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.
Prof. FANG Hongliang, and his team, from the Institute of Geographic Sciences and Natural Resources Research (IGSNRR),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 range between 25% and 60%. This indicates that current LAI products fail to meet the uncertainty requirement (15%) proposed by the Global Climate Observing System (GCOS).
They also showed that the performance of the LAI products varies at different crop growth stages: the global LAI tends to overestimate during the first stage, largely fluctuates during the middle stage, and underestimates during the late stage. Researchers concluded that the product uncertainties are 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.The related study has been published in a recent issue of Remote Sensing of Environment.