Photosynthesis is an essential biological process that fixes solar energy and retains it into the terrestrial ecosystem. It can be mainly divided into C3 and C4 plants defined by different processes to capture and transfer energy. Besides the photosynthesis process, C3 and C4 plants also differ in carbon fixation capacity, material composition and suitable environment for growth. Specifically, the C4 photosynthetic pathway relies on a coordinated system of anatomical and biochemical traits that function to concentrate CO2 around Rubisco, which in most C4 plants is localized to bundle sheath cells (Hatch, 1987). Although C4 species only comprising 3% of all vascular plant species (Sage, 2004), they account for some 25% of terrestrial photosynthesis (Still et al., 2003). Additionally, many studies have revealed that the material composition of C3 and C4 plants also differ. This may lead to C4 plants being more flammable than C3 plants, and as such, the carbon emission risk implied by their fire risk is also higher. Finally, the differing geographical and environmental distributions that C3 and C4 species occupy have been widely studied (Teeri and Stowe, 1967; Rundel, 1980; Williams et al., 1995; Ehleringer et al., 1997; Epstein et al., 1997; Edwards and Still, 2008), showing that C4 plants are more adaptable to arid environments and have stronger succession and expansion capabilities.
Given the premise of such large differences, the distribution pattern of C3 and C4 plants is important to accurately simulate land surface processes, such as exchanges of CO2, water, and energy (gradient?) between land surface and atmosphere (Ke et al., 2012). 60% of C4 species are grasses, and grasslands are a major component of terrestrial vegetation. Therefore, to estimate the carbon cycling via grass at each location, the relative amounts of C3 versus C4 grass biomass must be known (Ehleringer, 1978; Farquhar et al., 1989; Ehleringer and Monson, 1993; Lloyd and Farquhar, 1994; Tieszen eta al., 1997; Knapp and Medina, 1999; Long, 1999). The distribution of C3 and C4 grass is an important source of uncertainty in modelling. A more accurate mapping of the distribution pattern is of great significance for the subsequent discussion of fire risk, the evolution process of vegetation, the carbon cycle process of grasslands and homeostasis.
However, many problems exist in research regarding the distribution patterns of C3 and C4 grass. Most existing studies focus on a local scale, exploring the distribution pattern. Cavaganaro explored the distribution of native C3 and C4 grasses in a temperate arid region of Mendoza, Argentina in six areas at different altitudes (Cavaganaro, 1988). Similar field research has been carried out in Australia (Hattersley et al., 1983), South America (Powell et al., 2009) and so on. Such research areas are relatively limited and temporal and spatial resolution relatively rough. As for the global mapping of C3 and C4 grass, the earliest map was developed from interpolations based on a small number of sample plot data (Rowan, 1999), giving rise to much uncertainty in the resulting map.
Fortunately, the difference in remote sensing characteristics of C3 and C4 grass makes it possible to map the distribution pattern on a large scale. For example, because of the seasonal differences between C3 and C4 plants, measurement of vegetation greenness (e.g., NDVI) derived from time series of remote sensing data have the potential to discriminate between C3 and C4 plants (Liu et al., 2015). Still et al. captured the heterogeneity by combining remote sensing products, physiological modeling and the distribution of grassland to simulate the biomass of C3 and C4 plant. A phenology assisted decision tree approach in identifying C3 and C4 grasses with frequent satellite observations, and in examining the linkage between their spatio-temporal variations and climate dynamics has also been established(Wang et al., 2013). To improve the spatial and temporal resolution, a high spatial and temporal surface reflectance dataset can be generated by combining MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper) data and using them for identification (Liu et al., 2015). Irisarri has claimed that the hyperspectral data could discriminate C3 and C4 plants inside a laboratory (Irisarri et al., 2009).
Although these researches have made meaningful attempts, there are more advanced and universality nonparametric machine learning algorithms, providing the possibility to map more accurate C3 and C4 plant distribution. Such algorithms include the partial least squares regression (PLSR) (Wold et al., 1984), Sparse PLSR (SPLSR), random forest (RF), Discriminant Analysis (DA), support vector machines (SVM), artificial neural network (ANN) and boosted regression trees (BRT), etc. Perhaps using these new tools can give us a fresher and comprehensive picture of C3 and C4 plant distribution.
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