Elsevier

Remote Sensing of Environment

Volume 118, 15 March 2012, Pages 60-72
Remote Sensing of Environment

Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery

https://doi.org/10.1016/j.rse.2011.11.012Get rights and content

Abstract

Light use efficiency (LUE) is an important variable characterizing plant eco-physiological functions and refers to the efficiency at which absorbed solar radiation is converted into photosynthates. The estimation of LUE at regional to global scales would be a significant advantage for global carbon cycle research. Traditional methods for canopy level LUE determination require meteorological inputs which cannot be easily obtained by remote sensing. Here we propose a new algorithm that incorporates the enhanced vegetation index (EVI) and a modified form of land surface temperature (Tm) for the estimation of monthly forest LUE based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Results demonstrate that a model based on EVI × Tm parameterized from ten forest sites can provide reasonable estimates of monthly LUE for temperate and boreal forest ecosystems in North America with an R2 of 0.51 (p < 0.001) for the overall dataset. The regression coefficients (a, b) of the LUE–EVI × Tm correlation for these ten sites have been found to be closely correlated with the average EVI (EVI_ave, R2 = 0.68, p = 0.003) and the minimum land surface temperature (LST_min, R2 = 0.81, p = 0.009), providing a possible approach for model calibration. The calibrated model shows comparably good estimates of LUE for another ten independent forest ecosystems with an overall root mean square error (RMSE) of 0.055 g C per mol photosynthetically active radiation. These results are especially important for the evergreen species due to their limited variability in canopy greenness. The usefulness of this new LUE algorithm is further validated for the estimation of gross primary production (GPP) at these sites with an RMSE of 37.6 g C m 2 month 1 for all observations, which reflects a 28% improvement over the standard MODIS GPP products. These analyses should be helpful in the further development of ecosystem remote sensing methods and improving our understanding of the responses of various ecosystems to climate change.

Highlights

► The potential of EVI as a proxy of LUE depends on the standard deviation in EVI. ► A new LUE algorithm is proposed for temperate and boreal forests in North America. ► The RMSE of the new model for ten independent sites is 0.055 g C mol 1 PAR. ► A 28% improvement is observed in GPP using this LUE algorithm compared to MODIS GPP.

Introduction

Forest ecosystems play an important role in global carbon sequestration (Beer et al., 2010, Zhao and Running, 2010). Annual global carbon uptake by vegetation, also referred to as gross primary production (GPP), is around 123 ± 8 petagrams of carbon per year (Beer et al., 2010). However, substantial variations in GPP are observed among different models and ecoregions, both at plant and stand levels, and these discrepancies illustrate the limits to our full understanding of the global carbon cycle.

Light use efficiency (LUE), defined as the amount of carbon fixed in photosynthesis per unit of absorbed solar radiation, is an important variable for the estimation of GPP from satellite inputs when using the Monteith equation (Monteith, 1972):GPP=LUE×APARwhere APAR is the absorbed photosynthetically active radiation calculated as the product of an absorbed fraction (fAPAR) and the amount of incident photosynthetically active radiation (PAR).

Typical methods for the simulation of LUE require prior specification of a maximum LUE (ε0) for a specific land cover type and additional input of climate variables (e.g., temperature, water stress) representing canopy stresses that modulate this maximum LUE (Running et al., 2004). This method has been useful in productivity models, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (Running et al., 2004) and the Vegetation Photosynthesis Model (Xiao et al., 2004). However, the dependence on input climate variables and biome-scale maximum LUE can cause significant deviation from observed GPP (Heinsch et al., 2006, Mu et al., 2011, Zhao et al., 2006).

With ongoing improvements in our ability to remotely sense the land surface, there has been an increase in efforts to directly infer LUE using these observations. For example, the photochemical reflectance index (PRI), defined as a normalized difference index using reflectance at 531 and 570 nm, is suggested to have potential in tracking LUE both at the leaf scale based on ground spectral measurements (Filella et al., 2009, Gamon et al., 1997) and satellite observations (Drolet et al., 2005, Drolet et al., 2008, Goerner et al., 2009, Hall et al., 2008, Hall et al., 2011, Hilker et al., 2009). However, PRI shows high sensitivity to various extraneous effects such as canopy structure and the view observer geometry, which prevents its use at landscape and global scales and requires the appropriate upscaling algorithms to account for structural differences in vegetation (Hilker et al., 2010). Hilker et al. (2008) shows that while isotropic PRI scattering is correlated to LUE variation, geometric scattering can be attributed to canopy level shading. Therefore, remote sensing of forest LUE from space would be achieved by measuring PRI as a function of shadow fraction using multi-angle observations (Hall et al., 2008), which is further confirmed by the relationship between spaceborne PRI and canopy shadow fractions (Hilker et al., 2009). A theoretical concept using a canopy reflectance model proposed by Hall et al. (2011) recently further validates that using PRI alone to predict canopy LUE is confounded by the shadow fraction viewed by the sensor.

