Snow Cover Maps from MODIS Images at 250 m Resolution, Part 1: Algorithm Description

Remote Sensing

Claudia Notarnicola²  Martial Duguay²  Nico Moelg³  Thomas Schellenberger¹  Anke Tetzlaff²  Roberto Monsorno²  Armin Costa²  Christian Steurer² 

[1] Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, N-0316 Oslo, Norway; E-Mail:;Institute for Applied Remote Sensing, EURAC, Viale Druso 1, I-39100 Bolzano, Italy; E-Mails:;Department of Geography, Glaciology, Geomorphodynamics and Geochronology, University of Zürich-Irchel, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland; E-Mail:

关键词: MODIS;    snow;    snow covered area;    topography;    NDVI;   

DOI  :  10.3390/rs5010110

来源: mdpi

【 摘 要 】

A new algorithm for snow cover monitoring at 250 m resolution based on Moderate Resolution Imaging Spectroradiometer (MODIS) images is presented. In contrast to the 500 m resolution MODIS snow products of NASA (MOD10 and MYD10), the main goal was to maintain the resolution as high as possible to allow for a more accurate detection of snow covered area (SCA). This is especially important in mountainous regions characterized by extreme landscape heterogeneity, where maps at a resolution of 500 m could not provide the desired amount of spatial details. Therefore, the algorithm exploits only the 250 m resolution bands of MODIS in the red (B1) and infrared (B2) spectrum, as well as the Normalized Difference Vegetation Index (NDVI) for snow detection, while clouds are classified using also bands at 500 m and 1 km resolution. The algorithm is tailored to process MODIS data received in real-time through the EURAC receiving station close to Bolzano, Italy, but also standard MODIS products are supported. It is divided into three steps: first the data is preprocessed, including reprojection, calculation of physical reflectance values and masking of water bodies. In a second step, the actual classification of snow, snow in forested areas, and clouds takes place based on MODIS images both from Terra and Aqua satellites. In the third step, snow cover maps derived from images of both sensors of the same day are combined to reduce cloud coverage in the final SCA product. Four different quality indices are calculated to verify the reliability of input data, snow classification, cloud detection and viewing geometry. Using the data received through their own station, EURAC can provide SCA maps of central Europe to end users in near real-time. Validation of the algorithm is outlined in a companion paper and indicates good performance with accuracies ranging from 94% to around 82% compared to in situ snow depth measurements, and around 93% compared to SCA derived from Landsat ETM+ images.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.