Title :
An error and sensitivity analysis of the atmospheric- and soil-correcting variants of the NDVI for the MODIS-EOS
Author :
Huete, Alfred R. ; Liu, Hui Qing
Author_Institution :
Dept. of Soil & Water Sci., Arizona Univ., Tucson, AZ, USA
fDate :
7/1/1994 12:00:00 AM
Abstract :
Several soil- and atmospheric-correcting variants of the normalized difference vegetation index (NDVI) have been proposed to improve the accuracy in estimating biophysical plant parameters. In this study, a sensitivity analysis, utilizing simulated model data, was conducted on the NDVI and variants by analyzing the atmospheric- and soil-perturbed responses as a continuous function of leaf area index. Percent relative error and vegetation equivalent “noise” (VEN) were calculated for soil and atmospheric influences, separately and combined. The NDVI variants included the soil-adjusted vegetation index (SAVI), the atmospherically resistant vegetation index (ARVI), the soil-adjusted and atmospherically resistant vegetation index (SARVI), the modified SAVI (MSAVI), and modified SARVI (MSARVI). Soil and atmospheric error were of similar magnitudes, but varied with the vegetation index. All new variants outperformed the NDVI. The atmospherically resistant versions minimized atmospheric noise, but enhanced soil noise, while the soil adjusted variants minimized soil noise, but remained sensitive to the atmosphere. The SARVI, which had both a soil and atmosphere calibration term, performed the best with a relative error of 10 percent and VEN of ±0.33 LAI. By contrast, the NDM had a relative error of 20 percent and VEN of ±0.97 LAI
Keywords :
geophysical techniques; infrared imaging; remote sensing; ARVI; EOS; IR method; MODIS; NDVI; SARVI; SAVI; atmospheric correction; atmospherically resistant vegetation index; biophysical plant parameter; error analysis; geophysical measurement technique; leaf area index; normalized difference vegetation index; optical; remote sensing; sensitivity analysis; simulated model; soil-adjusted; soil-correcting variant; vegetation mapping; visible infrared; Analytical models; Atmosphere; Atmospheric modeling; Calibration; Equations; Immune system; Parameter estimation; Sensitivity analysis; Soil; Vegetation;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on