Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves

Wang, Zhihui; Skidmore, Andrew K.; Darvishzadeh, Roshanak; Hearne, John

doi:10.1016/j.rse.2015.07.007

Cited by 113 publications

(86 citation statements)

References 61 publications

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“…The applicability of vegetation indices can be further evaluated by generating a large synthetic dataset through coupling the modified PROSPECT leaf optical properties model [102] with a canopy reflectance model [103,104]. With the aid of upcoming earth observation systems, regional to global maps of foliar nitrogen can be generated for the sake of biodiversity assessment.…”

Section: Discussionmentioning

confidence: 99%

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

et al. 2016

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Hyperspectral remote sensing serves as an effective tool for estimating foliar nitrogen using a variety of techniques. Vegetation indices (VIs) are a simple means of retrieving foliar nitrogen. Despite their popularity, few studies have been conducted to examine the utility of VIs for mapping canopy foliar nitrogen in a mixed forest context. In this study, we assessed the performance of 32 vegetation indices derived from HySpex airborne hyperspectral images for estimating canopy mass-based foliar nitrogen concentration (%N) in the Bavarian Forest National Park. The partial least squares regression (PLSR) was performed for comparison. These vegetation indices were classified into three categories that are mostly correlated to nitrogen, chlorophyll, and structural properties such as leaf area index (LAI). %N was destructively measured in 26 broadleaf, needle leaf, and mixed stand plots to represent the different species and canopy structure. The canopy foliar %N is defined as the plot-level mean foliar %N of all species weighted by species canopy foliar mass fraction. Our results showed that the variance of canopy foliar %N is mainly explained by functional type and species composition. The normalized difference nitrogen index (NDNI) produced the most accurate estimation of %N (R 2 CV = 0.79, RMSE CV = 0.26). A comparable estimation of %N was obtained by the chlorophyll index Boochs2 (R 2 CV = 0.76, RMSE CV = 0.27). In addition, the mean NIR reflectance (800-850 nm), representing canopy structural properties, also achieved a good accuracy in %N estimation (R 2 CV = 0.73, RMSE CV = 0.30). The PLSR model provided a less accurate estimation of %N (R 2 CV = 0.69, RMSE CV = 0.32). We argue that the good performance of all three categories of vegetation indices in %N estimation can be attributed to the synergy among plant traits (i.e., canopy structure, leaf chemical and optical properties) while these traits may converge across plant species for evolutionary reasons. Our findings demonstrated the feasibility of using hyperspectral vegetation indices to estimate %N in a mixed temperate forest which may relate to the effect of the physical basis of nitrogen absorption features on canopy reflectance, or the biological links between nitrogen, chlorophyll, and canopy structure.

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Section: Discussionmentioning

confidence: 99%

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

et al. 2016

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“…Additional spectral bands from the SWIR region were often selected on the airborne image. These bands have been linked to leaf water, cellulose, and lignin contents in previous studies [87,88], but remained of less contribution to TPH predictions in our case. In contrast, SNV, Log(1/R), and AUCN transformation degraded TPH predictions, and did not provide a clear interpretation of the contributing spectral bands ( Figure 5).…”

Section: Elastic Net Based Methodsmentioning

confidence: 51%

Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery

et al. 2019

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Recent remote sensing studies have suggested exploiting vegetation optical properties for assessing oil contamination, especially total petroleum hydrocarbons (TPH) in vegetated areas. Methods based on the tracking of alterations in leaf biochemistry have been proposed for detecting and quantifying TPH under controlled and field conditions. In this study, we expand their use to airborne imagery, in order to monitor oil contamination at a larger scale. Airborne hyperspectral images with very high spatial and spectral resolutions were acquired over an industrial site with oil-contamination (mud pits) and control sites both colonized by Rubus fruticosus L. The method of oil detection exploiting 14 vegetation indices succeeded in classifying the sites in the case of high TPH contamination (overall accuracy ≥ 91.8%). Two methods, based on either the PROSAIL (PROSPECT + SAIL) radiative transfer model or elastic net multiple regression, were also developed for quantifying TPH. Both methods were tested on reflectance measurements in the field, at leaf and canopy scales, and on the image, and achieved accurate predictions of TPH concentrations (RMSE ≤ 3.28 g/kg −1 and RPD ≥ 1.90). The methods were validated on additional sites and open up promising perspectives of operational application for oil and gas companies, with the emergence of new hyperspectral satellite sensors.

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“…When considering the direction changes of particle scattering ratios, and are exactly the first left items on the right-hand side of Equation (14) and Equation (15). Thus, the second left items are same and represent the contributions of the lateral particles to sublayer backscattering and forward scattering ratios.…”

Section: Reassessment Of the Sublayer Backscattering And Forward Scatmentioning

confidence: 99%

“…In global SA, both individual parameter and interactions between parameters are supposed to explain the global variability while in local SA, only an individual parameter is considered. The theory of global SA can be found in previous literature such as by Ceccato, et al [14] and Wang, et al [15]. Suppose a model needs three input parameters.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Limitations and Improvements of the Leaf Optical Properties Model Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields (LIBERTY)

Wang

2017

Remote Sensing

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Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields (LIBERTY) models the effects of leaf biochemical concentrations on reflectance spectra on the basis of Melamed theory, which has several limitations. These are: (1) the radiation components are not treated satisfactorily; (2) the directional changes of both particle and sublayer scattering ratios are not considered; and (3) the boundary constraint which makes needle leaves different from broadleaves is not included. Proofs of these limitations as well as theoretical improvements are given in this study. Global sensitivity analysis (SA) of three models: the original LIBERTY, our improved LIBERTY (LIBERTY im ) and The optical PROperties SPECTra model (PROSPECT) suggests that compared with LIBERTY, the global reflectance and transmittance of LIBERTY im are more sensitive to diametrical absorbance αd-a parameter related to leaf biochemistry. Moreover, the global reflectance and transmittance of LIBERTY im and PROSPECT had similar sensitivity patterns to the input variables, demonstrating indirectly the validity of our improvements over LIBERTY. However, neither LIBERTY nor LIBERTY im considers boundary constraints, which limits their applications in modelling needle leaf optical properties. We introduced a particle string model, which might be used to simulate needle leaf optical properties in the future.

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Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves

Cited by 113 publications

References 61 publications

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery

Limitations and Improvements of the Leaf Optical Properties Model Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields (LIBERTY)

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