Abstract
Moderate resolution imaging spectroradiometry (MODIS) snow cover accuracy has been assessed in the past at different scales, with various approaches and in relation to the many factors influencing the remote observation of snow-covered areas (SCA). However, the challenge of fully characterizing MODIS accuracy over forest sites is still open. In this study, we exploit 5 years of data from the upper river Adige basin at Ponte Adige (Eastern Italian Alps) to condition an enhanced temperature index snowpack model accounting for model parameter uncertainty by using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The simulated SCA is then compared with MODIS retrievals through a range of different statistical metrics to investigate how land use and solar illumination conditions affect such comparison. In particular, the Overall Accuracy index (OA) is used to quantify the agreement between satellite-derived and simulated SCA on a pixel-by-pixel basis. Analyzing the spatial variability either of the median OA and its range shows that illumination conditions over forested canopies represent a major source of uncertainty in MODIS SCA. Exploiting this finding, we identify the minimum level of incoming short-wave radiation for accurate use of MODIS SCA in forest areas.
Highlights
Understanding and predicting snowpack dynamics is of crucial importance to characterize the water cycle in mountainous regions [1]
This paper presents an evaluation of Moderate resolution imaging spectroradiometry (MODIS)-based snow-covered areas (SCA) reliability on forest sites, carried out by means of a comparison with TOPMELT, a recently proposed distributed snowpack model
This analysis pointed out the significant impact of forest sites, especially during winter months
Summary
Understanding and predicting snowpack dynamics is of crucial importance to characterize the water cycle in mountainous regions [1]. Snowpack prediction models are an essential tool for water resources management activities, such as hydropower energy production planning, irrigation and providing early flood warnings. Approaches to snowpack prediction range from empirical models (e.g., simple temperature index models) to more sophisticated physically based energy-balance models [2,3]. The advantages of an energy balance model are that it, in theory, requires less model calibration and has the potential to provide forecasts that account for climate or land cover change [4]. Using an energy balance model may require more data, and model performance relies on the availability and quality of the additional needed climate input data. Prior studies comparing temperature index and energy balance models have not been conclusive as to whether one approach is better than the other [5,6,7]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call