| 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] |
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. |
| [2] |
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. |
| [3] |
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. |
| [4] |
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. |
| [5] |
XIA Tian1*, YANG Ke-ming2, FENG Fei-sheng3, GUO Hui4, ZHANG Chao2. A New Copper Stress Vegetation Index NCSVI Explores the Sensitive Range of Corn Leaves Spectral Under Copper Pollution[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2604-2610. |
| [6] |
WANG Xin-hui1, 2, GONG Cai-lan1, 2*, HU Yong1, 2, LI Lan1, 2, HE Zhi-jie1, 2. Spectral Feature Construction and Sensitivity Analysis of Water Quality Parameters Remote Sensing Inversion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1880-1885. |
| [7] |
KANG Xiao-yan1,2, ZHANG Ai-wu1,2*, PANG Hai-yang1,2. Estimation of Grassland Aboveground Biomass From UAV-Mounted Hyperspectral Image by Optimized Spectral Reconstruction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 250-256. |
| [8] |
CHEN Yan-long1,2, WANG Xiao-lan3, LI En3, SONG Mei-ping3, BAO Hai-mo4*. Research and Application of Band Selection Method Based on CEM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3778-3783. |
| [9] |
WANG Qiang-hui1, HUA Wen-shen1*, HUANG Fu-yu1, ZHANG Yan1, YAN Yang2. Hyperspectral Anomaly Detection Based on Approximate Posterior Information[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(08): 2538-2545. |
| [10] |
LIN Yi1, LIU Si-yuan1, YAN Lei1, FENG Hai-kuan2, ZHAO Shuai-yang1, ZHAO Hong-ying1*. Improvement of Hyperspectral Estimation of Nitrogen Content in Winter Wheat by Leaf Surface Polarized Reflection Measurement[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1956-1964. |
| [11] |
CHEN Yao1, 2, HUANG Chang-ping1*, ZHANG Li-fu1, QIAO Na1, 2. Spectral Characteristics Analysis and Remote Sensing Retrieval of COD Concentration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 824-830. |
| [12] |
CHEN Xiu-qing, YANG Qi, HAN Jing-ye, LIN Lin, SHI Liang-sheng*. Estimation of Winter Wheat Leaf Water Content Based on Leaf and Canopy Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 891-897. |
| [13] |
CHEN Wen-qian1, 2, DING Jian-li1, 2*, WANG Xin3, PU Wei3, ZHANG Zhe1, 2, SHI Teng-long3. Simulation of Spectral Albedo Mixing of Snow and Aerosol Particles[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(02): 446-453. |
| [14] |
YUAN Zi-ran, WEI Li-fei*, ZHANG Yang-xi, YU Ming, YAN Xin-ru. Hyperspectral Inversion and Analysis of Heavy Metal Arsenic Content in Farmland Soil Based on Optimizing CARS Combined with PSO-SVM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(02): 567-573. |
| [15] |
YE Fa-wang, WANG Jian-gang*, QIU Jun-ting, ZHANG Chuan. A Geological Application Oriented Comparison Research on Different Atmospheric Correction Methods for Airborne CASI-SASI Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2677-2685. |
|
|
|
|
