Leaf Area Index (LAI) indicates the amount of live green leaf above ground surface. Many agro-meteorology, atmospheric general circulation, and biogeochemical models rely on LAI to parameterize the vegetation interactions with the atmosphere. A series of LAI products have been generated from different satellite data. However, to effectively use these LAI products in various disciplines, it is important to know the uncertainties of these products.  

A research team led by Prof. FANG Hongliang from the Institute of Geographic Sciences and Natural Resources Research (IGSNRR) of the Chinese Academy of Sciences developed an initiative to validate seven major global LAI products over croplands in northeastern China. A series of field campaigns have been conducted over rice, maize, soybean, and sorghum fields since 2012.   

Their study revealed remarkable uncertainties in current LAI products for agricultural crops. They found that the uncertainties of current LAI products ranged between 25% and 60%, which indicated that current LAI products failed to meet the uncertainty requirement (15%) proposed by the Global Climate Observing System (GCOS).   

The researchers also showed that the performance of the LAI products varied at different crop growth stages: the global LAI tended to overestimate during the first stage, largely fluctuated during the middle stage, and underestimated during the late stage. They concluded that the product uncertainties were mainly due to the lack of regional tuning of the global LAI algorithms over agricultural areas.   

Prof. FANG suggested that future product development should consider the regional conditions in order to improve the product quality.