Sangam: A Confluence of Knowledge Streams

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Show simple item record

dc.creator Snapir, Boris
dc.creator Momblanch, Andrea
dc.creator Jain, S. K.
dc.creator Waine, Toby
dc.creator Holman, Ian P.
dc.date 2018-10-24T14:29:18Z
dc.date 2018-10-24T14:29:18Z
dc.date 2018-10-01
dc.date.accessioned 2022-05-25T16:39:11Z
dc.date.available 2022-05-25T16:39:11Z
dc.identifier Snapir B, Momblanch A, Jain SK, et al., A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin. International Journal of Applied Earth Observation and Geoinformation, Volume 74, February 2019, pp. 222-230
dc.identifier 0303-2434
dc.identifier https://doi.org/10.1016/j.jag.2018.09.011
dc.identifier http://dspace.lib.cranfield.ac.uk/handle/1826/13564
dc.identifier 19158964
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/182419
dc.description Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km2 river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.
dc.language en
dc.publisher Elsevier
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Snow
dc.subject MODIS
dc.subject Sentinel-1
dc.subject Google Earth Engine
dc.subject Himalayas
dc.title A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin
dc.type Article


Files in this item

Files Size Format View
A_method_for_mo ... _wet_and_dry_snow-2018.pdf 1.652Mb application/pdf View/Open

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse