Uncertainty in satellite remote sensing of snow fraction for water resources management

doi:10.1007/s11707-018-0720-1

Front. Earth Sci.

2018, Vol. 12

Issue (4) : 711-727 https://doi.org/10.1007/s11707-018-0720-1

RESEARCH ARTICLE

Uncertainty in satellite remote sensing of snow fraction for water resources management

Igor Appel(

)

TAG LLC, Washington, District of Columbia, 20024, USA

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Abstract

Snow fraction is an important component of land surface models and hydrologic models. Information on snow fraction also improves downstream products retrieved from remote sensing: vertical atmosphere profiles, soil moisture, heat fluxes, etc. The uncertainty of the fractional snow cover estimates must be determined, quantified, and reported to consider the suitability of the product for modeling, data assimilation and other applications. The reflectances of snow and non-snow are characterized by a very significant local variability and also by changes from one scene to another. The local snow and non-snow endmembers are approximated by the Normalized Difference Snow Index with a high accuracy. The magnitudes of snow and non-snow Normalized Difference Snow Indexes are scene-specific and calculated on the fly to retrieve snow fraction. The development of the Normalized Difference Snow Index based algorithms to estimate snow fraction including a scene-specific approach taking local snow and non-snow properties into account is considered an optimal way to fractional snow retrieval from moderate resolution optical remote sensing observations. The Landsat reference data are used to estimate the performance of the fractional snow cover algorithms at moderate resolution and to compare the quality of alternative algorithms. The validation results demonstrate that the performance of the algorithms using Normalized Difference Snow Index has advantages. The advantages achieved in snow fraction retrieval lead to improved estimate of snow water equivalent and changes in snow cover state contributing to better modeling of land surface and hydrologic regime. The success of managing water resources on the whole depends on coordinating described investigations with the works of other researchers developing further enhanced models.

Keywords algorithm remote sensing uncertainty validation snow cover fraction

Corresponding Author(s): Igor Appel

Just Accepted Date: 19 June 2018 Online First Date: 31 July 2018 Issue Date: 20 November 2018

Cite this article:

Igor Appel. Uncertainty in satellite remote sensing of snow fraction for water resources management[J]. Front. Earth Sci., 2018, 12(4): 711-727.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0720-1
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I4/711

Tab.1 Snow fraction sensor input for MODIS and VIIRS

Fig.1 Wavelength locations of the MODIS and VIIRS bands. Bands used in the NDSI calculations are labeled with band number.

Fig.2 Two dimensional probability density of pixels in spectral space defined by visible (X axis) and near-infrared (Y axis) reflectances (%).

Fig.3 The most probable snow (asterisks) and non-snow (squares) VIIRS reflectances for 16 scenes under consideration.

Fig.4 Example of NDSI probability density function.

Fig.5 Landsat binary snow cover classification.

Tab.2 Performance of averaged relationship for two regions tested on a third region

Tab.3 Testing “universal” relationship for Terra for independent scenes

Tab.4 Testing “universal” relationship for Terra (first lines) for independent Terra scenes and “universal” relationship for Aqua (second lines) for independent Aqua scenes

Fig.6 Aggregated and co-registered fraction data at 0.05° cells of a latitude/longitude grid. (a) Landsat fraction; (b) VIIRS fraction.

Tab.5 Statistics of VIIRS snow fraction validation

Fig.7 Stratified quantitative assessment of NDSI-based algorithm performance.

Tab.6 Comparison of results provided by the NDSI-based algorithm with the methods proposed by Barton and Kaufman for independent validation

Fig.8 Linear regressions of snow fraction on visible reflectance (a) and on NDSI (b).

Fig.9 Standard deviation (σ) for snow fraction regression on visible reflectance vs. standard deviation (σ) for regression on NDSI.

Fig.10 The errors in the MODIS subpixel snow fraction estimates. Thick line denotes a five-point running mean, gray line is a polynomial approximation.

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