| Intro |
Remote sensing can be the most effective means of scaling up grassland aboveground biomass (AGB) fromthe sample scale to the regional scale. Among the remote-sensing approaches, statistical models basedon the vegetation index (VI) are frequently used to retrieve grassland AGB because of their simplicity andreliability. However, these types of models have never been comprehensively optimized to overcome VIinsensitivity and soil effects. Because grassland AGB is related to grassland type, in our research the inte-grated orderly classification system for grassland (IOCSG) was used to differentiate grassland types. Thestudy 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 propor-tional relationship, PVI models were then constructed based on grassland type. The results demonstratedthat the PVI models can have clear advantages over the more commonly used VI models. They simplifythe parameterization of VI models and thus enhance models constructed for different regions with dif-ferent remote sensing data sources. Notably, detailed differentiation of grassland types can improve theaccuracy of AGB estimates. The methodology proposed in this study is particularly beneficial for AGBestimates at a national scale, especially for countries such as China with many grassland types. |
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