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United States Department of Agriculture

Agricultural Research Service

Title: Vegetation fraction mapping with high resolution multispectral data in the Texas High Plains

item O`shaughnessy, Susan
item Hernandez, Jairo
item Gowda, Prasanna
item Basu, Sukanta
item Colaizzi, Paul
item Howell, Terry
item Schulthess, Urs

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 12/10/2010
Publication Date: 12/17/2010
Citation: Oshaughnessy, S.A., Hernandez, J.E., Gowda, P., Basu, S., Colaizzi, P.D., Howell, T.A., Schulthess, U. 2010. Vegetation fraction mapping with high resolution multispectral data in the Texas High Plains [abstract]. American Geophysical Union Meeting, December 13-17, 2010, San Francisco, California. Paper No. H33F-1237

Interpretive Summary:

Technical Abstract: Land surface models use vegetation fraction to more accurately partition latent, sensible and soil heat fluxes from a partially vegetated surface as it affects energy and moisture exchanges between the earth's surface and atmosphere. In recent years, there is interest to integrate vegetation fraction data into intelligent irrigation scheduling systems to avoid false positive signals to irrigate. Remote sensing can facilitate the collection of vegetation fraction information on individual fields over large areas in a timely and cost-effective manner. In this study, we developed and evaluated a set of vegetation fraction models using least square regression and artificial neural network (ANN) techniques using RapidEye satellite data (6.5 m spatial resolution and on-demand temporal resolution). Four images were acquired during the 2010 summer growing season, covering bare soil to full crop cover conditions, over the USDA-ARS-Conservation and Production Research Laboratory in Bushland, Texas [350 11' N, 1020 06' W; 1,170 m elevation MSL]. Spectral signatures were extracted from 25 ground truth locations with geographic coordinates. Vegetation fraction information was derived from digital photos taken at the time of image acquisition using a supervised classification technique. Comparison of performance statistics indicate that ANN performed slightly better than least square regression models.

Last Modified: 06/23/2017
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