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

Claudia Notarnicola,Martial Duguay,Anke Tetzlaff,Armin Costa,Christian Steurer,Nico Moelg,Marc Zebisch,Roberto Monsorno,Thomas Schellenberger

doi:10.3390/rs5010110

Abstract

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.

Highlights

IntroductionSatellite sensors are efficient tools to monitor snow cover extent [2,3] and various authors have demonstrated that snow covered area (SCA) maps from optical satellite sensors can improve the prediction of the snow melt stream flow in hydrological models [1,4,5] and derived significant parameters such as the Snow Cover
Despite the availability and general performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10 and MYD10 Snow product [3,8,9,10,11,12], obvious limitations affect the monitoring of local scale details and stress the need for an adapted and robust algorithm to map snow cover with the highest possible amount of spatial details
The main objective of this paper is to introduce an alternative approach to improve the MODIS-based snow covered area (SCA) maps by exploiting only the 250 m resolution bands for snow detection

Summary

Introduction

Satellite sensors are efficient tools to monitor snow cover extent [2,3] and various authors have demonstrated that SCA maps from optical satellite sensors can improve the prediction of the snow melt stream flow in hydrological models [1,4,5] and derived significant parameters such as the Snow Cover. Duration (SCD), Snow Cover Start (SCS) and Snow Cover Melt (SCM) [6]. These derived parameters are useful to quantify the inter-annual changes in snow cover characteristics and represent a basis for future climate change studies. SCA maps are mainly derived from optical satellite images with high temporal resolution (AVHRR, MODIS) in order to ensure daily availability and medium ground resolution. The behavior of visible and near-infrared reflectances for snow in dry and wet conditions is well known [15]

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