Elsevier

Remote Sensing of Environment

Volume 115, Issue 4, 15 April 2011, Pages 1081-1089
Remote Sensing of Environment

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

https://doi.org/10.1016/j.rse.2010.12.013Get rights and content

Abstract

One of the most frequently applied methods for integrating controls on primary production through satellite data is the light use efficiency (LUE) approach, which links vegetation gross or net primary productivity (GPP or NPP) to remotely sensed estimates of absorbed photosynthetically active radiation (APAR). Eddy covariance towers provide continuous measurements of carbon flux, presenting an opportunity for evaluation of satellite estimates of GPP. Here we investigate relationships between eddy covariance estimated GPP, environmental variables derived from flux towers, Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and GPP across African savanna ecosystems. MODIS GPP was found to underestimate GPP at the majority of sites, particularly at sites in the Sahel. EVI was found to correlate well with estimated GPP on a site-by-site basis. Combining EVI with tower-measured PAR and evaporative fraction (EF, a measure of water sufficiency) improved the direct relationship between GPP and EVI at the majority of the sites. The slope of this relationship was strongly related to site peak leaf area index (LAI). These results are promising for the extension of GPP through the use of remote sensing data to a regional or even continental scale.

Research Highlights

► We explore relationships between GPP and MODIS data for sites across Africa. ► We assess the MODIS GPP product and whether MODIS EVI can be used for GPP modelling. ► We find that MODIS GPP underestimates GPP and that EVI is correlated with GPP. ► Including PAR and a water index (EF) improved relationships between GPP and EVI. ► We conclude that EVI, PAR, EF and LAI can be used to accurately model GPP at sites.

Introduction

Africa's role in the global carbon cycle has been increasingly recognized, particularly with the challenges it faces with respect to climate change (Hulme et al., 2001). According to Williams et al. (2007), the African continent contributes as much as one-fifth of the net primary production and a half of the interannual variability of the carbon balance at the global scale. Even though Africa as a whole appears to be approximately carbon neutral (Williams et al., 2007), estimates are highly uncertain and there is a need to better understand the temporal and spatial dynamics of ecosystem productivity across the continent. In 2006, CarboAfrica was established with the purpose of increasing our knowledge of Africa's role in the global carbon cycle (Bombelli et al., 2009). The project's objectives included a synthesis of flux data from existing eddy covariance sites in Africa (e.g. Merbold et al., 2009), as well as to support new observations. The eddy covariance technique (e.g. Aubinet et al., 1999, Baldocchi et al., 2001, Lindroth et al., 1998, Wofsy et al., 1993) has become a standard for measuring fluxes of CO2, water and energy between the land and atmosphere at the ecosystem scale and provides an excellent opportunity for validation of model estimates of carbon flux from terrestrial ecosystems.

Although eddy covariance towers can provide estimates at high temporal resolution, the number of stations is limited spatially across the African continent. Satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS), which has been acquiring data in 36 spectral bands across the entire globe since 2000, may therefore significantly contribute to our knowledge on vegetation dynamics and responses to changing environmental conditions. Data from MODIS are used in numerous biophysical products for which a number of studies over tropical dryland ecosystems have helped provide confidence too (Fensholt et al., 2004, Fensholt et al., 2006, Huemmrich et al., 2005, Kanniah et al., 2009, Privette et al., 2002). However, further attention is required in comparing spatially extensive satellite data to eddy covariance measurements over tropical dryland ecosystems to improve predictions and modelling of ecosystem primary productivity.

One of the most widely applied approaches to extrapolate ecosystem primary productivity measurements from the site-scale to larger spatial scales is the concept of light use efficiency (LUE, Monteith, 1972, Monteith, 1977). In this concept gross primary production (GPP), the total carbon assimilated by plants—which can be derived from eddy covariance measurements through estimates of net ecosystem exchange (NEE) and ecosystem respiration (Reco)—is a function of the absorbed photosynthetically active radiation (APAR) by plants and the conversion efficiency of absorbed light energy (ε). APAR is estimated as the product of incoming photosynthetically active radiation (PAR) and the fraction of PAR absorbed by the canopy (FAPAR), whereas ε is a quasi-constant, typically modified by functions of temperature and moisture. Through the established relationship between the normalized difference vegetation index (NDVI) and FAPAR (Asrar et al., 1984, Daughtry et al., 1983, Sellers et al., 1994) the concept of LUE has been applied in a number of satellite-based modeling studies (e.g. Fensholt et al., 2006, Seaquist et al., 2003). NDVI has however been reported to be sensitive to variations in background reflectance and to saturate at intermediate to high leaf area index (LAI) resulting in a lack of sensitivity to seasonal changes. With the advent of MODIS, the enhanced vegetation index (EVI) was developed to enhance the vegetation signal by reducing influences from the atmosphere and canopy background and to improve sensitivity in high biomass regions (Huete et al., 2002). Both the NDVI and EVI quantify the difference in reflectance in the visible red, where absorption in green leaves dominates, and the near-infrared (NIR) wavelengths, where light scattering by cell walls dominates (Tucker, 1979). However, unlike NDVI, EVI also incorporates a soil adjustment factor as well as an atmosphere resistance term using the blue band in its formulation:EVI=2.5×NIRREDNIR+(6×RED7.5×BLUE)+1

