Remote Sensing of Environment 112 (2008) 3958–3970

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Remote Sensing of Environment

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Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels

Gregory P. Asner ⁎, Roberta E. MartinDepartment of Global Ecology, Carnegie Institution, 260 Panama Street, Stanford, CA 94305, USA

⁎ Corresponding author. Tel.: +1 650 462 1047.E-mail addresses: gpa@stanford.edu (G.P. Asner), rem

(R.E. Martin).

0034-4257/$ – see front matter © 2008 Elsevier Inc. Aldoi:10.1016/j.rse.2008.07.003

a b s t r a c t

a r t i c l e i n f o

Article history:

Variation in the foliar che Received 29 April 2008Received in revised form 2 July 2008Accepted 4 July 2008

Keywords:Canopy chemistryRadiative transferCanopy structureHyperspectralImaging spectroscopyPartial least squaresTropical forest

mistry of humid tropical forests is poorly understood, and airborne imagingspectroscopy could provide useful information at leaf and canopy scales. However, variation in canopystructure affects our ability to estimate foliar properties from airborne spectrometer data, yet these structuralaffects remain poorly quantified. Using leaf spectral (400–2500 nm) and chemical data collected from 162Australian tropical forest species, along with partial least squares (PLS) analysis and canopy radiative transfermodeling, we determined the strength of the relationship between canopy reflectance and foliar propertiesunder conditions of varying canopy structure.At the leaf level, chlorophylls, carotenoids and specific leaf area (SLA) were highly correlated with leafspectral reflectance (r=0.90–0.91). Foliar nutrients and water were also well represented by the leaf spectra(r=0.79–0.85). When the leaf spectra were incorporated into the canopy radiative transfer simulations withan idealistic leaf area index (LAI)=5.0, correlations between canopy reflectance spectra and leaf propertiesincreased in strength by 4–18%. The effects of random LAI (=3.0–6.5) variation on the retrieval of leafproperties remained minimal, particularly for pigments and SLA (r=0.92–0.93). In contrast, correlationsbetween leaf nitrogen (N) and canopy reflectance estimates decreased from r=0.87 at constant LAI=5 tor=0.65 with randomly varying LAI=3.0–6.5. Progressive increases in the structural variability amongsimulated tree crowns had relatively little effect on pigment, SLA and water predictions. However, N andphosphorus (P) were more sensitive to canopy structural variability. Our modeling results suggest thatmultiple leaf chemicals and SLA can be estimated from leaf and canopy reflectance spectroscopy, and that thehigh-LAI canopies found in tropical forests enhance the signal via multiple scattering. Finally, the two factorswe found to most negatively impact leaf chemical predictions from canopy reflectance were variation in LAIand viewing geometry, which can be managed with new airborne technologies and analytical methods.

© 2008 Elsevier Inc. All rights reserved.

1. Introduction

Leaf and canopy chemical properties are principal determinants ofplant physiology and biogeochemical processes in terrestrial ecosys-tems (Hedin, 2004). However, we know little about the spatialvariation of canopy chemistry in humid tropical forests. What we doknow comes from sparse field-based data sets focused on just a fewchemicals, usually nitrogen (N) and phosphorus (P), and from just afew tropical forest sites globally. Geographic variation in leaf N and Pis, to some degree, driven by variation in climate and soil fertility (Johnet al., 2007; McGroddy et al., 2004; Vitousek and Sanford, 1986).However, within many forest stands, crown-by-crown variability in Nand P is often dominated by the taxonomic diversity of plant species(Townsend et al., 2007).

Leaf nutrients such asN and P are central to understanding plant andwhole ecosystem function, but so are other leaf properties, especiallywater content, pigments such as chlorophylls, carotenoids and antho-

artin@stanford.edu

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cyanins, and specific leaf area (SLA; cm2 g−1). Leaf water is an importantfactor regulating canopy temperature andmoisture stress, bothofwhichare particularly acute in tropical forest canopies (Nepstad et al., 2002;Williamson et al., 2000). Pigments are fundamental determinants oflight capture and utilization, and they provide protection against theharmful effects of high radiation, which is also common in the tropics(Björkman and Demmig-Adams, 1995; Evans et al., 2004). SLA is a leafstructural property linked to the entire constellation of foliar chemicalsand photosynthetic processes (Niinemets and Sack, 2006; Wright et al.,2004). Among tropical forest species, a few of these leaf properties areinter-correlated, butmanyare not, owing to thepronounced genetic andphenotypic diversity of plants in these systems (Asner, 2008; Townsendet al., 2007). Quantifying multiple leaf properties is a challenge in anyecosystem, and it is critical to understanding the role that canopyspeciesplay in determining tropical forest responses to climate change (Clark,2004).

High spectral resolution remote sensing, particularly imaging spec-troscopy, can provide estimates of leaf and canopy chemical properties(e.g., Curran et al., 1997; Martin and Aber, 1997; Matson et al., 1994;Smith et al., 2003; Wessman et al., 1988). Numerous leaf-level spectralreflectance studies have demonstrated connections to a variety of foliar

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characteristics, including N, chlorophyll, carotenoids, water, and evenSLA (summarized by Sims and Gamon, 2002; Ustin et al., 2004).Although canopy-level hyperspectral measurements have also beenlinked to plant chemistry, the relationships are frequently lower inprecision and accuracy compared to those of leaf-level studies. Thedecreased accuracy of spectroscopic methods at the whole-pixel scaleoften results from canopy structural variation in the measured spectra,and in relation to viewing and solar geometry (Sandmeier and Deering,1999; Zarco-Tejada et al., 2001). Although these scaling issues have beenidentified in field and modeling studies, it remains unclear as to howwell leaf properties can be, and often are, retrieved from spectralreflectance data acquired from airborne imaging spectrometers.

Most applied canopy chemistry studies have relied on regressionanalysis with hyperspectral data, or with a selection of narrow spectralbands, to estimate individual chemicals of interest (Curran et al., 1997;Ollinger et al., 2002; Peñuelas et al., 1994; Serrano et al., 2000; Smithet al., 2003). With these empirical approaches, it is difficult to quantifythe effects of canopy structure on leaf chemical retrievals. In some cases,variable canopy structure can decrease the sensitivity of remote spectralmeasurements to leaf chemicals (Asner, 1998). In other cases, biologi-cally-driven covariance among leaf chemical and canopy structuralproperties, especially leaf area index (LAI), may enhance the apparentsensitivity of spectral data to foliar properties (Sellers, 1987).

What we know about the covariance or decoupling of leaf andcanopy effects in hyperspectral data comes mostly from radiativetransfer models (Baret et al., 1994; Jacquemoud et al., 2000). Mostmodeling studies have focused on simulations of total chlorophyll andwater content, in addition to canopy structure. However, laboratorystudies have often empirically linked leaf spectra to six or morechemicals plus SLA (Curran, 1989). The leaf models are steadilyimproving (Faret et al., 2008) but do not yet represent the full suite ofleaf chemical characteristics known to control optical properties. Anempirical approach that links leaf spectroscopy to multiple chemicals,combinedwith a physicalmodeling approach at the canopy level, wouldfacilitate an analysis of how foliar chemical and spectral informationscales upward and is expressed in airborne imaging spectrometer data.

