine learning. Forests, 14.","DOI":"10.3390\/f14071418"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1007\/s00271-022-00798-8","article-title":"Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation","volume":"40","author":"Kang","year":"2022","journal-title":"Irrig. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s10462-024-11081-x","article-title":"Transfer learning in agriculture: A review","volume":"58","author":"Hossen","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Tak\u00e1ts, T., P\u00e1sztor, L., \u00c1rvai, M., Albert, G., and M\u00e9sz\u00e1ros, J. (2025). Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards. Land, 14.","DOI":"10.3390\/land14010163"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1051\/ctv\/20153001029","article-title":"Application of crop modelling to portuguese viticulture: Implementation and added-values for strategic planning","volume":"30","author":"Costa","year":"2015","journal-title":"Ci\u00eanc. E T\u00e9c. Vitivin\u00edc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106905","DOI":"10.1016\/j.compag.2022.106905","article-title":"Vineyard classification using OBIA on UAV-based RGB and multispectral data: A case study in different wine regions","volume":"196","author":"Matese","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"287","DOI":"10.14358\/PERS.84.5.287","article-title":"Classification of aerial photogrammetric 3D point clouds","volume":"84","author":"Becker","year":"2018","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Giovos, R., Tassopoulos, D., Kalivas, D., Lougkos, N., and Priovolou, A. (2021). Remote sensing vegetation indices in viticulture: A critical review. Agriculture, 11.","DOI":"10.3390\/agriculture11050457"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Stolarski, O., Fraga, H., Sousa, J.J., and P\u00e1dua, L. (2022). Synergistic use of sentinel-2 and uav multispectral data to improve and optimize viticulture management. Drones, 6.","DOI":"10.3390\/drones6110366"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Chiroque-Solano, P.M., Marques, P., Sousa, J.J., and Peres, E. (2022). Mapping the leaf area index of castanea sativa miller using uav-based multispectral and geometrical data. Drones, 6.","DOI":"10.3390\/drones6120422"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Banerjee, B.P., Spangenberg, G., and Kant, S. (2020). Fusion of spectral and structural information from aerial images for improved biomass estimation. Remote Sens., 12.","DOI":"10.3390\/rs12193164"},{"key":"ref_61","first-page":"10-70088","article-title":"A Comprehensive Study of Feature Selection Techniques in Machine Learning Models","volume":"1","author":"Cheng","year":"2024","journal-title":"Insights Comput. Signals Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2809","DOI":"10.1890\/02-3114","article-title":"Confronting multicollinearity in ecological multiple regression","volume":"84","author":"Graham","year":"2003","journal-title":"Ecology"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5512","DOI":"10.1002\/sim.3148","article-title":"Selection of important variables and determination of functional form for continuous predictors in multivariable model building","volume":"26","author":"Sauerbrei","year":"2007","journal-title":"Stat. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1197\/j.aem.2003.09.006","article-title":"Advanced statistics: Linear regression, part II: Multiple linear regression","volume":"11","author":"Marill","year":"2004","journal-title":"Acad. Emerg. Med."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.oregeorev.2015.01.001","article-title":"Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines","volume":"71","year":"2015","journal-title":"Ore Geol. Rev."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_67","unstructured":"Chen, T. (2025, November 30). Xgboost: Extreme Gradient Boosting; R Package Version 0.4-2; 2015; Volume 1. Available online: https:\/\/cran.r-project.org\/web\/packages\/xgboost\/index.html."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Natekin, A., and Knoll, A. (2013). Gradient boosting machines, a tutorial. Front. Neurorobotics, 7.","DOI":"10.3389\/fnbot.2013.00021"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","article-title":"Multi-class adaboost","volume":"2","author":"Hastie","year":"2009","journal-title":"Stat. Its Interface"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"240","DOI":"10.3390\/agriengineering6010015","article-title":"Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction","volume":"6","author":"Fraga","year":"2024","journal-title":"AgriEngineering"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"102194","DOI":"10.1016\/j.ecoinf.2023.102194","article-title":"Winter wheat yield prediction in the conterminous United States using solar-induced chlorophyll fluorescence data and XGBoost and random forest algorithm","volume":"77","author":"Joshi","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"107346","DOI":"10.1016\/j.compag.2022.107346","article-title":"Extreme Gradient Boosting for yield estimation compared with Deep Learning approaches","volume":"202","author":"Huber","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_74","unstructured":"Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., and Strachan, I. (2002, January 16\u201320). Effects of chlorophyll concentration on green LAI prediction in crop canopies: Modelling and assessment. Proceedings of the First International Sysmposium on Recent Advances in Quantitative Remote Sensing, Valencia, Spain."