Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach

López-Burgos, V.; Gupta, Hoshin; Clark, Martyn P.

doi:10.5194/hess-17-1809-2013

Cited by 55 publications

(32 citation statements)

References 38 publications

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“…In order to minimise the cloud data gaps from the validated MODIS snow products, we have applied a rigorous non-spectral cloud removal technique, partially following Wang et al (2009), Gurung et al (2011) and López-Burgos et al (2013). These studies have found the adopted cloud removal technique remarkably efficient in cloud reduction/removal from the MODIS snow products, with few season-dependent trade-offs, resulting in snow maps in good agreement with the ground snow observations.…”

Section: Cloud Removal Technique and Its Validationmentioning

confidence: 99%

Early 21st century snow cover state over the western river basins of the Indus River system

Hasson

Lucarini

Khan

et al. 2014

Hydrol. Earth Syst. Sci.

132

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In this paper we assess the snow cover and its dynamics for the western river basins of the Indus River system (IRS) and their sub-basins located in Afghanistan, China, India and Pakistan for the period 2001-2012. First, we validate the Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products from Terra (MOD10A1) and Aqua (MYD10A1) against the Landsat Thematic Mapper/Enhanced Thematic Mapper plus (TM/ETM+) data set, and then improve them for clouds by applying a validated non-spectral cloud removal technique. The improved snow product has been analysed on a seasonal and annual basis against different topographic parameters (aspect, elevation and slope). Our results show a decreasing tendency for the annual average snow cover for the westerlies-influenced basins (upper Indus basin (UIB), Astore, Hunza, Shigar and Shyok) and an increasing tendency for the monsooninfluenced basins (Jhelum, Kabul, Swat and Gilgit). Seasonal average snow cover decreases during winter and autumn, and increases during spring and summer, which is consistent with the observed cooling and warming trends during the respective seasons. Sub-basins at relatively higher latitudes/altitudes show higher variability than basins at lower latitudes/middle altitudes. Northeastern and northwestern aspects feature greater snow cover. The mean end-of-summer regional snow line altitude (SLA) zones range from 3000 to 5000 m a.s.l. for all basins. Our analysis provides an indication of a descending end-of-summer regional SLA zone for most of the studied basins, which is significant for the Shyok and Kabul basins, thus indicating a change in their water resources. Such results are consistent with the observed hydro-climatic data, recently collected local perceptions and glacier mass balances for the investigated period within the UIB. Moreover, our analysis shows a significant correlation between winter season snow cover and the North Atlantic Oscillation (NAO) index of the previous autumn. Similarly, the inter-annual variability of spring season snow cover and spring season precipitation explains well the interannual variability of the summer season discharge from most of the basins. These findings indicate some potential for the seasonal stream flow forecast in the region, suggesting snow cover as a possible predictor.

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Section: Cloud Removal Technique and Its Validationmentioning

confidence: 99%

Early 21st century snow cover state over the western river basins of the Indus River system

Hasson

Lucarini

Khan

et al. 2014

Hydrol. Earth Syst. Sci.

132

View full text Add to dashboard Cite

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“…However, optical images are obscured by clouds, which limit the direct usage of optical data (Gafurov & Bárdossy, ). Currently, there have been many mature cloud removal methods, including methods based on temporal and spatial continuity (Gafurov & Bárdossy, ; López‐Burgos et al, ; Paudel & Andersen, ) and methods combining multiple sensor data (Gao, Xie, Lu, et al, ; Gao, Xie, Yao, et al, ; Liang et al, ), which can reduce cloud cover and increase data availability by gap filling approaches and/or compositing.…”

Section: Introductionmentioning

confidence: 99%

Variations of Lake Ice Phenology on the Tibetan Plateau From 2001 to 2017 Based on MODIS Data

Cai

Cao

et al. 2019

JGR Atmospheres

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Lake ice is a robust indicator of climate change. The availability of information contained in Moderate Resolution Imaging Spectroradiometer daily snow products from 2000 to 2017 could be greatly improved after cloud removal by gap filling. Thresholds based on open water pixel numbers are used to extract the freezeup start and breakup end dates for 58 lakes on the Tibetan Plateau (TP); 18 lakes are also selected to extract the freezeup end and breakup start dates. The lake ice durations are further calculated based on freezeup and breakup dates. Lakes on the TP begin to freezeup in late October and all the lakes start the ice cover period in mid‐January of the following year. In late March, some lakes begin to break up, and all the lakes end the ice cover period in early July. Generally, the lakes in the northern Inner‐TP have earlier freezeup dates and later breakup dates (i.e., longer ice cover durations) than those in the southern Inner‐TP. Over 17 years, the mean ice cover duration of 58 lakes is 157.78 days, 18 (31%) lakes have a mean extending rate of 1.11 day/year, and 40 (69%) lakes have a mean shortening rate of 0.80 day/year. Geographical location and climate conditions determine the spatial heterogeneity of the lake ice phenology, especially the ones of breakup dates, while the physico‐chemical characteristics mainly affect the freezeup dates of the lake ice in this study. Ice cover duration is affected by both climatic and lake specific physico‐chemical factors, which can reflect the climatic and environmental change for lakes on the TP.

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“…Şensoy and Uysal (2012) presented the probability approach in snow depletion forecasting with a limited number of MODIS snow cover data. López-Burgos et al (2013) used the locally-weighted logistic regression (LWLR) method to estimate probabilistic snow occurrences for developing the cloud removal technique. Gafurov et al (2015) presented a methodology mainly based on correlations between station records and spatial snow-cover patterns, for reconstructing past snow cover using historical in situ snow-depth data, remote sensing snow-cover data and topographic data.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Snow Cover and Ensemble Streamflow Estimations in the Upper Euphrates Basin

Şorman

Uysal

Şensoy

2018

Journal of Hydrology and Hydromechanics

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Predicting snow cover dynamics and relevant streamflow due to snowmelt is a challenging issue in mountainous basins. Spatio-temporal variations of snow extent can be analyzed using probabilistic snow cover maps derived from satellite images within a relatively long period. In this study, Probabilistic Snow Depletion Curves (P-SDCs) and Probabilistic Snow Lines (P-SLs) are acquired from Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-filtered daily snow cover images. Analyses of P-SDCs show a strong correlation with average daily runoff (R 2 = 0.90) and temperature (R 2 = 0.96). On the other hand, the challenge lies in developing noteworthy methods to use P-SDCs in streamflow estimations. Therefore, the main objective is to explore the feasibility of producing probabilistic runoff forecasts with P-SDC forcing in a snow dominated basin.Upper Euphrates Basin in Turkey has large snow extent and high snowmelt contribution during spring and summer periods. The melting characteristics are defined by P-SDCs using MODIS imagery for 2001-2012. The value of snow probability maps on ensemble runoff predictions is shown with Snowmelt Runoff Model (SRM) during 2013-2015 where the estimated runoff values indicate good consistency (NSE: 0.47-0.93) with forecasts based on the derived P-SDCs. Therefore, the probabilistic approach distinguishes the snow cover characteristics for a region and promotes a useful methodology on the application of probabilistic runoff predictions especially for snow dominated areas.

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Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach

Cited by 55 publications

References 38 publications

Early 21st century snow cover state over the western river basins of the Indus River system

Early 21st century snow cover state over the western river basins of the Indus River system

Variations of Lake Ice Phenology on the Tibetan Plateau From 2001 to 2017 Based on MODIS Data

Probabilistic Snow Cover and Ensemble Streamflow Estimations in the Upper Euphrates Basin

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