Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from airborne hyperspectral imagery

Zarco‐Tejada, Pablo J.; Catalina, A.; Rebollo, Miguel Ángel González; Martín, Pedro

doi:10.1016/j.rse.2013.05.011

Cited by 140 publications

(101 citation statements)

References 41 publications

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“…However, local fluorescence measurement techniques are rapidly advancing and show their value in studying the ecophysiology of photosynthesis at the leaf level and in situ ( [52] and references therein). During the last decade, SiF has been successfully measured from tower [26,[64][65][66], aircraft [27,67,68] and satellite platforms [32,34,35,41]. For evaluation purposes, vegetation indices from satellite measurements (such as the NDVI) can be used as first proxies for SiF from spaceborne measurements due to their similar spatio-temporal patterns; they can therefore be used as a qualitative indicator of vegetation activity.…”

Section: Comparison With Ndvimentioning

confidence: 99%

Spaceborne Sun-Induced Vegetation Fluorescence Time Series from 2007 to 2015 Evaluated with Australian Flux Tower Measurements

et al. 2016

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Section: Comparison With Ndvimentioning

confidence: 99%

Spaceborne Sun-Induced Vegetation Fluorescence Time Series from 2007 to 2015 Evaluated with Australian Flux Tower Measurements

et al. 2016

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“…The resulting false color mosaic, the generated hyperspectral image as an image cube, and a vegetation health index map are given in Figure 10, covering the area shown by the white polygon in Figure 3. A hyperspectral image was then estimated from the multispectral mosaic using the fusion imputation methods described in Equations (6) and (7). To demonstrate the utility and advantage of such a hyperspectral image, a narrow band index, the Photo-chemical Reflectance Index (PRI) [33] was calculated as: (R570 − R531)/(R570 + R531), where R is reflectance extracted from the estimated hyperspectral imagery.…”

Section: Fused Hyperspectral Imagerymentioning

confidence: 99%

“…Compared with multispectral imagery, hyperspectral data were reported to perform better in modelling vegetation chlorophyll content [5]. They can also be used to calculate narrow band indices for modelling crown temperature, carotenoids, fluorescence, and plant disease [6,7], as well as crop growth period [8], soil status [9], net photosynthesis, and crop water stress [10], amongst other vegetation parameters. In precision farming applications, hyperspectral data with high spatial resolution are required [4,11], but such…”

Section: Introductionmentioning

confidence: 99%

Fusion of Multispectral Imagery and Spectrometer Data in UAV Remote Sensing

et al. 2017

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High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unmanned aerial vehicles (UAVs) are emerging as lower cost and more flexible means to acquire very high resolution imagery. Miniaturized hyperspectral sensors have been developed for UAVs, but the sensors, associated hardware, and data processing software are still cost prohibitive for use by individual farmers or small remote sensing firms. This study simulated hyperspectral image data by fusing multispectral camera imagery and spectrometer data. We mounted a multispectral camera and spectrometer, both being low cost and low weight, on a standard UAV and developed procedures for their precise data alignment, followed by fusion of the spectrometer data with the image data to produce estimated spectra for all the multispectral camera image pixels. To align the data collected from the two sensors in both the time and space domains, a post-acquisition correlation-based global optimization method was used. Data fusion, to estimate hyperspectral reflectance, was implemented using several methods for comparison. Flight data from two crop sites, one being tomatoes, and the other corn and soybeans, were used to evaluate the alignment procedure and the data fusion results. The data alignment procedure resulted in a peak R 2 between the spectrometer and camera data of 0.95 and 0.72, respectively, for the two test sites. The corresponding multispectral camera data for these space and time offsets were taken as the best match to a given spectrometer reading, and used in modelling to estimate hyperspectral imagery from the multispectral camera pixel data. Of the fusion approaches evaluated, principal component analysis (PCA) based models and Bayesian imputation reached a similar accuracy, and outperformed simple spline interpolation. Mean absolute error (MAE) between predicted and observed spectra was 17% relative to the mean of the observed spectra, and root mean squared error (RMSE) was 0.028. This approach to deriving estimated hyperspectral image data can be applied in a simple fashion at very low cost for crop assessment and monitoring within individual fields.

