Using crowdsourced and weather station data to fill cloud gaps in MODIS snow cover datasets. (September 2017)

• Record Type: Journal Article
• Authors: Kadlec, Jiří; Ames, Daniel P.
• Is Part Of: Environmental modelling & software. Volume 95(2017)
• Page Start: 258
• Page End: 270
• Publication Date: 2017-09
• Subjects: Snow cover -- Crowdsourcing -- Interpolation -- Winter sports; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.06.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We present the design, development, and testing of a new software package for generating snow cover maps. Using a custom inverse distance weighting method, we combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product. The method is demonstrated by producing a continuous daily time step snow probability map dataset for the Czech Republic region. For validation, we checked the ability of our method to reconstruct MODIS snow cover under cloud by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. The software is available as an R package. The output data sets are published on the HydroShare website for download and through a web map service for re-use in third-party applications. Highlights: Crowdsourcing data from cross-country skiers is used to update snow cover maps. The data is combined with satellite datasets create gap-free daily snow cover maps. The resulting daily snow cover map has been published as a web map service.