Analysis of crop water stress index (CWSI) for estimating stem water potential in grapevines: comparison between natural reference and baseline approaches

Poblete-Echeverría, Carlos; Espinace, D.; Sepulveda-Reyes, D.; Zúñiga, María del Carmén; Sánchez, Marcela

doi:10.17660/actahortic.2017.1150.27

Cited by 28 publications

(24 citation statements)

References 6 publications

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“…It is undeniable that the factor that has exponentially encouraged the spread of UAV application in agriculture is the continuous advance in sensor technologies, providing higher resolution, lower weight and dimensions, and cost reduction [23,[25][26][27][28]. Several authors describe a wide range of UAV applications for PV purposes: vigor and biomass [29][30][31][32][33][34], yield and quality monitoring [35,36], water stress [37][38][39][40][41], canopy management [42], diseases [43][44][45][46], weeds [47][48][49], and missing plants [50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Several studies have successfully utilized the CWSI approach to quantify water stress [8,9]. More recently, the CWSI was applied in vineyards to map vine water stress and demonstrated that CWSI was related to stem water potential and yield parameters in grapevines [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Upscaling Evapotranspiration with Parsimonious Models in a North Carolina Vineyard

et al. 2019

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Water stress can positively or negatively impact grape yield and yield quality, and there is a need for wine growers to accurately regulate water use. In a four-year study (2010–2013), energy balance fluxes were measured with an eddy-covariance (EC) system in a North Carolina vineyard (Vitis vinifera cv. Chardonnay), and evapotranspiration (ET) and the Crop Water Stress Index (CWSI) calculated. A multiple linear regression model was developed to upscale ET using air temperature (Ta), vapor pressure deficit (VPD), and Landsat-derived Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI). Daily ET reached values of up to 7.7 mm day−1, and the annual ET was 752 ± 59 mm, as measured with the EC system. The grapevine CWSI was between 0.53–0.85, which indicated moderate water stress levels. Median vineyard EVI was between 0.22 and 0.72, and the EVI range (max–min) within the vineyard was 0.18. The empirical models explained 75%–84% of the variation in ET, and all parameters had a positive linear relationship to ET. The Root Mean Square Error (RMSE) was 0.52–0.62 mm. This study presents easily applicable approaches to analyzing water dynamics and ET. This may help wine growers to cost-effectively quantify water use in vineyards.

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“…The crop water stress index (CWSI) was developed as a normalized index to quantify stress and reduce the disturbance of the environmental parameters affecting the relationship between water stress and canopy temperature [11,15]. It has been widely used as a water status indicator [19,20]. It provides the crop stress level based on canopy-air temperature differences.…”

Section: Crop Water Stress Index Calculationmentioning

confidence: 99%

“…The variability in environmental conditions, plant morphology, and meteorological factors could affect leaf temperature, and thus its precision in leaf transpiration and stomatal conductance (g s ) indicators [17]. Many indexes have been developed to normalize leaf temperature by taking previous reference measurements (reducing the disturbance of the environmental factor over canopy temperature) [18,19], with the crop water stress index (CWSI) [15] being one of the most widely used for a variety of crops [17,19,20]. The calculation of the CWSI relies on two thresholds: the non-water-stressed baseline (T wet ), which represents a fully transpiring crop, and the maximum stressed baseline (T dry ), which corresponds to a non-transpiring crop [17].…”

Section: Introductionmentioning

confidence: 99%

A New Low-Cost Device Based on Thermal Infrared Sensors for Olive Tree Canopy Temperature Measurement and Water Status Monitoring

et al. 2020

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In recent years, many olive orchards, which are a major crop in the Mediterranean basin, have been converted into intensive or super high-density hedgerow systems. This configuration is more efficient in terms of yield per hectare, but at the same time the water requirements are higher than in traditional grove arrangements. Moreover, irrigation regulations have a high environmental (through water use optimization) impact and influence on crop quality and yield. The mapping of (spatio-temporal) variability with conventional water stress assessment methods is impractical due to time and labor constraints, which often involve staff training. To address this problem, this work presents the development of a new low-cost device based on a thermal infrared (IR) sensor for the measurement of olive tree canopy temperature and monitoring of water status. The performance of the developed device was compared to a commercial thermal camera. Furthermore, the proposed device was evaluated in a commercially managed olive orchard, where two different irrigation treatments were established: a full irrigation treatment (FI) and a regulated deficit irrigation (RDC), aimed at covering 100% and 50% of crop evapotranspiration (ETc), respectively. Predawn leaf water potential (ΨPD) and stomatal conductance (gs), two widely accepted indicators for crop water status, were regressed to the measured canopy temperature. The results were promising, reaching a coefficient of determination R2 ≥ 0.80. On the other hand, the crop water stress index (CWSI) was also calculated, resulting in a coefficient of determination R2 ≥ 0.79. The outcomes provided by the developed device support its suitability for fast, low-cost, and reliable estimation of an olive orchard’s water status, even suppressing the need for supervised acquisition of reference temperatures. The newly developed device can be used for water management, reducing water usage, and for overall improvements to olive orchard management.

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Analysis of crop water stress index (CWSI) for estimating stem water potential in grapevines: comparison between natural reference and baseline approaches

Cited by 28 publications

References 6 publications

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

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

Upscaling Evapotranspiration with Parsimonious Models in a North Carolina Vineyard

A New Low-Cost Device Based on Thermal Infrared Sensors for Olive Tree Canopy Temperature Measurement and Water Status Monitoring

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