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VIIRS Vegetation Health Product: Algorithm Description

One of the most important long-term (34-year) satellite-based data records characterizing land surface, air temperature near the ground and climate was created from the Advanced Very High Resolution Radiometer (AVHRR) flown on the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites. Several global data sets have been developed from the AVHRR records since the early 1980s. They were the NOAA's Global Vegetation Index (GVI and GVI-2), National Aeronautics and Space Administration (NASA)'s Pathfinder, GIMMS and LTDR (Tarpley et al. 1984, James and Kalluri 1994, Kidwell 1997, Tucker et al. 2004). These datasets focused only on the Normalized Difference Vegetation Index (NDVI), ignoring infrared measurements, which are very useful for monitoring land, climate and socioeconomics. Therefore, NOAA developed a dataset entitled the Global Vegetation Health Product (VHP). The VHP has advantages over other long-term global data sets, being the longest (34-year), having the highest spatial resolution (4km), containing, in addition to NDVI, data and products from infrared channels, originally observed reflectance and emission, many indices with suppressed noise, biophysical climatology and more importantly, products used for monitoring environmental and socioeconomic activities (Kogan 1995, 1997).

The Visible Infrared Imager Radiometer Suite (VIIRS) provides advanced imaging and radiometric capabilities from NPP spacecraft and the next generation JPSS.VIIRS Visible and near infrared channels (I1 - 0.64µm, I2 - 0.86µm) are used to produce the NDVI and infrared band (I5 - 11.0-12.0µm) are used to produce Brightness Temperature (BT). Derived indices from NDVI and BT will be used to develop VH product.

For each day, VIIRS data are projected to a grid map with geo-graphic projection by selecting pixels closest to the center of grid cell. In order to pick cloud free pixels, 7 days maximum NDVI compositing is applied. Noise in NDVI and BT time series is further reduced by a digital filter.

After noise removal, weather-driven differences in NDVI and BT between the years become apparent: lower NDVI and higher BT in dry years and opposite in normal and wet years. This principle of comparing NDVI and BT for a particular year with their dry-wet range calculated from 30-year observations was laid down in the VH algorithm development and was based on the three laws: Law-of-Minimum, Law of Tolerance and Carrying Capacity. The absolute maximum and minimum of NDVI and BT during 1981-2005 were calculated for each of the 52 weeks and for each pixel. They were then used as the criteria to estimate the upper (favorable weather) and the lower (unfavorable weather) limits of the ecosystem resources. Further, for estimation of weather impacts on vegetation condition, NDVI and BT values for a particular time (year and week) were normalized relative to the absolute max/min interval. Following this procedure, NDVI and BT were rescaled based on equations below. They were named the Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI). These indices are designed to characterize moisture (VCI), thermal (TCI) and total vegetation health (VHI) conditions in response to weather impacts:


VCI=100*(NDVI-NDVImin)/(NDVImax-NDVImin) (1)
TCI=100*(BTmax - BT)/(BTmax - BTmin) (2)
VHI = a*VCI + (1- a )*TCI (3)

where NDVI, NDVImax, and NDVImin (BT, BTmax, and BTmin) are the noise reduced (smoothed) weekly NDVI (BT), their multi-year absolute maximum, and minimum, respectively. The VCI, TCI and VHI approximate the weather component in NDVI, BT and their combination values. They fluctuate from 0 to 100, reflecting changes in vegetation conditions from extremely bad to optimal. The weighting factor (a) in equation 3 was determined by experience, currently, a=0.5).

Further information may be found in the Algorithm Theoretical Basis Document.