Modeling grassland aboveground biomass using a pure vegetation index

Remote sensing can be the most effective means of scaling up grassland aboveground biomass (AGB) from the sample scale to the regional scale. Among the remote-sensing approaches, statistical models based on the vegetation index (VI) are frequently used to retrieve grassland AGB because of their simplicity and reliability. However, these types of models have never been comprehensively optimized to overcome VI insensitivity and soil effects. Because grassland AGB is related to grassland type, in our research the integrated orderly classification system for grassland (IOCSG) was used to differentiate grassland types. The study area, located in Inner Mongolia, China, included desert steppe, typical steppe and meadow steppe. A pure VI (PVI) was extracted from the normal VI using spectral mixture analysis (SMA). Using a proportional relationship, PVI models were then constructed based on grassland type. The results demonstrated that the PVI models can have clear advantages over the more commonly used VI models. They simplify the parameterization of VI models and thus enhance models constructed for different regions with different remote sensing data sources. Notably, detailed differentiation of grassland types can improve the accuracy of AGB estimates. The methodology proposed in this study is particularly beneficial for AGB estimates at a national scale, especially for countries such as China with many grassland types.