Remote sensing of subpixel snow cover using 0.66 and 2.1 μm channels

Kaufman, Yoram J.; Kleidman, R. G.; Hall, Dorothy K.; Martins, J. V.; Barton, Jonathan S.

doi:10.1029/2001gl013580

Cited by 48 publications

(31 citation statements)

References 11 publications

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“…The algorithm's heritage traces back to retrieval of snow-covered area and qualitative grain size from the Landsat Thematic Mapper using normalized band differences (Dozier, 1984(Dozier, , 1989 and the recognition that snow could be discriminated from clouds in the shortwave-infrared region (Crane & Anderson, 1984). Version 005 of MOD10A1 contains a new fractional snow cover product developed from a linear fit of binary Thematic Mapper snow cover (averaged into 500 m bins for fractional snow cover of MODIS) to the normalized difference snow index of MODIS bands 2 and 5 for a collection of snow covered regions (Kaufman et al, 2002;Salomonson & Appel, 2004. Vikhamar and Solberg (2003) mapped fractional snow cover in forests with MODIS using a land cover fraction map and linear mixture analysis.…”

Section: Remote Sensing Of Snow-covered Area and Grain Sizementioning

confidence: 99%

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

Painter

Rittger

McKenzie

et al. 2009

Remote Sensing of Environment

507

487

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We describe and validate a model that retrieves fractional snow-covered area and the grain size and albedo of that snow from surface reflectance data (product MOD09GA) acquired by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The model analyzes the MODIS visible, near infrared, and shortwave infrared bands with multiple endmember spectral mixtures from a library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model specific to a scene's illumination geometry; spectra for vegetation, rock, and soil were collected in the field and laboratory. We validate the model with fractional snow cover estimates from Landsat Thematic Mapper data, at 30 m resolution, for the Sierra Nevada, Rocky Mountains, high plains of Colorado, and Himalaya. Grain size measurements are validated with field measurements during the Cold Land Processes Experiment, and albedo retrievals are validated with in situ measurements in the San Juan Mountains of Colorado. The pixelweighted average RMS error for snow-covered area across 31 scenes is 5%, ranging from 1% to 13%. The mean absolute error for grain size was 51 µm and the mean absolute error for albedo was 4.2%. Fractional snow cover errors are relatively insensitive to solar zenith angle. Because MODSCAG is a physically based algorithm that accounts for the spatial and temporal variation in surface reflectances of snow and other surfaces, it is capable of global snow cover mapping in its more computationally efficient, operational mode.

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Section: Remote Sensing Of Snow-covered Area and Grain Sizementioning

confidence: 99%

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

Painter

Rittger

McKenzie

et al. 2009

Remote Sensing of Environment

507

487

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“…Other work using MODIS data (Barton et al 2001 ;Kaufman et al 2002 andAppel, 2004) has been done to develop algorithms to map FSC globally. Using the algorithm developed by Salomonson and Appel, FSC will be provided in the MODiO-L2 swath snow maps at 500-m resofution (see for example, Figure 2).…”

Section: Daily Snow Fractionmentioning

confidence: 99%

MODIS Snow and Sea Ice Products

Hall

Riggs

Salomonson

Earth Science Satellite Remote Sensing

Self Cite

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Popular SummaryIn this chapter, we desuibe the suite of Earth Observing System (EOS)Moderate-Resolution Imaging Spectromdiometer (MODIS) Terra and Aqua snow and sea ice products. Global, daily products, developed at Goddard Space Flight Center, are archived and distributed through the National Snow and Ice Data Center at various resolutions and on different grids useful for different communities Snow products indude binary snow cover, snow albedo, and in the near future, fraction of snow in a 5OO-m pixel. Sea ice products include ice extent determined with two different algorithms, and sea ice surface temperature.The algorithms used to develop these products are described. Both the snow and sea ice products, available since February 24,2000, are useful for modelers.Validation of the products is also discussed. SignificanceIt is important to describe, in detail, the EOS MODIS snow and ice algorithms and products. Users need this information in order to use the products. Users include university students and researchers and modelers. Many such users do not want to develop their own products from the original MODIS data, a Herculean task, but prefer to use products that have already been developed.

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“…Barton et al (2001) [8], Kaufman et al (2002) [10] and Salomonson et al (2004) [13] successively established regression models employing FSCs extracted from Landsat data as true values and normalized difference snow index (NDSI) data derived from MODIS data, which they used to estimate FSC at a subpixel scale. Of those regression models, the univariate regression model proposed by Salomonson et al (2004) [14] was used by the National Snow and Ice Data Center (NSIDC, US) to produce version 005 of the MODIS global FSC product (denoted as MOD10A1 FSC/MYD10A1 FSC).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, there are three main types of MODIS fractional snow-cover mapping algorithms: regression analysis algorithms [8][9][10][11][12][13][14][15][16][17], spectral unmixing algorithms [18][19][20][21][22] and machine learning algorithms [23][24][25][26][27]. Barton et al (2001) [8], Kaufman et al (2002) [10] and Salomonson et al (2004) [13] successively established regression models employing FSCs extracted from Landsat data as true values and normalized difference snow index (NDSI) data derived from MODIS data, which they used to estimate FSC at a subpixel scale.…”

Section: Introductionmentioning

confidence: 99%

Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

et al. 2017

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Moderate-resolution imaging spectroradiometer (MODIS) snow-cover products have relatively low accuracy over the Tibetan Plateau because of its complex terrain and shallow, fragmented snow cover. In this study, fractional snow-cover (FSC) mapping algorithms were developed using a linear regression model (LR), a linear spectral mixture analysis model (LSMA) and a back-propagation artificial neural network model (BP-ANN) based on MODIS data (version 006) and unmanned aerial vehicle (UAV) data. The accuracies of the three models were validated against Landsat 8 Operational Land Imager (OLI) snow-cover maps (Landsat 8 FSC) and compared with the MODIS global FSC product (MOD10A1 FSC, version 005) for the purpose of finding the optimal algorithm for FSC extraction for the Tibetan Plateau. The results showed that (1) the overall retrieval results of the LR and BP-ANN models based on MODIS and UAV data were relatively similar to the OLI snow-cover maps; the accuracy and stability were greatly improved, with even some reduction in errors; compared to the Landsat 8 FSC, the correlation coefficients (r) were 0.8222 and 0.8445 respectively and the root-mean-square errors (RMSEs) were 0.2304 and 0.2201, respectively. (2) The accuracy and stability of the fully constrained LSMA model using the pixel purity index (PPI) endmember extraction method based only on MODIS data suffered the worst performance of the three models; r was only 0.7921 and the RMSE was as large as 0.3485. There were some serious omission phenomena in the study area, specifically for the largest mean absolute error (MAE = 0.2755) and positive mean error (PME = 0.3411). (3) The accuracy of the MOD10A1 FSC product was much lower than that of the LR and BP-ANN models, although its accuracy slightly better that of the LSMA based on comprehensive evaluation of six accuracy indices. (4) The optimal model was the BP-ANN model with combined inputs of surface reflectivity data (R1-R7), elevation (DEM) and temperature (LST), which can easily incorporate auxiliary information (DEM and LST) on the basis of (R1-R7) during the relationship training period and can effectively improve the accuracy of snow area monitoring-it is the ideal algorithm for retrieving FSC for the Tibetan Plateau.

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Remote sensing of subpixel snow cover using 0.66 and 2.1 μm channels

Cited by 48 publications

References 11 publications

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

MODIS Snow and Sea Ice Products

Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau

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