Assessing canopy PRI from airborne imagery to map water stress in maize

Rossini, Micol; Fava, Francesco; Cogliati, Sergio; Meroni, Michele; Marchesi, Andrea; Panigada, Cinzia; Giardino, Claudia; Busetto, Lorenzo; Migliavacca, Mirco; Amaducci, Stefano; Colombo, R.

doi:10.1016/j.isprsjprs.2013.10.002

Cited by 92 publications

(58 citation statements)

References 64 publications

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“…However, NDVI is expected to work only when the stress induces a variation in canopy biomass and structure. In a previous study by the authors [12], NDVI was not able to detect a moderate water stress condition, affecting the plant physiological status but not the canopy structure. PRI and fluorescence appears to be more discriminant in the afternoon overpass.…”

Section: Synthesis Of Thermal and Optical Datamentioning

confidence: 65%

“…Even if previous studies have demonstrated that the PRI index is sensitive to water stress levels, they also report that it is affected by the confounding effects of absorption of photosynthetic pigments, canopy structure, and background [12,42,43].…”

Section: Diurnal Variations Of Optical Indices and Fluorescencementioning

confidence: 91%

“…The heat dissipation can be detected using the Photochemical Reflectance Index (PRI), originally proposed [10] to track changes in the de-epoxidation state of the xanthophyll cycle pigments, related to heat dissipation. PRI has been used to track photosynthesis changes caused by water stress conditions with varying degrees of success [11][12][13][14]. In particular, PRI has been shown to be able to track the evolution of maize water stress from the early phase when only the plant physiology is affected [12] to the later ones when also plant structure changes [11], showing higher potentiality than the traditional greenness indices that hardly detect early stress conditions.…”

Section: Introductionmentioning

confidence: 99%

“…PRI has been used to track photosynthesis changes caused by water stress conditions with varying degrees of success [11][12][13][14]. In particular, PRI has been shown to be able to track the evolution of maize water stress from the early phase when only the plant physiology is affected [12] to the later ones when also plant structure changes [11], showing higher potentiality than the traditional greenness indices that hardly detect early stress conditions. More recently, the estimation of sun-induced chlorophyll fluorescence in the far-red region (at 760 nm) has been proven feasible from remote sensors, and fluorescence has been evaluated as a novel remote measure to detect plant physiological changes due to environmental constraints [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps

Rossini

Panigada

Cilia

et al. 2015

IJGI

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This study evaluates the potential of airborne remote sensing images to detect water stress in maize. Visible and near infrared CASI (Itres Research Ltd., Calgary, AL, Canada) and thermal AHS-160 (Sensytech Inc., Beverly, MA, USA) data were acquired at three different times during the day on a maize field (Zea mays L.) grown with three different irrigation treatments. An intensive field campaign was also conducted concurrently with image acquisition to measure leaf ecophysiological parameters and the leaf area index. The analysis of the field data showed that maize plants were experiencing moderate to severe water stress in rainfed plots and a weaker stress condition in the plots with a water deficit imposed between stem elongation and flowering. Vegetation indices including the normalized difference vegetation index (NDVI) and the photochemical reflectance index (PRI) computed from the CASI images, sun-induced chlorophyll fluorescence (F760) and canopy temperature (Tc) showed different performances in describing the water stress during the day. During the OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 627 morning overpass, NDVI was the index with the highest discriminant power due to the sensitivity of NDVI to maize canopy structure, affected by the water irrigation treatment. As the day progressed, processes related to heat dissipation through plant transpiration became more and more important and at midday Tc showed the best performances. Furthermore, Tc retrieved from the midday image was the only index able to distinguish all the three classes of water status. Finally, during the afternoon, PRI and F760 showed the best performances. These results demonstrate the feasibility to detect water stress using thermal and optical airborne data, pointing out the importance of careful planning of the airborne surveys as a function of the specific aims of the study.

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Section: Synthesis Of Thermal and Optical Datamentioning

confidence: 65%

Section: Diurnal Variations Of Optical Indices and Fluorescencementioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps

Rossini

Panigada

Cilia

et al. 2015

IJGI

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“…Several studies have focused on the use of shortwave infrared (SWIR) data to detect crop water stress (e.g., [28,29]). Alternatively, VIs derived from hyperspectral data of the visible and red-edge (sharp transition of vegetation's reflectance between red and near-infrared spectral ranges) regions of the electromagnetic spectrum have been considered for assessing crop water status and detecting crop water stress at the canopy level in crops like maize [30], barley [31], olive orchards [11], and vineyards [4,22].…”

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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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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