A thorough analysis of PRI is shown in Garbulsky et al. (2011), indicating that calibration of the PRI-LUE relation across biomes and a careful attention to potentially confounding factors are both needed for future improvement. Apart from PRI, Inoue et al. (2008) demonstrate that a number of bands centered at the red edge and near-infrared ranges also have potential in deriving indicators of LUE for a wheat canopy. Correlations are reported between LUE and a number of vegetation indices, including the normalized difference vegetation index (NDVI, Rouse et al., 1974) and the enhance vegetation index (EVI, Huete et al., 2002) in crop (Wu et al., 2010, Wu et al., 2009) and peatland ecosystems (Schubert et al., 2010). However, the potential of a certain vegetation index as a proxy of LUE across biomes and the underlying mechanism are still unknown (Huemmrich et al., 2010, Zhang et al., 2009), probably due to a number of influencing factors, including temperature, soil moisture, vapor pressure deficit (VPD) and light quality (Coops et al., 2010).

Two specific aspects of known variations in LUE need to be considered for use in operational algorithms related to the use of vegetation indices. First, environmental effects of temperature and water stress considerably modify LUE and cannot be interpreted by a single vegetation index. These environmental controls likely limit the competency of productivity models that incorporate vegetation indices under drought conditions (Mu et al., 2011, Sims et al., 2008, Sims et al., 2006). A second uncertainty is the application of only a single vegetation index in evergreen biomes that show low dynamic ranges in greenness but potentially large variations in LUE (Garbulsky et al., 2008, Nakaji et al., 2008). Hence, environmental variables are potentially helpful in resolving these present limitations in remotely sensed LUE. A possible candidate among those climate variables is air temperature (Ta) because of its importance in influencing the magnitude and timing of plant growth (Chen et al., 2003), ecosystem respiration (Tang et al., 2008), LUE (Schwalm et al., 2006), and its correlation with other environmental variables, such as vapor pressure deficit (VPD) and PAR (Sims et al., 2008). This potential has been demonstrated in previous studies, such as the MODIS GPP product which uses temperature and VPD to reduce LUE under unfavorable conditions (Running et al., 2004, Zhao et al., 2006). However, the dependence on the requirements of meteorological inputs at desired temporal and spatial scales generally limits its global application (Mu et al., 2011).

With the availability of global carbon flux data that can be used to calculate canopy level LUE for multiple ecosystems (Baldocchi, 2008), it is possible to validate and compare the candidate LUE models and their impacts on GPP for a broad array of forest types. Here we present a methodology for estimating monthly LUE using MODIS observations and compare simulated values against LUE derived from flux measurements obtained from multiple temperate and boreal forest sites within North America. MODIS-derived EVI and land surface temperature (LST) are examined for their potential in estimating monthly LUE across biomes. The objectives of this study are: (1) to analyze the potential of EVI in evaluating monthly LUE for both deciduous forests and evergreen forests, (2) to derive a new model that can provide better estimates of monthly LUE using the MODIS EVI and LST observations, and (3) to show the usefulness of the new LUE algorithm in the estimation of forest GPP. This effort could result in an improved algorithm for remote sensing of GPP and aid in understanding of terrestrial carbon cycle-climate feedbacks in forested ecosystems.

Section snippets

Study sites

We focused on twenty forest sites in the North American flux networks, including twelve deciduous forests (DF) and eight evergreen forests (EF) (Fig. 1). Half of these sites, composed of five DF and five EF, were used to derive the LUE model and the remaining sites were used for validation. Detailed descriptions of these sites are shown in Table 1.

Flux and meteorological measurements

Flux data for the nine Canadian sites were downloaded from the Fluxnet-Canada Data Information System (http://www.fluxnet-canada.ca) while data for

Relationship between LUE and EVI

The relationship between all tower-derived monthly LUE and remotely sensed EVI reveals a relationship with a coefficient of determination (R2) equal to 0.46 (Fig. 2a) with evident differences among forest functional types. An R2 of 0.62 was found for deciduous forest (DF) sites, suggesting that EVI can better simulate the dynamics of LUE for ecosystems with wider dynamical ranges in EVI. For example, EVI values for DF sites generally fluctuated between 0.15 and 0.75 with a mean standard

Application of this LUE algorithm to estimate GPP

When the new model was applied to estimate GPP, we also observed a better performance than the standard MODIS GPP products. An R2 of 0.73 (p < 0.001) and an overall RMSE of 52.7 g C m 2 month 1 are obtained between the flux-measured GPP and the MODIS GPP products for all sites (Fig. 9a). Similar to our findings, MODIS GPP also shows better suitability for DF sites (R2 = 0.71, RMSE = 60.7 g C m 2 month 1) than for EF sites (R2 = 0.67, RMSE = 35.7 g C m 2 month 1). For all observations, there is a clear pattern for

Conclusions

Light use efficiency is an important variable characterizing plant eco-physiological function and has been widely used to estimate forest productivity from remotely sensed data. By incorporation of both canopy greenness and temperature, here we propose a new algorithm of EVI × Tm that shows reasonably good estimates of monthly LUE in various forest ecosystems in North America. We also find that a simple metric based on the average of EVI and Tmin allows for calibration of model coefficients.

