| 光谱学与光谱分析 |
|
|
|
|
|
| Hyperspectral Remote Sensing Estimation Models for Snow Grain Size |
| WANG Jian-geng1,2, FENG Xue-zhi1,2, XIAO Peng-feng1,2*, LIANG Ji3, ZHANG Xue-liang1,2, LI Hai-xing1,2, LI Yun1,2 |
1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, China
2. Department of Geographical Information Science, Nanjing University, Nanjing 210093, China
3. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China |
|
|
|
|
Abstract Snow grain size is a key parameter not only to affect the energy budget of the global or local region but also characterizing the status of snow vapor transport and temperature gradient. It is significant to monitor and estimate the snow grain size in large area for global or local climate change and water resource management. Recently, remote sensing technology has become a useful tool for snow grain size monitoring and estimating. In the present paper, the estimate models were built based on simulating the snow surface spectral reflectance curve in visible-infrared region and the sensitive bands and snow indices for snow grain size were selected. These models help estimate snow grain size by hyperspectral remote sensing. Through validating with ground true data, the results show that these models have higher explorative accuracy using 1 030, 1 260 nm and normalized difference snow index (460 and 1 030 nm). In addition, the correlation slopes of estimated and observed valves are 1.37, 0.61 and 0.62, respectively. R2 are 0.82, 0.86 and 0.93 and RMSE are 55.65, 50.83 and 35.91 μm, respectively. The result can provide a scientific basis for snow grain size monitoring and estimating.
|
|
Received: 2012-06-28
Accepted: 2012-09-21
|
|
|
|
Corresponding Authors:
XIAO Peng-feng
E-mail: xiaopf@gmail.com
|
|
[1] Barry R G. Polar Geography, 2011, 34(4): 219.
[2] Schlaepfer D R, Lauenroth W K, Bradford J B. Global Change Biology, 2012, 18(6): 1988.
[3] Dozier J, Green R O, Nolin A W, et al. Remote Sensing of Environment, 2009, 113: S25.
[4] Dietz A J, Kuenzer C, Gessner U, et al. International Journal of Remote Sensing, 2012, 33(13): 4094.
[5] Domine F, Albert M, Huthwelker T, et al. Atmospheric Chemistry and Physics 2008, 8(2): 171.
[6] Dozier J. Remote Sensing of Environment, 1989, 28: 9.
[7] Fily M, Bourdelles B, Dedieu J P, et al. Remote Sensing of Environment, 1997, 59(3): 452.
[8] Warren S G. Reviews of Geophysics and Space Physics, 1982, 20(1): 67.
[9] Hyvarinen T, Lammasniemi J. Optical Engineering, 1987, 26(4): 342.
[10] Nolin A W, Dozier J. Remote Sensing of Environment, 2000, 74(2): 207.
[11] Jin Z H, Charlock T P, Yang P, et al. Remote Sensing of Environment, 2008, 112(9): 3563.
[12] Lyapustin A, Tedesco M, Wang Y J, et al. Remote Sensing of Environment, 2009, 113(9): 1976.
[13] Nakamura T, Abe O, Hasegawa T, et al. Cold Regions Science and Technology, 2001, 32(1): 13.
[14] Stamnes K, Tsay S C, Wiscombe W, et al. Applied Optics, 1988, 27(12): 2502.
