Sentinel-2 Imagery Monitoring Vine Growth Related to Topography in a Protected Designation of Origin Region

Tassopoulos, Dimitris; Kalivas, Dionissios; Giovos, Rigas; Lougkos, Nestor; Priovolou, Anastasia

doi:10.3390/agriculture11080785

Cited by 12 publications

(4 citation statements)

References 63 publications

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“…Informally, the zone is divided into three subzones on an altitude basis, to the mountainous (600-850 m), the semi-mountainous (350-600 m), and the lowland (250-350 m) subzones. Tus, the zone is characterized by variations both in terrain and climate conditions, which impact the maturation process, the microbial ecology, and the physicochemical parameters of the grapes [1].…”

Section: Introductionmentioning

confidence: 99%

The Microbial Terroir of the Nemea Zone Agiorgitiko cv.: A First Metataxonomic Approach

Κάζου

Pagiati

Ḏotsika

et al. 2023

Australian Journal of Grape and Wine Research

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Vitis vinifera L. cv. Agiorgitiko is one of the most popular indigenous wine grape varieties in Greece, cultivated almost exclusively in the Nemea Protected Designation of Origin (PDO) zone. Here, the microbiota of soil, grapes, and wine, during controlled (CF) and spontaneous (SF) fermentations of Agiorgitko cv. from three vineyards in the PDO Nemea zone were explored, using both classical microbiological analysis and metataxonomics to get evidence about the microbial terroir of the PDO Nemea zone. The classical microbiological analysis revealed higher total mesophilic counts in soil, while in both grapes and wine samples, yeasts prevailed. Lactic acid bacteria and acetic acid bacteria counts were lower in grapes compared to wine and soil. Metataxonomic analysis revealed that, regarding yeasts/fungi, genera Fusarium, Sarea, and Alternaria dominated in soil; Aureobasidium, Cladosporium, and Penicillium in grapes; Saccharomyces in wine during CF; and Hanseniaspora and Saccharomyces in wine during SF. Regarding bacteria, genera Skermanella, Acidobacterium, and Ohtaekwangia dominated in soil, Sphingomonas, Micrococcus, and Rubrobacter in grapes, while Tatumella, Alcanivorax, and Komagateibacter in wine during both CF and SF. Finally, the factors that significantly influence the microbiota of soil, grapes, and wine samples were assessed, and potential microbial biomarkers were identified for the first time in a Greek grape variety.

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Section: Introductionmentioning

confidence: 99%

The Microbial Terroir of the Nemea Zone Agiorgitiko cv.: A First Metataxonomic Approach

Κάζου

Pagiati

Ḏotsika

et al. 2023

Australian Journal of Grape and Wine Research

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“…Remote sensing, and Sentinel-2 data in particular, has emerged as an alternative [32,[68][69][70] for estimating vine parameters, offering advantages over traditional time-consuming techniques that are commonly employed to assess plant water status [71] and vegetative growth [72,73]. However, remote sensing-based deductions can be affected by exceptional local conditions, namely the presence of plastic coverings (or nets), that can lead to unexpected results if not properly addressed [29].…”

Section: Discussionmentioning

confidence: 99%

Estimation of Intercepted Solar Radiation and Stem Water Potential in a Table Grape Vineyard Covered by Plastic Film Using Sentinel-2 Data: A Comparison of OLS-, MLR-, and ML-Based Methods

Farbo,

Trombetta,

de Palma

et al. 2024

Plants

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In the framework of precision viticulture, satellite data have been demonstrated to significantly support many tasks. Specifically, they enable the rapid, large-scale estimation of some viticultural parameters like vine stem water potential (Ψstem) and intercepted solar radiation (ISR) that traditionally require time-consuming ground surveys. The practice of covering table grape vineyards with plastic films introduces an additional challenge for estimation, potentially affecting vine spectral responses and, consequently, the accuracy of estimations from satellites. This study aimed to address these challenges with a special focus on the exploitation of Sentinel-2 Level 2A and meteorological data to monitor a plastic-covered vineyard in Southern Italy. Estimates of Ψstem and ISR were obtained using different algorithms, namely, Ordinary Least Square (OLS), Multivariate Linear Regression (MLR), and machine learning (ML) techniques, which rely on Random Forest Regression, Support Vector Regression, and Partial Least Squares. The results proved that, despite the potential spectral interference from the plastic coverings, ISR and Ψstem can be locally estimated with a satisfying accuracy. In particular, (i) the OLS regression-based approach showed a good performance in providing accurate ISR estimates using the near-infrared spectral bands (RMSE < 8%), and (ii) the MLR and ML algorithms could estimate both the ISR and vine water status with a higher accuracy (RMSE < 7 for ISR and RMSE < 0.14 MPa for Ψstem). These results encourage the adoption of medium–high resolution multispectral satellite imagery for deriving satisfying estimates of key crop parameters even in anomalous situations like the ones where plastic films cover the monitored vineyard, thus marking a significant advancement in precision viticulture.

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“…Specifically, ref. [18] attempts to monitor vine growth in a Protected Designation of Origin (PDO) zone using freely available and high-temporal-resolution Sentinel-2 imagery. They selected 27 vineyards for their study and calculated vegetation indices (i.e., NDVI, EVI) for each one.…”

Section: State Of the Art And Related Productsmentioning

confidence: 99%

Business Intelligence through Machine Learning from Satellite Remote Sensing Data

Kyriakos,

Vavalis

2023

Future Internet

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Several cities have been greatly affected by economic crisis, unregulated gentrification, and the pandemic, resulting in increased vacancy rates. Abandoned buildings have various negative implications on their neighborhoods, including an increased chance of fire and crime and a drastic reduction in their monetary value. This paper focuses on the use of satellite data and machine learning to provide insights for businesses and policymakers within Greece and beyond. Our objective is two-fold: to provide a comprehensive literature review on recent results concerning the opportunities offered by satellite images for business intelligence and to design and implement an open-source software system for the detection of abandoned or disused buildings based on nighttime lights and built-up area indices. Our preliminary experimentation provides promising results that can be used for location intelligence and beyond.

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Sentinel-2 Imagery Monitoring Vine Growth Related to Topography in a Protected Designation of Origin Region

Cited by 12 publications

References 63 publications

The Microbial Terroir of the Nemea Zone Agiorgitiko cv.: A First Metataxonomic Approach

The Microbial Terroir of the Nemea Zone Agiorgitiko cv.: A First Metataxonomic Approach

Estimation of Intercepted Solar Radiation and Stem Water Potential in a Table Grape Vineyard Covered by Plastic Film Using Sentinel-2 Data: A Comparison of OLS-, MLR-, and ML-Based Methods

Business Intelligence through Machine Learning from Satellite Remote Sensing Data

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