The Photochemical Reflectance Index (PRI) as a water-stress index

Thénot, F.; Méthy, M.; Winkel, Thierry

doi:10.1080/01431160210163100

Cited by 126 publications

(60 citation statements)

References 7 publications

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“…Rodríguez-Pérez et al [4] obtained good results in the estimation of water potential at canopy level using a RGRI adjusted for specific wavelengths. Differently, other authors preferred the PRI computed with the standard formulation as a good indicator of crop water stress [34,35,72]. However, issues related with viewing and illumination geometry effects, changes due to wilting or in leaf pigments content, as well as canopy structure, can affect the performance of PRI as a water stress indicator [22,34,73].…”

Section: Discussionmentioning

confidence: 99%

“…The first process is a proxy for water stress detection, while the second is associated with stomatal conductance under water stress conditions, as discussed by Zarco-Tejada et al [22]. Thus, although there is no water absorption in the visible spectral region, several VIs based on this spectral domain have shown high correlation with crop water status and are used as its proxy due to the physiological dependence of leaf pigments on water [4,22,30,34,35].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Pôças

Rodrigues²,

Gonçalves

et al. 2015

Remote Sensing

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Several vegetation indices (VI) derived from handheld spectroradiometer reflectance data in the visible spectral region were tested for modelling grapevine water status estimated by the predawn leaf water potential (Ψpd). The experimental trial was carried out in a vineyard in Douro wine region, Portugal. A statistical approach was used to evaluate which VI and which combination of wavelengths per VI allows the best correlation between VIs and Ψpd. A linear regression was defined using a parameterization dataset. The correlation analysis between Ψpd and the VIs computed with the standard formulation showed relatively poor results, with values for squared Pearson correlation coefficient (r 2 ) smaller than 0.67. However, the results of r 2 highly improved for all VIs when computed with the selected best combination of wavelengths (optimal VIs). The optimal Visible Atmospherically Resistant Index (VARI) and Normalized Difference Greenness Vegetation Index (NDGI) showed the higher r 2 and stability index results. The equations obtained through the regression between measured Ψpd (Ψpd_obs) and optimal VARI and between Ψpd_obs and optimal NDGI when using the parameterization dataset were adopted for predicting Ψpd using a testing dataset. The comparison of Ψpd_obs with Ψpd predicted based on VARI led to R 2 = 0.79 and a regression coefficient b = 0.96. Similar R 2 was achieved for the prediction based on NDGI, but b was smaller (b = 0.93). Results obtained allow the future use of optimal VARI and NDGI for estimating Ψpd, supporting vineyards irrigation management.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Pôças

Rodrigues²,

Gonçalves

et al. 2015

Remote Sensing

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“…The PRI index (Equation (6)) is sensitive to the de-epoxidation state of the xanthophyll cycle pigments and to the efficiency of photosynthesis [39]. Several studies demonstrated that the sensitivity of PRI to the de-epoxidation state is affected by crop water stress conditions [40].…”

Section: Optical and Thermal Indices To Assess The Crop Water Statusmentioning

confidence: 99%

Assessing the Reliability of Thermal and Optical Imaging Techniques for Detecting Crop Water Status under Different Nitrogen Levels

et al. 2017

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Efficient management of irrigation water is fundamental in agriculture to reduce the environmental impacts and to increase the sustainability of crop production. The availability of adequate tools and methodologies to easily identify the crop water status in operating conditions is therefore crucial. This work aimed to assess the reliability of indices derived from imaging techniques-thermal indices (I g (stomatal conductance index) and CWSI (Crop Water Stress Index)) and optical indices (NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index))-as operational tools to detect the crop water status, regardless the eventual presence of nitrogen stress. In particular, two separate experiments were carried out in a greenhouse, on two spinach varieties (Verdi F1 and SV2157VB), with different microclimatic conditions and under different levels of water and nitrogen application. Statistical analysis based on ANOVA test was carried out to assess the independence of thermal and optical indices from the crop nitrogen status. These imaging indices were successively compared through correlation analysis with reference destructive and non-destructive measurements of crop water status (stomatal conductance, chlorophyll a fluorescence, and leaf and soil water content), and linear regression models of thermal and optical indices versus reference measurements were calibrated. All models were significant (Fisher p-value lower than 0.05), and the highest R 2 values (greater than 0.6) were found for the regression models between CWSI and the soil water content, NDVI and the leaf water content, and PRI and the stomatal conductance. Further analysis showed that imaging indices acquired by thermal cameras (especially CWSI) can be used as operational tools to detect the crop water status, since no dependence on plant nitrogen conditions was observed, even when the soil water depletion was very limited. Our results confirmed that imaging indices such as CWSI, NDVI and PRI can be used as operational tools to predict soil water status and to detect drought stress under different soil nitrogen conditions.

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“…The PRI has been used as a pre-visual indicator of water stress at the leaf scale (Thenot et al, 2010) and canopy scale (Dobrowski et al, 2005). The PRI is calculated to obtain the average per plant of that index.…”

Section: Primentioning

confidence: 99%

Configuration and Specifications of an Unmanned Aerial Vehicle for Precision Agriculture

Erena¹,

Montesinos

Portillo³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Unmanned Aerial Vehicles (UAVs) with multispectral sensors are increasingly attractive in geosciences for data capture and map updating at high spatial and temporal resolutions. These autonomously-flying systems can be equipped with different sensors, such as a six-band multispectral camera (Tetracam mini-MCA-6), GPS Ublox M8N, and MEMS gyroscopes, and miniaturized sensor systems for navigation, positioning, and mapping purposes. These systems can be used for data collection in precision viticulture. In this study, the efficiency of a light UAV system for data collection, processing, and map updating in small areas is evaluated, generating correlations between classification maps derived from remote sensing and production maps. Based on the comparison of the indices derived from UAVs incorporating infrared sensors with those obtained by satellites (Sentinel 2A and Landsat 8), UAVs show promise for the characterization of vineyard plots with high spatial variability, despite the low vegetative coverage of these crops. Consequently, a procedure for zoning map production based on UAV/UV images could provide important information for farmers.

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The Photochemical Reflectance Index (PRI) as a water-stress index

Cited by 126 publications

References 7 publications

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Assessing the Reliability of Thermal and Optical Imaging Techniques for Detecting Crop Water Status under Different Nitrogen Levels

Configuration and Specifications of an Unmanned Aerial Vehicle for Precision Agriculture

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