In the specific case of humid tropical forests, the radiative conditionsare defined by canopies often having high LAI, with interlocking andoverlapping tree crowns, many of which differ in leaf angle distribution.In many ways, a densely foliated canopy presents a best-case scenariofor remote sensing of leaf chemicals, since foliar spectral properties areenhanced at the canopy scale in high-LAI conditions (Asner, 1998). Werecognize that the vertical distribution of foliage also affects leaf opticalproperties (Lee et al., 1990), as does phenology and extra-foliar, epiphyllgrowth (Roberts et al.,1998). Nonetheless, the spectral characteristics ofupper canopy foliage in tropical forests are driven by chemicalproperties (Asner and Martin, 2008), and these plant tissues in theupper reaches of the canopy strongly affect the reflectance properties oftropical forest canopies (Clark et al., 2005; Zhang et al., 2006). Despitethe progress made in understanding these basic principles, we do notknowthedegree towhich leaf chemicals are expressed in the reflectanceproperties of tropical forest canopies, particularly in the context ofvarying architecture and LAI.

Using a combination of empirical and physicalmodeling approaches,wequantifiedhowwell foliar chemical properties andSLAare expressedin leaf and canopy spectral data of humid tropical forest species. Firstwedeveloped quantitative linkages between multiple foliar chemical andoptical properties measured in 162 Australian rainforest canopy speciesusing Partial Least Squares (PLS) regression analysis. We used PLSbecause it has proven the most successful empirical approach forderiving foliar properties from canopy spectral data (Ollinger et al.,2002; Smith et al., 2003, 2002).We thenused a canopy radiative transfermodel and themeasured leaf spectra to simulate the canopies of the 162forest species, with prescribed combinations of simulated canopystructural properties, to test the strength of the relationship betweencanopy reflectance and foliar properties. With each set of simulations,

we increased the variability in LAI and other key structural propertiesamong the species to define the degree to which canopy structuralvariation can be accommodated in PLS analyses of leaf properties withairborne imaging spectroscopy. In doing so, we tested the followinghypotheses: (i) leaf spectral properties quantitatively represent a suite ofbiochemicals and SLA in the foliage of tropical forest tree species; (ii)high-LAI conditions increase the strength of the relationship betweenleaf properties and canopy-level spectral reflectance; and (iii) variationin canopy structural properties decreases the strength of the connectionbetween canopy reflectance and leaf properties.

2. Materials and methods

2.1. Leaf chemical and spectral data

This study utilized foliarmaterial collected at 11 tropical forest sitesacross Queensland, Australia, as reported by Asner et al. (in press). Afull description of the sites can also be obtained at http://spectra-nomics.stanford.edu. The foliage comprised a taxonomically diversedata set that includes 51 families,121 genera, and 162 species of canopytrees (Appendix A). Only fully sunlit portions of the uppermost treecrowns were selected for foliage collection. Laboratory assays werealso described in detail by Asner et al. (in press). Chlorophyll a and b(Chl-a, Chl-b), total carotenoids (Car), anthocyanins (Anth), leaf waterconcentration, total N and P, and SLAwere quantified for each species.

Hemispherical reflectance and transmittance from 400–2500 nmwas measured on each leaf taken for pigment analysis (n=5 perspecies). The measurements were made immediately after detachingthe leaf from each branch at the field site. The leaf spectra werecollected with a custom-designed, full-range spectrometer using1.4 nm sampling (FR-Pro with Select Test detectors and a customexit slit; Analytical Spectra Devices, Inc., Boulder, CO USA), anintegrating sphere modified for high resolution spectroscopic assays(Labsphere Inc., Durham, NH), and a custom illumination collimator.Measurements were collected with 200 ms integration time perspectrum. The spectra were then calibrated for dark current and straylight, and referenced to a calibration block (Spectralon, Labsphere Inc.,Durham, NH) within the integrating sphere.

2.2. Spectral–chemical analyses

We devised an approach to test the strength of the relationshipbetween multiple foliar properties and leaf or canopy spectral data(Table 1). The goal was to develop a set of controlled and comparablespectral analyses that scale from leaf to canopy levels, with thestructure of the canopies becoming increasingly variable, as describedbelow. All leaf spectral measurements and canopy simulations used220 spectral bands with 10 nm band-width (FWHM) spanning the400–2500 nm wavelength range. This configuration simulatedmeasurements acquired by the Airborne Visible Infrared ImagingSpectrometer (AVIRIS) (Green et al., 1998). The full-range leaf spectraldata were convolved to actual AVIRIS spectral response functionsprovided by the Jet Propulsion Laboratory, Pasadena, CA.

Starting at the foliar level, we used PLS regression analysis todetermine the relative contribution of each chemical constituent to the220-band leaf hemispherical reflectance and transmittance spectra ofthe species. Candidate chemicals included Chl-a, Chl-b, Car, Anth, N, Pand water as well as SLA. The PLS approach is advantageous because ituses the continuous spectrum as a single measurement rather than aband-by-band analysis. Spectral weightings generated by the PLScalculation directly relate the features in the spectra to the chemicalconstituents analyzed (Haaland and Thomas,1988). To avoid overfitting,the number of factors used in the PLS analysis was determined byminimizing the Prediction Residual Error Sum of Squares (PRESS)statistic (Chen et al., 2004). The PRESS statistic was calculated through across-validation prediction for each model. This cross-validation

Table 1Leaf and canopy parameters used in PLS and canopy radiative transfer model simulations

Analysis Description/model parameters

Leaf reflectance Field-measured hemispherical reflectanceLeaf transmittance Field-measured hemispherical transmittance

Canopy simulationsCase 1 LAI=5.0Case 2 LAI=3.0–6.5Case 3 LAI=1.5–8.0Case 4 LAD=four different distributionsa

Case 5 BR=0.5–2.5, HB=1.0–5.0Case 6 VZA=0°–20°, VAZ=0° or 180°Case 7 SSAI=0.1–0.6Case 8 LAD=four distributions, SSAI=0.1–0.6, BR=0.5–2.5,

HB=1.0–5.0, VZA=0°–20°, VAZ=0° or 180Case 9 LAI=3.0–6.5 plus all case 8 parametersCase 10 LAI=1.5–8.0 plus all case 9 parameters

Base-case Parameters LAD=uniform, SSAI=0.4, SAD=erectophile, BR=1.0,HB=3.0, SZA=30°, SAZ=0°, VZA=10°, VAZ=0°

Unless specified, all model parameters were fixed to a base-case set of values shown atthe bottom of the table.

a Leaf angle distributions included planophile, erectophile, plagiophile, and uniform(see text).

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procedure iteratively generates regression models while reserving onesample from the input data set until the root mean square error (RMSE)of the PRESS statistic is minimized. The PLS models were then used toestimate each leaf chemical and SLA from the original spectral data. Thisprovided a means to determine the absolute and relative importance ofeach foliar property in predicting the spectra. All PLS-PRESS analyseswere carried out using SAS JMP 7.0 statistical software package (SASInstitute Inc., Cary, NC). In addition, we processed a subset of the datathrough a second software package, GRAMS/AI 7.0 (Thermo Galactic,Durham, NH, USA), to verify the algorithms used in SAS JMP 7.0.

