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Remote Sensing in Viticulture

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

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 51644

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Special Issue Editors

Dr. Emmanuelle Vaudour

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Associate Professor, AgroParisTech/UMR ECOSYS, AgroParisTech, INRA, Université Paris Saclay, 78850 Thiverval-Grignon, France
Interests: remote sensing of agroecosystems; viticultural zoning; terroir; remote sensing of agricultural soils; sentinel time series; soil carbon storage
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Dr. Alessandro Matese

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Institute of BioEconomy, National Research Council (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy
Interests: environmental governance; institutional and ecological economics; climate change; biodiversity protection; land management
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Dr. Jose M. Peña

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Guest Editor

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

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Guest Editor

Remote Sensing Laboratory, National Technical University of Athens, 15780 Athens, Greece
Interests: hyperspectral imaging; UAVs; earth observation; data fusion; machine learning; computer vision; crop type classification; precision agriculture
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Special Issue Information

Dear Colleagues,

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

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

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

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

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

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Keywords

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

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Published Papers (8 papers)

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17 pages, 1894 KiB

Open AccessArticle

Medium-Resolution Multispectral Data from Sentinel-2 to Assess the Damage and the Recovery Time of Late Frost on Vineyards

by Alessia Cogato, Franco Meggio, Cassandra Collins and Francesco Marinello

Remote Sens. 2020, 12(11), 1896; https://doi.org/10.3390/rs12111896 - 11 Jun 2020

Cited by 25 | Viewed by 4515

Abstract

In a climate-change context, the advancement of phenological stages may endanger viticultural areas in the event of a late frost. This study evaluated the potential of satellite-based remote sensing to assess the damage and the recovery time after a late frost event in 2017 in northern Italian vineyards. Several vegetation indices (VIs) normalized on a two-year dataset (2018–2019) were compared over a frost-affected area (F) and a control area (NF) using unpaired two-sample t-test. Furthermore, the must quality data (total acidity, sugar content and pH) of F and NF were analyzed. The VIs most sensitive in the detection of frost damage were Chlorophyll Absorption Ratio Index (CARI), Enhanced Vegetation Index (EVI), and Modified Triangular Vegetation Index 1 (MTVI1) (−5.26%, −16.59%, and −5.77% compared to NF, respectively). The spectral bands Near-Infrared (NIR) and Red Edge 7 were able to identify the frost damage (−16.55 and −16.67% compared to NF, respectively). Moreover, CARI, EVI, MTVI1, NIR, Red Edge 7, the Normalized Difference Vegetation Index (NDVI) and the Modified Simple Ratio (MSR) provided precise information on the full recovery time (+17.7%, +22.42%, +29.67%, +5.89%, +5.91%, +16.48%, and +8.73% compared to NF, respectively) approximately 40 days after the frost event. The must analysis showed that total acidity was higher (+5.98%), and pH was lower (−2.47%) in F compared to NF. These results suggest that medium-resolution multispectral data from Sentinel-2 constellation may represent a cost-effective tool for frost damage assessment and recovery management. Full article

(This article belongs to the Special Issue Remote Sensing in Viticulture)

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19 pages, 5444 KiB

Open AccessArticle

Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications

by Francisco-Javier Mesas-Carrascosa, Ana I. de Castro, Jorge Torres-Sánchez, Paula Triviño-Tarradas, Francisco M. Jiménez-Brenes, Alfonso García-Ferrer and Francisca López-Granados

