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

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## 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

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**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