Stéphane Jacquemoud

Hyperspectral remote sensing of foliar nitrogen content

A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact—it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423–855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710–790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.

Use of spectral analogy to evaluate canopy reflectance sensitivity to leaf optical properties

Abstract The spectral variation of canopy reflectance is mostly governed by the optical properties of the elements such as the leaves. Since leaf intrinsic scattering properties show very little spectral variation, leaf optical properties are related to their absorption properties. Spectral analogies are thus observed between two wavelengths for which the optical properties (absorption, reflectance, or transmittance) of the elements are similar. The red edge for green plants shows the full range of variation of leaf optical properties. The relationship between canopy reflectance and leaf reflectance measured concurrently at the red edge over sugar beet canopies was thus used to simulate canopy reflectance over the whole spectral domain from leaf reflectance spectra measured over the whole spectral domain. The results show that the spectral analogies found allows accurate reconstruction of canopy reflectance spectra. Explicit assumptions about the very low spectral variation of leaf intrinsic scattering properties are thus indirectly justified. The sensitivity of canopy reflectance (ρc) to leaf optical properties is then investigated from concurrent spectral variations of canopy (∂ρc/∂λ) and leaf reflectance (∂ρl / ∂λ): ∂ρc / ∂ρl = (∂ρc / ∂λ) (∂ρl / ∂λ)−1. This expression is strictly valid only when the optical properties of the soil background or of the other vegetation elements such as bark are either spectrally flat or do not contribute significantly to canopy reflectance. Simulations using the SAIL and PROSPECT models demonstrate that the sensitivity of canopy reflectance to leaf reflectance is significant for large vegetation cover fractions in spectral domains where absorption is low. In these conditions, multiple scattering enhances the leaf absorption features by a factor that can be greater than 2.0. To override the limitations of the SAIL model for the description of the canopy architecture, we tested the previous simulation results on experimental data. Concurrent canopy and leaf reflectance spectra were measured for a range of sugar beet canopies. The results show good agreement with the theoretical findings. Conclusions are drawn about the applicability of these findings, with particular attention to the potential detectability of leaf biochemical composition from canopy reflectance sensed from space.

Chlorophyll fluorescence emission spectrum inside a leaf

Chlorophyll a fluorescence can be used as an early stress indicator. Fluorescence is also connected to photosynthesis so it can be proposed for global monitoring of vegetation status from a satellite platform. Nevertheless, the correct interpretation of fluorescence requires accurate physical models. The spectral shape of the leaf fluorescence free of any re-absorption effect plays a key role in the models and is difficult to measure. We present a vegetation fluorescence emission spectrum free of re-absorption based on a combination of measurements and modelling. The suggested spectrum takes into account the photosystem I and II spectra and their relative contribution to fluorescence. This emission spectrum is applicable to describe vegetation fluorescence in biospectroscopy and remote sensing.

Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis

Leaf water content is an important variable for understanding plant physiological properties. This study evaluates a spectral analysis approach, continuous wavelet analysis (CWA), for the spectroscopic estimation of leaf gravimetric water content (GWC, %) and determines robust spectral indicators of GWC across a wide range of plant species from different ecosystems. CWA is both applied to the Leaf Optical Properties Experiment (LOPEX) data set and a synthetic data set consisting of leaf reflectance spectra simulated using the leaf optical properties spectra (PROSPECT) model. The results for the two data sets, including wavelet feature selection and GWC prediction derived using those features, are compared to the results obtained from a previous study for leaf samples collected in the Republic of Panamá (PANAMA), to assess the predictive capabilities and robustness of CWA across species. Furthermore, predictive models of GWC using wavelet features derived from PROSPECT simulations are examined to assess their applicability to measured data. The two measured data sets (LOPEX and PANAMA) reveal five common wavelet feature regions that correlate well with leaf GWC. All three data sets display common wavelet features in three wavelength regions that span 1732-1736 nm at scale 4, 1874-1878 nm at scale 6, and 1338-1341 nm at scale 7 and produce accurate estimates of leaf GWC. This confirms the applicability of the wavelet-based methodology for estimating leaf GWC for leaves representative of various ecosystems. The PROSPECT-derived predictive models perform well on the LOPEX data set but are less successful on the PANAMA data set. The selection of high-scale and low-scale features emphasizes significant changes in both overall amplitude over broad spectral regions and local spectral shape over narrower regions in response to changes in leaf GWC. The wavelet-based spectral analysis tool adds a new dimension to the modeling of plant physiological properties with spectroscopy data.

