# Snow Grain-Size Estimation Using Hyperion Imagery in a Typical Area of the Heihe River Basin, China

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methodology

#### 2.1. Study Area and Data

^{2}in size, the C, F, G and H plots were 120 × 120 m

^{2}in size, and the D and E plots were 90 × 90 m

^{2}in size. Figure 1 shows the plot locations.

#### 2.2. Data Collection and Pre-Processing of Hyperion Imagery

#### 2.3. Spectrum Observation and Measurements of Snow Grain Size

#### 2.4. Analysis of the Measured Spectral Data

_{s}is the downward radiation flux at the snow surface. For the wavelengths near 1,030 nm, the penetration depth of a snow layer was normally within 10 cm. Therefore, this research used the average particle size of the surface and the 0–10 cm depth snow layer to establish the relationships between the spectral curves and snow particle sizes.

#### 2.5. The Optimal Band Selection to Estimate Grain Size

_{i}is snow grain sizes, Δh is 0.1 mm, which is the reading accuracy of the portable handheld microscope, n is the number of categories of grain sizes.

#### 2.6. Extraction of Snow Cover and Histogram Statistics

#### 2.7. Band Histogram Statistics

## 3. Results and Discussion

#### 3.1. Expression of the Grain-Size Estimation Model

^{−0.51}, the correlation coefficient r was 0.84. The root mean square error (RMSE) was 0.24 mm and 0.21 mm for the linear fitting and curve fitting, respectively. These results indicate that the pixel value had an index function and a linear relation with the grain size.

#### 3.2. Mapping the Snow Grain Size Using the Hyperion Imagery

#### 3.3. Improvement of the Snow Grain-Size Estimation Model

#### 3.4. Assessment of Snow Grain-Size Estimation Results

_{i}is the measured value, ŷ is an estimated value, and n is the number of samples. The RMSE were 0.14 mm and 0.12 mm for the linear model and the exponential model, respectively. The linear relationship between the estimated and measured snow grain sizes was calculated and shown in Figure 15. The linear relationship coefficient r

^{2}is 0.86. The kappa coefficient of the estimate accuracy was calculated (Table 1).

