Location: Location not imported yet.Title: Evaluation of drought indices via 1 remotely sensed data with hydrological variables: Little river experimental watershed, Georgia U.S.) Author
Submitted to: Remote Sensing of Environment
Publication Type: Peer reviewed journal
Publication Acceptance Date: 10/26/2012
Publication Date: 1/1/2013
Publication URL: http://handle.nal.usda.gov/10113/59949
Citation: Choi, M., Jacobs, J., Anderson, M.C., Bhat, S., Bosch, D.D. 2013. Evaluation of drought indices via 1 remotely sensed data with hydrological variables: Little river experimental watershed, Georgia U.S.. Remote Sensing of Environment. 476:265-273. Interpretive Summary: To evaluate the utility of new drought mapping products over the US, they are typically compared at continental scales with maps of precipitation anomalies or with classifications recorded in the U.S. Drought Monitor. In this paper, a new drought index based on remote sensing of evapotranspiration (ET) (the Evaporative Stress Index, or ESI) is evaluated regionally over the southeastern US in comparison with spatially distributed soil moisture and streamflow observations and other remote sensing drought indices. The ESI was able to capture fluctuations in the hydrologic observables, and shows promise for drought monitoring in this region.
Technical Abstract: An intercomparison among standard and remotely sensed drought indices was conducted using streamflow and soil moisture measurements collected in the Little River experimental watershed, Georgia US, during the period from 2000 to 2008. The remotely sensed Evaporative Stress Index (ESI) was identified as a promising drought index for characterizing streamflow and soil moisture anomalies. Of the indices examined, the ESI had the best performance with about 80% accuracy in comparison with observed soil moisture and streamflow drought conditions. Both the Palmer Drought Severity Index (PDSI) and the Vrije Universiteit Amsterdam (VUA) AMSR-E soil moisture products predicted streamflow and soil water variability with squared correlation coefficients (R2) of 0.65 and 0.79, respectively. While the remote sensing Vegetation Health Index (VHI) tracked drought, its performance lagged the other indices. A regression analysis revealed that the joint use of the PDSI and appropriate remotely sensed drought indices, ESI and VUA, could improve predictions of streamflow and soil water variability.