Assessing canopy PRI for water stress detection with diurnal airborne imagery

Suárez, Lola; Zarco‐Tejada, Pablo J.; Sepulcre-Cantó, G.; Pérez-Priego, Óscar; Miller, John R.; Jiménez-Muñoz, Juan C.; Sobrino, José A.

doi:10.1016/j.rse.2007.05.009

Cited by 247 publications

(180 citation statements)

References 51 publications

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“…Additionally, factors like leaf age can also influence the spectral response thus impacting these VIs responses [70]. Contrarily, PRI is a stress/physiology-oriented hyperspectral VI [49] designed for measuring subtle decreases of reflectance around 531 nm due to changes in the xanthophyll cycle pigment activity resulting from stress conditions, including water stress conditions [32,34,68].…”

Section: Discussionmentioning

confidence: 99%

“…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: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Pôças

Rodrigues²,

Gonçalves

et al. 2015

Remote Sensing

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“…The PRI is chosen as it is based on subtle absorption features of bio-chemicals (xanthophyll, carotenoids and chlorophylls), and is related to photosynthetic efficiency, light use efficiency as well as water stress [32,33]. The PRI is frequently used on leaf, canopy and ecosystem level, but the meaningfulness when applied at satellite scale is critically discussed [32], and high-spectral resolution instruments such as EnMAP have the potential to overcome some of the problems.…”

Section: Further Influence On Thematic Productsmentioning

confidence: 99%

Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices

et al. 2015

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As part of the EnMAP preparation activities this study aims at estimating the uncertainty in the EnMAP L2A ground reflectance product using the simulated scene of Barrax, Spain. This dataset is generated using the EnMAP End-to-End Simulation tool, providing a realistic scene for a well-known test area. Focus is set on the influence of the expected radiometric calibration stability and the spectral calibration stability. Using a Monte-Carlo approach for uncertainty analysis, a larger number of realisations for the radiometric and spectral calibration are generated. Next, the ATCOR atmospheric correction is conducted for the test scene for each realisation. The subsequent analysis of the generated ground reflectance products is carried out independently for the radiometric and the spectral case. Findings are that the uncertainty in the L2A product is wavelength-dependent, and, due to the coupling with the estimation of atmospheric parameters, also spatially variable over the scene. To further illustrate the impact on subsequent data analysis, the influence on two vegetation indices is briefly analysed. Results show that the radiometric and spectral stability both have a high impact on the uncertainty of the narrow-band Photochemical Reflectance Index (PRI), and also the broad-band Normalized Difference Vegetation Index (NDVI) is affected.

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“…This is probably connected with the fact that, apart from the contents of carotenoid pigments and light use efficiency, it is also influenced by several other factors, such as reflection from the soil background (as stated above -a highly significant factor in the case of the crops analysed in this study); proportions between direct and diffuse incoming radiation; structure of the canopy; the angle of solar rays; and sun-vegetation cover-sensor geometry (relative to the Sun as well as elements of the vegetation cover) (Grace et al, 2007;Hall et al, 2008;Louis et al, 2005;Suarez et al, 2008). Due to the measurement errors caused by these factors, although PRI works very well as an indicator of LUE at the leaf level (Filela et al, 1996;Gamon and Surfus, 1999;Penuelas et al, 1995Penuelas et al, , 1997, the accuracy of models based on this index is more limited if they are applied at the canopy level.…”

Section: Discussionmentioning

confidence: 88%

Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

Uździcka¹,

Stróżecki²,

Urbaniak³

et al. 2017

International Agrophysics

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'Assessment of a seasonal and spatial variation of plant biophysical and spectral indices in the context of CO 2 , CH 4 , H 2 O gas exchange between wetland, forest and arable ecosystems and the atmosphere', 2010-2013.A b s t r a c t. The aim of this paper is to demonstrate that spectral vegetation indices are good indicators of parameters describing the intensity of CO 2 exchange between crops and the atmosphere. Measurements were conducted over 2011-2013 on plots of an experimental arable station on winter wheat, winter rye, spring barley, and potatoes. CO 2 fluxes were measured using the dynamic closed chamber system, while spectral vegetation indices were determined using SKYE multispectral sensors. Based on spectral data collected in 2011 and 2013, various models to estimate net ecosystem productivity and gross ecosystem productivity were developed. These models were then verified based on data collected in 2012. The R 2 for the best model based on spectral data ranged from 0.71 to 0.83 and from 0.78 to 0.92, for net ecosystem productivity and gross ecosystem productivity, respectively. Such high R 2 values indicate the utility of spectral vegetation indices in estimating CO 2 fluxes of crops. The effects of the soil background turned out to be an important factor decreasing the accuracy of the tested models.

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Assessing canopy PRI for water stress detection with diurnal airborne imagery

Cited by 247 publications

References 51 publications

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Predicting Grapevine Water Status Based on Hyperspectral Reflectance Vegetation Indices

Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices

Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

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