Improving MODIS Spatial Resolution for Snow Mapping Using Wavelet Fusion and ARSIS Concept

Sirguey, Pascal; Mathieu, Renaud; Arnaud, Yves; Khan, Muhammad Murtaza; Chanussot, Jocelyn

doi:10.1109/lgrs.2007.908884

Cited by 103 publications

(42 citation statements)

References 15 publications

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“…A comparison with Terra snow maps indicated a lower accuracy at least in forested areas as expected . Alternative snow cover detection algorithms were developed for MODIS reflectance data, which showed better accuracies (Sirguey et al, 2008;Rittger et al, 2013). However these codes are not operational globally and imply the processing of a large amount of data to get a snow map, which restricts the number of potential users.…”

Section: Introductionmentioning

confidence: 99%

A snow cover climatology for the Pyrenees from MODIS snow products

Gascoin

Hagolle

Huc

et al. 2015

Hydrol. Earth Syst. Sci.

149

118

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Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97 % (κ = 0.85) for MOD10A1 and 96 % (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96 %, κ = 0.77; MYD10A1: 95 %, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50 % of the time above 1600 m between December and April. We finally analyze the snow patterns for the atypical winter 2011-2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

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Section: Introductionmentioning

confidence: 99%

A snow cover climatology for the Pyrenees from MODIS snow products

Gascoin

Hagolle

Huc

et al. 2015

Hydrol. Earth Syst. Sci.

149

118

View full text Add to dashboard Cite

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“…In this paper, one fine band is selected from bands 1 and 2. The CC (R 2 and RMSD) of seven bands for MODIS data has been investigated widely, which were used to explore the possible correlation between MODIS bands [15,51]. Therefore, based on the CC of spectral similarity between MODIS bands, only the fine band with strongest CC was determined as the one spectrally closest to the coarse band.…”

Section: Usability Of CC Ow Psf For Downscaling Methodsmentioning

confidence: 99%

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

et al. 2017

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Lake extent is an indicator of water capacity as well as the aquatic ecological and environmental conditions. Due to the small sizes and rapid water dynamics, monitoring the extent of small lakes fluctuating between 2.5 and 30 km 2 require observations with both high spatial and temporal resolutions. The paper applied an improved surface reflectance (SR) downscaling method (i.e., IMAR (Improved Modified Adaptive Regression model)) to downscale the daily SR acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra platform to a consistent 250-m resolution, and derived monthly water extent of four small lakes in the Tibetan Plateau (Longre Co, Ayonggongma Co, Ayonggama Co, and Ayongwama Co)) from 2000 to 2014. Using Landsat ETM+ acquired on the same date, the downscaled MODIS SR and identified water extent were compared to the original MODIS, observations downscaled using an early SR downscaling method (MAR (Modified Adaptive Regression model)) and Wavelet fusion. The results showed IMAR achieved the highest correlation coefficients (R 2 ) (0.89-0.957 for SR and 0.79-0.933 for water extent). The errors in the derived water extents were significantly decreased comparing to the results of MAR and Wavelet fusion, and lakes morphometry of IMAR is more comparable to Landsat results. The detected lake extents dynamic between 2000 and 2014 were analyzed using the trend and season decomposition model (BFAST), indicating an increasing trend after 2005, and it likely had higher correlations with temperature and precipitation variation in the Tibetan region (R 2 : 0.598-0.728 and 0.61-0.735, respectively).

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“…We associate to each pixel the scale of the most significant region containing it computed on the high resolution Pan image P according to Equation (5), noted as E P (x). With the help of this value, we incorporate Equations (2) and (1). If the scale E P (x) of the most significant region containing x is large (i.e.…”

Section: Proposed Pansharpening Methodsmentioning

confidence: 99%

“…Since the satellite moves during this duration the image captured is blurred, thus resulting in low resolution MS images. Generally, satellite images with both high spectral and spatial resolution are required for improving the results of automated tasks like classification, segmentation and object detection [1]. The process of pansharpening helps in synthesizing MS images having both high spectral and spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Pansharpening with a decision fusion based on the local size information

Luo

Khan

Bienvenu

et al. 2010

2010 IEEE International Conference on Image Processing

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Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. Different pansharpening methods produce images with different characteristics. In the 2006 IEEE Data Fusion Contest,À-trous Wavelet Transform based pansharpening (AWLP) and Context Adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, it was observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to develop a method which could produce results locally approximately similar to the best method, among the two pansharpening methods. In this paper we propose a method which selects either of the two methods for performing pansharpening on local regions, based upon the size of the objects. The results obtained demonstrate that the proposed method produces images with quantitative results approximately similar to the method which is better among the AWLP and CBD pansharpening methods.

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Improving MODIS Spatial Resolution for Snow Mapping Using Wavelet Fusion and ARSIS Concept

Cited by 103 publications

References 15 publications

A snow cover climatology for the Pyrenees from MODIS snow products

A snow cover climatology for the Pyrenees from MODIS snow products

Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance

Pansharpening with a decision fusion based on the local size information

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