#### 3.6. Uncertainties

## 4. Conclusions

## Acknowledgments

## References

- Rinne, J.; Aurela, M.; Manninen, T. A simple method to determine the timing of snow melt by remote sensing with application to the CO2 balances of northern mire and heath ecosystems. Remote Sens
**2009**, 1, 1097–1107. [Google Scholar] - Kropacek, J.; Feng, C.; Alle, M.; Kang, S.; Hochschild, V. Temporal and spatial aspects of snow distribution in the Nam Co Basin on the Tibetan Plateau from MODIS data. Remote Sens
**2010**, 2, 2700–2712. [Google Scholar] - Wiscombe, W.J.; Warren, S.G. A model for the spectral albedo of snow: I Pure snow. J. Atoms. Sci
**1980**, 37, 2712–2733. [Google Scholar] - Dozier, J.; Davis, R.E.; Perla, R. On the Objective Analysis of Snow Microstructure. In Avalanche Formation, Movement and Effects; Salm, B., Gubler, H., Eds.; International Association of Hydrological Sciences Publication: Wallingford, UK, 1987; pp. 49–59. [Google Scholar]
- Dozier, J.; Marks, D. Snow mapping and classification from Landsat Thematic Mapper data. Ann. Glaciol
**1987**, 9, 97–103. [Google Scholar] - Fily, M.; Bourdells, B.; Dedieu, J.P.; Sergent, C. Comparison of in situ and Landsat Thematic Mapper derived snow grain characteristics in the Alps. Remote Sens. Environ
**1997**, 59, 452–460. [Google Scholar] - Nolin, A.; Dozier, J. Estimating snow grain size using AVIRIS data. Remote Sens. Environ
**1993**, 44, 231–238. [Google Scholar] - Nolin, A.; Dozier, J. Hyperspectral method for remotely sensing the grain size of snow. Remote Sens. Environ
**2000**, 74, 207–216. [Google Scholar] - Jennifer, E.K.; Alan, R.G.; Gary, B.H. Spatial relationships between snow contaminant content, grain size, and surface temperature from multispectral images of Mt. Rainier, Washington (USA). Remote Sens. Environ
**2003**, 86, 216–231. [Google Scholar] - Scambos, T.A.; Haran, T.M.; Fahnestock, M.A. MODIS-based Mosaic of Antarctica (MOA) data sets: Continent-wide surface morphology and snow grain size. Remote Sens. Environ
**2007**, 111, 367–375. [Google Scholar] - Lyapustin, A.; Tedesco, M.; Wang, Y.; Aoki, T.; Hori, M.; Kokhanovsky, A. Retrieval of snow grain size over greenland from MODIS. Remote Sens. Environ
**2009**, 113, 1976–1987. [Google Scholar] - Painter, T.H.; Rittger, K.; McKenzie, C.; Slaughter, P.; Davis, R.E.; Dozie, J. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sens. Environ
**2009**, 113, 868–879. [Google Scholar] - Jiang, T.; Zhao, S.; Xiao, P.; Feng, X.; Zhang, Y.; Hu, W. Spectral analysis of different snow grain size based on field measurement. J. Glaci. Geoc
**2009**, 31, 227–232. [Google Scholar] - Stamnes, K.; Li, W.; Eide, H.; Aoki, T.; Hori, M.; Storvold, R. ADEOS-II/GLI snow/ice products-Part I: Scientific basis. Remote Sens. Environ
**2007**, 111, 258–273. [Google Scholar] - Warren, S.G.; Wiscombe, W.J. A model for the spectral albedo of snow. II: Snow containing atmospheric aerosols. J. Atoms. Sci
**1980**, 37, 2734–2745. [Google Scholar] - Painter, T.H.; Molotch, N.P.; Cassidy, M.; Flanner, M.; Steffen, K. Contact spectroscopy for determination of stratigraphy of snow optical grain size. J. Glaciol
**2007**, 53, 121–127. [Google Scholar] - Aoki, T.; Hori, M.; Motoyoshi, H.; Tanikawa, T.; Hachikubo, A.; Sugiura, K.; Yasunari, T.J.; Storvold, R.; Eide, H.A.; Stamnes, K.; et al. ADEOS-II/GLI snow/ice products Part II: Validation results using GLI and MODIS data. Remote Sens. Environ
**2007**, 111, 274–290. [Google Scholar] - Grenfell, T.C.; Warren, S.G. Representation of a nonspherical ice particle by a collection of independent spheres for scattering and absorption of radiation. J. Geophys. Res
**1999**, 104, 31697–31709. [Google Scholar] - Painter, T.H.; Dozier, J.; Roberts, D.A.; Davis, R.E.; Green, R.O. Retrieval of sub-pixel snow-covered area and grain size from imaging spectrometer data. Remote Sens. Environ
**2003**, 85, 64–77. [Google Scholar] - Jin, Z.H.; Charlock, T.; Yang, P.; Xie, Y.; Miller, W. Snow optical properties for different particle shapes with application to snow grain size retrieval and MODIS/CERES radiance comparison over Antarctica. Remote Sens. Environ
**2008**, 112, 3563–3581. [Google Scholar] - Picard, G.; Arnaud, L.; Domine, F.; Fily, M. Determining snow specific surface area from near-infrared reflectance measurements: Numerical study of the influence of grain shape. Cold Reg. Sci. Techol
**2009**, 56, 10–17. [Google Scholar]

**Figure 12.**Distribution of the snow grain size estimated using the linear and exponential models. (

**a**) Linear model, (

**b**) Exponential model.

Measured Snow Grain Sizes | Estimated Snow Grain Sizes | Total | ||||
---|---|---|---|---|---|---|

<0.5 | 0.5–0.7 | 0.7–1.0 | 1.0–1.5 | 1.5–2.0 | ||

<0.5 | 19 | 6 | 0 | 0 | 0 | 25 |

0.5–0.7 | 6 | 11 | 5 | 4 | 0 | 26 |

0.7–1.0 | 1 | 2 | 22 | 4 | 0 | 29 |

1.0–1.5 | 0 | 2 | 7 | 31 | 7 | 47 |

1.5–2.0 | 0 | 0 | 0 | 6 | 30 | 36 |

Total | 26 | 21 | 34 | 45 | 37 | 163 |

Total of diagonal | 113 | Kappa coefficient | 0.611 |

## Share and Cite

**MDPI and ACS Style**

Zhao, S.; Jiang, T.; Wang, Z.
Snow Grain-Size Estimation Using Hyperion Imagery in a Typical Area of the Heihe River Basin, China. *Remote Sens.* **2013**, *5*, 238-253.
https://doi.org/10.3390/rs5010238

**AMA Style**

Zhao S, Jiang T, Wang Z.
Snow Grain-Size Estimation Using Hyperion Imagery in a Typical Area of the Heihe River Basin, China. *Remote Sensing*. 2013; 5(1):238-253.
https://doi.org/10.3390/rs5010238

**Chicago/Turabian Style**

Zhao, Shuhe, Tenglong Jiang, and Zhaojun Wang.
2013. "Snow Grain-Size Estimation Using Hyperion Imagery in a Typical Area of the Heihe River Basin, China" *Remote Sensing* 5, no. 1: 238-253.
https://doi.org/10.3390/rs5010238