Swiss GCOS Office
Swiss GCOS Office
Trishchenko A.P.,Canada Center For Remote Sensing |
Leblanc S.G.,Canada Center For Remote Sensing |
Wang S.,Canada Center For Remote Sensing |
Li J.,Canada Center For Remote Sensing |
And 3 more authors.
Canadian Journal of Remote Sensing | Year: 2016
Abstract. Snow and ice are important hydrological resources. Their minimum spatial extent over land, here referred to as annual minimum snow/ice (MSI) cover, plays a very important role as an indicator of long-term changes and baseline capacity for surface water storage. Data from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite for the period of 2000–2014 were utilized in this study. The level-2 MODIS swath imagery for bands B1 to B7 was employed and the 500-m bands B3–B7 were spatially downscaled to a 250-m swath grid. The imagery is available daily with multiple overpasses. This allows for more accurate identification of annual minimum in comparison to high-resolution imagery (e.g., Landsat, ASTER, etc.) available at much coarser temporal rates. Atmospherically corrected 10-day clear-sky composites converted into normalized surface reflectance over the warm season (April 1 to September 20) were employed to identify persistent snow and ice presence. Results were compared with our previous results derived from the MODIS Circumpolar Arctic clear-sky composites, generated for the end of melting season, and showed smaller MSI extent by 24%, on average. Produced MSI distributions were also compared with the permanent snow and ice maps available from 6 global land cover datasets: (i) Global Land Cover GLC-2000, (ii & iii) European Space Agency's (ESA) Globcover circa 2005 and 2009, (iv–vi) land cover maps derived under the ESA Climate Change Initiative (CCI) for 2000, 2005, and 2010. Significant biases were discovered between various land cover datasets and our results. For example, GLC-2000 overestimated snow/ice extent by 194% (325,400 km2) for the Canadian Arctic. The biases over the entire landmass (excluding Greenland) are 135% (3.7 × 105 km2), 113% (3.0 × 105 km2), 89% (2.2 × 105 km2), and 28% (0.8 × 105 km2) between our results and GLC-2000, ESA Globcover 2005, ESA Globcover 2009, and ESA CCI datasets, correspondingly. The derived MSI extent was compared with Randolph Glacier Inventory (RGI) 4.0 and showed much better consistency (ranging from 1% to 15%). © 2016, Copyright © Crown copyright.
Foppa N.,Swiss GCOS Office |
Seiz G.,Swiss GCOS Office
Cryosphere | Year: 2012
Snow cover plays a vital role in the Swiss Alps and therefore it is of major interest to determine and understand its variability on different spatiotemporal scales. Within the activities of the National Climate Observing System (GCOS Switzerland) inter-annual variations of snow days over Switzerland were derived from 2000 to 2010 based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. To minimize the impact of cloud cover on the MODIS snow product MOD10C1, we implemented a post-processing technique based on a forward and backward gap-filling approach. Using the proposed methodology it was possible to determine the total number of annual snow days over Switzerland from 2000 to 2010 (SCDMODIS). The accuracy of the calculated snow days per year were quantitatively evaluated against three in situ snow observation sites representing different climatological regimes (SCDin-situ). Various statistical indices were computed and analysed over the entire period. The overall accuracy between SCDMODIS and SCD in-situ on a daily basis over 10 yr is 88% to 94%, depending on the regional characteristics of each validation site. Differences between SCDMODIS and SCDin-situ vary during the snow accumulation period in autumn and smaller differences after spring, in particularly for the Central Alps. © 2013 Author(s).
Fontana F.,Swiss GCOS Office |
Lugrin D.,Swiss GCOS Office |
Seiz G.,Swiss GCOS Office |
Meier M.,Swiss GCOS Office |
Foppa N.,Swiss GCOS Office
Atmospheric Research | Year: 2013
Satellite data provide the opportunity for systematic and continuous observation of cloud cover over large spatial scales. In this paper, we describe the generation of two new high spatial resolution (0.05°) daytime cloud fraction data sets over Switzerland. The data sets are based on the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask products. The data sets cover the period from March 1, 2000 to February 29, 2012 (Terra/MODIS) and July 1, 2002 to February 29, 2012 (Aqua/MODIS) and represent mid-morning and early-afternoon cloud cover over Switzerland. Time series clearly reflected seasonal variations in cloud fraction over Switzerland. A comparison with cloud fraction observations at four Synop stations (Chur, Locarno/Monti, Payerne, Zurich/Kloten) revealed an agreement of monthly mean mid-morning cloud fraction (MMCF) within ± 1 octa (i.e., 12.5%). Relative to Synop observations, MMCF was positively biased by 0.3-5.0%, except at Payerne (-2.5%). Linear correlation coefficients ranged from 0.878 to 0.972. Results were similar for monthly mean early-afternoon cloud fraction (MACF). Cloud fraction was found to be higher in the early-afternoon when compared to mid-morning, except at Payerne and Zurich/Kloten in fall, which is explained by typical daytime cloud cover patterns in Switzerland. Analysis of daily mid-morning cloud fraction showed that largest discrepancies were observed in partly cloudy conditions, which is mainly explained by differences in observation times and observation geometry. Our results demonstrate that the newly processed cloud fraction data sets from the MODIS sensor can play an important role in complementing traditional Synop observations in support of systematic cloud cover monitoring within the National Climate Observing System (GCOS Switzerland). © 2013 Elsevier B.V.