Remote Sens. 2020, 12(2), 317; https://doi.org/10.3390/rs12020317 - 18 Jan 2020

Cited by 36 | Viewed by 6784

Abstract

Remote sensing applied in the digital transformation of agriculture and, more particularly, in precision viticulture offers methods to map field spatial variability to support site-specific management strategies; these can be based on crop canopy characteristics such as the row height or vegetation cover fraction, requiring accurate three-dimensional (3D) information. To derive canopy information, a set of dense 3D point clouds was generated using photogrammetric techniques on images acquired by an RGB sensor onboard an unmanned aerial vehicle (UAV) in two testing vineyards on two different dates. In addition to the geometry, each point also stores information from the RGB color model, which was used to discriminate between vegetation and bare soil. To the best of our knowledge, the new methodology herein presented consisting of linking point clouds with their spectral information had not previously been applied to automatically estimate vine height. Therefore, the novelty of this work is based on the application of color vegetation indices in point clouds for the automatic detection and classification of points representing vegetation and the later ability to determine the height of vines using as a reference the heights of the points classified as soil. Results from on-ground measurements of the heights of individual grapevines were compared with the estimated heights from the UAV point cloud, showing high determination coefficients (R² > 0.87) and low root-mean-square error (0.070 m). This methodology offers new capabilities for the use of RGB sensors onboard UAV platforms as a tool for precision viticulture and digitizing applications. Full article

(This article belongs to the Special Issue Remote Sensing in Viticulture)

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23 pages, 10176 KiB

Open AccessFeature PaperArticle

Mapping Cynodon Dactylon Infesting Cover Crops with an Automatic Decision Tree-OBIA Procedure and UAV Imagery for Precision Viticulture

by Ana I. de Castro, José M. Peña, Jorge Torres-Sánchez, Francisco M. Jiménez-Brenes, Francisco Valencia-Gredilla, Jordi Recasens and Francisca López-Granados

Remote Sens. 2020, 12(1), 56; https://doi.org/10.3390/rs12010056 - 21 Dec 2019

Cited by 29 | Viewed by 5271

Abstract

The establishment and management of cover crops are common practices widely used in irrigated viticulture around the world, as they bring great benefits not only to protect and improve the soil, but also to control vine vigor and improve the yield quality, among others. However, these benefits are often reduced when cover crops are infested by Cynodon dactylon (bermudagrass), which impacts crop production due to its competition for water and nutrients and causes important economic losses for the winegrowers. Therefore, the discrimination of Cynodon dactylon in cover crops would enable site-specific control to be applied and thus drastically mitigate damage to the vineyard. In this context, this research proposes a novel, automatic and robust image analysis algorithm for the quick and accurate mapping of Cynodon dactylon growing in vineyard cover crops. The algorithm was developed using aerial images taken with an Unmanned Aerial Vehicle (UAV) and combined decision tree (DT) and object-based image analysis (OBIA) approaches. The relevance of this work consisted in dealing with the constraint caused by the spectral similarity of these complex scenarios formed by vines, cover crops, Cynodon dactylon, and bare soil. The incorporation of height information from the Digital Surface Model and several features selected by machine learning tools in the DT-OBIA algorithm solved this spectral similarity limitation and allowed the precise design of Cynodon dactylon maps. Another contribution of this work is the short time needed to apply the full process from UAV flights to image analysis, which can enable useful maps to be created on demand (within two days of the farmer´s request) and is thus timely for controlling Cynodon dactylon in the herbicide application window. Therefore, this combination of UAV imagery and a DT-OBIA algorithm would allow winegrowers to apply site-specific control of Cynodon dactylon and maintain cover crop-based management systems and their consequent benefits in the vineyards, and also comply with the European legal framework for the sustainable use of agricultural inputs and implementation of integrated crop management. Full article

(This article belongs to the Special Issue Remote Sensing in Viticulture)

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19 pages, 3276 KiB

Open AccessArticle

Assessing the Feasibility of Using Sentinel-2 Imagery to Quantify the Impact of Heatwaves on Irrigated Vineyards

by Alessia Cogato, Vinay Pagay, Francesco Marinello, Franco Meggio, Peter Grace and Massimiliano De Antoni Migliorati