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Towers, P.C., Strever, A., and Poblete-Echeverr\u00eda, C. (2019). Comparison of vegetation indices for leaf area index estimation in vertical shoot positioned vine canopies with and without grenbiule hail-protection netting. Remote Sens., 11.","DOI":"10.3390\/rs11091073"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Darra, N., Psomiadis, E., Kasimati, A., Anastasiou, A., Anastasiou, E., and Fountas, S. (2021). Remote and proximal sensing-derived spectral indices and biophysical variables for spatial variation determination in vineyards. Agronomy, 11.","DOI":"10.3390\/agronomy11040741"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Vera-Esmeraldas, A., Pizarro-Ote\u00edza, S., Labb\u00e9, M., Rojo, F., and Salazar, F. (2025). UAV-Based Spectral and Thermal Indices in Precision Viticulture: A Review of NDVI, NDRE, SAVI, GNDVI, and CWSI. Agronomy, 15.","DOI":"10.3390\/agronomy15112569"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Di Gennaro, S.F., Toscano, P., Cinat, P., Berton, A., and Matese, A. (2019). A low-cost and unsupervised image recognition methodology for yield estimation in a vineyard. Front. Plant Sci., 10.","DOI":"10.3389\/fpls.2019.00559"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1007\/s11119-020-09717-3","article-title":"Vineyard yield estimation by combining remote sensing, computer vision and artificial neural network techniques","volume":"21","author":"Ballesteros","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1111\/j.1755-0238.2003.tb00258.x","article-title":"Temporal stability of an NDVI-LAI relationship in a Napa Valley vineyard","volume":"9","author":"Johnson","year":"2003","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., Jim\u00e9nez-Brenes, F.M., Torres-S\u00e1nchez, J., Pe\u00f1a, J.M., Borra-Serrano, I., and L\u00f3pez-Granados, F. (2018). 3-D characterization of vineyards using a novel UAV imagery-based OBIA procedure for precision viticulture applications. Remote Sens., 10.","DOI":"10.3390\/rs10040584"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1080\/01431161.2016.1226002","article-title":"Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging","volume":"38","author":"Matese","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.fcr.2010.01.010","article-title":"Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index\u2014The canopy chlorophyll content index (CCCI)","volume":"116","author":"Fitzgerald","year":"2010","journal-title":"Field Crops Res."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1016\/j.compag.2019.05.038","article-title":"Methods to compare the spatial variability of UAV-based spectral and geometric information with ground autocorrelated data. A case of study for precision viticulture","volume":"162","author":"Matese","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Matese, A., and Di Gennaro, S.F. (2018). Practical applications of a multisensor UAV platform based on multispectral, thermal and RGB high resolution images in precision viticulture. Agriculture, 8.","DOI":"10.3390\/agriculture8070116"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s11119-019-09699-x","article-title":"Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery","volume":"21","author":"Comba","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_87","first-page":"215","article-title":"Robust nonlinear regression in applications","volume":"67","author":"Lim","year":"2013","journal-title":"J. Indian Soc. Agric. Stat. Indian Soc. Agric. Stat."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1007\/s11590-021-01765-6","article-title":"Distributionally-robust machine learning using locally differentially-private data","volume":"16","author":"Farokhi","year":"2022","journal-title":"Optim. Lett."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"5377","DOI":"10.1080\/01431161.2018.1471548","article-title":"Vineyard properties extraction combining UAS-based RGB imagery with elevation data","volume":"39","author":"Marques","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_90","first-page":"103069","article-title":"Using NDVI, climate data and machine learning to estimate yield in the Douro wine region","volume":"114","author":"Barriguinha","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s00271-018-0614-8","article-title":"Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals","volume":"37","author":"White","year":"2019","journal-title":"Irrig. Sci."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Ghiat, I., Mackey, H.R., and Al-Ansari, T. (2021). A review of evapotranspiration measurement models, techniques and methods for open and closed agricultural field applications. Water, 13.","DOI":"10.3390\/w13182523"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S1672-6308(07)60027-4","article-title":"New vegetation index and its application in estimating leaf area index of rice","volume":"14","author":"Wang","year":"2007","journal-title":"Rice Sci."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Marques, P., Martins, L., Sousa, A., Peres, E., and Sousa, J.J. (2020). Monitoring of chestnut trees using machine learning techniques applied to UAV-based multispectral data. Remote Sens., 12.","DOI":"10.3390\/rs12183032"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"L08403","DOI":"10.1029\/2005GL022688","article-title":"Remote estimation of canopy chlorophyll content in crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Albetis, J., Jacquin, A., Goulard, M., Poilv\u00e9, H., Rousseau, J., Clenet, H., Dedieu, G., and Duthoit, S. (2018). On the potentiality of UAV multispectral imagery to detect Flavescence dor\u00e9e and Grapevine Trunk Diseases. Remote Sens., 11.","DOI":"10.3390\/rs11010023"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/17\/23\/3915\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T11:40:27Z","timestamp":1764762027000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/17\/23\/3915"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,3]]},"references-count":99,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["rs17233915"],"URL":"https:\/\/doi.org\/10.3390\/rs17233915","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,3]]}}}