Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data

Zarco‐Tejada, Pablo J.; Miller, John R.; Noland, Thomas L.; Mohammed, Gina H.; Sampson, P. H.

doi:10.1109/36.934080

Cited by 582 publications

(194 citation statements)

References 48 publications

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“…Once SPAD readings were converted to CC, each individual leaf CC was compared with its corresponding spectral response to establish the CC and spectral characteristics at the leaf level. (ρ734 − ρ747) (ρ715 + ρ720) [65] Photochemical Reflectance Index PRI (ρ531 − ρ570) (ρ531 + ρ570) [66] Transformed Chlorophyll Absorption Ratio Index TCARI 3 × (ρ700 − ρ670) − 0.2 × (ρ700 − ρ550) × ( ρ700 ρ670 ) [67] Modified Chlorophyll Absorption Index mCARI 705 Spectral bands used to compute broad band VIs were: Blue (436-528), green (512-610), red (625-691), and NIR (829-900 nm). Broad band VIs were computed using the Landsat 8 spectral response function available in the Landsat Science website (http://landsat.gsfc.nasa.gov/).…”

Section: Spad Calibrationmentioning

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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Section: Spad Calibrationmentioning

confidence: 99%

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

et al. 2015

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“…Therefore vegetation indices developed with leaf-level relationships must be ''scaled-up'' to test their utility at the crown and canopy level. Vegetation indices can be incorporated into the ''scaling-up'' process for remote sensing by a number of methods (Zarco-Tejada et al 2001). These include direct application of leaf-level relationships between optical indices and leaf properties to the canopy-measured reflectance, often with single-crown delineation (Coops et al 2003a.…”

Section: A Crown-scale Approachmentioning

confidence: 99%

Crown‐scale evaluation of spectral indices for defoliated and discoloured eucalypts

Barry

Stone²,

Mohammed

2008

International Journal of Remote Sensing

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Remote sensing for evaluation of canopy health in plantation eucalypts is a realistic option for forest managers in the near future if reliable and robust methods of spectral analysis can be developed. Pot-grown eucalypts of three species important to the Australian plantation industry were used for crown-scale spectral (400-1000 nm) evaluations of vegetation indices as indicators of common symptoms of stress. When defoliation treatments (in E. globulus) or exposure to cold and nutrient deprivation (in E. pilularis) resulted in large differences in leaf cover, the red edge position and slope indices, two normalized difference vegetation indices (NDVIs), modified chlorophyll absorption ratio index 2 (MCARI2) or modified triangular vegetation index 2 (MTVI2) were most strongly correlated to leaf cover. However the NDVIs were significantly affected by soil background in a study with E. globulus. The percentage of red leaves resulting from stress treatment was most strongly correlated with the anthocyanin reflectance index (ARI) and red-green index (RGI) in both E. grandis and E. pilularis, however the RGI was affected by background type in the E. globulus study while the ARI was not. Exposure to cold and nutrient deprivation led to marked changes in leaf cover for E. pilularis but not in E. grandis and a much more reduced level of chlorophyll in E. pilularis than is suspected in E. grandis. In E. globulus, defoliation from the upper crown was easier to detect with spectral data than from the lower crown. Results were generally comparable to studies of eucalypt crown condition from native forests.

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“…Moreover, the approach is sensitive to perturbing factors such as atmospheric conditions, canopy characteristics and differences in viewing or solar position geometry [23,24]. In an attempt to generate more generic relationships, RTM-based dataset simulations have been used to assess for the most sensitive SIs [18,[25][26][27][28][29]. A spectral index can quite often be used as an estimator for a biophysical parameter using a fitting function through the data; usually by simple linear regression, but also exponential, power, logarithmic and polynomial fitting functions, among others, are commonly applied [22,30,31].…”

Section: Introductionmentioning

confidence: 99%

On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization

et al. 2014

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Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This paper describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic retrieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox facilitates the assessment of biophysical parameter retrieval accuracy of established as well as new and generic SIs. For instance, based on the SI formulation used, all possible band combinations of formulations with up to ten bands can be defined and evaluated. Several options are available in the SI assessment: calibration/validation data partitioning, the addition of noise and the definition of curve fitting models. To illustrate its functioning, all two-band combinations according to simple ratio (SR) and normalized difference (ND) formulations as well as various fitting functions (linear, exponential, power, logarithmic, polynomial) functions led to similar best correlations, though spatial differences emerged when applying the functions to HyMap imagery. We suggest that a systematic SI assessment is a strong requirement in the quality assurance approach for accurate biophysical parameter retrieval.

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Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data

Cited by 582 publications

References 48 publications

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8

Crown‐scale evaluation of spectral indices for defoliated and discoloured eucalypts

On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization

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