Remote Sens. 2019, 11(23), 2869; https://doi.org/10.3390/rs11232869 - 2 Dec 2019

Cited by 36 | Viewed by 5988

Abstract

Heatwaves are common in many viticultural regions of Australia. We evaluated the potential of satellite-based remote sensing to detect the effects of high temperatures on grapevines in a South Australian vineyard over the 2016–2017 and 2017–2018 seasons. The study involved: (i) comparing the normalized difference vegetation index (NDVI) from medium- and high-resolution satellite images; (ii) determining correlations between environmental conditions and vegetation indices (Vis); and (iii) identifying VIs that best indicate heatwave effects. Pearson’s correlation and Bland–Altman testing showed a significant agreement between the NDVI of high- and medium-resolution imagery (R = 0.74, estimated difference −0.093). The band and the VI most sensitive to changes in environmental conditions were 705 nm and enhanced vegetation index (EVI), both of which correlated with relative humidity (R = 0.65 and R = 0.62, respectively). Conversely, SWIR (short wave infrared, 1610 nm) exhibited a negative correlation with growing degree days (R = −0.64). The analysis of heat stress showed that green and red edge bands—the chlorophyll absorption ratio index (CARI) and transformed chlorophyll absorption ratio index (TCARI)—were negatively correlated with thermal environmental parameters such as air and soil temperature and growing degree days (GDDs). The red and red edge bands—the soil-adjusted vegetation index (SAVI) and CARI2—were correlated with relative humidity. To the best of our knowledge, this is the first study demonstrating the effectiveness of using medium-resolution imagery for the detection of heat stress on grapevines in irrigated vineyards. Full article

(This article belongs to the Special Issue Remote Sensing in Viticulture)

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15 pages, 3097 KiB

Open AccessArticle

Sentinel-2 Validation for Spatial Variability Assessment in Overhead Trellis System Viticulture Versus UAV and Agronomic Data

by Salvatore Filippo Di Gennaro, Riccardo Dainelli, Alberto Palliotti, Piero Toscano and Alessandro Matese

Remote Sens. 2019, 11(21), 2573; https://doi.org/10.3390/rs11212573 - 2 Nov 2019

Cited by 51 | Viewed by 6967

Abstract

Several remote sensing technologies have been tested in precision viticulture to characterize vineyard spatial variability, from traditional aircraft and satellite platforms to recent unmanned aerial vehicles (UAVs). Imagery processing is still a challenge due to the traditional row-based architecture, where the inter-row soil provides a high to full presence of mixed pixels. In this case, UAV images combined with filtering techniques represent the solution to analyze pure canopy pixels and were used to benchmark the effectiveness of Sentinel-2 (S2) performance in overhead training systems. At harvest time, UAV filtered and unfiltered images and ground sampling data were used to validate the correlation between the S2 normalized difference vegetation indices (NDVIs) with vegetative and productive parameters in two vineyards (V1 and V2). Regarding the UAV vs. S2 NDVI comparison, in both vineyards, satellite data showed a high correlation both with UAV unfiltered and filtered images (V1 R² = 0.80 and V2 R² = 0.60 mean values). Ground data and remote sensing platform NDVIs correlation were strong for yield and biomass in both vineyards (R² from 0.60 to 0.95). These results demonstrate the effectiveness of spatial resolution provided by S2 on overhead trellis system viticulture, promoting precision viticulture also within areas that are currently managed without the support of innovative technologies. Full article

(This article belongs to the Special Issue Remote Sensing in Viticulture)

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23 pages, 13097 KiB

Open AccessArticle

An Empirical Assessment of Angular Dependency for RedEdge-M in Sloped Terrain Viticulture

by Chizhang Gong, Henning Buddenbaum, Rebecca Retzlaff and Thomas Udelhoven

Remote Sens. 2019, 11(21), 2561; https://doi.org/10.3390/rs11212561 - 31 Oct 2019

Cited by 9 | Viewed by 4023

Abstract

For grape canopy pixels captured by an unmanned aerial vehicle (UAV) tilt-mounted RedEdge-M multispectral sensor in a sloped vineyard, an in situ Walthall model can be established with purely image-based methods. This was derived from RedEdge-M directional reflectance and a vineyard 3D surface model generated from the same imagery. The model was used to correct the angular effects in the reflectance images to form normalized difference vegetation index (NDVI) orthomosaics of different view angles. The results showed that the effect could be corrected to a certain scope, but not completely. There are three drawbacks that might restrict a successful angular model construction and correction: (1) the observable micro shadow variation on the canopy enabled by the high resolution; (2) the complexity of vine canopies that causes an inconsistency between reflectance and canopy geometry, including effects such as micro shadows and near-infrared (NIR) additive effects; and (3) the resolution limit of a 3D model to represent the accurate real-world optical geometry. The conclusion is that grape canopies might be too inhomogeneous for the tested method to perform the angular correction in high quality. Full article

