Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling

Colombo, R.; Meroni, Michele; Marchesi, Andrea; Busetto, Lorenzo; Rossini, Micol; Giardino, Claudia; Panigada, Cinzia

doi:10.1016/j.rse.2007.09.005

Cited by 229 publications

(152 citation statements)

References 69 publications

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“…The high spectral resolution provided by current sensors allows computing narrow-band VIs that can detect subtle changes in reflectance, e.g., the red-edge position that relates to both the LAI and leaf chlorophyll content (Cho and Skidmore 2006;Clevers and Kooistra 2012;Guyot and Baret 1988). These VIs can be used to estimate either structural properties such as LAI (Darvishzadeh et al, 2011;Haboudane et al, 2004) and green fraction (Comar et al, 2012), or biochemical properties such as leaf chlorophyll content ) and leaf water content (Colombo et al, 2008). However, interactions between biochemical and structural canopy parameters (e.g., LAI and leaf chlorophyll content) may add some uncertainties in the retrieval, the measured signal not only depending on the leaf biochemistry but also on the amount of leaves within the sensor field of view (Baret et al 2007;Colombo et al 2008).…”

Section: Introductionmentioning

confidence: 99%

“…These VIs can be used to estimate either structural properties such as LAI (Darvishzadeh et al, 2011;Haboudane et al, 2004) and green fraction (Comar et al, 2012), or biochemical properties such as leaf chlorophyll content ) and leaf water content (Colombo et al, 2008). However, interactions between biochemical and structural canopy parameters (e.g., LAI and leaf chlorophyll content) may add some uncertainties in the retrieval, the measured signal not only depending on the leaf biochemistry but also on the amount of leaves within the sensor field of view (Baret et al 2007;Colombo et al 2008). Alternatively, canopy integrated biochemical parameters (obtained by multiplying the leaf biochemical content by the LAI) can generally be estimated more accurately (Clevers and Kooistra 2012;Colombo et al 2008;Darvishzadeh et al 2011;Jacquemoud et al 1995), while still representing physically-sound quantities (Baret et al 2007).…”

Section: Introductionmentioning

confidence: 99%

“…However, interactions between biochemical and structural canopy parameters (e.g., LAI and leaf chlorophyll content) may add some uncertainties in the retrieval, the measured signal not only depending on the leaf biochemistry but also on the amount of leaves within the sensor field of view (Baret et al 2007;Colombo et al 2008). Alternatively, canopy integrated biochemical parameters (obtained by multiplying the leaf biochemical content by the LAI) can generally be estimated more accurately (Clevers and Kooistra 2012;Colombo et al 2008;Darvishzadeh et al 2011;Jacquemoud et al 1995), while still representing physically-sound quantities (Baret et al 2007). Interestingly, the correlation between chlorophyll and nitrogen contents makes it possible to use chlorophyll VIs for quantifying the nitrogen status of crops (Clevers and Gitelson 2013;Clevers and Kooistra 2012;Schlemmer et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Jay

Maupas

Bendoula³

et al. 2017

Field Crops Research

203

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Remote sensing has gained much attention for agronomic applications such as crop management or yield estimation. Crop phenotyping under field conditions has recently become another important application that requires specific needs: the considered remote-sensing method must be (1) as accurate as possible so that slight differences in phenotype can be detected and related to genotype, and (2) robust so that thousands of cultivars potentially quite different in terms of plant architecture can be characterized with a similar accuracy over different years and soil and weather conditions. In this study, the potential of nadir and off-nadir ground-based spectro-radiometric measurements to remotely sense five plant traits relevant for field phenotyping, namely, the leaf area index (LAI), leaf chlorophyll and nitrogen contents, and canopy chlorophyll and nitrogen contents, was evaluated over fourteen sugar beet (Beta vulgaris L.) cultivars, two years and three study sites. Among the diversity of existing remote-sensing methods, two popular approaches based on various selected Vegetation Indices (VI) and PROSAIL inversion were compared, especially in the perspective of using them for phenotyping applications.Overall, both approaches are promising to remotely estimate LAI and canopy chlorophyll content ( ≤ 10 %). In addition, VIs show a great potential to retrieve canopy nitrogen content ( = 10 %). On the other hand, the estimation of leaf-level quantities is less accurate, the best accuracy being obtained for leaf chlorophyll content estimation based on VIs ( = 17 %). As expectedAuthor-produced version of the article published in Field Crops Research, 2017, N°210, p.33-46. The original publication is available at http://www.sciencedirect.com Doi: 10.1016Doi: 10. /j.fcr.2017 when observing the relationship between leaf chlorophyll and nitrogen contents, poor correlations are found between VIs and mass-based or area-based leaf nitrogen content. Importantly, the estimation accuracy is strongly dependent on sun-sensor geometry, the structural and biochemical plant traits being generally better estimated based on nadir and off-nadir observations, respectively.Ultimately, a preliminary comparison tends to indicate that, providing that enough samples are included in the calibration set, (1) VIs provide slightly more accurate performances than PROSAIL inversion, (2) VIs and PROSAIL inversion do not show significant differences in robustness across the different cultivars and years. Even if more data are still necessary to draw definitive conclusions, the results obtained with VIs are promising in the perspective of high-throughput phenotyping using UAVembedded multispectral cameras, with which only a few wavebands are available.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Jay

