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

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

doi:10.3390/rs5010110

Cited by 71 publications

(58 citation statements)

References 35 publications

(49 reference statements)

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“…MOD03-MYD03 are used for correct geolocation. Daily snow maps at 250-m resolution obtained with the algorithm described in [36] allow improved snow cover mapping with respect to the 500 m standard MODIS product, especially in mountainous terrain where snow cover shows high spatial variability due to highly irregular topography [37].…”

Section: Input Data Consideredmentioning

confidence: 99%

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Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

Callegari

Mazzoli

Gregorio

et al. 2015

Water

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Abstract:In this contribution we analyze the performance of a monthly river discharge forecasting model with a Support Vector Regression (SVR) technique in a European alpine area. We considered as predictors the discharges of the antecedent months, snow-covered area (SCA), and meteorological and climatic variables for 14 catchments in South Tyrol (Northern Italy), as well as the long-term average discharge of the month of prediction, also regarded as a benchmark. Forecasts at a six-month lead time tend to perform no better than the benchmark, with an average 33% relative root mean square error (RMSE%) on test samples. However, at one month lead time, RMSE% was 22%, a non-negligible improvement over the benchmark; moreover, the SVR model reduces the frequency of higher errors associated with anomalous months. Predictions with a lead time of three months show an intermediate performance between those at one and six months lead time. Among the considered predictors, OPEN ACCESSWater 2015, 7 2495SCA alone reduces RMSE% to 6% and 5% compared to using monthly discharges only, for a lead time equal to one and three months, respectively, whereas meteorological parameters bring only minor improvements. The model also outperformed a simpler linear autoregressive model, and yielded the lowest volume error in forecasting with one month lead time, while at longer lead times the differences compared to the benchmarks are negligible. Our results suggest that although an SVR model may deliver better forecasts than its simpler linear alternatives, long lead-time hydrological forecasting in Alpine catchments remains a challenge. Catchment state variables may play a bigger role than catchment input variables; hence a focus on characterizing seasonal catchment storage-Rather than seasonal weather forecasting-Could be key for improving our predictive capacity.

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Section: Input Data Consideredmentioning

confidence: 99%

“…The snow products have been derived with a specific algorithm adapted to mountain areas [36,37]. This algorithm takes 250 m resolution, atmospherically corrected surface reflectance MOD09GQ-MYD09GQ (Terra-Aqua satellites) products as input.…”

Section: Input Data Consideredmentioning

confidence: 99%

Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

Callegari

Mazzoli

Gregorio

et al. 2015

Water

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“…To improve the snow detection, especially in heterogeneous areas, some previous works focused on the estimation of the snow cover fraction in a 500 m MODIS pixel using the additional information of the two MODIS 250 m channels [16,17]. In this context, a new algorithm based on MODIS images has been proposed in a companion paper [18]. The algorithm exploits the MODIS 250 m in order to produce resolution improved binary SCA maps (here after, called EURAC SCA).…”

Section: Introductionmentioning

confidence: 99%

Snow Cover Maps from MODIS Images at 250 m Resolution, Part 2: Validation

et al. 2013

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Abstract:The performance of a new algorithm for binary snow cover monitoring based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images at 250 m resolution is validated using snow cover maps (SCA) based on Landsat 7 ETM+ images and in situ snow depth measurements from ground stations in selected test sites in Central Europe. The advantages of the proposed algorithm are the improved ground resolution of 250 m and the near real-time availability with respect to the 500 m standard National Aeronautics and Space Administration (NASA) MODIS snow products (MOD10 and MYD10). It allows a more accurate snow cover monitoring at a local scale, especially in mountainous areas characterized by large landscape heterogeneity. The near real-time delivery makes the product valuable as input for hydrological models, e.g., for flood forecast. A comparison to sixteen snow cover maps derived from Landsat ETM/ETM+ showed an overall accuracy of 88.1%, which increases to 93.6% in areas outside of forests. OPEN ACCESSRemote Sens. 2013, 5 1569A comparison of the SCA derived from the proposed algorithm with standard MODIS products, MYD10 and MOD10, indicates an agreement of around 85.4% with major discrepancies in forested areas. The validation of MODIS snow cover maps with 148 in situ snow depth measurements shows an accuracy ranging from 94% to around 82%, where the lowest accuracies is found in very rugged terrain restricted to in situ stations along north facing slopes, which lie in shadow in winter during the early morning acquisition.