(This article belongs to the Special Issue Remote Sensing in Viticulture)

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23 pages, 2764 KiB

Open AccessArticle

Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality

by Matthew Maimaitiyiming, Vasit Sagan, Paheding Sidike and Misha T. Kwasniewski

Remote Sens. 2019, 11(7), 740; https://doi.org/10.3390/rs11070740 - 27 Mar 2019

Cited by 46 | Viewed by 9346

Abstract

Reliable assessment of grapevine productivity is a destructive and time-consuming process. In addition, the mixed effects of grapevine water status and scion-rootstock interactions on grapevine productivity are not always linear. Despite the potential opportunity of applying remote sensing and machine learning techniques to predict plant traits, there are still limitations to previously studied techniques for vine productivity due to the complexity of the system not being adequately modeled. During the 2014 and 2015 growing seasons, hyperspectral reflectance spectra were collected using a handheld spectroradiometer in a vineyard designed to investigate the effects of irrigation level (0%, 50%, and 100%) and rootstocks (1103 Paulsen, 3309 Couderc, SO4 and Chambourcin) on vine productivity. To assess vine productivity, it is necessary to measure factors related to fruit ripeness and not just yield, as an over cropped vine may produce high-yield but poor-quality fruit. Therefore, yield, Total Soluble Solids (TSS), Titratable Acidity (TA) and the ratio TSS/TA (maturation index, IMAD) were measured. A total of 20 vegetation indices were calculated from hyperspectral data and used as input for predictive model calibration. Prediction performance of linear/nonlinear multiple regression methods and Weighted Regularized Extreme Learning Machine (WRELM) were compared with our newly developed WRELM-TanhRe. The developed method is based on two activation functions: hyperbolic tangent (Tanh) and rectified linear unit (ReLU). The results revealed that WRELM and WRELM-TanhRe outperformed the widely used multiple regression methods when model performance was tested with an independent validation dataset. WRELM-TanhRe produced the highest prediction accuracy for all the berry yield and quality parameters (R² of 0.522–0.682 and RMSE of 2–15%), except for TA, which was predicted best with WRELM (R² of 0.545 and RMSE of 6%). The results demonstrate the value of combining hyperspectral remote sensing and machine learning methods for improving of berry yield and quality prediction. Full article

(This article belongs to the Special Issue Remote Sensing in Viticulture)

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11 pages, 2098 KiB

Open AccessTechnical Note

A New, Satellite NDVI-Based Sampling Protocol for Grape Maturation Monitoring

by James M. Meyers, Nick Dokoozlian, Casey Ryan, Cella Bioni and Justine E. Vanden Heuvel

Remote Sens. 2020, 12(7), 1159; https://doi.org/10.3390/rs12071159 - 4 Apr 2020

Cited by 26 | Viewed by 6078

Abstract

Vineyards are sampled on multiple occasions during the growing season for a range of purposes, particularly to assess fruit maturation. The objective of this work was to determine if satellite normalized difference vegetation index (NDVI) vineyard images could be used to compute optimal spatially explicit sampling protocols for determining fruit maturation and quality, and minimize the number of locations physically sampled in a vineyard. An algorithm was designed to process Landsat images to locate three consecutive pixels that best represent the three quantile means representing the left tail, center, and right tail of the NDVI pixel population of a vineyard block. This new method (NDVI3) was compared to a commonly used method (CM8) and random sampling (R20) in 13 and 16 vineyard blocks in 2016 and 2017, respectively, in the Central Valley of California. Both NDVI3 and CM8 were highly correlated with R20 in pairwise comparisons of soluble sugars, pH, titratable acidity, and total anthocyanins. Kolmogorov-Smirnov tests indicated that NDVI pixels sampled via the NDVI3 method generally better represented the block population than pixels selected by CM8 or R20. Analysis of 24 blocks over a 3-year period indicated that sampling solutions were temporally stable. Full article

(This article belongs to the Special Issue Remote Sensing in Viticulture)

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