HR: 1340h
AN: H23E-1171    [Abstracts]
TI: Evaluation of MODIS snow-cover products with constraints from streamflow and SNOTEL data
AU: * Xie, H
EM: hxie@utsa.edu
AF: University of Texas at San Antonio, 6900 N. Loop 1604 W., San Antonio, TX 78249 United States
AU: Zhou, X
AF: New Mexico Tech, 801 Leroy Place, Socorro, NM 87801 United States
AU: Hendrickx, J
AF: New Mexico Tech, 801 Leroy Place, Socorro, NM 87801 United States
AB: Evaluation of the Moderate Resolution Imaging Spectroradiometer (MODIS) daily and 8-day snow-cover products is carried out by utilizing the streamflow and the Snowpack Telemetry (SNOTEL) measurements as constraints. Time series of the snow areal extent (SAE) of the Upper Rio Grande Basin are retrieved from MODIS during the period 2000 to 2004 using an automatic Geographic Information System (GIS) based algorithm developed for this study. Statistical analysis between the streamflow at Otowi (NM) station and the SAE retrieved from the two MODIS snow-cover products shows that there is a statistically significant correlation between the streamflow and SAE for both products. This relationship can be disturbed by heavy rainstorms in the later springtime, especially in May. Correlation analyses show that the MODIS 8-day product has a better correlation (r = -0.404) with streamflow and has less percentage of spurious snowmelt events in wintertime than the MODIS daily product (r = -0.300). Intercomparison of these two products, with the SNOTEL datasets as the ground truth, shows that the MODIS 8-day product has higher classification accuracy for both snow and land. The major cause that reduces the overall accuracy of the MODIS daily product is due to the cloud. Improvement in suppressing cloud in the 8-day product is obvious from this comparison study. The sacrifice is the temporal resolution which is reduced from one day to 8 days. The significance of the results is that the 8-day product may be more useful in evaluating the streamflow response to the snow-cover extent changes, especially from the long-term point of view considering its lower temporal resolution than the daily product. For clear days, the MODIS daily algorithm works quite well or even better than the MODIS 8-day algorithm.
DE: 6062 Satellites
DE: 3360 Remote sensing
DE: 1854 Precipitation (3354)
DE: 1863 Snow and ice (1827)
DE: 0910 Data processing
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting