Author's Personal Copy Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors. Part 2: Implementation, Analysis and Validation Remote Sensing of Environment Author's Personal Copy

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t a r t i c l e i n f o Keywords: Leaf area index Validation Accuracy Climate data Long term data record AVHRR LAI MODIS LAI CYCLOPES Spectral invariants Recollision probability Radiative transfer Scaling Single scattering albedo Data uncertainties The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the inter-comparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies. Long term global vegetation monitoring requires temporally and spatially consistent data sets of vegetation biophysical variables characteristic of vegetation structure and functioning like the Leaf area index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR). Such data sets are useful in many applications ranging from ecosystem monitoring to modeling of the exchange of energy, …