Canopy water content is an important variable for forestry and agriculture management. This study was aimed at
building calibration models to estimate vegetation canopy (VC) equivalent water thickness (EWT) from high temporal
resolution and large areal coverage MODIS images. The models were developed for a semi-arid area in Arizona
(SMEX04) and the best one was applied to MODIS images covering a forest area in Southern Indiana. EWT derived
from hyperspectral data in the process of atmospheric correction was used for calibrating MODIS spectral indices.
Tested in this study were four vegetation indices: Normalized Difference Water Index (NDWI), Shortwave Infrared
Water Stress Index (SIWSI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI),
which were designed based on either water (NDWI and SIWSI) or chlorophyll absorptions (NDVI and EVI). Validating
these indices on field measured EWT for the SMEX04 site resulted in R2 correlations of 0.7547, 0.7509, 0.7299 and
0.7547, respectively. According to regression equations, however, EWT estimated using NDWI and SIWSI shows a
slope more close to 1 than those using NDVI and EVI when validated with ground measured EWT, thus showing a better
prediction ability than the two chlorophyll indices. The SIWSI-EWT model was chosen to apply to a time series of
MODIS images covering the Southern Indiana areas and the relationship of EWT derived from these images to
precipitation was examined.