Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products

Understanding those processes in which snow dynamics has a significant influence requires long-term and high spatio-temporal resolution observations. While new optical space-borne sensors overcome many previous snow cover monitoring limitations, their short temporal length limits their application i...

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Autores principales:	Jesús Revuelto, Esteban Alonso-González, Simon Gascoin, Guillermo Rodríguez-López, Juan Ignacio López-Moreno
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	snow distribution mountain areas optical satellite sensors high resolution downscaling snow cover area Science Q
Acceso en línea:	https://doaj.org/article/0f7ade03a5b24adc87cf342ff10d5a28
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spelling	oai:doaj.org-article:0f7ade03a5b24adc87cf342ff10d5a282021-11-25T18:53:50ZSpatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products10.3390/rs132245132072-4292https://doaj.org/article/0f7ade03a5b24adc87cf342ff10d5a282021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4513https://doaj.org/toc/2072-4292Understanding those processes in which snow dynamics has a significant influence requires long-term and high spatio-temporal resolution observations. While new optical space-borne sensors overcome many previous snow cover monitoring limitations, their short temporal length limits their application in climatological studies. This work describes and evaluates a probabilistic spatial downscaling of MODIS snow cover observations in mountain areas. The approach takes advantage of the already available high spatial resolution Sentinel-2 snow observations to obtain a snow probability occurrence, which is then used to determine the snow-covered areas inside partially snow-covered MODIS pixels. The methodology is supported by one main hypothesis: the snow distribution is strongly controlled by the topographic characteristics and this control has a high interannual persistence. Two approaches are proposed to increase the 500 m resolution MODIS snow cover observations to the 20 m grid resolution of Sentinel-2. The first of these computes the probability inside partially snow-covered MODIS pixels by determining the snow occurrence frequency for the 20 m Sentinel-2 pixels when clear-sky conditions occurred for both platforms. The second approach determines the snow probability occurrence for each Sentinel-2 pixel by computing the number of days in which snow was observed on each grid cell and then dividing it by the total number of clear-sky days per grid cell. The methodology was evaluated in three mountain areas in the Iberian Peninsula from 2015 to 2021. The 20 m resolution snow cover maps derived from the two probabilistic methods provide better results than those obtained with MODIS images downscaled to 20 m with a nearest-neighbor method in the three test sites, but the first provides superior performance. The evaluation showed that mean kappa values were at least 10% better for the two probabilistic methods, improving the scores in one of these sites by 25%. In addition, as the Sentinel-2 dataset becomes longer in time, the probabilistic approaches will become more robust, especially in areas where frequent cloud cover resulted in lower accuracy estimates.Jesús RevueltoEsteban Alonso-GonzálezSimon GascoinGuillermo Rodríguez-LópezJuan Ignacio López-MorenoMDPI AGarticlesnow distributionmountain areasoptical satellite sensorshigh resolutiondownscalingsnow cover areaScienceQENRemote Sensing, Vol 13, Iss 4513, p 4513 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	snow distribution mountain areas optical satellite sensors high resolution downscaling snow cover area Science Q
spellingShingle	snow distribution mountain areas optical satellite sensors high resolution downscaling snow cover area Science Q Jesús Revuelto Esteban Alonso-González Simon Gascoin Guillermo Rodríguez-López Juan Ignacio López-Moreno Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products
description	Understanding those processes in which snow dynamics has a significant influence requires long-term and high spatio-temporal resolution observations. While new optical space-borne sensors overcome many previous snow cover monitoring limitations, their short temporal length limits their application in climatological studies. This work describes and evaluates a probabilistic spatial downscaling of MODIS snow cover observations in mountain areas. The approach takes advantage of the already available high spatial resolution Sentinel-2 snow observations to obtain a snow probability occurrence, which is then used to determine the snow-covered areas inside partially snow-covered MODIS pixels. The methodology is supported by one main hypothesis: the snow distribution is strongly controlled by the topographic characteristics and this control has a high interannual persistence. Two approaches are proposed to increase the 500 m resolution MODIS snow cover observations to the 20 m grid resolution of Sentinel-2. The first of these computes the probability inside partially snow-covered MODIS pixels by determining the snow occurrence frequency for the 20 m Sentinel-2 pixels when clear-sky conditions occurred for both platforms. The second approach determines the snow probability occurrence for each Sentinel-2 pixel by computing the number of days in which snow was observed on each grid cell and then dividing it by the total number of clear-sky days per grid cell. The methodology was evaluated in three mountain areas in the Iberian Peninsula from 2015 to 2021. The 20 m resolution snow cover maps derived from the two probabilistic methods provide better results than those obtained with MODIS images downscaled to 20 m with a nearest-neighbor method in the three test sites, but the first provides superior performance. The evaluation showed that mean kappa values were at least 10% better for the two probabilistic methods, improving the scores in one of these sites by 25%. In addition, as the Sentinel-2 dataset becomes longer in time, the probabilistic approaches will become more robust, especially in areas where frequent cloud cover resulted in lower accuracy estimates.
format	article
author	Jesús Revuelto Esteban Alonso-González Simon Gascoin Guillermo Rodríguez-López Juan Ignacio López-Moreno
author_facet	Jesús Revuelto Esteban Alonso-González Simon Gascoin Guillermo Rodríguez-López Juan Ignacio López-Moreno
author_sort	Jesús Revuelto
title	Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products
title_short	Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products
title_full	Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products
title_fullStr	Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products
title_full_unstemmed	Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products
title_sort	spatial downscaling of modis snow cover observations using sentinel-2 snow products
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0f7ade03a5b24adc87cf342ff10d5a28
work_keys_str_mv	AT jesusrevuelto spatialdownscalingofmodissnowcoverobservationsusingsentinel2snowproducts AT estebanalonsogonzalez spatialdownscalingofmodissnowcoverobservationsusingsentinel2snowproducts AT simongascoin spatialdownscalingofmodissnowcoverobservationsusingsentinel2snowproducts AT guillermorodriguezlopez spatialdownscalingofmodissnowcoverobservationsusingsentinel2snowproducts AT juanignaciolopezmoreno spatialdownscalingofmodissnowcoverobservationsusingsentinel2snowproducts
_version_	1718410570530553856

Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products

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