Abstract
This article presents a cloud-free snow cover dataset with a daily temporal resolution and 0.05° spatial resolution from March 2000 to February 2017 over the contiguous United States (CONUS). The dataset was developed by completely removing clouds from the original NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Area product (MOD10C1) through a series of spatiotemporal filters followed by the Variational Interpolation (VI) algorithm; the filters and VI algorithm were evaluated using bootstrapping test. The dataset was validated over the period with the Landsat 7 ETM+ snow cover maps in the Seattle, Minneapolis, Rocky Mountains, and Sierra Nevada regions. The resulting cloud-free snow cover captured accurately dynamic changes of snow throughout the period in terms of Probability of Detection (POD) and False Alarm Ratio (FAR) with average values of 0.955 and 0.179 for POD and FAR, respectively. The dataset provides continuous inputs of snow cover area for hydrologic studies for almost two decades. The VI algorithm can be applied in other regions given that a proper validation can be performed.
Highlights
Background & SummaryThe Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover area (SCA) product[1] serves as a reliable source of snow measurements for hydrologic studies as well as for data assimilation in climate models
Major concerns about MODIS snow maps polluted by clouds and snow/cloud discrimination have been repeatedly mentioned in its assessment studies[9,10] as well as in the MODIS product user guide[11]
The spatial domain of the dataset developed in this study is the contiguous United States (CONUS) which covers about 8,080,464.3 km[2], ranges between 24o30N and 49o25N in latitude and from 66o57W to 124o46W in longitude
Summary
The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover area (SCA) product[1] serves as a reliable source of snow measurements for hydrologic studies as well as for data assimilation in climate models. Dozier et al.[16] considered snow data as a sparse space-time cube that could be filled by temporal cubic spline interpolation Their results demonstrated that the interpolated and smoothed product has more consistent snow-covered area in the Tuolumne and Merced River basins throughout the water year 2005 than the raw, cloud cover filtered data. To completely remove clouds and delineate dynamic snow boundaries, Xia et al.[21] implemented the Variational Interpolation (VI) method[22] for interpolating the three-dimensional space-time cube of snow cover proposed by Dozier et al.[16]. The improved VI method is implemented to reconstruct a three-dimensional time-varying snow cover boundary. This 3-d surface can be used to obtain cloud-free snow cover images
Sign-in/Register to view highlights & summary 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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