Several studies have previously shown consistent linear relationships between eddy covariance GPP and EVI in various environments, whereas NDVI has either shown little variation in seasonality or a poorer correspondence with GPP (Huete et al., 2008, Xiao et al., 2004). It remains unclear to what extent EVI can be used to model GPP, since the relationships have been shown to differ greatly between ecosystems (e.g. Rahman et al., 2005, Sims et al., 2006). Xiao et al. (2004) distinguished between the photosynthetically active and non-photosynthetically active components of FAPAR and incorporated EVI, together with scalars of ε, into the satellite-based Vegetation Photosynthesis Model (VPM) as equivalent to the photosynthetically active chlorophyll component of FAPAR. Recently, Zhang et al. (2005) reported EVI to be closely related to FAPAR if the effects of chlorophyll were taken into account. In this study, we focus on exploring MODIS GPP and EVI and their relationships with eddy covariance GPP in African ecosystems. Using the LUE concept, we further determine whether the inclusion of other environmental variables (derived from eddy covariance towers) can improve upon the direct relationship of eddy covariance based GPP with EVI for assessing whether EVI can be a useful input for satellite-based modeling of GPP over tropical dryland ecosystems, specifically in Africa.

Section snippets

Eddy covariance and meteorological data

Seven CarboAfrica-associated sites were used, representing a variety of African ecosystems and rainfall regimes (Table 1, Fig. 1). The sites covers a diversity of vegetation and climate types with three in the semi-arid Sahel (Wankama millet and fallow in Niger and Demokeya in the Sudan), three in the semi-arid and sub-humid regions of Southern Africa (Maun in Botswana, Mongu in Zambia and Skukuza in South Africa) and one in the more humid region close to the equator (Tchizalamou in the

Results

Averaged 8-day GPP estimated from eddy covariance data and 8-day original EVI and TIMESAT-smoothed EVI show reasonable agreement (Fig. 2, Table 2). Mean 8-day GPP throughout the growing season varied between sites from 1.59 g C m−2 d−1 at Wankama millet to 5.67 g C m−2 d−1 at the woodland site in Mongu and was most variable for the mixed wooded grassland site at Skukuza. TIMESAT-smoothed EVI presented fairly similar patterns as GPP during the vegetation period with lowest mean EVI at Wankama millet and

Discussion

In this paper we examined the degree of correspondence between eddy covariance estimated GPP, MODIS EVI, GPP from the MOD17A2 algorithm, and tower-measured PAR, EF and Tair. It was anticipated that other than vegetation greenness, represented by EVI, the availability of water, here represented by EF, should have a fairly strong control on 8-day estimated GPP. EF was found to correlate closely with GPP at all of the sites. At the grassland site at Tchizalamou, GPP was also found to be correlated

Conclusion

In this study we evaluated environmental controls on GPP in African ecosystems, the use of MODIS EVI as input to production efficiency models (an adaptation of the concept of LUE) and the accuracy of the MODIS GPP product. Although challenges remain on how to combine remote sensing data with eddy covariance measurements, the MODIS GPP product was found to underestimate GPP at the majority of sites. This suggests that the BPLUT parameters used in the MODIS GPP algorithm may require further

Acknowledgements

Financial support was provided by the Swedish National Space Board (contracts 120/06 and 74/08), the Swedish Research Council (VR 621-2004) and through the EU-funded CARBOAFRICA project (contract 037137). The Wankama stations in Niger are part of the French-funded AMMA-CATCH observatory (amma-catch.org), a component of the AMMA international programme (amma-international.org). We would like to thank Dr. Maozheng Zhao for providing the MODIS GPP data and the anonymous reviewers for their helpful

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