Water Stress Impacts on Grapevines (Vitis vinifera L.) in Hot Environments: Physiological and Spectral Responses

Cogato, Alessia; Jewan, Shaikh Yassir Yousouf; Wu, L. H.; Marinello, Francesco; Meggio, Franco; Sivilotti, Paolo; Sozzi, Marco; Pagay, Vinay

doi:10.3390/agronomy12081819

Cited by 8 publications

(3 citation statements)

References 57 publications

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“…The stomatal conductance, chlorophyll content, and leaf transpiration rate are also influenced by water stress. The accumulated effect of these physiological parameters is evidenced in the leaf spectral response tracked by RS technologies [15]. Hence, in this study, the canopy spectral response was related to the accumulated effect of water stress through the S ψ .…”

Section: Introductionmentioning

confidence: 73%

“…Unlike most of the works found in the bibliography that estimated water status using punctual measures such as stem or leaf water potential, in our study the S ψ was used. This variable enabled the analysis of the effect of the cumulative water deficit duration and intensity throughout the growing season considering midday Ψ stem measurements over a chosen period of time [35], coinciding with the fact that the accumulated effects of water stress are shown in plant tissues affecting the spectral response of leaves [15,55]. Some studies have employed this parameter, as an index of water stress, and it was correlated with volume growth, pre-dawn xylem water potential, soil volumetric water content [11], productivity [56], and mortality [57].…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning-Based Processing of Multispectral and RGB UAV Imagery for the Multitemporal Monitoring of Vineyard Water Status

et al. 2022

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The development of unmanned aerial vehicles (UAVs) and light sensors has required new approaches for high-resolution remote sensing applications. High spatial and temporal resolution spectral data acquired by multispectral and conventional cameras (or red, green, blue (RGB) sensors) onboard UAVs can be useful for plant water status determination and, as a consequence, for irrigation management. A study in a vineyard located in south-eastern Spain was carried out during the 2018, 2019, and 2020 seasons to assess the potential uses of these techniques. Different water qualities and irrigation application start throughout the growth cycle were imposed. Flights with RGB and multispectral cameras mounted on a UAV were performed throughout the growth cycle, and orthoimages were generated. These orthoimages were segmented to include only vegetation and calculate the green canopy cover (GCC). The stem water potential was measured, and the water stress integral (Sψ) was obtained during each irrigation season. Multiple linear regression techniques and artificial neural networks (ANNs) models with multispectral and RGB bands, as well as GCC, as inputs, were trained and tested to simulate the Sψ. The results showed that the information in the visible domain was highly related to the Sψ in the 2018 season. For all the other years and combinations of years, multispectral ANNs performed slightly better. Differences in the spatial resolution and radiometric quality of the RGB and multispectral geomatic products explain the good model performances with each type of data. Additionally, RGB cameras cost less and are easier to use than multispectral cameras, and RGB images are simpler to process than multispectral images. Therefore, RGB sensors are a good option for use in predicting entire vineyard water status. In any case, field punctual measurements are still required to generate a general model to estimate the water status in any season and vineyard.

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Section: Introductionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Processing of Multispectral and RGB UAV Imagery for the Multitemporal Monitoring of Vineyard Water Status

et al. 2022

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“…The CWSI method has been widely used in irrigated crops, and is considered a standard method. Many studies have been carried out in different crops, including nectarines [ 17 , 19 ], soybean [ 48 ], cotton [ 49 ], tomato [ 50 ], olive trees [ 51 ] and vines [ 52 , 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Using Aerial Thermal Imagery to Evaluate Water Status in Vitis vinifera cv. Loureiro