2.3. Canopy modeling

Using the leaf optical spectra collected in the field, we simulatedcanopy reflectance signatures for all species using an increasingamount of canopy structural variation with each successive set ofsimulations (Table 1). The canopy model has been presented by Asner(2000) and Asner and Vitousek (2005), which combines radiativetransfer (Iaquinta et al., 1997) and geometric–optical (Li and Strahler,1992) models, with extra attention on computational speed for highspectral resolution simulations. Spectrally, our model provides canopysimulations that are similar to the output of other models we havetested, including SAIL (Verhoef, 1984), DISORD (Myneni and Asrar,1993), and Nadime (Gobron et al., 1997).

The model simulates top-of-canopy spectral reflectance based onthe following scale-dependent factors:

R ¼ f ρtissue; τtissue; LAI; LAD; SSAI; SAD;GO�params;Geometryð Þ ð1Þ

where ρtissue and τtissue are the hemispherical reflectance and transmit-tance properties of plant tissues, LAI is the canopy leaf area index, LAD isthe canopy leaf angle distribution, SSAI is the stem silhouette area index,and SAD is the stem angle distribution. The tissues can include both livegreen foliage and senescent foliage or wood surfaces (Asner andWessman,1997). GO-params are three crown geometric–optical proper-ties that include the areal density of tree stems, the ratio of crownvertical to horizontal radius (BR), and the ratio of tree height (ground tocrown center) and vertical crown radius (HB). Geometry includes fourparameters of solar zenith and azimuth angles (SZA, SAZ), and sensorviewing zenith and azimuth angles (VZA, VAZ).

For our purposes, we are implicitly modeling high spatialresolution airborne data (e.g., b3 m) as would be acquired fromsensors such as AVIRIS (Green et al., 1998), HyMap (Cocks et al., 1998),

and the Carnegie Airborne Observatory (Asner et al., 2007). This isimportant here because, in the context of mapping humid tropicalforests, the spectrawould be collected at a spatial resolution finer thanmost tree crowns and vegetation clusters, thus simplifying themodeling problem, especially in terms of the geometric–opticalparameters. Specifically, we do not address tree density, intra-crowngaps and shadows in this study.

The first and simplest simulations set all canopy LAI values to 5.0(Table 1),which is about themean for bothAustralian (Nightingale et al.,2008) and global humid tropical forests (Asner et al., 2003). All othermodel parameters were held to a constant “base-case” ensemble asdescribed in Table 1. The purpose of the LAI=5 simulations (hereafterreferred to as case 1) was to establish the strength of the basic linkbetween canopy spectra, which convolves the contributions of leafreflectance and transmittance properties in a highly foliated canopy,with leaf chemical and SLAproperties. The PLS analyses described abovefor the leaf-level analyses were repeated using the simulated case 1spectra against the measured leaf chemical and SLA properties.

Following the above procedures,weused the PLS equations resultingfrom case 1 analysis, but with spectra from simulated canopies ofincreasing structural variability, to quantify the error resulting fromuncertainty in canopy properties (Table 1). For case 2, we allowed LAI tovary randomly from 3.0–6.5, which is one standard deviation of a globalLAI distribution compiled by Asner et al. (2003) for humid tropicalforests, and it is nearly the full range for all old growth tropical forests inAustralia (Nightingale et al., 2008). Eachcanopysimulationwas repeatedfive times, each with a different randomly selected LAI value between3.0 and 6.5, thereby providing five canopy spectra for each species.Witheach of the five sets of simulations (n=162 species per simulation), weused the case 1 PLS equations to estimate leaf properties. This entireprocedure was repeated with a random selection of LAI values in therange of 1.5 to 8.0 (case 3), which are minimum and maximum valuesreported for tropical forests in general (Asner et al., 2003).We think thisrange is highly unlikely in any single tropical forest stand (notwithstanding tree fall gaps, standing dead trees, etc.), so we consider ita very conservative test of how LAI variation affects remotely sensedchemical determinations.

Following the analyses focused on LAI variability, we added othersources of canopy structural variation to the simulations (Table 1).Case 4 focused on variation in leaf angle distribution, which wasrandomly selected for each canopy among four choices: planophile,erectophile, plagiophile, and uniform (Myneni et al., 1989). Moststudies using either forward or inverse modeling employ uniform orrandom LAD. However, tropical forest canopies often contain a mix ofcanopy types, usually with planophile or uniform distributions(Richards, 1952). Therefore, we also considered this test to berelatively conservative. Again, the simulations and PLS predictioncalculations were repeated five separate times.

Cases 5, 6 and 7 focused on the uncertainty in leaf chemical andSLA estimates caused by variation in crown shape, viewing geometry,and projected woody stem area, respectively (Table 1). Variability incrown dimensions was taken from the literature for humid tropicalforests (Clark et al., 2004; Gerard and North, 1997; Read et al., 2003).Viewing geometry was simulated to randomly select VZA values fromnadir (0°) to the edge of a scan-line typical of most airborne imagingspectrometers (20°), and VAZ was randomly selected at 0° or 180° tosimulate cross-track view azimuth. The solar geometry was fixed at 0°azimuth and 30° zenith, thereby simulating airborne scanning alongthe solar principal plane. Woody SSAI was randomly selected betweenthe range 0.1–0.6, which meets or exceeds typical values for mostforests (Asner,1998). Throughout the case 5 and 6 simulations, LAI washeld constant at 5 along with other base-case model inputs (Table 1).

Finally, cases 8–10 allowed for random selection among multiplecanopy parameters (Table 1). Case 8 held LAI constant at 5, with allother inputs varying between the ranges shown in Table 1. Cases 9 and10 were treated similarly, but with added LAI variability in the ranges

Table 2Leaf chemical properties and specific leaf area (SLA) for 162 tropical forest tree speciescollected in Queensland, Australia

Leaf property Mean±S.E. Minimum Maximum

SLA (cm2 g−1) 80.27±2.74 37.32 274.42Water (%) 58±1 43 79N (%) 1.74±0.05 0.75 3.50P (%) 0.12±0.00 0.05 0.34Chl-a (mg g−1) 3.45±0.12 0.68 10.67Chl-b (mg g−1) 1.37±0.05 0.24 4.79Carotenoids (mg g−1) 1.13±0.03 0.36 2.68Anthocyanins (mmol g−1) 0.36±0.03 0.00 2.10

All values are reported on a dry-mass basis.

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of 3.0–6.5 and 1.5–8.0, respectively. In effect, cases 9–10 representedworst-case scenarios in which no information or constraints would beapplied to a canopy chemical analysis using airborne imaging spec-trometers (discussed later). Again, each of these simulations wasrepeated five times (n=5×162 canopies) and the case 1 PLS equationswere used to estimate leaf properties.

