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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 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 reectance was largely attrib-
utable to structure (which is in turn caused
by variation in fraction of broadleaf can-
opy). Our conclusion that the observed cor-
relation with %N was spurious (i.e., lacking
a causal basis) is, thus, clearly justied: we
demonstrated that structure explained the
great majority of observed correlation, where
the structural inuence was derived precisely
via reconciling the observed correlation with
radiative-transfer theory. What this also sug-
gests is that such correlations, although ob-
served, do not uniquely provide information
on canopy biochemical constituents. We,
therefore, disagree with the assertion in ref.
1thatwedid not provide an adequate ra-
tionale 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
423855 nm (gure 7B and gure 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 ac-
curately estimated without accounting for
canopy structural effects.
We identied a structural variable, the
directional area scattering factor (DASF),
which was determined entirely by canopy
geometrical properties such as shape and
size of the tree crowns, spatial distribution
of trees on the ground, within-crown foliage
arrangement, and properties of the leaf sur-
faces. In dense vegetation, this parameter
can be directly retrieved from the reectance
spectrum without the use of canopy-reec-
tance models, prior knowledge, or ancillary
information regarding leaf optical properties
(3). Equations S4.1S5.3 in ref. 3 explain the
background physics, but Townsend et al.
(1), nonetheless, misinterpret this as the
authors used a single leaf spectrum derived
from one PROSPECT simulation.We
clearly demonstrated that DASF provides
information critical to accounting for struc-
tural contributions to measurements of leaf
biochemistry from remote sensing.
Lastly, we do not claim that links be-
tween leaf biochemistry (e.g., %N) and
hyperspectralreectance data are ob-
scured by variation in leaf-surface albedo,
as overstated in ref. 1. We emphasized that
some radiation is scattered at the surface
of leaves and, therefore, contains no infor-
mation on leaf biochemistry; this presents
an additional confounding factor, unless it
can be accounted for.
Statistical relationships between leaf bio-
chemistry and canopy reectance spectra
have indeed been repeatedly demonstrated.
However, analyses of underlying physical
mechanisms that generate the remotely
measured signal, which are required to
distinguish causality from correlation (4),
such as ours, have been lacking thus far.
This is absolutely necessary to obtain ac-
curate information on leaf biochemistry
from space (5). We agree that analyses in-
cluding both biologically and physically
based approaches will help reveal the sub-
tleties of the empirical relationships.
Yuri Knyazikhin
, Philip Lewis
Mathias I. Disney
, Pauline Stenberg
Matti Mõttus
, Miina Rautiainen
Robert K. Kaufmann
, Alexander
, Mitchell A. Schull
, Pedro
Latorre Carmona
, Vern Vanderbilt
Anthony B. Davis
, Frédéric Baret
Stéphane Jacquemoud
, Alexei Lyapustin
Yan Yang
, and Ranga B. Myneni
Department of Earth and Environment,
Boston University, Boston, MA 02215;
Department of Geography and National
Centre for Earth Observation, University
College London, London WC1E 6BT, United
Kingdom; Departments of
Forest Sciences and
Geosciences and Geography, University of
Helsinki, FI-00014, Helsinki, Finland;
and Radiation Laboratory, Code 613, National
Aeronautics and Space Administration
Goddard Space Flight Center, Greenbelt, MD
Hydrology and Remote Sensing
Laboratory, US Department of Agriculture
Agricultural Research Service, Beltsville, MD
Departamento de Lenguajes y Sistemas
Informáticos, Universidad Jaume I, 12071
Castellón, Spain;
Biospheric Science Branch,
Earth Science Division, National Aeronautics
and Space Administration Ames Research
Center, Moffet Field, CA 94035;
Jet Propulsion
Laboratory, California Institute of Technology,
Pasadena, CA 91109;
Unité Mixte de
Recherche 1114 Environnement Méditerranéen
et Modélisation des Agro-Hydrosystèmes,
Institut National de la Recherche Agronomique
Site Agroparc, 84914 Avignon, France; and
Institut de Physique du Globe de Paris
Sorbonne Paris Cité, Université Paris Diderot,
Unité Mixte de Recherche Centre National de la
Recherche Scientique 7154, 75013 Paris,
1Townsend PA, Serbin SP, Kruger EL, Gamon JA (2013)
Disentangling the contribution of biological and physical properties
of leaves and canopies in imaging spectroscopy data. Proc Natl Acad
Sci USA, 10.1073/pnas.1300952110.
2Ollinger SV, et al. (2008) Canopy nitrogen, carbon assimilation, and
albedo in temperate and boreal forests: Functional relations and potential
climate feedbacks. Proc Natl Acad Sci USA 105 (49):1933619341.
