Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey

Tekeli, Ahmet Emre; Akyürek, Zuhal; Şorman, Ali Arda; Şensoy, Aynur; Şorman, A. Ünal

doi:10.1016/j.rse.2005.03.013

Cited by 258 publications

(190 citation statements)

References 14 publications

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“…They also had a combined daily and eight-day MODIS snow cover products and achieved better accuracy than with individual MODIS products. Elsewhere, the study by Tekeli et al (2005) stated that "among 96 observations, 74 observations mapped as snow or land by MODIS agreed with the ground measurements with a matched ratio of 77%".…”

Section: Introductionmentioning

confidence: 92%

Cloud removal methodology from MODIS snow cover product

Gafurov

Bàrdossy

2009

Hydrol. Earth Syst. Sci.

256

238

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Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) employed by Terra and Aqua satellites provides spatially snow covered data with 500 m and daily temporal resolution. It delivers public domain data in raster format. The main disadvantage of the MODIS sensor is that it is unable to record observations under cloud covered regions. This is why this study focuses on estimating the pixel cover for cloud covered areas where no information is available. Our step to this product involves employing methodology based on six successive steps that estimate the pixel cover using different temporal and spatial information. The study was carried out for the Kokcha River basin located in northeastern part of Afghanistan. Snow coverage in catchments, like Kokcha, is very important where the melt-water from snow dominates the river discharge in vegetation period for irrigation purposes. Since no snow related observations were available from the region, the performance of the proposed methodology was tested using the cloud generated MODIS snow cover data as possible "ground truth" information. The results show successful performances arising from the methods applied, which resulted in all cloud coverage being removed. A validation was carried out for all subsequent steps, to be outlined below, where each step removes progressively more cloud coverage. Steps 2 to 5 (step 1 was not validated) performed very well with an average accuracy of between 90-96%, when applied one after another for the selected valid days in this study. The sixth step was the least accurate at 78%, but it led to the removal of all remaining cloud cover.

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Section: Introductionmentioning

confidence: 92%

Cloud removal methodology from MODIS snow cover product

Gafurov

Bàrdossy

2009

Hydrol. Earth Syst. Sci.

256

238

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“…A popular approach is to derive snow depletion curves from MODIS SCF and use them in the Snowmelt Runoff Model (SRM) (Martinec, 1975). This approach is still popular and used in recent studies (Lee et al, 2005;Tekeli et al, 2005;Li and Williams, 2008;Butt and Bilal, 2011;Tahir et al, 2011;Bavera et al, 2012). However, the SRM studies are focussed mostly on the winter half-year and are limited to study sites where snowmelt processes are dominant.…”

Section: T Berezowski Et Al: Spatial Sensitivity Analysis Of Snow Cmentioning

confidence: 99%

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Berezowski

Nossent

Chormański

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction -SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by cooccurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.

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“…Fine 500 m resolution, daily revisit time, well scientific developer team, and ease of access have made MODIS images a popular dataset for hydrological modeling [1][2][3][4] and climatologic studies [5][6][7][8][9]. However, the obscuration of earth surface by clouds in visible and near-infrared wavelengths causes a major challenge in snow cover mapping through MODIS images [10][11][12][13]. Different methods have been proposed to overcome this problem [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Developing a Cloud-Reduced MODIS Surface Reflectance Product for Snow Cover Mapping in Mountainous Regions

Khoramian

Dariane

2017

Geosciences

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Cloud obscuration is a major problem for using Moderate Resolution Imaging Spectroradiometer (MODIS) images in different applications. This issue poses serious difficulties in monitoring the snow cover in mountainous regions due to high cloudiness in such areas. To overcome this, different cloud removal methods have been developed in the past where most of them use MODIS snow cover products and spatiotemporal dependencies of snow to estimate the undercloud coverage. In this study, a new approach is adopted that uses surface reflectance data in the cloud-free pixels and estimates the surface reflectance of a cloudy pixel as if there were no cloud. This estimation is obtained by subsequently applying the k-nearest neighbor and dynamic time compositing methods. The modified surface reflectance data are then utilized as inputs of a Normalized Difference Snow Index (NDSI)-based algorithm to map snow cover in the study area. The results indicate that the suggested approach is able to appropriately estimate undercloud surface reflectance in bands 2, 4 and 6, and can map the snow cover with 97% accuracy, which is a substantial improvement over the conventional method with an accuracy of 86%. Finally, although a clear underestimation of snow cover (about 15%) is observed by applying the proposed approach, still, it is much better than the 30% underestimation obtained by the conventional method.

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Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey

Cited by 258 publications

References 14 publications

Cloud removal methodology from MODIS snow cover product

Cloud removal methodology from MODIS snow cover product

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

Developing a Cloud-Reduced MODIS Surface Reflectance Product for Snow Cover Mapping in Mountainous Regions

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