3. Results and discussion

3.1. Leaf properties

Table 2 provides a summary of leaf chemical properties and SLAreported by Asner et al. (in press). The data are synthesized here tohighlight the wide range of values represented in the forthcoming leafand canopy spectral analyses. The leaf N and P data ranged from 0.75–3.5% and 0.05–0.34%, respectively, which nearly matches globaltropical forest compilations (McGroddy et al., 2004; Townsend et al.,2007). Total chlorophyll and carotenoid concentrations spanned a 16-fold and 7-fold range of values, respectively. The SLA data ranged from

Fig. 1. (a) Mean, +/−standard deviation, minimum, and maximum of leaf hemispherical refltransmittance. (c) PLS regression weightings for seven leaf chemicals and SLA predicted utransmittance spectra in panel (b).

37 to 274 cm2 g−1, whichmatches global tropical forest values (Wrightet al., 2004). As an ensemble of leaf properties, this Australia tropicalforest data set is highly variable from biochemical, physiological, andremote sensing perspectives.

3.2. Leaf PLS analyses

A summary of our leaf hemispherical reflectance and transmit-tancemeasurements for 162 tropical forest canopy species is shown inFig. 1a–b. Analogous to the chemical data, the variability in opticalproperties matched, and often exceeded, those reported for othertropical leaf data sets (Castro-Esau et al., 2006; Clark et al., 2005;Cochrane, 2000; Lee et al., 1990). Reflectance variation was highest inthe near-infrared (NIR; 700–1300 nm), whereas transmittance wasmost variable in the shortwave-IR (SWIR; 1500–2500 nm). The NIRspectral range is dominated by variation in leaf water content and leafthickness, related to SLA (Ceccato et al., 2001; Jacquemoud and Baret,1990). Transmittance variation in the SWIR is caused by leaf waterconcentration, with important contributions from protein N, celluloseand lignin (Curran,1989). The shape of the spectra in the visible region(400–700 nm) is associated with chlorophyll, carotenoid, andanthocyanin pigments (Sims and Gamon, 2002).

Leaf-level PLS analyses showed that seven of eight foliar propertieswere highly correlatedwith reflectance and transmittance spectra, withPLS weightings indicating chemical contributions throughout much ofthe wavelength range (Figs. 1c–d, 2). Particularly strong weightings(±50) showed that chlorophylls contributed most to the 510–730 nmrange,whereas SLAwas expressed throughout a broad spectral region of750–2500 nm. These results are typical of other leaf-level empirical(Gitelson and Merzlyak, 1997) and modeling (Jacquemoud and Baret,1990) studies. Chl-a and -b, as well as SLA, were estimated with high r-values of 0.90–0.91, and with RMSE values of just 0.63 mg g−1 (15% ofmean), 0.27 mg g−1 (13%), and 15.1 cm2 g−1 (14%), respectively, usingreflectance spectra (Fig. 2). Transmittance spectra yielded comparable

ectance for 162 tropical rainforest species collected in Australia. (b) Leaf hemisphericalsing the leaf reflectance spectra in panel (a). (d) PLS regression weightings using leaf

Fig. 2. (a) Relationships between predicted and measured leaf chemical properties and SLA using hemispherical reflectance spectra from Fig. 1a. (b) Similar but using transmittancespectra from Fig. 1b. Dashed lines indicate 95% confidence interval of the regression; solid outermost lines indicate 95% confidence interval of prediction.

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results. Although the regressions were linear, there were signs ofsaturation at high chlorophyll and SLA levels.

Leaf carotenoids were also well represented in both reflectance( r = 0.89; RMSE = 0.19 mg g− 1) and transmittance ( r = 0.85;RMSE=0.22 mg g−1) measurements (Fig. 2). PLS weightings indicatedthat carotenoids were expressed in the visible (550–707 nm), but withabout a 27-nm shift to lower wavelengths in comparison to thosepredicting chlorophyll concentrations (Fig.1c–d). Gitelson et al. (2002)reported similar results in laboratory analyses of chestnut and beechspecies. Anthocyanins were poorly represented by our foliar reflec-tance and transmittance data (Fig. 2), likely due to the overall lowconcentrations in these particular forest canopy leaves. This poor

anthocyanin retrieval was useful since it demonstrated that the con-strained PLS-PRESS regression approach does not simply over-fit toany parameter given, a problem reported early in the canopy che-mistry literature (Grossman et al., 1996).

Leaf water was represented in the spectra, with PLS weightingsbeing largest in the SWIR for reflectance and NIR+SWIR fortransmittance (Fig. 1c–d). The r-values for reflectance- and transmit-tance-based water analyses were 0.83 and 0.87, respectively, withRMSE values of 0.04 g g−1 (6% of mean) by dry weight. Leaf N was alsocorrelated with leaf reflectance (r=0.85; RMSE=0.52 mg g−1), and to alesser degree, with leaf transmittance (r=0.72; RMSE=0.42 mg g−1)(Fig. 2). PLS weightings show strong contributions from nitrogen in

Fig. 3. (a) Case 1 canopy reflectance spectra simulated using a radiative transfer modelwith leaf reflectance and transmittance properties from 162 rainforest species (Fig. 1a–b) and a constant LAI=5.0. The remaining model parameters were set to an averagebase-case determined from the literature and shown in Table 1. (b) PLS regressionweightings relating the canopy spectra from panel (a) to seven leaf chemicals and SLAmeasured in the field and laboratory.

Fig. 4. Relationships between predicted and measured leaf chemical properties and SLAusing the case 1 canopy reflectance data from Fig. 3.

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the visible range associated with chlorophylls and in the SWIR relatedto proteins (Curran, 1989). Together, the water and N results agreewith previous leaf optical–chemical studies (Ceccato et al., 2001;Kokaly, 2001).We unexpectedly found correlations between leaf P andleaf reflectance (r=0.76), although with relatively high RMSE values of0.03 mg g−1 (20% of mean) (Fig. 2). Given that elemental P is notdirectly expressed in the shortwave spectrum (Gillon et al., 1999), wethink that this correlation simply results from the stoichiometric linkbetween P and N, a link that has been used to develop maps of leaf Pfrom airborne imaging spectroscopy over a tropical forest landscape(Porder et al., 2005).

In sum, the leaf-level results indicate that a constellation of foliarproperties, especially pigments, SLA and N, is quantitatively repre-sented by the foliar spectra. Water and P were also represented,although we think P is indirectly connected via stoichiometry.Anthocyanins were poorly modeled by our particular leaf spectra,but this is likely due to our low chemical values compared to otherstudies (Gitelson et al., 2001). Previous studies have often focused onthe estimation of one leaf chemical from reflectance spectra or from afew bands taken from the spectra. Here we show that PLS analysis canprovide estimates of multiple leaf chemicals for humid tropicalspecies, and it can be donewithout statistical overfitting (Haaland andThomas, 1988). Our findings also highlight the utility of apportioningthe contributions of several chemicals to the spectrum, to quantify andintercompare the relative importance of each chemical determiningspectral variation among species. Most important to this study, theseleaf analyses provide a basis fromwhich to test the potential gains andlosses incurred when scaling up to the canopy level.