3Knyazikhin Y, et al. (2013) Hyperspectral remote sensing of foliar
nitrogen content. Proc Natl Acad Sci USA 110(3):E185E192.
4Fisher JB (2009) Canopy nitrogen and albedo from remote sensing:
What exactly are we seeing? Proc Natl Acad Sci USA 106(7):E16E16,
author reply E17.
5Ustin SL (2013) Remote sensing of canopy chemistry. Proc Natl
Acad Sci USA 110(3):804805.
Author contributions: Y.K., P.L., M.I.D., P.S., M.M., M.R., R.K.K.,
A.M., M.A.S., P.L.C., V.V., A.B.D., F.B., S.J., A.L., Y.Y., and R.B.M.
wrote the paper.
The authors declare no conict of interest.
To whom correspondence should be addressed. E-mail: jknjazi@ PNAS Early Edition
... With regard to the canopy trait models our findings supported our hypothesis that multispectral satellite data is able to model the investigated canopy traits along the elevational gradient and within single study sites. However, there is an ongoing debate whether there is a direct or an indirect relationship between spectral reflectance data and biochemical leaf traits (Knyazikhin et al., 2013a;Lepine et al., 2016;Ollinger, 2011;Townsend et al., 2013). On the one hand our results are in line with previous studies showing that the characteristics of multispectral wavelength bands and indices are relevant to predict canopy traits (Ali et al., 2017a;Asner and Martin, 2008;Lepine et al., 2016;Lymburner et al., 2000). ...
... foliar N (Lepine et al., 2016). On the other hand Knyazikhin et al. (2013bKnyazikhin et al. ( , 2013a proposed that foliar N cannot be estimated by the NIR without accounting for canopy structure. A different opinion is that the relationship between NIR and foliar N might be derived via the correlation of foliar N with structural traits (Ollinger et al., 2013;Townsend et al., 2013). ...
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... In addition, since this paper uses empirical models, although Zhou et al. [49], Niu et al. [50], Yue et al. [15], Wu et al. [42], and Liang et al. [51] used the vegetation index from the estimation model used herein, the crop parameters were estimated by using empirical methods. However, to estimate crop canopy variables, one must consider confounding factors that also affect the estimation, such as leaf or canopy structure and the understory in multiple-scattering processes, soil parameters, and some external parameters [56][57][58][59][60][61]. To determine how canopy structure affects estimates of crop parameters, Bendig et al. [62] used UAV RGB imagery to obtain crop surface models, extract crop heights, and use the extracted crop heights to estimate biomass, which improved the accuracy of the estimates. ...
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Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results are as follows: (1) The RGB-imagery indices red reflectance (r), the excess-red index (EXR), the vegetation atmospherically resistant index (VARI), and the modified green-red vegetation index (MGRVI), as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index (MSR), the simple ratio vegetation index (SR), and the normalized difference vegetation index (NDVI), are more strongly correlated with the CGI than a single growth-monitoring indicator. (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stages in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.
... Researchers from the radiative transfer community are less optimistic that proteins (nitrogen) can be retrieved by means of physically-based methods Jacquemoud et al., 1996;Knyazikhin et al., 2013a). This is also mirrored by our literature review with only three studies having used RTM-based approaches within the agricultural context to derive N. It is therefore questionable if satellite-based reflectance will allow for an accurate, transferable and physically-based retrieval of N. According to a broad body of research, the accurate estimation of N is mainly possible through indirect correlations with those traits that are more explicitly linked to the radiative transfer -mostly chlorophyll content. ...
Nitrogen (N) is considered as one of the most important plant macronutrients and proper management of N therefore is a prerequisite for modern agriculture. Continuous satellite-based monitoring of this key plant trait would help to understand individual crop N use efficiency and thus would enable site-specific N management. Since hyperspectral imaging sensors could provide detailed measurements of spectral signatures corresponding to the optical activity of chemical constituents, they have a theoretical advantage over multi-spectral sensing for the detection of crop N. The current study aims to provide a state-of-the-art overview of crop N retrieval methods from hyperspectral data in the agricultural sector and in the context of future satellite imaging spectroscopy missions. Over 400 studies were reviewed for this purpose, identifying those estimating mass-based N (N concentration , N%) and area-based N (N content, N area) using hyperspectral remote sensing data. Retrieval methods of the 125 studies selected in this review can be grouped into: (1) parametric regression methods, (2) linear nonparametric regression methods or chemometrics, (3) nonlinear nonparametric regression methods or machine learning regression algorithms, (4) physically-based or radiative transfer models (RTM), (5) use of alternative data sources (sun-induced fluorescence, SIF) and (6) hybrid or combined techniques. Whereas in the last decades methods for estimation of N area and N% from hyperspectral data have been mainly based on simple parametric regression algorithms, such as narrowband vegetation indices, there is an increasing trend of using machine learning, RTM and hybrid techniques. Within plants, N is invested in proteins and chlorophylls stored in the leaf cells, with the proteins being the major nitrogen-containing biochemical constituent. However, in most studies, the relationship between N and chlorophyll content was used to estimate crop N, focusing on the visible-near infrared (VNIR) spectral domains, and thus neglecting protein-related N and reallocation of nitrogen to non-photosynthetic compartments. Therefore, we recommend exploiting the estimation of nitrogen via the proxy of proteins using hyperspectral data and in particular the shortwave infrared (SWIR) spectral domain. We further strongly encourage a standardization of nitrogen terminology, distinguishing between N% and N area. Moreover, the exploitation of physically-based approaches is highly recommended combined with machine learning regression algorithms, which represents an interesting perspective for future research in view of new spaceborne imaging spectroscopy sensors.
