Leaf Area Index (LAI)

The GLASS LAI product is generated from time-series AVHRR/MODIS reflectance data using general regression neural networks (GRNN). Different from the existing neural network methods that use only remote sensing data acquired at a specific time to retrieve LAI, the reprocessed MODIS reflectance data from an entire year were inputted into the GRNNs to estimate one-year LAI profiles. The training data was generated from MODIS and CYCLOPES LAI products and MODIS/AVHRR reflectance products of the BELMANIP sites during the period from 2001-2004. The effective CYCLOPES LAI was first converted to the true LAI, which was then combined with the MODIS LAI according to the uncertainties of each as determined from the ground-measured true LAI. The MODIS and AVHRR reflectance was reprocessed to remove remaining effects of cloud contamination and other factors. GRNNs were then trained using the fused LAI and reprocessed MODIS reflectance for each biome type.

The LAI product was generated from AVHRR and MODIS reflectance. The MODIS reflectance product (MOD09A1) provides the surface reflectance for each of the MODIS land spectral bands with 500 m spatial resolution and 8 days temporal sampling period. The AVHRR reflectance data are from the NASA's Land Long Term Data Record (LTDR) project which reprocessed Global Area Coverage (GAC) data from AVHRR sensors onboard NOAA satellites and created daily surface reflectance product with 0.05° spatial resolution. And the maximum value composite approach (MVC) is used to composite the daily surface reflectance data into composites of 8-day intervals in order to maintain a consistent time resolution with MODIS surface reflectance data. The red and near-infrared (NIR) reflectance time series data of AVHRR and MODIS are used to generate GLASS LAI product.

For further details please consult the publication:

Xiao Z., S. Liang, J. Wang, et al., Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product from Time Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 2013, doi:10.1109/TGRS.2013.2237780.

Leaf Area Index
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