3.3. Leaf estimates from canopy spectra

Fig. 3 shows the case 1 canopy spectra simulated using the leaf opticsmeasured on the 162 species and a constant LAI=5. All other canopy

parameters were fixed to the base-case described in Table 1. Fig. 3 alsoshows the PLSweightings that link the field-measured leaf properties tothese simulated canopy spectra. Correlations between canopy reflec-tance and leaf pigments, nutrients and SLAwere 3–4% higher thanwithanalogous tests using leaf-level reflectance (Fig. 2). Leaf water predic-tions improved by 18% at the canopy scale (Fig. 4). Moreover, theapparent saturation in the leaf-level regressions observed for thepigments (Fig. 2) was less obvious in the canopy-based predictions(Fig. 4). These canopy-scale improvements are predicted by radiativetransfer theory, which suggests that multiple scattering in the uppercanopy leaf layers enhances the expression of leaf properties, at least inhighly foliated canopies (Baret et al., 1994). Here we found that canopy-level PLS regressionswith leaf properties are equally ormore robust thanthose made at the leaf level when LAI and canopy structure are heldartificially constant.

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As an additional measure, we attempted to estimate each foliarproperty using the leaf-level PLS equations (Fig. 1) but with canopyspectra (Fig. 3), rather than using the PLS equations generated at thecanopy scale for case 1. This is equivalent to developing a leaf spectral–chemical library in the field and laboratory, and then directly applyingthat library to airborne imaging spectrometer data. We found that leafproperties were poorly estimated using this approach, with regressioncoefficients r=0.35–0.54 for pigments, and r=0.17–0.22 for nutrientsand SLA (data not shown). The exception was leaf water, which wasmodeled with an r-value of 0.81, although slope of the regression wasfar above the 1:1 line. These tests suggest that caution should be usedwhen directly applying leaf-level PLS regression equations to canopy-scale measurements.

3.4. Effects of LAI variation

Cases 2 and 3 tested the effects of random LAI variation on leafchemical and SLA estimation from canopy reflectance spectra (Table 1).The case 2 variation for LAI=3.0–6.5 could easily be experienced in asingle airborne overpass of a tropical forest. We found that SLA, water,Chl-a, Chl-b andCar remainedhighlycorrelatedwith canopy reflectance,with r-values ranging from 0.84–0.93, and little change in RMSE whencompared to the leaf-level or case 1 results (Fig. 5). RMSE valuesincreased only slightly for carotenoids (0.17 to 0.23 mg g−1)in comparison to the case 1 results. In contrast, leaf N and P estimatesdecreased in strength from case 1 r-values of 0.87 and 0.79, respectively,to case 2 r-values of 0.65 and 0.36. Leaf water correlations remainedrelatively robust in the case 2 simulations, falling betweenpigments andnutrients (Fig. 5, Appendix B).

The case 3 simulations, which extended the LAI variability to 1.5–8.0, showed that leaf Chl-a, Chl-b and SLA remained highly correlatedwith canopy reflectance (r=0.88, 0.85, and 0.90, respectively). RMSEvalues for SLA increased from 12.9 cm2 g−1 in the ideal case 1 scenarioto 15.4 cm2 g−1 in the case 3 simulations. This increase is small from

Fig. 5. Correlation coefficients (r) between canopy reflectance and leaf properties forcases 1–10 described in Table 1. The r-values are medians±range of five independentsets of simulations for each case (Appendix B), with the exception that case 1 with LAIfixed at 5.0 did not require replicates.

either a physiological or biogeochemical perspective (Niinemets andKull, 2003; Wright et al., 2004). A similar result was found for chloro-phylls, thus demonstrating the very robust potential for retrievalacross a wide range of LAI conditions.

Correlations between canopy reflectance and leaf N, P and waterconcentrations diminished with the extreme LAI variation repre-sented by case 3. Comparing the ideal case 1 and these case 3 results,RMSE values for nitrogen increased from 0.30% to 0.58%, which issubstantial from an ecological perspective (Vitousek and Sanford,1986), and the r-value for N correlations dropped from 0.87 to 0.28.Phosphorus results paralleled those of N. Leaf water correlationsdecreased from r=0.93 in the case 1 simulations to just 0.37 in thecase 3 results.

The effects of LAI variation on the relationship between canopyreflectance and leaf properties depends upon the specific chemical inquestion. For pigments, we know that the path length of lightinteraction within the canopy is very short (e.g., LAIb2) due to stronglight absorption. Thus, we would expect pigments to be the leastaffected by LAI variation in either range selected for the case 2 or 3simulations. SLA, which has not been a focus of applied remotesensing until very recently (Asner, 2008; Asner et al., 2008a), alsoappears to be very insensitive to LAI variation. This may be theunderlying reason why past studies successfully estimated leafchlorophyll on either a concentration or a content basis, the latter ofwhich convolves the effects of SLA and chlorophyll concentration(Gitelson and Merzlyak, 1997; Yoder and Pettigrew-Crosby, 1995;Zarco-Tejada et al., 2001).

In contrast to the relatively low sensitivity of SLA and pigmentestimates to canopy LAI variation, N, P, and water are highly sensitiveto changing canopy leaf area. These chemicals are strongly expressedin the near-infrared portion of the reflectance spectrum (Fig. 1)(Kokaly, 2001; Smith et al., 2003; Ustin et al., 2004), and infrared lightis highly scattered in canopies. Since LAI also has a large impact on thescattering of light in the near-infrared, variations in LAI have aproportionally larger impact on the estimation of these chemicals.

3.5. Other structural variation

Cases 4–10 tested the effects of other structural variation andviewing geometry on leaf chemical and SLA estimation from canopyspectra (Table 1). We found that inter-crown variability in leaf angledistribution (LAD; case 4), simulated across a wide range of leaforientations, had little impact on the estimation of foliar propertiesfrom canopy reflectance (Fig. 5, Appendix B). Leaf pigments and waterwere estimated with r-values of 0.91–0.93; leaf N and P werecorrelated with r=0.86 and 0.69, respectively. Case 4 RMSE valuesremained at or close to those acquired in the case 1 analysis. Similarlyvariation in crown dimensions (case 5), mainly canopy depth, hadalmost no effect on correlations between leaf properties and canopyreflectance (Fig. 5). This was not surprising, given that our simulationswere set up for spectral analysis at a spatial unit size smaller than anentire tree crown, so changes in crown shape should have relativelylittle impact at the sub-canopy level.

Variability in viewing geometry (case 6) caused measurabledecreases in the relationships between leaf N and P and canopyspectra (Fig. 5). For N, r-values decreased from 0.87 to 0.66, and RMSEof the estimates increased from 0.30% to 0.45% (Appendix B). Leaf Pfared poorly, with r=0.30 and RMSE=0.05%. All other parameterswere affected by a small amount, despite the wide range of viewzenith angles (0°±20°) modeled along the solar principal plane, whichharbors the greatest cross-track bidirectional reflectance effects inaircraft imagery (Abuelgasim and Strahler, 1994). Variability in stemsilhouette area index (SSAI; case 7) also had negligible impacts on leafchemical and SLA determinations from simulation canopy spectra.This result could have been caused by the treatment of stems in thecanopy radiative transfer model, which combines the stem and leaf

3965G.P. Asner, R.E. Martin / Remote Sensing of Environment 112 (2008) 3958–3970

tissues in a turbidmedium. In reality, stems can protrude outward andcause noticeable changes in sub-canopy reflectance, which can bedetected and/or avoided in high resolution imagery (Bohlman, 2008).

