Hyperspectral remote sensing of foliar nitrogen content

Abstract

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

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

“…3 was "determined as the mean of mass-based foliar %N over all species in each plot (weighted by the relative abundance of each)." In both cases we found canopy structure dominated variations in NIR reflectance with %N, resulting in spurious correlation (2). We therefore disagree with Ollinger et al (1,3) that the observed NIR vs. %N relationship alone adequately justifies its use in remote sensing: reflectance data must be corrected for canopy structure effects to extract information about %N and other chemical constituents.…”

“…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.…”

Tropospheric emissions: Monitoring of pollution (TEMPO)

“…Spectral approaches for estimating pigment contents apply generally to leaves and not the full canopy. A single spectrally invariant parameter, the Directional Area Scattering Factor (DASF), relates canopy-measured spectral indices to pigment concentrations at the leaf scale [57]. The DASF retrieval can be retrieved using reflectances in TEMPO's 710-740 nm spectral range [57] and does not require canopy reflectance models or prior information on canopy structure.…”

Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence

“…13,14). The signal of the so-called spectral vegetation indices convolves leaf chlorophyll content, biomass, canopy structure, and cover (15,16), such that estimating actual productivity from vegetation indices requires additional data and modeling steps, both associated with considerable uncertainty. Complementing reflectance-based indices, global space-based estimates of sun-induced chlorophyll fluorescence (SIF) became available recently.…”

“…The first global maps of SIF were derived using data from the Greenhouse Gases Observing Satellite (GOSAT) (20)(21)(22)(23). Despite the complicated photosynthesis-SIF relationships and the convolution of the signal with canopy structure (16), SIF retrievals showed high correlations with data-driven GPP estimates at global and annual scales (21,22), as well as intriguing patterns of seasonal drought response in Amazonia (24,25). Recently, a global SIF data set with better spatial and temporal sampling than that from GOSAT was produced using spectra from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument onboard the MetOp-A platform (26) (see SI Appendix, SIF Retrievals).…”