The accuracy of snow melt-off day derived from optical and microwave radiometer data — A study for Europe

Abstract This paper describes the methodology for deriving yearly pixel-wise snow melt-off day maps from optical data-based FSC (Fractional Snow Cover) without conducting any interpolation for cloud-obscured pixels or otherwise missing data. The Copernicus CryoLand Pan-European FSC time series for 2001–2016 re-gridded to 0.1° serves as input for the production of 16 years of melt-off day maps for Europe. These maps are compared with passive microwave radiometer (MWR) melt retrievals, to compare the performance of these two independent datasets, particularly concerning the effect of physiographic and snow conditions on the accuracy of the melt-off day estimates. Both these datasets are evaluated against melt-off day derived from in situ snow depth (SD) time series observed at European weather stations. We also present the relationship of these snow melt-off day products to a passive microwave radiometer-derived landscape freeze/thaw product. Our results show that the melt-off day derived from optical springtime FSC time series provides the strongest correlation with the snow melt-off day with respect to the in situ data. Overall the deviation of CryoLand FSC data derived melt-off day to that indicated by in situ observations is quite small, with positive bias of 0.9 days, and RMSE of 13.1 days. For 85% of the analyzed cases the differences are between ±10 days. Across Europe the MWR-based detection of melt-off day is less accurate; the investigated method performs the best for areas with sustained seasonal snow cover. Based on the time series for MWR-based melt-off day (1980–2016) and FT-ESDR (1980–2014), separately for boreal forests and tundra, we also found a clear trend towards earlier snow clearance: a decrease of melt-off day by as much as ~5 days per decade in boreal forest region was observed.

[1]  P. Kushner,et al.  Snow cover response to temperature in observational and climate model ensembles , 2017 .

[2]  Tuomas Laurila,et al.  Spring initiation and autumn cessation of boreal coniferous forest CO2 exchange assessed by meteorological and biological variables , 2009 .

[3]  Chris Derksen,et al.  Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections , 2012 .

[4]  Andreas Wiesmann,et al.  Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment , 2015 .

[5]  Xubin Zeng,et al.  A global 0.05° maximum albedo dataset of snow‐covered land based on MODIS observations , 2005 .

[6]  N. Gobron,et al.  Predictive power of remote sensing versus temperature‐derived variables in modelling phenology of herbivorous insects , 2018 .

[7]  Jouni Pulliainen,et al.  Detection of Snowmelt Using Spaceborne Microwave Radiometer Data in Eurasia From 1979 to 2007 , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  David A. Robinson,et al.  Changing Northern Hemisphere Snow Seasons , 2010 .

[9]  A. Hall,et al.  What Controls the Strength of Snow-Albedo Feedback? , 2007 .

[10]  Sari Metsämäki,et al.  A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model , 2005 .

[11]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[12]  Youngwook Kim,et al.  Developing a Global Data Record of Daily Landscape Freeze/Thaw Status Using Satellite Passive Microwave Remote Sensing , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Claudia Kuenzer,et al.  European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products , 2012, Remote. Sens..

[14]  Sergio M. Vicente-Serrano,et al.  The impact of snow depth and snowmelt on the vegetation variability over central Siberia , 2005 .

[15]  Chris Derksen,et al.  Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements , 2011 .

[16]  D. Hall,et al.  Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data , 1995 .

[17]  O. Bulygina,et al.  Evaluation of North Eurasian snow-off dates in the ECHAM5.4 atmospheric general circulation model , 2014 .

[18]  Jiang Zhu,et al.  Deriving Snow Cover Metrics for Alaska from MODIS , 2015, Remote. Sens..

[19]  Tuomas Laurila,et al.  The timing of snow melt controls the annual CO2 balance in a subarctic fen , 2004 .

[20]  Ross D. Brown,et al.  Recent Northern Hemisphere snow cover extent trends and implications for the snow‐albedo feedback , 2007 .

[21]  M. K. Hughes,et al.  Influence of snowfall and melt timing on tree growth in subarctic Eurasia , 1999, Nature.

[22]  Alexander Kislov,et al.  The snow cover characteristics of northern Eurasia and their relationship to climatic parameters , 2002 .

[23]  Kari Luojus,et al.  An optical reflectance model-based method for fractional snow cover mapping applicable to continental scale , 2012 .

[24]  C. Fletcher,et al.  The influence of canopy snow parameterizations on snow albedo feedback in boreal forest regions , 2014 .

[25]  S. Dullinger,et al.  Late snowmelt delays plant development and results in lower reproductive success in the High Arctic. , 2011, Plant science : an international journal of experimental plant biology.