The final three simulations (cases 8–10) randomly selected a suiteof structural properties to vary in the canopy reflectancemodels of therainforest species. Case 8 held LAI to 5.0, to allow for a direct com-parison to case 1, while LAD, view angle, crown shape, and SSAI variedwidely. Again, N and P estimates were the most negatively affected bycanopy structural variability (Fig. 5); correlation coefficientsdecreased by about the same magnitude as was caused by LAI andview-angle variation alone (cases 2 and 6). Cases 9–10 created whatcan arguably be described as worst possible or even unrealisticconditions, given the fact that airborne remote sensing data can bemanaged for poor viewing conditions and low LAI, as discussed below.For case 9, LAI ranged from 3.0–6.5 while all other parameters variedwidely as well. Whereas the correlation coefficients for leaf N, P and

Fig. 6. New fully integrated (a) high-fidelity imaging spectrometers (HiFIS) and (b) light decomparable units for chemical analysis. (c) Simple pre-screening of the data based on a minim(d) Sun-target-view geometry (here, 20°) and minimum canopy height (here, 5 m) is contr(e) Combined, these filters provide a map of canopies suitable for chemometric determinationexample images and data products were collected over a Hawaiian rainforest reserve by th

water decreased by 32%, 45%, and 18%, respectively; leaf pigments andSLA decreased by only a few percent (Fig. 5; Appendix B). Probably themost impressive result was found in the case 10 simulations thatcontained maximum random variation in LAI (=1.5–8.0) and allstructural parameters. Even in these simulations SLA, Chl-a and Chl-bmaintained relatively high correlation (r=0.85–0.88) with canopyreflectance and acceptable RMSE values (Appendix B). Estimates ofother leaf properties increased in uncertainty, but similarly to those ofcase 3 in which only LAI varied from 1.5 to 8.0. These results suggestthat LAI variability trumps the effects of all other parameters wetested.

3.6. Implications for airborne mapping

This study quantified how canopy structural variation affects leafchemical and SLA determinations from high resolution imaging

tection and ranging (LiDAR) systems provide a means to filter rainforest canopies intoum NDVI, here set to 0.8, ensures that only high-LAI canopies are analyzed (red color).

olled for using the LiDAR data thus pre-screening for view-angle effects (white color).s similar to those represented in the modeling analyses from cases 1–2 in Table 1. These

e Carnegie Airborne Observatory (Asner et al., 2007).

3966 G.P. Asner, R.E. Martin / Remote Sensing of Environment 112 (2008) 3958–3970

spectroscopy. Our modeling results suggest that variability in canopyLAI and viewing geometry have the largest negative impact on em-pirical (PLS) estimates of leaf properties from canopy spectra, with leafnutrients and water being most sensitive to these factors. In contrast,leaf chlorophyll and SLA are weakly affected by variation in LAI, vieworientation, and canopy structural variability.

These findings are promising because, in actual high resolutionimaging spectrometer data, LAI and view angle are among the mostcontrollable of variables. Pre-screening spectra for a minimumallowable LAI value is straightforward using vegetation indices. Forexample, the normalized difference vegetation index (NDVI) saturatesat LAI values of 2–3 at the canopy scale (Roberts et al., 1997), so onlythe pixels with high, saturated NDVI can be selected for use in leafchemical determinations. View angle is more difficult to control, butrecent progress has been made to integrate imaging spectrometer andlidar (light detection and ranging) sensors onboard aircraft, providinga straightforward way to locate each measured spectrum on the topsurface of canopies and to select pixels by sun-canopy-sensor angle(Asner et al., 2007). Our results presented here, along with those fromactual airborne studies (Asner et al., 2008b; Zarco-Tejada et al., 2001),argue for the development and deployment of more integratedspectrometer–lidar systems.

How might it work operationally? The spectral signature of acanopy is measured from the air, along with the detailed angularinformation from sun to canopy surface to sensor using a lidar and theintegrated inertial measurement unit between sensors onboard theaircraft (Fig. 6). The first pre-screening step might involve thederivation of a crown, shade, and view-angle mask using the top-of-canopy (“surface”) lidar model, demonstrated by Asner et al. (2008b)and shown in Fig. 6d. A minimum vegetation height can also be easilyapplied to isolate trees from shrubs and grasses. The second pre-screening step applies a narrowband vegetation index to identify lowLAI pixels for masking (Fig. 6c). The spectra remaining after thesefiltering steps will represent a well-controlled set of upper canopy,sunlit leaf surfaces with sufficient leaf biomass for chemometricanalysis (Fig. 6e). The final step can then apply empirical equationsderived from a PLS-PRESS analysis linking canopy spectra, simulatedusing a database of leaf reflectance and transmittance species, withtheir chemical signatures. This database may need to be biome orregionally specific, since leaf morphology (e.g. needleleaf vs. broad-leaf) imparts a control over leaf spectral–chemical relationships(Dawson et al., 1998). We are focused on humid tropical forests, sotaxonomic variation among canopy species is a dominant driver of leafspectral–chemical properties (Asner et al., in press; Clark et al., 2005;Cochrane, 2000), and thus a database will need to incorporate a verylarge number of species.

4. Conclusions

The spectral and chemical diversity of humid tropical forests isreceiving increasing attention in small-scale field studies taking placearound the world. Spatial and taxonomic variation in leaf chemistry isrecognized as important both for the functional role that trees play intropical systems as well as their response to climate change (reviewedby Townsend et al. (2007). Given the large geographic extent and in-accessibility of tropical forests, remote sensing has a role to play inquantifying and monitoring changes in canopy composition, physiol-ogy, and chemistry in these systems. Our study combined field,laboratory and modeling analyses to identify limits to and opportu-nities for scaling leaf spectral–chemical relationships to canopies.The analysis was done in the context of humid tropical forest ra-diative transfer as well as high spatial resolution airborne imagingspectroscopy, both of which simplify the problem of remote chemicaldetection to some degree.

A well-recognized step needed to advance the role of remotesensing in canopy chemistry research lies in developing the quan-

titative linkages between foliar chemical and spectral properties.Much work has been done at this scale, cited throughout this paper,with this literature demonstrating that multiple leaf properties, manynot yet incorporated into physical models, can be estimated withhigh-quality spectroscopic measurements. Our foliar results provideadditional data specific to tropical forest canopy species, showing thata suite of chemicals and SLA can be predicted using full-rangehemispherical reflectance and transmittance measurements collectedin the field. Leaf pigments and SLA were highly correlated with leafoptical properties (r=0.90–0.91). Leaf nutrients and water were alsowell represented by the leaf spectral signatures. Anthocyanins werepoorly estimated due to low concentrations in the foliage of theAustralian rainforest species. From an analytical standpoint, theuncertainty (RMSE) in the regression equations for all leaf propertiesother than anthocyanin ranged from about 3–20% of the means. Froman ecological perspective, these uncertainty levels are well within thevariability usually experienced by way of field sample collections andlaboratory processing. We conclude that a suite of leaf propertiesamong tropical forest species can be estimated using full-range leafspectra of fresh foliage collected in the field.

