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

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.</p

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

(c) The Author/sfals

Continuous Plant-Based and Remote Sensing for Determination of Fruit Tree Water Status

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment of plant water status is crucial for understanding plant physiological responses to water stress and optimizing water management practices in agriculture. Proximal and remote sensing techniques have emerged as powerful tools for the non-destructive, efficient, and spatially extensive monitoring of plant water status. This review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops. Several proximal sensing tools have proved useful in the continuous estimation of tree water status but have strong limitations in terms of spatial variability. On the contrary, remote sensing technologies, although less precise in terms of water status estimates, can easily cover from medium to large areas with drone or satellite images. The integration of proximal and remote sensing would definitely improve plant water status assessment, resulting in higher accuracy by integrating temporal and spatial scales. This paper consists of three parts: the first part covers current plant-based proximal sensing tools, the second part covers remote sensing techniques, and the third part includes an update on the on the combined use of the two methodologies

Decision-support system for water stress assessment and deficit irrigation management in wine grapes

A timely and appropriate level of water deficit is desirable in wine grape production to optimize fruit quality for winemaking. Regulated deficit irrigation (RDI) is an irrigation management strategy which applies less water than the full water requirement in some growing phases (e.g., from fruit set to veraison) to achieve a mild to moderate water stress. The implementation of RDI in wine grape production requires a combination of technical knowledge, accurate assessment and monitoring, and effective decision-making to achieve the desired balance between water stress and adequate water availability to the plants. This research aimed to develop and validate a comprehensive decision-support system for precision RDI management in vineyards. The proposed system was aimed to accurately assess the soil and plant water status through hyperspectral imaging (HSI). By doing so, the system sought to provide appropriate irrigation plans to achieve the desired soil water content threshold optimally.This research comprised three studies aimed at developing and validating several data-driven models as decision-support tools for precision deficit irrigation management in wine grapes. The first two studies focused on developing ground-based approaches to detect grapevine water status using HSI. The first study aimed to develop ground-based approaches for detecting soil and grapevine water status using HSI obtained in diffused lighting conditions. It was found that using spectral data obtained under diffused light resulted in improved model performance compared to using spectral data obtained under direct sunlight. This allowed for high-resolution sensing of grapevine water status by estimating leaf water potential and stomatal conductance. The second study fused HSI with 3D point clouds to address the effect of varied leaf orientations and enabled a Multiblock Partial Least Squares-based model to estimate leaf water potential with high accuracy. The third study aimed to develop a decision-support system for managing precision RDI in vineyards. The system consists of a soil moisture prediction model and an RDI scheduling model developed based on artificial neural networks. Validation tests showed that the soil moisture prediction model could predict the soil moisture in the following week with an R2 of 0.93 and RMSE of 0.86 %, and the RDI scheduling model could estimate the weekly irrigation water amount for maintaining a target soil moisture with an R2 of 0.94 and RMSE of 8.85 L per drip irrigation emitter. These studies contribute to developing efficient data-driven approaches to assess grapevine water status and optimize deficit irrigation plans for dynamic soil water threshold. The outcomes of this research could aid in achieving a balance between yield and fruit quality in wine grape production

Development of an effective and sustainable system to monitor fruit tree water status with precision devices

In recent years, sustainable water resource management has become a significant and debated issue in the agro-environmental context. Agriculture, as one of the major water-consuming sectors, plays a crucial role in water resource management. Indeed, global climate change is leading to a general temperature rising, with a consequent increase in drought phenomena. As a result, this leads to an overuse of water resources for irrigation. Therefore, understanding tree crop responses to water availability is becoming increasingly urgent, aiming to increase their water use efficiency.In this regard, one of the primary objectives of scientific research today is to optimize the use of water resources, minimizing inputs without compromising outputs. Water resource savings alone will lead to increased profits. In recent years, deficit irrigation methods, such as regulated deficit irrigation (RDI) and partial rootzone drying (PRD), have allowed farmers to save water while increasing profit by irrigating only during specific phenological stages or with reduced volumes on alternated sides of the rootzone, inducing the plant to activate physiological mechanisms (partial stomatal closure) useful for maximizing water use efficiency. However, real-time knowledge of fruit tree water requirements with consequent automation of precise irrigation applications would allow farmers to further increase water use efficiency. In this regard, last-generation sensors allow continuous data acquisition directly from the plant, greatly increasing the level of information. The combined use of plant-based proximal sensors can provide highly precise information about its water status. Furthermore, remote sensing technologies allow strategic use of proximal sensors, taking into account the spatial variability of the orchard.Based on these premises, the main objective of this dissertation was to develop an effective and sustainable system for monitoring the water status of fruit trees using proximal and remote sensing technologies. Firstly, the use of plant-based proximal and remote sensing technologies, as well as the combination of the two techniques, was reviewed. Subsequently, some techniques for assessing the water status of young olive trees placed in a growth chamber were tested. In the subsequent trial, fruit growth sensors (fruit gauges) were used to study responses of fruit growth from five different species (peach, mango, olive, orange, and loquat) to vapor pressure deficit. In the last trial, the combined use of proximal and remote sensing technologies was tested for estimating the water status of 'Calatina' olive trees under open field conditions