Acknowledgements

We like to thank Dr. Andrew Richardson for the suggestions to the initial manuscript. Though comments from both reviewers are also appreciated. This work used data from flux sites from both the AmeriFlux and Fluxnet-Canada and we appreciate the flux PIs providing these valuable data and helpful explanations. This work was funded by an NSERC Strategic Grant (381474-09), the National Natural Science Foundation (Grant No. 41001210), the Knowledge Innovation Program of CAS (KZCX2-EW-QN302), and the

References (69)

  • G.G. Drolet et al.

    A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest

    Remote Sensing of Environment

    (2005)
  • G.G. Drolet et al.

    Regional mapping of gross light-use efficiency using MODIS spectral indices

    Remote Sensing of Environment

    (2008)
  • R. Fensholt et al.

    Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements

    Remote Sensing of Environment

    (2004)
  • M.F. Garbulsky et al.

    The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies. A review and meta-analysis

    Remote Sensing of Environment

    (2011)
  • M.A. Giasson et al.

    Ecosystem-level CO2 fluxes from a boreal cutover in eastern Canada before and after scarification

    Agricultural and Forest Meteorology

    (2006)
  • A. Goerner et al.

    Tracking seasonal drought effects on ecosystem light use efficiency with satellite-based PRI in a Mediterranean forest

    Remote Sensing of Environment

    (2009)
  • S.J. Goetz et al.

    Advances in satellite remote sensing of environmental variables for epidemiological applications

    Advances in Parasitology

    (2000)
  • F.G. Hall et al.

    PHOTOSYNSAT, photosynthesis from space: Theoretical foundations of a satellite concept and validation from tower and spaceborne data

    Remote Sensing of Environment

    (2011)
  • F.G. Hall et al.

    Multi-angle remote sensing of forest light use efficiency by observing PRI variation with shadow fraction

    Remote Sensing of Environment

    (2008)
  • T. Hilker et al.

    Separating physiologically and directionally induced changes in PRI using BRDF models

    Remote Sensing of Environment

    (2008)
  • T. Hilker et al.

    Remote sensing of photosynthetic light-use efficiency across two forested biomes: Spatial scaling

    Remote Sensing of Environment

    (2010)
  • T. Hilker et al.

    An assessment of photosynthetic light use efficiency from space: Modeling the atmospheric and directional impacts on PRI reflectance

    Remote Sensing of Environment

    (2009)
  • K.F. Huemmrich et al.

    Remote sensing of tundra gross ecosystem productivity and light use efficiency under varying temperature and moisture conditions

    Remote Sensing of Environment

    (2010)
  • A. Huete et al.

    Overview of the radiometric and biophysical performance of the MODIS vegetation indices

    Remote Sensing of Environment

    (2002)
  • Y. Inoue et al.

    Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice

    Remote Sensing of Environment

    (2008)
  • J.P. Jenkins et al.

    Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements

    Agricultural and Forest Meteorology

    (2007)
  • Q. Mu et al.

    Improvements to a MODIS global terrestrial evapotranspiration algorithm

    Remote Sensing of Environment

    (2011)
  • T. Nakaji et al.

    Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Japan

    Agricultural and Forest Meteorology

    (2008)
  • P. Schubert et al.

    Impact of nutrients on peatland GPP estimations using MODIS time series data

    Remote Sensing of Environment

    (2010)
  • C.R. Schwalm et al.

    Photosynthetic light use efficiency of three biomes across an east–west continental-scale transect in Canada

    Agricultural and Forest Meteorology

    (2006)
  • D.A. Sims et al.

    A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS

    Remote Sensing of Environment

    (2008)
  • M. Sjöström et al.

    Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems

    Remote sensing of environment

    (2011)
  • J. Tang et al.

    Ecosystem respiration and its components in an old-growth forest in the Great Lakes region of the United States

    Agricultural and Forest Meteorology

    (2008)
  • T. Teklemariam et al.

    Eight years of carbon dioxide exchange above a mixed forest at Borden, Ontario

    Agricultural and Forest Meteorology

    (2009)
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