When these leaf spectra were incorporated into the canopyradiative transfer model simulations with an idealistic LAI=5.0,relationships between leaf chemical and SLA properties and canopyreflectance were enhanced by 4–18%. This was likely due to multiplescattering of photons in the canopies, which serves to enhance theexpression of leaf properties in canopy reflectance spectra (Asner,1998; Baret et al., 1994). The impact of a more realistic randomvariation in LAI of 3.0–6.5 on the retrieval of leaf properties remainedminimal, particularly for pigments and SLA (r=0.92–0.93). Nitrogencorrelations with canopy reflectance decreased from r=0.87 atconstant LAI =5, to r=0.65 with randomly varying LAI=3–6.5.Progressive increases in the structural variability among simulatedtree crowns had relatively little effect on pigment, SLA and waterestimates. In contrast, N and P were far more sensitive to canopystructural variability. Nonetheless, our modeling results suggest thatmultiple leaf chemicals and SLA can be estimated from canopyreflectance spectroscopy, and that the high-LAI canopies found intropical forests may enhance the signal via multiple scattering.

Finally, the two factors we found to most negatively impact modelfits at the canopy level were LAI and viewing geometry. However,when applying these with actual airborne imaging spectroscopy, webelieve that LAI can be readily pre-screened for a minimum valueusing traditional vegetation index techniques. The view-angle issue ismore challenging, but new hybrid spectrometer–lidar systems havesolved this problem and are now being actively deployed over keytropical forest regions.

Acknowledgements

Thisworkwas supported by the JohnD. and Catherine T.MacArthurFoundation and the Carnegie Institution.

Appendix AList of Australian tropical forest canopy species measured in the field and laboratory,which served as the basis for canopy radiative transfer model simulations

Family

Genus Species

Achariaceae

Baileyoxylon lanceolatum Ryparosa kurrangii

Annonaceae

Goniothalamus australis Polyalthia australis Xylopia maccreae

Apocynaceae

Alstonia scholaris Cerbera floribunda Wrightia laevis

Aquifoliaceae

Ilex arnhemensis Araliaceae Polyscias australiana

bellendenkerensis

3967G.P. Asner, R.E. Martin / Remote Sensing of Environment 112 (2008) 3958–3970

(continued)Appendix A (continued)

Family

Genus Species

Meliaceae

Araucariaceae Agathis robusta Arecaceae Archontophoenix alexandrae

Calamus

sp. Licuala ramsayi Normanbya normanbyi

Atherospermataceae

Daphnandra repandula Doryphora aromatica Dryadodaphne trachyphloia

Bignoniaceae

Deplanchea tetraphylla Boraginaceae Cordia dichotoma Burseraceae Canarium australianum var australianum Calycanthaceae Idiospermum australiense Cardiopteridaceae Citronella smythii Clusiaceae Calophyllum sil

Garcinia

gibbsiae Convolvulaceae Merremia peltata Corynocarpaceae Corynocarpus cribbianus Cunoniaceae Caldcluvia australiensis

Ceratopetalum

hylandiimacrophyllumsuccirubrum

Davidsonia

pruriens Gillbeea adenopetala Pseudoweinmannia lachnocarpa Pullea stutzeri

Ebenaceae

Diospyros hebecarpapentamera

Family

Genus Species Elaeocarpaceae Elaeocarpus bancroftii

ferruginiflorusgrahamiigrandisruminatussp. (Mt Bellenden Ker L.J.Brass 18336)

Peripentadenia

phelpsii Sloanea langii

Ericaceae

Dracophyllum sayeri Trochocarpa bellendenkerensis

Euphorbiaceae

Aleurites moluccana Macaranga inamoena Mallotus paniculatus

philippensis

Fabaceae Acacia celsa

Archidendron

kanisii Austrosteenisia blackii Castanospermum australe Storckiella australiensis

Gentianaceae

Fagraea berteroana Hamamelidaceae Ostrearia australiana Lauraceae Beilschmiedia bancroftii

collinatooram

Cinnamomum

propinquum Cryptocarya angulata

bellendenkeranacorrugatagrandisleucophyllamackinnonianaoblatapleurospermasaccharata

Endiandra

microneuramontana

Litsea

fawcettianagraniticaleefeana

Neolitsea

dealbata Lythraceae Lagerstroemia archeriana Malvaceae Argyrodendron peralatum

polyandrum

Brachychiton acerifolius Franciscodendron laurifolium

Meliaceae

Dysoxylum gaudichaudianummollissimumoppositifoliumpapuanum

(continued)Appendix A (continued)

Family

Genus Species

parasiticumpettigrewianum

Synoum

glandulosum ssp. paniculosum Toona ciliata

Monimiaceae

Levieria acuminata Moraceae Ficus variegata Myristicaceae Myristica globosa Myrsinaceae Myrsine oreophila

sp.

Myrtaceae Acmena divaricata

Backhousia

bancroftii Corymbia torrelliana Gossia shepherdii Lindsayomyrtus racemoides Rhodamnia blairiana Rhodomyrtus sericea Ristantia pachysperma Syzygium cormiflorum

dansieiendophloiumerythocalyxgustavioideskurandapapyraceumwesa

Xanthostemon

chrysanthus Oleaceae Chionanthus axillaris

Olea

paniculata Phyllanthaceae Antidesma erostre

Cleistanthus

myrianthus Glochidion hylandii

Picrodendraceae

Austrobuxus megacarpus Podocarpaceae Podocarpus smithii Polygalaceae Xanthophyllum octandrum Polyosmaceae Polyosma rigidiuscula Proteaceae Athertonia diversifolia

Austromuellera

trinervia Buckinghamia celsissima Cardwellia sublimis Carnarvonia araliifolia Catalepidia heyana Darlingia darlingiana

ferruginea

Musgravea stenostachya Neorites kevediana Placospermum coriaceum Sphalmium racemosum

Quintiniaceae

Quintinia quatrefagesii Rhamnaceae Alphitonia excelsa

petriei

Rosaceae Prunus brachystachya Rousseaceae Abrophyllum ornans Rubiaceae Gardenia ovularis

Psydrax

montigena Rutaceae Acronychia crassipetala

Brombya

platynema Euodia hylandii Flindersia brayleyana Halfordia kendack Medicosma fareana Melicope affinis

Salicaceae

Scolopia braunii Sapindaceae Castanospora alphandii

Cupaniopsis

papillosa Diploglottis smithii Guioa montana Mischocarpus macrocarpus Sarcoteryx montana Sarcotoechia cuneata

Sapotaceae

Niemeyera pruniferasp. (Mt Lewis AKI 1402)

Pouteria

euphlebiasinguliflora

Sphenostemonaceae

Sphenostemon lobosporus Symplocaceae Symplocos stawellii var. montana

Appendix BLinear regression coefficients between sevenmeasured leaf properties and predictionsmade from canopy reflectance simulations, cases 2–10 (see Table 1). The lettersm and b indicate theslope and y-intercept, respectively, for each regression

Leaf property Canopy simulations m Minimum Maximum b Minimum Maximum r Minimum Maximum RMSE