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“…(2) Estimate the weights of the reflectance PCs using the apparent reflectance (ˆ() R  ) without absorption bands according to Equation (8) and then reconstruct the reflectance () R  using the weights of the different reflectance PCs according to Equation (6). (3) Estimate the weights of the SIF PCs according to Equation (10) using the least-squares fitting method for the parts of the spectrum within the absorption bands and reconstruct the SIF spectrum () SIF  according to Equation (7).…”

Section: Spectral Fittingmentioning

confidence: 99%

“…In earlier studies, the Photochemical Reflectance Index (PRI) [4], which is linked to the xanthophyll cycle, has been used as a proxy for photosynthesis but is strongly affected by the canopy structure, leaf pigments and background [5]. In contrast, SIF seems to be a better indicator of photosynthesis [6]. Numerous studies have shown that making measurements of SIF is a reliable way of obtaining physiological information about plants quickly and in a non-invasive way.…”

Section: Introductionmentioning

confidence: 99%

New Spectral Fitting Method for Full-Spectrum Solar-Induced Chlorophyll Fluorescence Retrieval Based on Principal Components Analysis

Liu

Zhang

et al. 2015

Remote Sensing

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Abstract:The full-spectrum Solar-Induced chlorophyll Fluorescence (SIF) within the 650800 nm spectral region can provide important information regarding physiological and biochemical activities in vegetation. This paper proposes a new Full-spectrum Spectral Fitting Method (F-SFM) for the retrieval of SIF spectra based on Principal Components Analysis (PCA). Using F-SFM, both the full-spectrum reflectance and SIF within the 650800 nm region were modeled by PCA based on a training dataset simulated by the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model, and the weighting coefficients of the principal components were estimated by the least-squares fitting method. An iterative process was employed to improve the accuracy of the estimation of the reflectance. In each iteration, the SIF spectra retrieved from the last run were removed from the total upwelling radiance to minimize the small contribution of the SIF to the apparent reflectance outside the absorption bands. Then, the F-SFM algorithm was tested using both simulated and field-measured data with different Spectral Resolutions (SRs) and Signal-to-Noise Ratios (SNRs). For data with an SR of 0.3 nm and without noise, the Relative Root Mean Square Error (RRMSE) was less than 14% within the spectral region that was studied, and the peak-value ratio (SIF735/SIF685) was accurately estimated with an RRMSE of 3.56%. In addition, the F-SFM algorithm proved less sensitive to the SR than the three-band Fraunhofer Line Discrimination (3 FLD) and improved FLD (iFLD) methods. In OPEN ACCESSRemote Sens. 2015, 7 10627 the case of the field spectral data with SRs of 3 nm and 0.3 nm, the double-peak shape and the diurnal variation trend of the SIF spectra could be reasonably reconstructed by F-SFM, and the retrieved SIF values at the O2-A and O2-B bands were consistent with those retrieved by 3FLD from data with a high SR (0.3 nm) and SNR (1000). Therefore, the F-SFM method can provide full-spectrum SIF information with high accuracy even at relatively low SRs and SNRs, and shows promise for use in applications involving the SIF shape information.

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Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from airborne hyperspectral imagery

Cited by 140 publications

References 41 publications

Spaceborne Sun-Induced Vegetation Fluorescence Time Series from 2007 to 2015 Evaluated with Australian Flux Tower Measurements

Spaceborne Sun-Induced Vegetation Fluorescence Time Series from 2007 to 2015 Evaluated with Australian Flux Tower Measurements

Fusion of Multispectral Imagery and Spectrometer Data in UAV Remote Sensing

New Spectral Fitting Method for Full-Spectrum Solar-Induced Chlorophyll Fluorescence Retrieval Based on Principal Components Analysis

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