Characterization and intercomparison of global moderate resolution leaf area index (LAI) products: Analysis of climatologies and theoretical uncertainties

Leaf area index (LAI) is a critical variable for land surface and climate modeling studies. Several global LAI products exist and it is important to know how these products perform and what their uncertainties are. Five major global LAI products: MODIS, GEOV1, GLASS, GLOBMAP, and JRC-TIP were compared between 2003 and 2010 at a 0.01° spatial resolution and with a monthly time step. The daily Land-SAF product was used as a regional reference in order to evaluate the performance of other global products in Africa. Cross-sensor LAI conversion equations were derived for different biome types. Product uncertainties were assessed by looking into the product quantitative quality indicators (QQIs) attached to MODIS, GEOV1, and JRC-TIP. MODIS, GEOV1, GLASS, and GLOBMAP are generally consistent and show strong linear relationships between the products (R2 > 0.74), with typical deviations of < 0.5 for non-forest and < 1.0 for forest biomes. JRC-TIP, the only effective LAI product, is about half the values of the other LAI products. The average uncertainties and relative uncertainties are in the order: MODIS (0.17, 11.5%) < GEOV1 (0.24, 26.6%) < Land-SAF (0.36, 37.8%) < JRC-TIP (0.43, 114.3%). The highest relative uncertainties usually appear in ecological transition zones. More than 75% of MODIS, GEOV1, JRC-TIP, and Land-SAF pixels are within the absolute uncertainty requirements (± 0.5) set by the Global Climate Observing System (GCOS), whereas more than 78.5% of MODIS and 45.0% of GEOV1 pixels are within the threshold for relative uncertainty (20%). This study reveals that the discrepancies are mainly due to differences between definitions, retrieval algorithms, and input data. Future product development and validation studies should focus on areas (e.g., sparsely vegetated and savanna areas) and periods (e.g., winter time) with higher uncertainties.

JRC.H.7 - Climate Risk Management