Maupas

Bendoula³

et al. 2017

Field Crops Research

203

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“…In recent years, studies of vegetation water content using remote sensing techniques have received widespread attention, and have become a focus point during vegetation monitoring and parameter inversion. Currently, a few studies have been conducted on the estimation of vegetation water content using hyperspectral data [5][6][7]10,24,25]. In these studies, two major approaches, physically-based radiative transfer models [5,6,10,24,25] and statistical models mainly including the first derivative reflectance spectra [6,7,25], spectral reflectance indices [5,7,10,24,26], continuum removed spectra analysis [6,10], and full spectrum methods [26] were used.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Canopy Water Content by Means of Hyperspectral Indices Based on Drought Stress Gradient Experiments of Maize in the North Plain China

Zhang

Zhou

2015

Remote Sensing

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Here, we conducted drought stress gradient experiments of maize, and used ten water content related vegetation indices (VIs) to estimate widely variable canopy water content (CWC) and mean leaf equivalent water thickness at canopy level ( ̅̅̅̅̅̅̅ ) based on in situ measurements of Lambertian equivalent reflectance and important biological and environmental factors during the 2013−2014 growing seasons in the North China Plain. Among ten VIs, the performances of green chlorophyll index (CIgreen), red edge chlorophyll index (CIred edge), and the red edge normalized ratio (NRred edge) were most sensitive to the variations of CWC and ̅̅̅̅̅̅̅ . Simulated drought in two differently managed irrigation years did not affect the sensitivities of VIs to the variations in CWC and ̅̅̅̅̅̅̅ . However, the relationships between CWC and VIs were more noticeable in 2014 than in 2013. In contrast, ̅̅̅̅̅̅̅ and VIs were more closely related in 2013 than in 2014. CWC and relative soil water content (RSWC) obviously exhibited a two-dimensional trapezoid space, which illustrated that CWC was determined not only by soil water status but also by crop growth and stage of development. This study demonstrated that nearly half of the variation in CWC explained by spectral information was derived from the variation in leaf area index (LAI).

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“…So, it is necessary to develop new and accurate techniques for the remote estimation of RWC and leaf ion content under saline conditions. In analysis of plant spectral data for extracting characteristic information on target objects many methods have been adopted, including vegetation parameters, and other derived functions, in attempt to minimize background noise and enhancing capacity of spectral information utilization and accuracy of estimating models [Claudio et al, 2006;Colombo et al, 2008;Jacquemoud and Baret, 1990;Wu et al, 2009]. However, among them, normalized difference vegetation indices (NDVI) and ratio vegetation indices (RVI) have been widely used for analyzing multispectral information in crop plants because they are constructed with simple form and for easy calculation [Claudio et al, 2006;Gao, 1996;Penuelas et al, 1997;Seelig et al, 2008;Yilmaz et al, 2008].…”

Section: Introductionmentioning

confidence: 99%

Monitoring cotton (Gossypium hirsutum L.) leaf ion content and leaf water content in saline soil with hyperspectral reflectance

Zhang

Zhou

Zhang

et al. 2014

European Journal of Remote Sensing

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The objectives of this study were to establish quantitative models for monitoring the leaf ion and water content under saline conditions. The best spectral indices for estimating leaf ion content and leaf water content were found to be normalized difference spectral indices (NDSI (R 1340 , R 2306 )), ratio spectral indices (RSI (R 2306 , R 1347 )) for K 2-and NDSI (R 1222 , R 2264 ), RSI (R 2264 , R 1321 ) for RWC, respectively. The regression models based on the above spectral indices were formulated with R 2 greater than 0.46. The high fit between the measured and estimated values indicate that the present models based on RSI could be used to estimate leaf ion and water content feasibly under saline conditions.

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Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling

Cited by 229 publications

References 69 publications

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping

Estimation of Canopy Water Content by Means of Hyperspectral Indices Based on Drought Stress Gradient Experiments of Maize in the North Plain China

Monitoring cotton (Gossypium hirsutum L.) leaf ion content and leaf water content in saline soil with hyperspectral reflectance

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