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“…Since the availability of daily optical satellite data at the global scale (NOAA-AVHRR) at end of the 1980s, and at the regional scale (MODIS) since the year 2000, different methods have been developed to compute changes in terms of snow cover area (SCA) expressed in km 2 and snow cover duration (SCD) expressed in days (Chokmani et al, 2005(Chokmani et al, , 2013Parajka and Blösch, 2006;Flueraru et al, 2007;Foppa et al, 2007;Ertürk et al, 2008;Sirguey et al, 2009;Notarnicola et al, 2013aNotarnicola et al, , 2013bZhang et al, 2012;Hüsler et al, 2012Hüsler et al, , 2013. The main parameters analyzed are the timing and duration of the melting season under current and future climate conditions.…”

Section: Remote Sensingmentioning

confidence: 99%

Shifting mountain snow patterns in a changing climate from remote sensing retrieval

Dedieu

Lessard-Fontaine

Ravazzani

et al. 2014

Science of The Total Environment

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• MODIS satellite images analysis for snow cover dynamics over 2000-2010 time period.• Application to river basins at medium and large scale (Europe, Central Asia).• Statistical bias as standard deviation values express sensitivity to climate change.• Below 1200 m, a winter time continuous snow-pack is becoming increasingly rare. Observed climate change has already led to a wide range of impacts on environmental systems and society. In this context, many mountain regions seem to be particularly sensitive to a changing climate, through increases in temperature coupled with changes in precipitation regimes that are often larger than the global average (EEA, 2012). In mid-latitude mountains, these driving factors strongly influence the variability of the mountain snow-pack, through a decrease in seasonal reserves and earlier melting of the snow pack. These in turn impact on hydrological systems in different watersheds and, ultimately, have consequences for water management. Snow monitoring from remote sensing provides a unique opportunity to address the question of snow cover regime changes at the regional scale. This study outlines the results retrieved from the MODIS satellite images over a time period of 10 hydrological years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) and applied to two case studies of the EU FP7 ACQWA project, namely the upper Rhone and Po in Europe and the headwaters of the Syr Darya in Kyrgyzstan (Central Asia). The satellite data were provided by the MODIS Terra MOD-09 reflectance images (NASA) and MOD-10 snow products (NSIDC). Daily snow maps were retrieved over that decade and the results presented here focus on the temporal and spatial changes in snow cover. This paper highlights the statistical bias observed in some specific regions, expressed by the standard deviation values (STD) of annual snow duration. This bias is linked to the response of snow cover to changes in elevation and can be used as a signal of strong instability in regions sensitive to climate change: with alternations of heavy snowfalls and rapid snow melting processes. The interest of the study is to compare the methodology between the medium scales (Europe) and the large scales (Central Asia) in order to overcome the limits of the applied methodologies and to improve their performances. Results show that the yearly snow cover duration increases by 4-5 days per 100 m elevation during the accumulation period, depending of the watershed, while during the melting season the snow depletion rate is 0.3% per day of surface loss for the upper Rhone catchment, 0.4%/day for the Syr Darya headwater basins, and 0.6%/day for the upper Po, respectively. Then, the annual STD maps of snow cover indicate higher values (more than 45 days difference compared to the mean values) for (i) the Po foothill region at medium elevation (SE orientation) and (ii) the Kyrgyzstan high plateaux (permafrost areas). These observations cover only a timeperiod of 10 years, but exhibit a signal under current climate that is alread...

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

Cited by 71 publications

References 35 publications

Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

Snow Cover Maps from MODIS Images at 250 m Resolution, Part 2: Validation

Shifting mountain snow patterns in a changing climate from remote sensing retrieval

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