Leaf Area Index Retrieval

Following products are available for LAI:

Copernicus BioPAR GEOv2

Period: 1999 – present Sensor: SPOT-VGT & PROBA-V Frequency: 10 days Resolutions: 1km Extent: Global Data can be accessed here. Algorithm description: The algorithm is essentially similar to GEOV1 with respect to actual retrieval of instantaneous values. Additional features are (1) multistep filtering approach to eliminated data affected by atmospheric effects and snow cover, (2) temporal smoothing, gap filling and consistent adjustment of…

Copernicus BioPAR GEOv3

Sensor: PROBA-V Frequency: 10 days Resolutions: 300m Extent: Global Data can be accessed here. Algorithm description: The algorithm is similar to GEOv2 algorithm – (1) Estimation (neural networks) of instantaneous biophysical variables atmospherically corrected surface reflectance and then (2) smoothing and gap-filling. The main advantage is finer resolution than GEOv2. Note that the training datasets are still the once developed for SPOT-VGT, hence PROBA-V…

NASA MODIS (MCD15A3H v6)

Period: 2000 – present Sensor: MODIS Terra+Aqua Frequency: 4 and 8 days Resolutions: 500m (1km older versions) Extent: Global Data can be accessed here. Algorithm description: The algorithm consists of a Look-up-Table (LUT) based procedure that exploits the spectral information content of the MODIS red (648 nm) and near-infrared (NIR, 858 nm) surface reflectances. The LUT was generated using 3D radiative transfer…

NCDC AVHRR

Period: 1981 – present Sensor: AVHRR Frequency: 1 day Resolutions: 5km Extent: Global Data can be accessed here. Algorithm description: The product has been developed to ensure continuity of AVHRR based LAI/FAPAR products. The algorithm relies on Artificial Neural Networks (ANN) relating LAI to BRDF corrected AVHRR surface reflectance products for five biomes. MODIS LAI/FAPAR data was used as training dataset. It…

LSASAF MSG SEVIRI (MDLAI)

Period: 2006 – present Sensor: MSG SEVIRI Frequency: 1 days Resolutions: full SEVIRI spatial resolution (3km at equator) Extent: MSG disk Data can be accessed here. Algorithm description: The algorithm is uses a semi-empirical approach, wherein LAI is retrieved from the Fractional Vegetation Cover (FCV) data, where FCV calculation relies on k0 BRDF parameters in the red, NIR and MIR bands. Accuracy:Accuracy was…

GLASS

Period: 1982 – 2012 Sensor: AVHRR (1982 to 1999) & MODIS (2000 to 2012) Frequency: 8 days Resolutions: 5km Extent: Global Data can be accessed here. Algorithm description: The algorithm theoretical approach is similar to Copernicus GEOV1 LAI product. It uses General Regression Neural Networks (GRNN) to retrieve LAI from time-series MODIS reflectance data. The network was trained (1 year period) on the…

UCL GlobAlbedo-derived

Period: 2002 – 2011 Sensor: ATSR2, AATSR, MERIS, VGT1, VGT2 Frequency: 8 days Resolutions: 1km, 5km, 25km Extent: Regional and Global Data access: Please contact Mathias Disney: mathias.disney@ucl.ac.uk Algorithm description: The algorithm uses the JRC-TIP (1D) radiative transfer model (2 stream) inversion scheme, where TIP refers to Two-stream Inversion Package. LAI is estimated from GlobAlbedo albedo values in the VIS and the NIR/SWIR…

Copernicus BioPAR GEOv1

Period: 1999 – present Sensor: SPOT-VGT and PROBA-V Frequency: 10 days Resolutions: 1km Extent: Global Data can be accessed here. Algorithm description: This product has been developed to build up on the already existing and widely validated MODIS and CYCLOPES products while removing limitations observed in these products. MODIS and CYCLOPES were chosen as they were more spatially and temporally consistent. The…