Hyperspectral remote sensing of foliar nitrogen content Knyazikhin, Yuri; Schull, Mitchell A.; Stenberg, Pauline ...
Proceedings of the National Academy of Sciences - PNAS,
01/2013, Letnik:
110, Številka:
3
Journal Article
Recenzirano
Odprti dostop
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.
Identification of materials based on spectral reflectance is confounded by variations in reflectance magnitude that are independent of the spectral shape. Local variations such as ...viewing/illumination angles, multiscale soil surface roughness that causes shadows and redistributes light, and soil moisture content, all drive changes in magnitude that are distinct from the spectral variations, and complicate identification and modeling of targets based on spectral features. Normalization metrics that remove magnitude variations can greatly clarify the nature of spectral differences, simplifying interpretation of reflectance features in spectral imagery. Normalized difference measures are particularly useful because of the simplicity of the computation, the convenient scaling, and the ease with which the normalized difference procedure can be extended to multiple dimensions. The two-dimensional normalized difference space described here allows for improved discrimination among bare soils and emergent vegetation when there are multiple soil types in the scene. A 2-D model of soil-specific change in vegetation density is presented. An application of the vector index to mineral identification and mapping is also presented, with an emphasis on band selection.
•Variations in reflectance magnitude obscure spectral reflectance differences.•Normalization emphasizes spectral differences by minimizing magnitude differences.•Two-dimensional normalized difference maps facilitate modeling and classification.•Achieves refined soil type identification and soil-dependent emergent vegetation.
Leaf mass per area (LMA) is an important leaf trait but challenging to be accurately estimated. This article proposes a simple leaf radiative transfer model called ISPECT. It explains the difference ...in optical properties observed on the adaxial (upper) and abaxial (lower) sides of leaves, i.e., their dorsiventrality, with a limited number of structural parameters. The performance of ISPECT in estimating LMA is compared to that of five other leaf radiative transfer models (FASPECT, DLM, PROSPECT-D, PROSPECT-5B, and Leaf-SIP). We tested six experimental datasets with 962 leaf samples and two spectral ranges: the solar domain (0.4–2.5 µm) and the shortwave infrared (1.7–2.4 µm). Results show that PROSPECT-D and PROSPECT-5B accurately estimate LMA using the shortwave infrared spectra, while ISPECT and FASPECT perform well in both spectral ranges. Further analysis demonstrates that leaf dorsiventrality is likely to be an influential factor for LMA estimation: thus ISPECT can accurately estimate LMA in the solar and shortwave infrared domains, with NRMSE of 26.0% and 28.8%, respectively. This motivates further studies on LMA mapping from spaceborne imaging spectrometers (e.g., PRISMA, GaoFen-5, EnMAP) by coupling ISPECT with canopy radiative transfer models.
•Whether or not surface effects are important for LMA estimation is debated.•We argue that leaf dorsiventrality is likely an influential factor.•A simple leaf radiative transfer model called ISPECT is developed.•LMA can be accurately estimated from ISPECT from leaf spectra between 0.4 and 2.5 µm.
We used synthetic reflectance spectra generated by a radiative transfer model, PROSPECT-5, to develop statistical relationships between leaf optical and chemical properties, which were applied to ...experimental data without any readjustment. Four distinct synthetic datasets were tested: two unrealistic, uniform distributions and two normal distributions based on statistical properties drawn from a comprehensive experimental database. Two methods used in remote sensing to retrieve vegetation chemical composition, spectral indices and Partial Least Squares (PLS) regression, were trained both on the synthetic and experimental datasets, and validated against observations. Results are compared to a cross-validation process and model inversion applied to the same observations. They show that synthetic datasets based on normal distributions of actual leaf chemical and structural properties can be used to optimize remotely sensed spectral indices or other retrieval methods for analysis of leaf chemical constituents. This study concludes with the definition of several polynomial relationships to retrieve leaf chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area using spectral indices, derived from synthetic data and validated on a large variety of leaf types. The straightforward method described here brings the possibility to apply or adapt statistical relationships to any type of leaf.
► Leaf reflectance datasets are simulated using radiative transfer modeling. ► Different distributions of leaf chemical and structural properties are tested. ► Statistical relationships linking chemistry to simulated reflectance are adjusted. ► These relationships are successfully applied to experimental data. ► Simulated data help optimizing spectral indices already published.
The PROSPECT leaf optical model has, to date, combined the effects of photosynthetic pigments, but a finer discrimination among the key pigments is important for physiological and ecological ...applications of remote sensing. Here we present a new calibration and validation of PROSPECT that separates plant pigment contributions to the visible spectrum using several comprehensive datasets containing hundreds of leaves collected in a wide range of ecosystem types. These data include leaf biochemical (chlorophyll
a, chlorophyll
b, carotenoids, water, and dry matter) and optical properties (directional–hemispherical reflectance and transmittance measured from 400 nm to 2450 nm). We first provide distinct
in vivo specific absorption coefficients for each biochemical constituent and determine an average refractive index of the leaf interior. Then we invert the model on independent datasets to check the prediction of the biochemical content of intact leaves. The main result of this study is that the new chlorophyll and carotenoid specific absorption coefficients agree well with available
in vitro absorption spectra, and that the new refractive index displays interesting spectral features in the visible, in accordance with physical principles. Moreover, we improve the chlorophyll estimation (RMSE
=
9 µg/cm
2) and obtain very encouraging results with carotenoids (RMSE
=
3 µg/cm
2). Reconstruction of reflectance and transmittance in the 400–2450 nm wavelength domain using PROSPECT is also excellent, with small errors and low to negligible biases. Improvements are particularly noticeable for leaves with low pigment content.
