Improving MODIS snow products with a HMRF-based spatio-temporal modeling technique in the Upper Rio Grande Basin

Abstract Seasonal snow cover and its melt dominate regional climate and hydrology in many mountainous regions in the world. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products have been widely used for regional hydrological modeling. However, data gaps in snow products due to frequent clouds remain a serious problem, particularly for daily products. This paper presents a spatio-temporal modeling technique for filling up data gaps in daily snow cover estimates, based on time series of Terra/Aqua MODIS images. The spatio-temporal modeling technique integrates MODIS spectral information, spatial and temporal contextual information, and environmental association within a Hidden Markov Random Field (HMRF) framework. The performance of our new technique is quantitatively evaluated by comparing our snow cover estimates with in situ observations at 33 SNOTEL stations as well as to original MODIS snow cover products over the Upper Rio Grande Basin during 2006–2008 snow seasons. Mainly due to cloud obscuration, there are as high as 32% data gaps in original Terra/Aqua combined MODIS snow products. Our HMRF technique reduced cloud-cover related data gaps to

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