A review of current and potential applications of remote sensing to study the water status of horticultural crops

Published: 17 January 2020With increasingly advanced remote sensing systems, more accurate retrievals of crop water status are being made at the individual crop level to aid in precision irrigation. This paper summarises the use of remote sensing for the estimation of water status in horticultural crops. The remote measurements of the water potential, soil moisture, evapotranspiration, canopy 3D structure, and vigour for water status estimation are presented in this comprehensive review. These parameters directly or indirectly provide estimates of crop water status, which is critically important for irrigation management in farms. The review is organised into four main sections: (i) remote sensing platforms; (ii) the remote sensor suite; (iii) techniques adopted for horticultural applications and indicators of water status; and, (iv) case studies of the use of remote sensing in horticultural crops. Finally, the authors’ view is presented with regard to future prospects and research gaps in the estimation of the crop water status for precision irrigation.Deepak Gautam and Vinay Paga

Monitoring of emerging water stress situations by thermal and vegetation indices in different almond cultivars

In recent years, the area dedicated to modern irrigated almond plantations has increased significantly in Spain. However, the legal irrigation allocations are lower than the maximum water requirements of the crop in most cases. Therefore, almond growers are forced to implement regulated deficit irrigation strategies on their farms, applying water stress in certain resistant phenological periods and avoiding it in sensitive periods. Given the need to monitor the water status of the crop, especially in the most sensitive periods to water stress, the objective of this work was to evaluate the sensitivity of two UAV-based crop water status indicators to detect early water stress conditions in four almond cultivars. The field trial was conducted during 2020 in an experimental almond orchard, where two irrigation strategies were established: full irrigation (FI), which received 100% of irrigation requirements (IR), and regulated deficit irrigation (RDI), which received 70% of IR during the whole irrigation period except during the kernel-filling stage when received 40% IR. The UAV flights were performed on four selected dates of the irrigation season. The Crop Water Status Index (CWSI) and the Normalized Difference Vegetation Index (NDVI) were derived from thermal and multispectral images, respectively, and compared to classical water status indicators, i.e., stem water potential (Ψstem ), stomatal conductance (gs ), and photosynthetic rate (AN ). Of the four flights performed, three corresponded to mild water stress conditions and a single flight was performed under moderate water stress conditions. Under mild water stress, CWSI was not able to capture the differences between FI and RDI trees that were observed with Ψstem . Under moderate stress conditions, CWSI was sensitive to the water deficit reached in the trees and showed significant differences among both irrigation treatments. No differences were observed in the CWSI and NVDI response to water stress among cultivars. Although NDVI and CWSI were sensitive to water stress, the low signal intensity observed in NDVI makes this index less robust than CWSI to monitor crop water stress. It can be concluded that UAV-based CWSI measurements are reliable to monitor almond water status, although for early (mild) levels of water stress, Ψstem seems to be the preferred option.Junta de Andalucía AVA.AVA2019.05

Modern viticulture in southern Europe: Vulnerabilities and strategies for adaptation to water scarcity

Water is now considered the most important but vulnerable resource in the Mediterranean region. Nev ertheless, irrigation expanded fast in the region (e.g. South Portugal and Spain) to mitigate environmental stress and to guarantee stable grape yield and quality. Sustainable wine production depends on sustain able water use in the wine’s supply chain, from the vine to the bottle. Better understanding of grapevine stress physiology (e.g. water relations, temperature regulation, water use efficiency), more robust crop monitoring/phenotyping and implementation of best water management practices will help to mitigate climate effects and will enable significant water savings in the vineyard and winery. In this paper, we focused on the major vulnerabilities and opportunities of South European Mediterranean viticulture (e.g. in Portugal and Spain) and present a multi-level strategy (from plant to the consumer) to overcome region’s weaknesses and support strategies for adaptation to water scarcity, promote sustainable water use and minimize the environmental impact of the sector

Spectral Pattern of Paddy as Response to Drought Condition: An Experimental Study

Every single physical object has a different response to the electromagnetic wave emitted to it. The response is in the form of how it absorbs and reflects the energy in every range of wavelength. The absorption and reflection curve is known as a spectral pattern. The spectral pattern of each object can be used to determine the object. In agriculture, the spectral pattern of plants can be used to determine the health condition of the plant. Drought is one factor that can affect the health of the plant. By identifying the spectral pattern of the plants, the effect of drought on paddy can be identified. This experimental study tried to identify the spectral pattern of some varieties of paddy and different growth stages. A spectrophotometer with a wavelength range of 350-1052 nm was used. Four varieties of paddy were planted in a greenhouse and being treated in drought conditions from the stage of vegetative, generative, and reproductive. Based on the result, the spectral response from the generative phase of all varieties gave the most different pattern compared to the control. This result compromising the rapid detection of paddy fields affected by drought using optical remote sensing data. Especially for plants in the stage of generative