... Studies performed in grasslands and forest (see Section 2.6.2) on leaf traits such as specific leaf area (SLA), leaf mass per area (LMA), and water content have shown sensitivity to wavelengths in the shortwave infrared (SWIR) region of the electromagnetic spectrum (1200-2500 nm) (Ali et al., 2017a,b;Mirzaie et al., 2014;Romero et al., 2012). While the spectral bands sensitive to foliar chlorophyll and carotenoids, exist in the visible and red edge region (400-750 nm) (Curran, 1989;Dawson et al., 1999), leaf nitrogen has been related to different spectral regions (Curran, 1989;Fourty et al., 1996;Kokaly et al., 2009;Ollinger et al., 2008;Knyazikhin et al., 2013;Wang et al., 2017). This may be explained by the interrelation and interactions of leaf nitrogen with other leaf and structural traits. ...
... One study reported that canopy nitrogen contents scale with NIR reflectance in temperate to boreal forests (Ollinger et al., 2008). However, another group argued that the good correlation between canopy nitrogen contents and NIR reflectance was an artifact caused by variations in canopy structure across sites (Knyazikhin et al., 2013b), which led to further debates (Knyazikhin et al., 2013a;Townsend et al., 2013). In fact, it is well known that the most direct absorption signal related to protein and nitrogen content is located in the short-wave infrared part of the spectrum (Curran, 1989;Kokaly, 2001). ...
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... An albedo-nitrogen relation for closed-canopy temperate and boreal forests was used in the previous study. However, a recent study showed that such relation might not be direct at the satellite-level, without accounting for canopy structural effects (Knyazikhin et al., 2013). Therefore, the relationship was not used in this study. ...
Several global gross primary production (GPP) and evapotranspiration (ET) remote sensing products exist, mainly provided by machine-learning (e.g. MPI-BGC) and semi-empirical (e.g. MODIS) approaches. Process-based approaches have the advantage of representing the atmosphere-vegetation-soil system and associated fluxes as an organic integration, but their sophistication results in a lack of high spatiotemporal resolution continuous products. Targeting this gap, we reported a new set of global 8-day composite 1-km resolution GPP and ET products from 2000 to 2015, using a simplified process-based model, the Breathing Earth System Simulator (BESS). BESS couples atmosphere and canopy radiative transfer, photosynthesis and evapotranspiration, and uses MODIS atmosphere and land data and other satellite data sources as inputs. We evaluated BESS products against FLUXNET observations at site scale (total of 113 sites, 742 site years), and against MPI-BGC products at global scale. At site scale, BESS 8-day products agreed with FLUXNET observations with R² = 0.67 and RMSE = 2.58 gC m− 2 d− 1 for GPP, and R² = 0.62 and RMSE = 0.78 mm d− 1 for ET, respectively, and they captured a majority of seasonal variability, about half of spatial variability, and a minority of interannual variability in FLUXNET observations. At global scale, BESS mean annual sum GPP and ET maps agreed with MPI-BGC products with R² = 0.93 and RMSE = 229 gC m− 2 y− 1 for GPP, and R² = 0.90 and RMSE = 118 mm y− 1 for ET, respectively. Over the period of 2001–2011, BESS quantified the mean global GPP and ET as 122 ± 25 PgC y− 1 and 65 × 10³ ± 11 × 10³ km³ y− 1, respectively, with a significant ascending GPP trend by 0.27 PgC y− 2 (p < 0.05), similar to MPI-BGC products as well. Overall, BESS GPP and ET estimates were comparable with FLUXNET observations and MPI-BGC products. The process-based BESS can serve as a set of independent GPP and ET products from official MODIS GPP and ET products.
