A cloud-free MODIS snow cover dataset for the contiguous United States from 2000 to 2017.

Sci Data

Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, USA.

Published: January 2019

AI Article Synopsis

  • - The article introduces a cloud-free snow cover dataset spanning from March 2000 to February 2017 for the contiguous United States, with daily updates and high spatial resolution (0.05°).
  • - The dataset was created by enhancing NASA's MODIS Snow Cover Area product by removing cloud interference using advanced filtering techniques and the Variational Interpolation (VI) algorithm, which was validated through testing.
  • - Validation against Landsat 7 ETM+ snow cover maps showed high accuracy in detecting snow cover changes, making this dataset valuable for hydrologic studies, and the VI algorithm can be adapted for use in other areas as long as proper validation is conducted.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6335612PMC
http://dx.doi.org/10.1038/sdata.2018.300DOI Listing

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