A review of research on grassland biomass estimation based on remote sensing and mechanistic models

Grassland degradation and desertification are increasingly becoming serious problems owing to anthropogenic and climate change impacts, making the monitoring and protection of grasslands evermore critical tasks. Biomass is an important indicator of the ecological status of grasslands, and its estimation is of great significance to grassland resource management and ecological restoration and conservation. With the development of remote sensing technology and the wide application of mechanistic vegetation models, two main methods for estimating grassland biomass have been developed: remote sensing inversion and mechanistic vegetation model estimation. In this paper, the empirical regression method and radiative transfer model in remote sensing inversion as well as the crop and grassland growth models based on vegetation mechanisms are systematically reviewed. Additionally, we introduce data assimilation techniques that have been widely used in the field of agricultural yield estimation, analyze the complementary advantages of remote sensing data and mechanistic models in grassland biomass assimilation estimation, and highlight current problems and future development needs. Aside from simplifying the models, the focus of future research on grassland biomass estimation should include ensuring the stability and accuracy of estimation and taking the vegetation mechanisms into account.