浏览全部资源
扫码关注微信
纸质出版日期： 2020-07-07 ，
扫 描 看 全 文
张子谦,赵天杰,施建成,李玉霖,冉有华,陈莹莹,赵少杰,王健,宁志英,杨红玲,韩丹.2020.青藏高原地区近地表冻融状态判别算法研究.遥感学报,24(7): 904-916
Zhang Z Q,Zhao T J,Shi J C,Li Y L,Ran Y H,Chen Y Y,Zhao S J,Wang J,Ning Z Y,Yang H L and Han D. 2020. Near-surface freeze/thaw state mapping over Tibetan Plateau. Journal of Remote Sensing(Chinese)，24(7): 904-916[DOI：10.11834/jrs.20209293]
青藏高原地区以其独特的气候水文特征被称为“亚洲水塔”
这一地区广泛分布的冻土及其冻融过程对地表非绝热加热与水文过程具有重要影响。然而
恶劣和复杂的地理环境为这一区域的地表冻融过程本地观测和遥感监测均带来极大挑战。本文利用AMSR-2传感器遥感数据开展青藏高原地区的近地表冻融判别算法研究
包括判别式算法和季节性阈值算法
并使用4个青藏高原典型地区的土壤温湿度密集观测网数据对算法进行区域适应性优化。研究特别针对季节性阈值算法进行了两点改进: 首先考虑到地表发射率的变化对于冻融相态的转变指示更为直接，故采用6.9 GHz水平(H)极化的准发射率替换季节性阈值算法中的原有冻结因子; 其次使用一种新的数据归一化方法：标准差归一化方法
用以替代原有的离差归一化方法
并通过阈值设定对判别精度的影响分析改进后的优势。结果证明
冻融判别式算法在升轨时期的整体精度最具优势
其优势在于能够减少夏季地表发射率复杂变化导致的误判
基于标准差归一化方法的季节性阈值算法在降轨时期的整体精度具有优势。通过对不同典型区域的冻融土辐射特征和判别精度的分析
发现地表发射率的变幅(初始液态含水量)大小是影响所有冻融判别算法精度的最关键因素。
The Tibetan Plateau (TP) area is recognized as the “Water Tower of Asia” owing to its significant climatic and hydrological characteristics. However
land surface freeze/thaw transition can hardly be detected in this area because of its harsh and complex geographical environment. This study intends to establish an algorithm to identify near-surface freeze/thaw state using the AMSR-2 satellite data
including discrimination function and seasonal threshold. The 6.925 GHz horizontal polarization Quasi-emissivity with high sensitivity to near-surface freeze/thaw cycle should replace the relative Frost Factor (FF
rel
). To minimize the effect of small-scale threshold selection
Min-Max normalization can be replaced with a new normalization method
namely
standard deviation normalization method. The parameterization of the discrimination function algorithm to the TP area is proposed to improve algorithm accuracy using four soil moisture and temperature dense observation network data.
Results reveal that the classification accuracy of the discriminant function algorithm exhibits the most advantage during ascend period. In addition
this algorithm reduces misclassification points due to complex changes of surface emissivity during summer. The seasonal threshold algorithm based on the standard deviation normalization method shows optimum performance during descend period. Moreover
the amplitude of surface emissivity (initial liquid water content) exhibits an important influence on the algorithm accuracy.
