Remote estimation of canopy chlorophyll content in crops

Gitelson, Anatoly A.; Viña, Andrés; Ciganda, Verónica; Rundquist, Donald C.; Arkebauer, Timothy J.

doi:10.1029/2005gl022688

Cited by 1,052 publications

(448 citation statements)

References 19 publications

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“…Non-destructive techniques based on leaf reflectance have been proposed as alternative, robust, and simple methods for pigment quantification in leaves (Collins, 1978;Curran and Milton, 1983;Buschmann and Nagel, 1993;Merzlyak, 1994, 1996;Richardson et al, 2002;Sims and Gamon, 2002;Gitelson et al, 2003;Hu et al, 2004;Le Maire et al, 2004) and in canopies (e.g., Barton, 2000;Gitelson et al, 2005). However, an important uncertainty remains when Chl content values for individual leaves are used to represent the Chl content in the +canopy.…”

Section: E-mail Address: Agitelsonz@unledu (A Gitelson)mentioning

confidence: 99%

“…However, an important uncertainty remains when Chl content values for individual leaves are used to represent the Chl content in the +canopy. Gitelson et al (2005) estimated total Chl in maize canopies during the growing season as Chl = Chi,,,,, x green LAI, where Chi,,,,, is the Chl content of the upper leaf and green LA1 is the green leaf area index of the canopy. This approach markedly improved current techniques proposed for Chl quantification in the canopy.…”

Section: E-mail Address: Agitelsonz@unledu (A Gitelson)mentioning

confidence: 99%

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Non-destructive determination of maize leaf and canopy chlorophyll content

Ciganda

Gitelson

Schepers

2009

Journal of Plant Physiology

128

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SummaryThe objective of this study was to develop a rapid non-destructive technique to estimate total chlorophyll (Chl) content in a maize canopy using Chl content in a single leaf. The approach was (1) to calibrate and validate a reflectance-based nondestructive technique to estimate leaf Chl in maize; (2) to quantify the relative contribution of each leaf Chl to the total Chl in the canopy; and (3) to establish a relationship between leaf Chl content and total Chl in a maize canopy. The Red Edge Chlorophyll Index Clred edge = (RNIRIRred edge)-l based on reflectances, R, in the red edge (720-730nm) and near infrared (770-800nm) was found to be an accurate measure of maize leaf Chl. It was able to predict leaf Chl ranging from 10 to 805 mg Chl m-2 with root mean-square error less than 38 mg Chl mP2. Relationships between Chl content in each maize leaf and total canopy Chl content were established and showed that Chl in the collar leaf before silking or ear leaves explained more than 80% and 87% of the variation in total Chl in a maize canopy, respectively. Thus, non-destructive measurements of both reflectance and area of a single leaf (either collar or ear) can be used to accurately estimate total Chl content in a maize canopy.

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Section: E-mail Address: Agitelsonz@unledu (A Gitelson)mentioning

confidence: 99%

Section: E-mail Address: Agitelsonz@unledu (A Gitelson)mentioning

confidence: 99%

Non-destructive determination of maize leaf and canopy chlorophyll content

Ciganda

Gitelson

Schepers

2009

Journal of Plant Physiology

128

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“…The analysis of chlorophyll efficiency is particularly suitable because non-destructive and remote techniques are readily available to effectively and accurately assess the amount of Chl present at multiple scales, from leaves (Gitelson and Merzlyak, 1997;Gitelson et al, 2006a;Fassnacht et al, 2015;Kira et al, 2015) to entire canopies (Dash and Curran, 2004;Gitelson et al, 2005;Clevers and Gitelson, 2013). Thus, given that limitations due to scaling are reduced, the use of models based on chlorophyll efficiency may allow more direct assessments of plant optimization patterns under different environmental conditions, and in different species/plant functional types.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of chlorophyll in gross primary productivity: A proof of concept and application in crops

Gitelson

Viña

et al. 2016

Journal of Plant Physiology

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“…It has been argued that this approach is sensor-specific, site-dependent, and does not account for variability in LAI. However, the VI approach offers computational simplicity and accuracy, and its potential for predicting vegetation variables is well supported by numerous published studies [40,44].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

et al. 2015

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There is a need to develop indicators of mangrove condition using remotely sensed data. However, remote estimation of leaf and canopy biochemical properties and vegetation condition remains challenging. In this paper, we (i) tested the performance of selected hyperspectral and broad band indices to predict chlorophyll concentration (CC) on mangrove leaves and (ii) showed the potential of Landsat 8 for estimation of mangrove CC at the landscape level. Relative leaf CC and leaf spectral response were measured at 12 Elementary Sampling Units (ESU) distributed along the northwest coast of the Yucatan Peninsula, Mexico. Linear regression models and coefficients of determination were computed to measure the association between CC and spectral response. At leaf level, the narrow band indices with the largest correlation with CC were Vogelmann indices and the MTCI (R 2 > 0.5). Indices with spectral bands around the red edge (705-753 nm) were more OPEN ACCESSRemote Sens. 2015, 7 14531 sensitive to mangrove leaf CC. At the ESU level Landsat 8 NDVI green, which uses the green band in its formulation explained most of the variation in CC (R 2 > 0.8). Accuracy assessment between estimated CC and observed CC using the leave-one-out cross-validation (LOOCV) method yielded a root mean squared error (RMSE) = 15 μg·cm −2 , and R 2 = 0.703. CC maps showing the spatiotemporal variation of CC at landscape scale were created using the linear model. Our results indicate that Landsat 8 NDVI green can be employed to estimate CC in large mangrove areas where ground networks cannot be applied, and mapping techniques based on satellite data, are necessary. Furthermore, using upcoming technologies that will include two bands around the red edge such as Sentinel 2 will improve mangrove monitoring at higher spatial and temporal resolutions.

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Remote estimation of canopy chlorophyll content in crops

Cited by 1,052 publications

References 19 publications

Non-destructive determination of maize leaf and canopy chlorophyll content

Non-destructive determination of maize leaf and canopy chlorophyll content

Efficiency of chlorophyll in gross primary productivity: A proof of concept and application in crops

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

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