Estimating near‐infrared leaf reflectance from leaf structural characteristics

Slaton, Michèle R.; Hunt, E. Raymond; Smith, William K.

doi:10.2307/2657019

Cited by 358 publications

(205 citation statements)

References 30 publications

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“…One of the most important of these changes (Nobel et al, 1975;Longstreth et al, 1985) is an increase in the ratio of leaf mesophyll cell surface area exposed to intercellular air spaces per unit leaf area (A mes /A). Slaton et al (2001) investigated plant anatomy in relation to NIR reflectance and found a strong correlation between NIR leaf reflectance and A mes /A, putting the relationship between scattering and internal structure described by earlier workers (e.g. Woolley, 1971) on a firm quantitative basis.…”

Section: Generality Of the Albedo-nitrogen Relationshipmentioning

confidence: 99%

Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration

Hollinger

Ollinger

Richardson

et al. 2010

Global Change Biology

154

106

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; Schmid, H. P.; Stoy, P. C.; Suyker, Andrew E.; and Verma, Shashi, "Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration" (2010 Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration AbstractVegetation albedo is a critical component of the Earth's climate system, yet efforts to evaluate and improve albedo parameterizations in climate models have lagged relative to other aspects of model development. Here, we calculated growing season albedos for deciduous and evergreen forests, crops, and grasslands based on over 40 site-years of data from the AmeriFlux network and compared them with estimates presently used in the land surface formulations of a variety of climate models. Generally, the albedo estimates used in land surface models agreed well with this data compilation. However, a variety of models using fixed seasonal estimates of albedo overestimated the growing season albedo of northerly evergreen trees. In contrast, climate models that rely on a common two-stream albedo submodel provided accurate predictions of boreal needle-leaf evergreen albedo but overestimated grassland albedos. Inverse analysis showed that parameters of the two-stream model were highly correlated. Consistent with recent observations based on remotely sensed albedo, the AmeriFlux dataset demonstrated a tight linear relationship between canopy albedo and foliage nitrogen concentration (for forest vegetation: albedo 5 0.01 1 0.071%N, r 2 5 0.91; forests, grassland, and maize: albedo 5 0.02 1 0.067%N, r 2 5 0.80). However, this relationship saturated at the higher nitrogen concentrations displayed by soybean foliage. We developed similar relationships between a foliar parameter used in the two-stream albedo model and foliage nitrogen concentration. These nitrogen-based relationships can serve as the basis for a new approach to land surface albedo modeling that simplifies albedo estimation while providing a link to other important ecosystem processes.

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Section: Generality Of the Albedo-nitrogen Relationshipmentioning

confidence: 99%

Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration

Hollinger

Ollinger

Richardson

et al. 2010

Global Change Biology

154

106

View full text Add to dashboard Cite

show abstract

“…Leaf spectral features in the visible and NIR regions of the spectrum have been associated to pigment concentration (e.g., chlorophyll) and leaf structure, respectively [77]. Chl peaks of absorbance are located in the blue and red regions of the spectrum.…”

Section: Spectral Signature and Chl Concentrationmentioning

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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“…Spectral reflectance is affected by strong chlorophyll absorption in the range of 670-680 nm and changes with variation in leaf anatomy or water content in response to stresses beyond 730 nm in the near-infrared region (Carter et al, 2001). Slaton et al (2001) considered that leaf reflectance in the near-infrared region is primarily affected by leaf structure and the position of the red edge correlated with chlorophyll content, plant phenological stage, as well as plant stresses. Leaf necrosis is characterized by chlorophyll loss and leaf drying as well as leaf structure variation.…”

Section: Resultsmentioning

confidence: 99%