遥感青藏高原土壤冻融微波遥感AMSR-2判别式算法季节性阈值算法
remote sensingTibetan Plateausoil freeze/thawmicrowave remote sensingAMSR-2discriminant function algorithmseasonal threshold algorithm
Chai L N, Zhang L X, Zhang Y Y, Hao Z G, Jiang L M and Zhao S J . 2014. Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery. International Journal of Remote Sensing, 35(22): 7631-7649 [DOI: 10.1080/01431161.2014.975376http://dx.doi.org/10.1080/01431161.2014.975376 ]
Chen Y Y, Yang K, Qin J, Cui Q, Lu H, La Z, Han M L and Tang W J . 2017. Evaluation of SMAP, SMOS, and AMSR2 soil moisture retrievals against observations from two networks on the Tibetan Plateau. Journal of Geophysical Research, 122(11): 5780-5792 [DOI: 10.1002/2016jd026388http://dx.doi.org/10.1002/2016jd026388 ]
Derksen C, Xu X L, Dunbar R S, Colliander A, Kim Y, Kimball J S, Black T A, Euskirchen E, Langlois A, Loranty M M, Marsh P, Rautiainen K, Roy A, Royer A and Stephens J . 2017. Retrieving landscape freeze/thaw state from Soil Moisture Active Passive (SMAP) radar and radiometer measurements. Remote Sensing of Environment, 194: 48-62 [DOI: 10.1016/j.rse.2017.03.007http://dx.doi.org/10.1016/j.rse.2017.03.007 ]
England A W. 1974. The effect upon microwave emissivity of volume scattering in snow, in ice , and in frozen soil//Proceedings of URSI Conference on Emission and Scattering from the Earth. Berne, Switzerland: [s.n.]:273-287
Entekhabi D, Njoku E, O'Neill P, Spencer M, Jackson T, Entin J, Im E and Kellogg K . 2008. The Soil Moisture Active/Passive Mission (SMAP)//Proceedings of 20008 IEEE International Geoscience and Remote Sensing Symposium. Boston, MA, USA: IEEE [DOI: 10.1109/IGARSS.2008.4779267http://dx.doi.org/10.1109/IGARSS.2008.4779267 ]
Han M L, Yang K, Qin J, Jin R, Ma Y M, Wen J, Chen Y Y, Zhao L, Lazhu and Tang W J . 2015. An algorithm based on the standard deviation of passive microwave brightness temperatures for monitoring soil surface freeze/thaw state on the Tibetan plateau. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2775-2783 [DOI: 10.1109/tgrs.2014.2364823http://dx.doi.org/10.1109/tgrs.2014.2364823 ]
Hu T X, Zhao T J, Shi J C, Wu S L, Liu D, Qin H M and Zhao K G . 2017. High-resolution mapping of freeze/thaw status in china via fusion of MODIS and AMSR2 data. Remote Sensing, 9(12): 1339 [DOI: 10.3390/rs9121339http://dx.doi.org/10.3390/rs9121339 ]
Hu T X, Zhao T J, Zhao K G and Shi J C . 2019. A continuous global record of near-surface soil freeze/thaw status from AMSR-E and AMSR2 data. International Journal of Remote Sensing, 40(18): 6993-7016 [DOI: 10.1080/01431161.2019.1597307http://dx.doi.org/10.1080/01431161.2019.1597307 ]
Hu W X, Chai L N, Zhao S J and Zhao T J . 2017. Improvementon soil freeze/thaw discriminant algorithm under complex surface conditions in cold regions. Remote Sensing Technology and Application, 32(3): 395-405
胡文星, 柴琳娜, 赵少杰, 赵天杰 . 2017. 寒区复杂地表冻融状态判别式算法改进. 遥感技术与应用, 32(3): 395-405 [DOI: 10.11873/j.issn.1004-0323.2017.3.0395http://dx.doi.org/10.11873/j.issn.1004-0323.2017.3.0395 ]
Imaoka K, Kachi M, Fujii H, Murakami H, Hori M, Ono A, Igarashi T, Nakagawa K, Oki T, Honda Y and Shimoda H . 2010. Global Change Observation Mission (GCOM) for monitoring carbon, water cycles, and climate change. Proceedings of the IEEE, 98(5): 717-734 [DOI: 10.1109/jproc.2009.2036869http://dx.doi.org/10.1109/jproc.2009.2036869 ]
