Reducing cloud obscuration on MODIS Snow Cover Area products by applying spatio-temporal techniques combined with topographic effects.

Abstract

Rapid population growth in Arizona is leading to increasing demand and decreasing availability of water, requiring a detailed quantification of hydrological processes. The integration of detailed spatial water fluxes information from remote sensing platforms, and hydrological models is one of the steps towards this goal. One example step is the use of MODIS Snow Cover Area (SCA) information to update the snow component of a land surface model (LSM). Because cloud cover obscures the images, this project explores a rule-based method to remove the clouds. The rules include: combination of SCA maps from two satellites; time interpolation method; spatial interpolation method; and the probability of snow occurrence in a pixel based on topographic variables. The application in sequence of these rules over the Upper Salt River Basin for WY 2005 resulted in a reduction of cloud obscuration by 93.7878% and the resulting images' accuracy is similar to the accuracy of the original SCA maps. The results of this research will be used on a LSM to improve the management of reservoirs on the Salt River. This research seeks to improve SCA data for further use in a LSM to increase the knowledge base used to manage water resources. It will be relevant for regions were snow is the primary source of water supply.