[15] Sergent C, Pougatch E, Sudul M, et al. Annals of Glaciology, 1993, 17: 281. |
| [1] |
FENG Hai-kuan1, 2, YUE Ji-bo3, FAN Yi-guang2, YANG Gui-jun2, ZHAO Chun-jiang1, 2*. Estimation of Potato Above-Ground Biomass Based on VGC-AGB Model and Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2876-2884. |
| [2] |
JIN Chun-bai1, YANG Guang1*, LU Shan2*, LIU Wen-jing1, LI De-jun1, ZHENG Nan1. Band Selection Method Based on Target Saliency Analysis in Spatial Domain[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2952-2959. |
| [3] |
GAO Yu1, SUN Xue-jian1*, LI Guang-hua2, ZHANG Li-fu1, QU Liang2, ZHANG Dong-hui1, CHANG Jing-jing2, DAI Xiao-ai3. Study on the Derivation of Paper Viscosity Spectral Index Based on Spectral Information Expansion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2960-2966. |
| [4] |
KONG Bo1, YU Huan2*, SONG Wu-jie2, 3, HOU Yu-ting2, XIANG Qing2. Hyperspectral Characteristics and Quantitative Remote Sensing Inversion of Gravel Grain Size in the North Tibetan Plateau[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2381-2390. |
| [5] |
ZHANG Xia1, WANG Wei-hao1, 2*, SUN Wei-chao1, DING Song-tao1, 2, WANG Yi-bo1, 2. Soil Zn Content Inversion by Hyperspectral Remote Sensing Data and Considering Soil Types[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2019-2026. |
| [6] |
NIU Fang-peng1, 2, LI Xin-guo1, 3*, BAI Yun-gang2, ZHAO Hui4. Hyperspectral Estimation Model of Soil Organic Carbon Content Based on Genetic Algorithm Fused With Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2232-2237. |
| [7] |
WANG Hui-min1, 2, YU Lei1, XU Kai-lei1, 2, JIANG Xiao-guang1, 2, WAN Yu-qing1, 2*. Estimation of Salt Content of Saline Soil in Arid Areas Based on GF-5 Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2278-2286. |
| [8] |
CAO Yang1, 2, LI Yan-hong1, 2*. Study on the Effects of NO2 Pollution Under COVID-19 Epidemic
Prevention and Control in Urumqi[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1981-1987. |
| [9] |
ZHANG Chao1*, SU Xiao-yu1, XIA Tian2, YANG Ke-ming3, FENG Fei-sheng4. Monitoring the Degree of Pollution in Different Varieties of Maize Under Copper and Lead Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1268-1274. |
| [10] |
XU Long-xin1, 2, 3, 4, SUN Yong-hua2, 3, 4*, WU Wen-huan1, ZOU Kai2, 3, 4, HE Shi-jun2, 3, 4, ZHAO Yuan-ming2, 3, 4, YE Miao2, 3, 4, ZHANG Xiao-han2, 3, 4. Research on Classification of Construction Waste Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3927-3934. |
| [11] |
FENG Tian-shi1, 2, 3, PANG Zhi-guo1, 2, 3*, JIANG Wei1, 2, 3. Remote Sensing Retrieval of Chlorophyll-a Concentration in Lake Chaohu Based on Zhuhai-1 Hyperspectral Satellite[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2642-2648. |
| [12] |
ZHANG Jie1, 2, XU Bo1, FENG Hai-kuan1, JING Xia2, WANG Jiao-jiao1, MING Shi-kang1, FU You-qiang3, SONG Xiao-yu1*. Monitoring Nitrogen Nutrition and Grain Protein Content of Rice Based on Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1956-1964. |
| [13] |
JING Xia1, ZHANG Jie1, 2, WANG Jiao-jiao2, MING Shi-kang2, FU You-qiang3, FENG Hai-kuan2, SONG Xiao-yu2*. Comparison of Machine Learning Algorithms for Remote Sensing
Monitoring of Rice Yields[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1620-1627. |
| [14] |
YANG En1, WANG Shi-bo2*. Study on Directional Near-Infrared Reflectance Spectra of Typical Types of Coal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 847-858. |
| [15] |
JIANG Jie1, YU Quan-zhou1, 2, 3*, LIANG Tian-quan1, 2, TANG Qing-xin1, 2, 3, ZHANG Ying-hao1, 3, ZHANG Huai-zhen1, 2, 3. Analysis of Spectral Characteristics of Different Wetland Landscapes Based on EO-1 Hyperion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 524-529. |
|
|
|
|