... Although estimation of canopy foliar %N has been tested by using the ratio of BRF and DASF spectra (canopy scattering coefficient, W ) after suppressing the impact of canopy structure (Knyazikhin et al., 2013a), the analyses were restricted to using information from each wavelength between 423 and 855 nm (Knyazikhin et al., 2013a(Knyazikhin et al., , 2013b. Canopy foliar %N estimates using the visible spectral region rely on the well-known correlation between chlorophyll and nitrogen (Evans 1989;Field and Mooney 1986;Kokaly et al., 2009). ...
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A precise estimate of canopy leaf nitrogen concentration (CNC, based on dry mass) is important for researching the carbon assimilation capability of forest ecosystems. Hyperspectral remote sensing technology has been applied to estimate regional CNC, which can adjust forest photosynthetic capacity and carbon uptake. However, the relationship between forest CNC and canopy spectral reflectance as well as its mechanism is still poorly understood. Using measured CNC, canopy structure and species composition data, four vegetation indices (VIs), and near-infrared reflectance (NIR) derived from EO-1 Hyperion imagery, we investigated the role of canopy structure traits and plant functional types (PFTs) in modulating the correlation between CNC and canopy reflectance in a temperate forest in northeast China. A plot-scale forest structure indicator, named broad foliar dominance index (BFDI), was introduced to provide forest canopy structure and coniferous and broadleaf species composition. Then, we revealed the response of forest canopy reflectance spectrum to BFDI and CNC. Our results showed that leaf area index had no significant effect on NIR (P>0.05) but indicated that there was a significant correlation (R2=0.76, P<0.0001) between CNC and BFDI. NIR had a more significant correlation with BFDI than with CNC for all PFTs, but it had no obvious correlation with CNC for single PFT. Partial correlation analysis showed that four VIs had better correlations with BFDI than with CNC. When the effect of BFDI was removed, the partial correlation between CNC and NIR was insignificant (R=0.273, P>0.05). On the contrary, removing the CNC effect, the partial correlation between BFDI and NIR was positively significant (R=0.69, P<0.0001). These findings proved that canopy structure and coniferous and broadleaf species composition had a greater influence on the remote sensing signal than canopy nitrogen concentration. The functional convergence of plant traits resulted in the relation of CNC and canopy structure and determined the positive correlation between CNC and NIR. We maintain that the repeatable relationship between CNC and NIR can be used in the remote sensing retrieval of CNC during various forest types. Nevertheless, the relationship cannot be considered as a feasible approach of CNC estimation for a single PFT. © 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
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One of the major uncertainties in predicting climate change comes from a full accounting of carbon-cycle feedbacks, which roughly double physical feedbacks (1, 2). Most of this uncertainty is a result of the many pathways and time scales at which ecosystems interact with the climate system and how these will respond to change (3). The relationship between leaf nitrogen and the carbon cycle is key to many ecosystem processes because photosynthesis provides the energy and carbon-cycle molecules for growth and reproduction (4⇓⇓–7) and decomposition for nutrient cycling (7, 8). Ecologists have long recognized that nitrogen was the most limited nutrient for plant growth (9, 10). Quantifying changes in canopy nitrogen content provides direct information about ecosystem functioning and a method to detect and monitor changes in response to climate forcing (9, 10); thus, it has been a long-term objective for airborne and spaceborne imaging spectroscopy (11⇓–13). Several papers have reported direct detection of canopy nitrogen from airborne imaging spectrometers (14⇓⇓–17). Ollinger (18) argues that selective pressure on plant competition for light, water, and nutrients should result in suites of biochemical and structural traits that integrate their functional strategies. Thus, structural traits affecting light scattering “over scales ranging from cells to canopies” (18) will be convergent with their biochemical traits. Knyazikhin et al. …
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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.
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The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earth's climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem CO(2) uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both CO(2) uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycle-climate models.
Ollinger et al. (1) report that albedo depends on canopy nitrogen. A critic may dismiss this as correlation-does-not-equal-causation because the underlying mechanisms linking albedo with nitrogen are unclear. Albedo is controlled by processes other than nitrogen, for instance, canopy water content (2, 3). Moist dark soil has a lower albedo than dry bright soil (4), so a drier canopy should change the albedo without changing the canopy nitrogen. Similarly, within the spatial resolution of a MODIS pixel, a tree may fall, revealing bare soil and thus changing the albedo of the pixel (5). Would the canopy nitrogen change appropriately? Ollinger et al. (1) excluded patterns in photosynthetically active radiation wavelengths, thus making it difficult to separate albedo from the canopy versus other objects (essentially the basis of vegetation indices). The albedo of the surface would increase with snowfall (not predicted by canopy nitrogen), though this effect can easily be excluded.