# Analysis and Applications of GlobeLand30: A Review
## 4 Application Analysis
### Coupling Analysis with Earth System Models  
Models have proven to be an important tool, both to conceptualize and test human understanding of the role of different drivers in land use and land cover change and to explore scenarios of possible future developments [81; 82]. In recent years, study on climate coupling models between the dynamic mechanism of land cover and environmental change has elicited considerable research interest in climate change, such as urban expansion and climate change. At present, the direct and indirect effects of land cover data on climate and climate change are receiving increasing attention. Models that use land cover information are mainly the climate model, urban change models, and environment model. For instance, urban expansion has been simulated by coupling one \"bottom-up\" cellular automata (CA)-based model and one \"top-down\" system dynamics (SD)-based model [83]. Here, we summarized some Earth system models that incorporate GlobeLand30 land cover information to simulate climate, hydrological cycle, and land surface fluxes.  
GlobeLand30 dataset was used in the Beijing Climate Center Climate System Model (BCC_CSM) to assess the influences of land cover dataset on land surface and climate simulations. Land cover is one of the most basic input elements of land surface and climate models [52]. GlobeLand30 data was merged with other satellite remote sensing datasets to regenerate the plant function types (PFT) fitted for the BCC_CSM. The area-weighted up-scaling approach was used to aggregate the 30 m-resolution GlobeLand30 data onto the coarser model grids and derive FFT, as well as percentage information. Results show that with the new LC data products, several model biases between simulations and observations in the BCC climate model with original LC datasets were effectively reduced, including the positive bias of precipitation in the mid-high latitude of the northern hemisphere and the negative bias in the Amazon, as well as the negative bias of air temperature in parts of the southern hemisphere. Therefore, the GlobeLand30 data are considered suitable for use in the BCC_CSM component models and can improve the performance of the land and atmosphere simulations.  
GlobeLand30 was also used as an input data in the Soil and Water Assessment Tool (SWAT) to model water vulnerability in the Yangtze River Basin, China [35]. [PERSON] et al. [84] assessed the uncertainties of seven GLC datasets and their propagation into the simulation of land surface fluxes (LSFs) in India by using a macro-scale land surface model. The GLC datasets, including UMD, IGBP DISCover, GLC2000, MODIS, GlobeCover, CCI-LC and GlobeLand30, were aggregated to 0.25 degrees and then input into the variable infiltration capacity (VIC) model to simulate LSFs. The results were validated by observed stream flow and MODIS-derived global evapotranspiration data. The results indicated that GlobeLand30 with the least difference in crop fraction has the least bias. These studies showed that finer-resolution GLC land cover dataset can improve the accuracy of model simulation at the regional scale.