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.radiative effect | spurious regression | plant ecology | carbon cycle

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Key Findings: Combining physical fractionation and pyrolysis–gas chromatography/mass spectrometry (py-GC/MS) technique can help better understand the dynamics of soil organic matter (SOM). Background and Objectives: SOM plays a critical role in the global carbon (C) cycle. However, its complexity remains a challenge in characterizing chemical molecular composition within SOM and under nitrogen (N) deposition. Materials and Methods: Three particulate organic matter (POM) fractions within SOM and under N treatments were studied from perspectives of distributions, C contents and chemical signatures in a subtropical forest. N addition experiment was conducted with two inorganic N forms (NH4Cl and NaNO3) applied at three rates of 0, 40, 120 kg N ha−1 yr−1. Three particle-size fractions (>250 μm, 53–250 μm and <53 μm) were separated by a wet-sieving method. Py-GC/MS technique was used to differentiate between chemical composition. Results: A progressive proportion transfer of mineral-associated organic matter (MAOM) to fine POM under N treatment was found. Only C content in fine POM was sensitive to N addition. Principal component analyses (PCA) showed that the coarse POM had the largest plant-derived markers (lignins, phenols, long-chain n-alkanes, and n-alkenes). Short-chain n-alkanes and n-alkenes, benzofurans, aromatics and polycyclic aromatic hydrocarbons mainly from black carbon prevailed in the fine POM. N compounds and polysaccharides from microbial products dominated in the MAOM. Factor analysis revealed that the degradation extent of three fractions was largely distinct. The difference in chemical structure among three particulate fractions within SOM was larger than treatments between control and N addition. In terms of N treatment impact, the MAOM fraction had fewer benzofurans compounds and was enriched in polysaccharides, indicating comparatively weaker mineralization and stronger stabilization of these substances. Conclusions: Our findings highlight the importance of chemical structure in SOM pools and help to understand the influence of N deposition on SOM transformation.

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Abstract-Scattering from a leaf responds differently at different wavelengths to changes in leaf properties such as pigment concentrations, chemical constituents, internal structure and leaf surface properties. Radiation scattered by leaves and exiting the vegetation canopy toward the sensor is affected by canopy structure. The concept of canopy spectral invariants is used to decompose multi-angular hyperspectral CHRIS-PROBA surface reflectances over agricultural crops during peak growth season into structural and optical components. The former called the Directional Area Scattering Factor is determined by the canopy geometrical properties and varies with crop type. The latter is a function of the leaf scattering properties and more directly related to the leaf interior. For dense crops the decomposition technique does not require the use of canopy radiation models, prior knowledge, or ancillary information regarding the leaf scattering properties and thus provides a powerful means to remove canopy structural influences in hyperspectral remote sensing of leaf biochemical constituents. Our results also suggest that leafsurface characteristics can increase canopy scattering spectra. This may decrease the ability to remotely sense leaf biochemistry.Index Terms-hyperspectral remote sensing; leaf optics; canopy structure, leaf biochemical constituents, spectral invariants.

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