Jin R, Li X and Che T . 2009. A decision tree algorithm for surface soil freeze/thaw classification over China using SSM/I brightness temperature. Remote Sensing of Environment, 113(12): 2651-2660 [DOI: 10.1016/j.rse.2009.08.003http://dx.doi.org/10.1016/j.rse.2009.08.003 ]
Judge J, Galantowicz J F, England A W and Dahl P . 1996. Freeze/thaw classification for prairie soils using SSM/I radiobrightnesses. IEEE Transactions on Geoscience and Remote Sensing, 35(4): 827-832 [DOI: 10.1109/36.602525http://dx.doi.org/10.1109/36.602525 ]
Kerr Y H, Waldteufel P, Wigneron J P, Martinuzzi J, Font J and Berger M . 2001. Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission. IEEE transactions on Geoscience and remote sensing, 39(8): 1729-1735 [DOI: 10.1109/36.942551http://dx.doi.org/10.1109/36.942551 ]
Kim Y, Kimball J S, Glassy J and Du J Y . 2017. An extended global Earth system data record on daily landscape freeze–thaw status determined from satellite passive microwave remote sensing. Earth System Science Data, 9(1): 133-147 [DOI: 10.5194/essd-9-133-2017http://dx.doi.org/10.5194/essd-9-133-2017 ]
Kou X K, Jiang L M, Yan S, Zhao T J, Lu H and Cui H Z . 2017. Detection of land surface freeze-thaw status on the Tibetan Plateau using passive microwave and thermal infrared remote sensing data. Remote Sensing of Environment, 199: 291-301 [DOI: 10.1016/j.rse.2017.06.035http://dx.doi.org/10.1016/j.rse.2017.06.035 ]
Mao K B, Shi J C, Li Z L, Qin Z H and Jia Y Y . 2005. The land surface temperature and emissivity retrieved from the AMSR passive microwave data. Remote Sensing For Land and Resources, 17(3): 14-17
毛克彪, 施建成, 李召良, 覃志豪, 贾媛媛 . 2005. 用被动微波AMSR数据反演地表温度及发射率的方法研究. 国土资源遥感, 17(3): 14-17 [DOI: 10.6046/gtzyyg.2005.03.04http://dx.doi.org/10.6046/gtzyyg.2005.03.04 ]
Mika S, Ratsch G, Weston J, Scholkopf B and Mullers K R 1999. Fisher discriminant analysis with kernels//Proceedings of the 1999 IEEE Signal Processing Society Workshop Neural Networks for Signal Processing IX. Madison, WI, USA: IEEE: 41-48 [DOI: 10.1109/NNSP.1999.788121http://dx.doi.org/10.1109/NNSP.1999.788121 ]
Prince M, Roy A, Brucker L, Royer A, Kim Y and Zhao T J . 2018. Northern Hemisphere surface freeze–thaw product from Aquarius L-band radiometers. Earth System Science Data, 10(4): 2055-2067 [DOI: 10.5194/essd-10-2055-2018http://dx.doi.org/10.5194/essd-10-2055-2018 ]
Shi J C, Du Y, Du J Y, Jiang L M, Chai L N, Mao K B, Xu P, Ni W J, Xiong C, Liu Q, Liu C Z, Guo P, Cui Q, Li Y Q, Chen J, Wang A Q, Luo H J and Wang Y H . 2012. Progresses on microwave remote sensing of land surface parameters. Science China Earth Sciences, 55(7): 1052-1078
施建成, 杜阳, 杜今阳, 蒋玲梅, 柴琳娜, 毛克彪, 徐鹏, 倪文俭, 熊川, 刘强, 刘晨洲, 郭鹏, 崔倩, 李云青, 陈晶, 王安琪, 罗禾佳, 王殷辉 . 2012. 微波遥感地表参数反演进展. 中国科学: 地球科学, 42(6): 814-842 [DOI: 10.1007/s11430-012-4444-xhttp://dx.doi.org/10.1007/s11430-012-4444-x ]
Su Z, Wen J, Dente L, van der Velde R, Wang L, Ma Y, Yang K and Hu Z . 2011. The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products. Hydrology and Earth System Sciences, 15(7): 2303-2316 [DOI: 10.5194/hess-15-2303-2011http://dx.doi.org/10.5194/hess-15-2303-2011 ]