PROSPECT+SAIL: 15 Years of Use for Land Surface Characterization

The combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, i.e. PROSAIL, has been used for about fifteen years to increase our understanding of plant canopy spectral and bidirectional reflectance in the solar domain and to develop new methods of vegetation biophysical properties retrieval. It links the spectral variation of canopy reflectance with its directional variation. This link is the key to simultaneously estimate biophysical/structural canopy variables for applications in agriculture, plant physiology, and forestry at different scales. PROSPECT and SAIL are still evolving: they have undergone recent improvements both at the leaf and the plant levels and became one of the most popular radiative transfer tools in these domains due to their ease of use, their robustness, and because they have been validated by many lab/field/space experiments over the years. This paper is intended to review this subject, which has been extensively researched in optical remote sensing.

3D segmentation of forest structure using a mean-shift based algorithm

Consistent and accurate information on 3D forest canopy structure is required by many applications like forest inventory, management, logging, fuel mapping, habitat studies or biomass estimate. Compared to other remote sensing techniques (e.g., SAR or photogrammetry), airborne laser scanning is an adapted tool to provide such information by generating a three-dimensional georeferenced point cloud. Vertical structure analysis consists in detecting the number of layers within a forest stand and their limits. Until now, there is no approach that properly segments the different strata of a forest. In this study, we directly work on the 3D point cloud and we propose a mean shift (MS) based procedure for vertical forest segmentation. The approach that is carried out on complex forest plots improves the discrimination of vegetation strata.

Modeling plant leaf bidirectional reflectance and transmittance with a 3-D ray tracing approach

A new radiative transfer model based on Monte Carlo ray tracing techniques of leaf optical properties has been developed, where the internal three-dimensional cellular structure is explicitly described to represent morphological properties of a typical dicotyledon leaf. The main objective of this work is to perform sensitivity analyses at different wavelengths to test the influence of the leaf internal structure as well as that of pigment and water concentrations on the light attenuation profile and the bidirectional scattering shape.

Regularization of discriminant analysis for the study of biodiversity in humid tropical forests

The performance of two supervised classifiers, linear and regularized discriminant analysis (LDA and RDA), is compared here for canopy species discrimination in humid tropical forest, based on airborne hyperspectral imagery acquired with the sensor Carnegie Airborne Observatory Alpha System (CAO-Alpha). Classification is performed to identify 13 species at pixel scale, crown scale, and using an object-based approach. The results show that for each scale of study, 70% to 75% overall accuracy is obtained with LDA. RDA allows improved classification for more than half species, and 5% increase of overall accuracy compared to LDA. The extended spectral range of the forthcoming CAO AToMS system (380–2500 nm) will allow for even more accurate classifications of tropical canopy species.

Reply to Townsend et al.: Decoupling contributions from canopy structure and leaf optics is critical for remote sensing leaf biochemistry

Townsend et al. (1) agree that we explained that the apparent relationship (2) between foliar nitrogen (%N) and near-infrared (NIR) canopy reflectance was largely attributable to structure (which is in turn caused by variation in fraction of broadleaf canopy). Our conclusion that the observed correlation with %N was spurious (i.e., lacking a causal basis) is, thus, clearly justified: we demonstrated that structure explained the great majority of observed correlation, where the structural influence was derived precisely via reconciling the observed correlation with radiative-transfer theory. What this also suggests is that such correlations, although observed, do not uniquely provide information on canopy biochemical constituents. We, therefore, disagree with the assertion in ref. 1 that we “did not provide an adequate rationale for the inference that %N and other leaf properties cannot be characterized from imaging spectroscopy”; our analysis showed precisely that. Our analysis also led to the conclusion that “NIR and/or SW broadband satellite data cannot be directly linked to leaf-level processes,” and any such link must be indirect and will be a function of structure. This is true for all wavelengths in the interval 423–855 nm (figure 7B and figure S2 in ref. 3), not primarily for the 800- to 850-nm spectral band, as misstated in ref. 1. None of the leaf biochemical constituents can be accurately estimated without accounting for canopy structural effects.

Reply to Ollinger et al.: Remote sensing of leaf nitrogen and emergent ecosystem properties.

Various physical, chemical, and physiological processes, including canopy structure, impact surface reflectance. Remote sensing aims to derive ecosystem properties and their functional relationships, given these impacts. Ollinger et al. (1) do not distinguish between the forward and inverse problems in radiative transfer and, hence, misrepresent our results (2). The authors also suggest our conclusions are based on a subset of data from ref. 3, which is not the case.