Leaf Area Index (LAI), a crucial biophysical variable, represents the one-sided green leaf area per unit ground surface area. LAI is fundamental for understanding vegetation structure and function, playing a key role in photosynthesis, evapotranspiration, and carbon cycling. It is widely applied in land surface and ecological modeling, agriculture, and climate studies. The VIIRS LAI product primarily aims to enhance Numerical Weather Prediction (NWP) by integrating LAI into the next-generation Noah Multi-parameterization Land Surface Model (Noah-MP). Beyond this, it is designed to serve the broader scientific community.
VIIRS LAI is generated Sfrom the VIIRS instruments onboard S-NPP, NOAA-20, and NOAA-21. It is a global, gap-free product at 1 km resolution, with updates provided every 8 days. Leveraging over two decades of satellite-derived LAI data, the LAI retrieval process employs a data-driven approach, estimating LAI from the surface reflectance data captured by VIIRS image bands (I1, I2, and I3). A fused and refined LAI training dataset has been developed using NASA LAI, Global Land Surface Satellite (GLASS), and Geoland2/BioPar (GEOV2) LAI products. The end-to-end process includes daily global surface reflectance compositing, LAI retrieval, weekly LAI compositing, temporal smoothing, and gap filling to produce the final global 1 km product.
The VIIRS LAI product is provided in NetCDF4 format and includes LAI values along with a set of global attributes. To optimize storage, LAI values are scaled and stored in 16-bit integer format, with scale factors, offsets, valid ranges, and fill values clearly documented in the data attributes.