SLA cm2 g−1) Case 2 0.99 0.98 1.00 1.83 1.17 2.30 0.93 0.93 0.93 13.20Case 3 0.96 0.93 0.99 8.08 4.13 11.55 0.90 0.88 0.90 15.38Case 4 0.98 0.96 1.00 1.46 0.14 2.30 0.93 0.92 0.93 13.23Case 5 1.00 1.00 1.01 −0.31 −0.38 −0.23 0.93 0.93 0.93 12.93Case 6 0.92 0.90 0.94 6.33 4.83 8.12 0.92 0.91 0.93 13.95Case 7 0.98 0.97 0.99 0.09 −0.54 0.77 0.93 0.93 0.93 13.00Case 8 0.92 0.88 0.93 5.58 4.10 8.34 0.92 0.90 0.93 14.14Case 9 0.89 0.86 0.91 8.21 6.31 10.08 0.92 0.91 0.92 13.80Case 10 0.85 0.83 0.87 13.61 12.04 15.33 0.88 0.88 0.91 16.42

Water (g/g) Case 2 0.77 0.72 0.79 0.12 0.10 0.15 0.83 0.78 0.84 0.04Case 3 0.15 0.10 0.19 0.48 0.46 0.51 0.37 0.27 0.42 0.07Case 4 0.98 0.98 1.00 0.01 0.00 0.01 0.93 0.93 0.93 0.03Case 5 1.00 1.00 1.01 0.00 0.00 0.00 0.93 0.93 0.93 0.03Case 6 0.67 0.66 0.68 0.18 0.17 0.19 0.76 0.73 0.79 0.05Case 7 0.91 0.88 0.93 0.07 0.05 0.08 0.91 0.90 0.91 0.03Case 8 0.68 0.63 0.72 0.18 0.15 0.21 0.81 0.77 0.82 0.04Case 9 0.55 0.51 0.58 0.25 0.23 0.27 0.75 0.69 0.76 0.05Case 10 0.15 0.12 0.17 0.48 0.47 0.50 0.37 0.29 0.42 0.07

N (%) Case 2 0.55 0.50 0.64 0.64 0.50 0.70 0.65 0.63 0.70 0.46Case 3 0.09 0.03 0.13 1.51 1.41 1.64 0.28 0.14 0.41 0.58Case 4 0.96 0.94 0.98 0.03 0.00 0.06 0.86 0.85 0.86 0.31Case 5 1.00 1.00 1.01 0.00 0.00 0.01 0.87 0.87 0.87 0.30Case 6 0.60 0.58 0.63 0.55 0.49 0.61 0.66 0.66 0.72 0.45Case 7 0.89 0.87 0.90 0.30 0.29 0.32 0.84 0.83 0.84 0.33Case 8 0.62 0.51 0.67 0.58 0.47 0.77 0.71 0.65 0.77 0.43Case 9 0.39 0.32 0.44 0.90 0.79 1.06 0.58 0.49 0.63 0.49Case 10 0.07 0.04 0.09 1.55 1.49 1.62 0.25 0.14 0.30 0.59

P (%) Case 2 0.26 0.21 0.33 0.08 0.07 0.09 0.36 0.34 0.48 0.05Case 3 0.03 0.01 0.07 0.11 0.10 0.12 0.11 0.06 0.31 0.05Case 4 0.75 0.72 0.77 0.02 0.02 0.03 0.69 0.64 0.71 0.04Case 5 1.00 0.99 1.01 0.00 0.00 0.00 0.78 0.78 0.79 0.03Case 6 0.20 0.14 0.25 0.09 0.08 0.10 0.30 0.22 0.42 0.05Case 7 0.72 0.70 0.74 0.05 0.05 0.05 0.67 0.63 0.70 0.04Case 8 0.24 0.16 0.31 0.08 0.08 0.10 0.41 0.33 0.52 0.05Case 9 0.15 0.09 0.17 0.09 0.09 0.10 0.34 0.22 0.40 0.05Case 10 0.02 0.01 0.03 0.11 0.11 0.12 0.09 0.05 0.13 0.05

Chl-a (mg g−1) Case 2 1.00 1.00 1.01 −0.07 −0.10 −0.04 0.93 0.93 0.93 0.56Case 3 0.88 0.84 0.95 0.16 −0.04 0.28 0.88 0.84 0.91 0.73Case 4 0.97 0.96 0.98 0.13 0.10 0.15 0.93 0.93 0.94 0.56Case 5 1.00 1.00 1.00 −0.01 −0.02 −0.01 0.94 0.94 0.94 0.55Case 6 0.95 0.94 0.96 0.19 0.17 0.21 0.93 0.93 0.94 0.55Case 7 0.95 0.94 0.96 0.14 0.10 0.17 0.93 0.93 0.93 0.57Case 8 0.89 0.87 0.89 0.42 0.36 0.51 0.93 0.92 0.93 0.59Case 9 0.89 0.88 0.90 0.34 0.30 0.37 0.93 0.92 0.93 0.58Case 10 0.80 0.77 0.82 0.45 0.37 0.56 0.88 0.88 0.89 0.73

Chl-b (mg g−1) Case 2 1.00 0.99 1.01 −0.03 −0.05 −0.02 0.92 0.92 0.92 0.25Case 3 0.81 0.77 0.89 0.13 0.04 0.18 0.85 0.79 0.88 0.34Case 4 0.97 0.97 0.98 0.05 0.04 0.06 0.92 0.92 0.92 0.25Case 5 1.00 1.00 1.00 0.00 −0.01 0.00 0.92 0.92 0.92 0.25Case 6 0.95 0.94 0.95 0.06 0.05 0.07 0.92 0.92 0.92 0.25Case 7 0.95 0.94 0.96 0.06 0.05 0.07 0.92 0.91 0.92 0.25Case 8 0.89 0.88 0.90 0.15 0.12 0.17 0.91 0.91 0.92 0.26Case 9 0.88 0.86 0.90 0.12 0.10 0.14 0.92 0.90 0.93 0.26Case 10 0.76 0.73 0.79 0.20 0.16 0.24 0.85 0.84 0.86 0.34

Car (mg g−1) Case 2 0.83 0.79 0.86 0.13 0.09 0.18 0.84 0.81 0.86 0.23Case 3 0.33 0.24 0.42 0.67 0.58 0.79 0.52 0.41 0.65 0.36Case 4 0.98 0.97 1.00 0.03 0.01 0.05 0.91 0.90 0.91 0.17Case 5 1.00 1.00 1.00 0.00 0.00 0.00 0.91 0.91 0.91 0.17Case 6 0.89 0.86 0.91 0.07 0.03 0.09 0.87 0.87 0.88 0.20Case 7 0.92 0.90 0.94 0.15 0.13 0.17 0.90 0.89 0.90 0.18Case 8 0.85 0.82 0.89 0.18 0.12 0.21 0.86 0.85 0.89 0.21Case 9 0.73 0.71 0.76 0.25 0.22 0.27 0.82 0.80 0.84 0.24Case 10 0.33 0.32 0.36 0.69 0.66 0.71 0.54 0.49 0.58 0.35

3968 G.P. Asner, R.E. Martin / Remote Sensing of Environment 112 (2008) 3958–3970

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