Tang Y, Zhang W J, Liu L and Li G C . 2019. Spring thaw classification based on AMSR-E brightness temperature in the central Tibetan Plateau. International Journal of Remote Sensing, 40(17): 6542-6552 [DOI: 10.1080/2150704x.2018.1554275http://dx.doi.org/10.1080/2150704x.2018.1554275 ]
Wang J, Jiang L M, Kou X K, Cui H Z and Yang J W . 2019. Downscaling method for near-surface freeze/thaw state monitoring in Genhe area of China. Journal of Remote Sensing, 23(6): 1209-1222
王健, 蒋玲梅, 寇晓康, 崔慧珍, 杨建卫 . 2019. 根河地区冻融监测和降尺度算法的验证分析. 遥感学报, 23(6): 1209-1222 [DOI: 10.11834/jrs.20198097http://dx.doi.org/10.11834/jrs.20198097 ]
Wang P K, Zhao T J, Shi J C, Hu T X, Roy A, Qiu Y B and Lu H . 2019. Parameterization of the freeze/thaw discriminant function algorithm using dense in-situobservation network data. International Journal of Digital Earth, 12: 980-994 [DOI: 10.1080/17538947.2018.1452300http://dx.doi.org/10.1080/17538947.2018.1452300 ]
Yang K, Qin J, Zhao L, Chen Y Y, Tang W J, Han M L, Lazhu, Chen Z Q, Lv N, Ding B H, Wu H and Lin C G . 2013. A multiscale soil moisture and freeze–thaw monitoring network on the third pole. Bulletin of the American Meteorological Society, 94(12): 1907-1916 [DOI: 10.1175/bams-d-12-00203.1http://dx.doi.org/10.1175/bams-d-12-00203.1 ]
Ye Q Y, Chai L N, Jiang L M and Zhao T J . 2014. A disaggregation approach for soil phase transition water content using AMSR2 and MODIS products. Journal of Remote Sensing, 18(6): 1147-1157
叶勤玉, 柴琳娜, 蒋玲梅, 赵天杰 . 2014. 利用AMSR2和MODIS数据的土壤冻融相变水量降尺度方法. 遥感学报, 18(6): 1147-1157 [DOI: 10.11834/jrs.20144093http://dx.doi.org/10.11834/jrs.20144093 ]
Zhang L X, Jiang L M, Chai L N, Zhao S J, Zhao T J and Li X X . 2011. Research advances in passive microwave remote sensing of freeze-thaw processes over complex landscapes. Advances in Earth Sciences, 26(10): 1023-1029
张立新, 蒋玲梅, 柴琳娜, 赵少杰, 赵天杰, 李欣欣 . 2011. 地表冻融过程被动微波遥感机理研究进展. 地球科学进展, 26(10): 1023-1029 [DOI: 10.11867/j.issn.1001-8166.2011.10.1023http://dx.doi.org/10.11867/j.issn.1001-8166.2011.10.1023 ]
Zhang L X, Zhao T J, Jiang L M and Zhao S J . 2010. Estimate of phase transition water content in freeze–thaw process using microwave radiometer. IEEE Transactions on Geoscience and Remote Sensing, 48(12): 4248-4255 [DOI: 10.1109/tgrs.2010.2051158http://dx.doi.org/10.1109/tgrs.2010.2051158 ]
Zhao T J, Shi J C, Hu T X, Zhao L, Zou D F, Wang T X, Ji D B, Li R and Wang P K . 2017. Estimation of high-resolution near-surface freeze/thaw state by the integration of microwave and thermal infrared remote sensing data on the Tibetan Plateau. Earth and Space Science, 4(8): 472-484 [DOI: 10.1002/2017ea000277http://dx.doi.org/10.1002/2017ea000277 ]
Zhao T J, Zhang L X, Jiang L M, Zhao S J, Chai L N and Jin R . 2011. A new soil freeze/thaw discriminant algorithm using AMSR-E passive microwave imagery. Hydrological Processes, 25(11): 1704-1716 [DOI: 10.1002/hyp.7930http://dx.doi.org/10.1002/hyp.7930 ]
Zuemdorfer B, England A W and Ulaby F T . 1990. An optimized approach to mapping freezing terrain with SMMR data//Proceedings of the 10th Annual International Symposium on Geoscience and Remote Sensing. College Park, Maryland, USA: IEEE [DOI: 10.1109/IGARSS.1990.688700http://dx.doi.org/10.1109/IGARSS.1990.688700 ]
相关作者
相关机构
京公网安备11010802024621