Publications

Case, Jonathan L.; LaFontaine, Frank J.; Bell, Jordan R.; Jedlovec, Gary J.; Kumar, Sujay V.; Peters-Lidard, Christa D. (2014). A Real-Time MODIS Vegetation Product for Land Surface and Numerical Weather Prediction Models. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 52(3), 1772-1786.

Abstract
A technique is presented to produce real-time, daily vegetation composites at 0.01 degrees resolution (similar to 1 km) over the Conterminous United States (CONUS) for use in the NASA Land Information System (LIS) and weather prediction models. Green vegetation fraction (GVF) is derived from direct-broadcast swaths of normalized difference vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observing System satellites. The real-time data and increased resolution compared to the 0.144 degrees (similar to 16 km) resolution monthly GVF climatology in community models result in an improved representation of vegetation in high-resolution models, especially in complex terrain. The MODIS GVF fields show seasonal variations that are similar to the community model climatology, and respond realistically to temperature and precipitation anomalies. The wet spring and summer 2010 over the U. S. Plains led to higher regional GVF than in the climatology. The GVF substantially decreased over the U.S. Southern Plains from 2010 to 2011, consistent with the transition to extreme drought in summer 2011. LIS simulations depict substantial sensitivity to the MODIS GVF, with regional changes in heat fluxes around 100 Wm(-2) over the northern U.S. in June 2010. CONUS LIS simulations during the 2010 warm season indicate that the larger MODIS GVF in the western U.S. led to higher latent heat fluxes and initially lower sensible heat fluxes, with a net drying effect on the soil. With time, the drier soil eventually lead to higher mean sensible heat fluxes such that the total surface energy output increased by late summer 2010 over the western U.S. A sensitivity simulation of a severe weather event using real-time MODIS GVF data results in systematic changes to low-level temperature, moisture, and instability fields, and improves the evolution of simulated precipitation.

DOI:
10.1109/TGRS.2013.2255059

ISSN:
0196-2892; 1558-0644