Araújo-Paredes

Portela

Mendes

et al. 2022

Sensors

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The crop water stress index (CWSI) is a widely used analytical tool based on portable thermography. This method can be useful in replacing the traditional stem water potential method obtained with a Scholander chamber (PMS Model 600) because the latter is not feasible for large-scale studies due to the time involved and the fact that it is invasive and can cause damage to the plant. The present work had three objectives: (i) to understand if CWSI estimated using an aerial sensor can estimate the water status of the plant; (ii) to compare CWSI from aerial-thermographic and portable thermal cameras with stem water potential; (iii) to estimate the capacity of an unmanned aerial vehicle (UAV) to calculate and spatialize CWSI. Monitoring of CWSI (CWSIP) using a portable device was performed directly in the canopy, by measuring reference temperatures (Tdry, Twet, and canopy temperature (Tc)). Aerial CWSI calculation was performed using two models: (i) a simplified CWSI model (CWSIS), where the Tdry and Twet were estimated as the average of 1% of the extreme temperature, and (ii) an air temperature model (CWSITair) where air temperatures (Tair + 7 °C) were recorded as Tdry and in the Twet, considering the average of the lowest 33% of histogram values. In these two models, the Tc value corresponded to the temperature value in each pixel of the aerial thermal image. The results show that it was possible to estimate CWSI by calculating canopy temperatures and spatializing CWSI using aerial thermography. Of the two models, it was found that for CWSITair, CWSIS (R2 = 0.55) evaluated crop water stress better than stem water potential. The CWSIS had good correlation compared with the portable sensor (R2 = 0.58), and its application in field measurements is possible.

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Evaluation of canopy fraction-based vegetation indices, derived from multispectral UAV imagery, to map water status variability in a commercial vineyard

Berry,

Vivier,

Poblete-Echeverría

2024

Irrig Sci

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Water stress is a major factor affecting grapevine yield and quality. Standard methods for measuring water stress, such as midday stem water potential (ΨSWP), are laborious and time-consuming for intra-block variability mapping. In this study, we investigate water status variability within a 2.42-ha commercial Cabernet Sauvignon block with a standard vertical trellis system, using remote sensing (RS) tools, specifically canopy fraction-based vegetation indices (VIs) derived from multispectral unmanned aerial vehicle (UAV) imagery, as well as standard reference methods to evaluate soil and plant water status. A total of 31 target vines were monitored for ΨSWP during the whole growing season. The highest variability was at véraison when the highest atmospheric demand occurred. The ΨSWP variability present in the block was contrasted with soil water content (SWC) measurements, showing similar patterns. With spatial and temporal water stress variability confirmed for the block, the relationship between the ΨSWP measured in the field and fraction-based VIs obtained from multispectral UAV data was analysed. Four UAV flights were obtained, and five different VIs were evaluated per target vine across the vineyard. The VI correlation to ΨSWP was further evaluated by comparing VI obtained from canopy fraction (VIcanopy) versus the mean (VImean). It was found that using canopy fraction-based VIs did not significantly improve the correlation with ΨSWP (NDVIcanopyr = 0.57 and NDVImeanr = 0.53), however fractional cover (fcover) did seem to show a similar trend to plant water stress with decreasing canopy size corresponding with water stress classes. A subset of 14 target vines were further evaluated to evaluate if additional parameters (maximum temperature, relative humidity (RH), vapour pressure deficit, SWC and fractional cover) could serve as potential water stress indicators for future mapping. Results showed that the integration of NDVIcanopy and NDREmean with additional information could be used as an indicator for mapping water stress variability within a block.

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Water Stress Impacts on Grapevines (Vitis vinifera L.) in Hot Environments: Physiological and Spectral Responses

Cited by 8 publications

References 57 publications

Machine Learning-Based Processing of Multispectral and RGB UAV Imagery for the Multitemporal Monitoring of Vineyard Water Status

Machine Learning-Based Processing of Multispectral and RGB UAV Imagery for the Multitemporal Monitoring of Vineyard Water Status

Using Aerial Thermal Imagery to Evaluate Water Status in Vitis vinifera cv. Loureiro

Evaluation of canopy fraction-based vegetation indices, derived from multispectral UAV imagery, to map water status variability in a commercial vineyard

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