This study presents a robust approach for characterization of multi-layered forests using airborne laser scanning (ALS) data. Fuel mapping or biomass estimation requires knowing the diversity and ...boundaries of the forest patches, as well as their spatial pattern. This includes the thickness of the main vegetation layers, but also the spatial arrangement and size of the individual plants that compose each stratum. In order to decompose the ALS point cloud into genuine 3-D segments corresponding to individual vegetation features, such as shrubs or tree crowns, we apply a statistical approach based on the mean shift algorithm. The segments are progressively assigned to a forest layer: ground vegetation, understory or overstory. Our method relies on a single biophysically meaningful parameter, the kernel bandwidth, which is related to the local forest structure. It is validated on 44 plots of a Portuguese forest, composed mainly of eucalyptus (Eucalyptus globulus Labill.) and maritime pine (Pinus pinaster Ait.) trees. The number of detected trees varies with the dominance position: from 98.6% for the dominant trees to 12.8% for the suppressed trees. Linear regression models explain up to 70% of the variability associated with ground vegetation and understory height.
► Robust approach for multi-layered forest characterization using ALS data. ► We adapt the mean shift algorithm to extract individual vegetation features. ► Applying the method directly to the 3-D point cloud improves the segmentation of forests. ► The segments are assigned to surface vegetation, understory or overstory. ► Validated on 44 plots of a heterogeneous forest mainly composed of eucalyptus and pine trees.
Life on Earth depends on photosynthesis. Photosynthetic systems evolved early in Earth history and have been stable for 2.5 billion years, providing prima facie evidence for the significance of ...pigments in plant functions. Photosynthetic pigments fill multiple roles from increasing the range of energy captured for photosynthesis to protective functions. Given the importance of pigments to leaf functioning, greater effort is needed to determine whether individual pigments can be identified and quantified
in vivo using high fidelity spectroscopy. We review recent advances in detecting plant pigments at the leaf level and discuss successes and reasons why challenges remain for robust remote observation and quantification. New methods to identify and quantify individual pigments in the presence of overlapping absorption features would provide a major advance in understanding their biological functions, quantifying net carbon exchange, and identifying plant stresses.
Many plant species have dorsiventral leaves that have significant differences in optical properties from one side to the other. Several studies have revealed that ignoring this asymmetry induces ...significant errors in plant canopy reflectance, and current leaf models simulating leaf dorsiventrality are limited to the 0.4–2.5 μm wavelength range. This article, partly based on two recently collected datasets in the 2.5–14 μm wavelength range, demonstrates that ignoring leaf dorsiventrality induces significant errors in brightness temperature and effective emissivity at the canopy scale. The PROLIB model, which inherits from the PROSPECT-VISIR and LIBERTY models, is the first radiative transfer model to simulate the reflectance and transmittance of both leaf sides from 0.4 to 5.7 μm. The palisade and spongy mesophylls are represented as plate and sphere layers, respectively, to account for the structural asymmetry of leaf cells. The sieve effect that explains the differences in transmittance between the adaxial and abaxial sides of the leaf is successfully incorporated into PROLIB. Evaluation of the model on several leaf datasets shows that: (1) It reproduces well the adaxial and abaxial optical properties of the leaves, with a root mean square error (RMSE) of 0.0109 for reflectance and transmittance. (2) It can be inversed to retrieve leaf traits, with RMSE values for leaf chlorophyll, carotenoid, anthocyanin, water, and dry matter content of 5.519 μg/cm2, 2.344 μg/cm2, 4.219 μg/cm2, 0.0022 g/cm2, and 0.0017 g/cm2, respectively (corresponding normalized RMSE values of 22.0%, 34.0%, 49.4%, 19.6%, and 24.7%). However, better and more complete leaf datasets are needed for leaf dorsiventrality analysis and model calibration.
•Adaxial and abaxial leaf surfaces have different spectral properties in 2.5–14 μm.•A leaf model called PROLIB is developed to simulate leaf dorsiventrality.•PROLIB takes into account the sieve effect.•PROLIB reconstructs leaf spectra well from multiple datasets, with an RMSE of 0.0109.
This paper outlines the first part of a series of research studies to investigate the potential and approaches for using optical remote sensing to assess vegetation water content. It first analyzes ...why most methods used as approximations of vegetation water content (such as vegetation stress indices, estimation of degree of curing and chlorophyll content) are not suitable for retrieving water content at leaf level. It then documents the physical basis supporting the use of remote sensing to directly detect vegetation water content in terms of Equivalent Water Thickness (EWT) at leaf level. Using laboratory measurements, the radiative transfer model PROSPECT and a sensitivity analysis, it shows that shortwave infrared (SWIR) is sensitive to EWT but cannot be used alone to retrieve EWT because two other leaf parameters (internal structure and dry matter) also influence leaf reflectance in the SWIR. A combination of SWIR and NIR (only influenced by these two parameters) is necessary to retrieve EWT at leaf level. These results set the basis towards establishing operational techniques for the retrieval of EWT at top-of-canopy and top-of-atmospheric levels.
Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf ...reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA).
Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R2: 0.51–0.82, p<0.0001). The best robustness (R2=0.74, RMSE=18.97g/m2 and Bias=0.12g/m2) was obtained using a combination of two low-scale features (1639nm, scale 4) and (2133nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the NDLMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or spaceborne imaging spectroscopy data for mapping canopy foliar dry biomass.
PDF
