An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data
The characterization of snow extent is critical for a wide range of applications. Since 1966, snow maps at different spatial resolutions have been produced using various satellite sensor images. Nowadays, the most widely used products are likely those derived from Moderate-Resolution Imaging Spectro...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-04-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/10/4/619 |
| _version_ | 1818971408361848832 |
|---|---|
| author | Théo Masson Marie Dumont Mauro Dalla Mura Pascal Sirguey Simon Gascoin Jean-Pierre Dedieu Jocelyn Chanussot |
| author_facet | Théo Masson Marie Dumont Mauro Dalla Mura Pascal Sirguey Simon Gascoin Jean-Pierre Dedieu Jocelyn Chanussot |
| author_sort | Théo Masson |
| collection | DOAJ |
| description | The characterization of snow extent is critical for a wide range of applications. Since 1966, snow maps at different spatial resolutions have been produced using various satellite sensor images. Nowadays, the most widely used products are likely those derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) data, which cover the whole Earth at a near-daily frequency. There are a variety of snow mapping methods for MODIS data, based on different methodologies and applied at different spatial resolutions. Up to now, all these products have been tested and evaluated separately. This study aims to compare the methods currently available for retrieving snow from MODIS data. The focus is on fractional snow cover, which represents the snow cover area at the subpixel level. We examine the two main approaches available for generating such products from MODIS data; namely, linear regression of the Normalized Difference Snow Index (NDSI) and spectral unmixing (SU). These two approaches have resulted in several methods, such as MOD10A1 (the NSIDC MODIS snow product) for NDSI regression, and MODImLAB for SU. The assessment of these approaches was carried out using higher resolution binary snow maps (i.e., showing the presence or absence of snow) at spatial resolutions of 10, 20, and 30 m, produced by SPOT 4, SPOT 5, and LANDSAT-8, respectively. Three areas were selected in order to provide landscape diversity: the French Alps (117 dates), the Pyrenees (30 dates), and the Moroccan Atlas (24 dates). This study investigates the impact of reference maps on accuracy assessments, and it is suggested that NDSI-based high spatial resolution reference maps advantage NDSI medium-resolution snow maps. For MODIS snow maps, the results show that applying an NDSI approach to accurate surface reflectance corrected for topographic and atmospheric effects generally outperforms other methods for the global retrieval of snow cover area. The improvements to the newer version of MOD10A1 (Collection 6) compared to the older version (Collection 5) are significant. Products based on SU provide a good alternative and more accurate retrieval of the snow fraction where wider ranges of land covers are concerned. The fusion process and its resulting 250 m spatial resolution product improve snow line retrieval. False detection in mixed pixels, probably due to the spectral variability associated with the various materials in the spectral mixture, has been identified as an area that will require improvement. |
| first_indexed | 2024-12-20T14:51:54Z |
| format | Article |
| id | doaj.art-88f7645879e54fdfb3ea621b9375dc8c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T14:51:54Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-88f7645879e54fdfb3ea621b9375dc8c2022-12-21T19:36:56ZengMDPI AGRemote Sensing2072-42922018-04-0110461910.3390/rs10040619rs10040619An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS DataThéo Masson0Marie Dumont1Mauro Dalla Mura2Pascal Sirguey3Simon Gascoin4Jean-Pierre Dedieu5Jocelyn Chanussot6Institute of Engineering, Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, FranceMétéo-France, CNRS, Centre National de Recherches MéTéOrologiques/Centre D’éTudes de la neige (CNRM/CEN), F-38000 Grenoble, FranceInstitute of Engineering, Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, FranceNational School of Surveying, University Otago, Dunedin 9054, New ZealandCESBIO, Université de Toulouse, CNES/CNRS/INRA/IRD/UPS, 31401 Toulouse, FranceUniv. Grenoble Alpes, CNRS, IRD, Institut des Géosciences de l’Environnement (IGE), F-38000 Grenoble, FranceInstitute of Engineering, Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, FranceThe characterization of snow extent is critical for a wide range of applications. Since 1966, snow maps at different spatial resolutions have been produced using various satellite sensor images. Nowadays, the most widely used products are likely those derived from Moderate-Resolution Imaging Spectroradiometer (MODIS) data, which cover the whole Earth at a near-daily frequency. There are a variety of snow mapping methods for MODIS data, based on different methodologies and applied at different spatial resolutions. Up to now, all these products have been tested and evaluated separately. This study aims to compare the methods currently available for retrieving snow from MODIS data. The focus is on fractional snow cover, which represents the snow cover area at the subpixel level. We examine the two main approaches available for generating such products from MODIS data; namely, linear regression of the Normalized Difference Snow Index (NDSI) and spectral unmixing (SU). These two approaches have resulted in several methods, such as MOD10A1 (the NSIDC MODIS snow product) for NDSI regression, and MODImLAB for SU. The assessment of these approaches was carried out using higher resolution binary snow maps (i.e., showing the presence or absence of snow) at spatial resolutions of 10, 20, and 30 m, produced by SPOT 4, SPOT 5, and LANDSAT-8, respectively. Three areas were selected in order to provide landscape diversity: the French Alps (117 dates), the Pyrenees (30 dates), and the Moroccan Atlas (24 dates). This study investigates the impact of reference maps on accuracy assessments, and it is suggested that NDSI-based high spatial resolution reference maps advantage NDSI medium-resolution snow maps. For MODIS snow maps, the results show that applying an NDSI approach to accurate surface reflectance corrected for topographic and atmospheric effects generally outperforms other methods for the global retrieval of snow cover area. The improvements to the newer version of MOD10A1 (Collection 6) compared to the older version (Collection 5) are significant. Products based on SU provide a good alternative and more accurate retrieval of the snow fraction where wider ranges of land covers are concerned. The fusion process and its resulting 250 m spatial resolution product improve snow line retrieval. False detection in mixed pixels, probably due to the spectral variability associated with the various materials in the spectral mixture, has been identified as an area that will require improvement.http://www.mdpi.com/2072-4292/10/4/619snow cover mappingspectral unmixingremote sensingMODISNDSI |
| spellingShingle | Théo Masson Marie Dumont Mauro Dalla Mura Pascal Sirguey Simon Gascoin Jean-Pierre Dedieu Jocelyn Chanussot An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data Remote Sensing snow cover mapping spectral unmixing remote sensing MODIS NDSI |
| title | An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data |
| title_full | An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data |
| title_fullStr | An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data |
| title_full_unstemmed | An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data |
| title_short | An Assessment of Existing Methodologies to Retrieve Snow Cover Fraction from MODIS Data |
| title_sort | assessment of existing methodologies to retrieve snow cover fraction from modis data |
| topic | snow cover mapping spectral unmixing remote sensing MODIS NDSI |
| url | http://www.mdpi.com/2072-4292/10/4/619 |
| work_keys_str_mv | AT theomasson anassessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT mariedumont anassessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT maurodallamura anassessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT pascalsirguey anassessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT simongascoin anassessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT jeanpierrededieu anassessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT jocelynchanussot anassessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT theomasson assessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT mariedumont assessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT maurodallamura assessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT pascalsirguey assessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT simongascoin assessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT jeanpierrededieu assessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata AT jocelynchanussot assessmentofexistingmethodologiestoretrievesnowcoverfractionfrommodisdata |