The Community Land Model version 4 with carbon and nitrogen components is coupled with data assimilation research testbed to assimilate remotely sensed leaf area index (LAI), to analyze the improvement in model performance for simulating land surface variables and land–atmospheric exchange fluxes. The results demonstrate that assimilation effectively addresses the issue of significant overestimation of LAI values, particularly noticeable in regions characterized by low latitudes and dense vegetation coverage. At a global scale, the disparities between simulated and assimilated LAI relative to observational data, are measured at 0.90 and −0.07, representing 54.1% and 3.9% of the observed values, respectively. The root mean square difference (RMSD) for assimilated LAI is 1.61 comparing with the simulated LAI of 1.85. Assimilating LAI globally leads to a noteworthy 1% reduction in the mean relative difference of the global average 2-m air temperature ( T _2m ) and a concurrent decrease of 0.15 °C in RMSD. However, at the global level, the assimilation of LAI does not yield a significant enhancement in the modeling capability of heat fluxes, although modeling capability of sensible heat (HS) slightly outperforms latent heat. Improvements in land surface variables after assimilation show significant variations at regional scales due to factors such as vegetation coverage and climatic conditions. Overall, in regions characterized by periodic changes in vegetation, such as forested areas in Western Eurasian Continent (region 5), the enhancements in T _2m and HS after assimilating LAI are particularly notable, with mean relative difference reduced by 7% and 20%, respectively.

中文翻译：

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同化遥感叶面积指数以提高全球范围内的地表模拟性能