Using MODIS Satellite Images to Confirm Distributed Snowmelt Model Results in a Small Arctic Watershed

Abstract

Environmental analysts face the problem of obtaining distributed measurements to evaluate the performance of models with increasingly small spatiotemporal resolution. While U.S. government agencies readily provide both measurement products and data tools for the study of global change occurring over entire seasons and across continental areas, analysts need access to the low-level data that provides the basis for global products. Finally, analysts need to consider sensor errors inherent in low-level products that are accounted for in global, composite products. Hydrologists using tools for managing low-level snow swath measurements, in particular, must consider how measurements are affected by sensor errors like snow-cloud confusion and sensor errors due to low ground illumination at night.

This thesis aims to explore the use of remotely sensed snow maps to confirm a time series of model maps. Specifically, snow covered area (SCA) measurements remotely sensed by the National Aeronautics and Space Administration (NASA) are used to confirm SCA predictions modeled by the United States Agriculture Department (USDA). The measurements come from the two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard near-polar, sun-synchronous satellites named Aqua and Terra. The USDA calls the model TOPMODEL-Based Land-Atmosphere Transfer Scheme (TOPLATS). The Upper Kuparuk River Watershed (UKRW) on the North Slope of Alaska acts as the case study location.

To meet the map-comparison goal, the Kappa statistic, Kappa statistic variants, and probability density functions expressing measurement uncertainty in discrete scenes all evaluate the ability of MODIS measurements to confirm the accuracy of TOPLATS model maps. Data management objectives to make measured data accessible and comparable to the model output comprise a supporting goal.

Results show that individual composite statistics, like the proportion of agreement between two maps, can easily obscure spatiotemporally distributed confirmation information without additional statistics and side-by-side images of measurement maps and model maps. These tools show some promise for using MODIS to confirm model predictions of snowmelt that occur across less than 150 km2 and less than a few days, however, clouds and malfunctioning sensors limit such use.