Leaf Area Index (LAI) measurements were collected using a Delta-T SunScan as part of the Network for Calibration and Validation of Earth Observation data (NCAVEO) 2006 Field Campaign. Data were collected from the following experiment fields: Rickyard (winter wheat), Fairpiece (winter oats) and Brockley (spring barley). The parameters required by the SunData program were set as follows: • Leaf Angle Distribution Parameter = 1.0 • Leaf Absorption Parameter = 0.85 Five sample points were located within tramlines in each field and their position determined using dGPS. Five 10 metre long transects were set-up, centred on each of these points and marked with coloured flags, marking the longitudinal extent of each Elementary Sampling Unit (ESU). The ‘width’ of the area sampled within the crop was determined by the reach of the instrument, around 1 metre. The geographic co-ordinates in the data file have been calculated to allow for the offset of sample measurements from the tramlines, and these should be taken as the definitive locations of the individual samples. SunScan measurements were made every metre along a 30 m transect, the central 10 m length of which coincided with the area between the coloured flags. Each of the extended ESUs was sampled in the same order in each field: Red, Green, Blue, Yellow, White flag, following a path along the tramlines shown in Figure 1. This gave a total of 165 LAI measurements per field. The geographic co-ordinates of each measurement were recorded and represented alongside the measured variables. For further details see the dataset's metadata document in linked documentation.

A new monthly long term average (climatology) of Leaf Area Index (LAI) has been developed for use as ancillary data with the Joint UK Land Environment Simulator (JULES) Land Surface Model and the UK Met Office Unified Model. It is derived from an improved version of long time series of LAI from the original Global LAnd Surface Satellite (GLASS) products (http://www.glass.umd.edu/LAI/MODIS/0.05D/). The GLASS data consists of a time series of LAI from Moderate Resolution Imaging Spectroradiometer (MODIS) surface-reflectance data for the period 2000-2014. The MODIS data was provided in a spatial resolution of 1km in a sinusoidal projection and is interpolated into 0.5deg on a geographic latitude/longitude projection in this dataset. The total LAI from MODIS is segregated into five different Plant Functional Types (PFTs) using the fractional coverage of each PFT from the Climate Change Initiative (CCI) Land Cover data. For this reason this new LAI climatology should be used in combination with the CCI PFT data, which is also provided here. Two variables are provided with the dataset containing LAI, each covering the same spatial and time extent. The PFT data provided with this dataset covers a time span of only one year, 2010. - Leaf Area Index (LAI) - LAI is an important parameter in land-surface models, influencing the surface roughness, transpiration rate and the soil water content and temperature. Numerous outputs of vegetation models such as net primary productivity (NPP), evapotranspiration (ET), light absorption by plants (FAPAR), nutrient dynamics etc., are influenced by LAI where it is a key variable in energy and water balance calculations. - Vegetation Canopy Height (H) - H plays an important role in the interface between the atmosphere and land surface and it impacts weather and climate at local to global scales by modulating aerodynamic conductance and vegetation dynamics. Therefore, H is fundamentally needed for the calculation of turbulent exchanges of energy and mass between the atmosphere and the terrestrial ecosystem. One variable is provided with the dataset containing CCI PFTs: - Fractional coverage of 5 PFTS or vegetation classes and 4 land use classes – The 5 PFTs are Broad Leaf, Needle Leaf, C3 Grass, C4 Grass and Shrub. The 4 land use classes are Urban area, Inland Water, Bare Soil and Snow/Ice. Full details about this dataset can be found at https://doi.org/10.5285/6d07d60a-4cb9-44e4-be39-89ea40365236