Please wait a minute...
img

官方微信

遥感技术与应用  2014, Vol. 29 Issue (6): 993-1000    DOI: 10.11873/j.issn.1004-0323.2014.6.0993
模型与反演     
一种融合遥感和地面观测资料的雪深空间插值方法
王宏伟1,2,3,黄春林1,3,侯金亮1,2,3
(1.中国科学院寒区旱区环境与工程研究所,甘肃 兰州730000;2.中国科学院大学,北京100049;
3.中国科学院寒区旱区环境与工程研究所黑河遥感试验研究站,甘肃 兰州730000)
A Novel Spatial Interpolation Method for Snow Depth by Integrating Satellite and Ground Observations
Wang Hongwei1,2,Huang Chunlin1,Hou Jinliang1
(1.Cold and Arid Regions Environmental and Engineering Research Institute,
Chinese Academy of Sciences,Lanzhou 730000,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China)
 全文: PDF(6390 KB)  
摘要:

针对积雪观测站点稀少的问题,提出一种考虑海拔影响,能够融合MODIS积雪面积产品和站点观测的雪深空间插值方法,该方法利用去云后MODIS积雪面积产品构建的无积雪“虚拟站点”弥补站点分布不均匀和稀少的不足,利用泛协克里金插值方法考虑海拔对雪深的影响。利用北疆地区50个气象站点的逐日雪深观测资料、逐日MODIS积雪面积产品和AMSR-E被动微波雪水当量和雪深产品,对普通克里金、泛克里金、普通协克里金和泛协克里金插值结果进行了比较研究。研究结果表明:积雪覆盖范围较大时,站点雪深与海拔之间相关系数较大,利用泛协克里金插值结果精度高且稳定;否则利用普通克里金插值精度较高且稳定。通过增加“虚拟站点”,能够提高雪深插值精度,并在一定程度上修正了克里金插值中存在的平滑效应。

关键词: 雪深遥感克里金空间插值MODIS    
Abstract:

For the problem of rare snow observation stations,this paper proposed a new scheme to produce spatial distribution of snow depth on based on different kriging interpolation methods,which can not only consider elevation effects,but also fuse MODIS snow cover products.This scheme uses snow\|free pixels in the cloud\|removed MODIS snow cover image to build virtual stations with zeros snow depth to compensate for the scarcity and uneven distribution of stations.Additionally,the universal co\|kriging interpolation method is used to consider the impact of elevation on snow depth.The daily snow depth observations at 50 meteorological stations in northern Xinjing province are chosen to evaluate the proposed scheme.Four types of kriging methods are also compared such as ordinary kriging,universal kriging,ordinary co\|kriging and universal co-kriging.Results show that universal co\|kriging can achieve the best performance with a larger snow cover area and a bigger value of correlation coefficient between snow depth and elevation.Otherwise,the best performance is achieved by the ordinary kriging.The added virtual stations can improve the accuracy of interpolation and reduce smoothing effect in kriging interpolation.

Key words: Snow depth    Remote sensing    Kriging    Spatial interpolation    MODIS
收稿日期: 2013-11-17 出版日期: 2015-01-15
:  TP 79  
基金资助:

国家自然科学基金项目(41271358)和中国科学院“百人计划”项目 (29Y127D01)资助。

通讯作者: 黄春林(1979-),男,宁夏青铜峡人,研究员,主要从事陆面数据同化研究。Email:huangcl@lzb.ac.cn。   
作者简介: 王宏伟(1986-),男,甘肃定西人,硕士研究生,主要从事定量遥感、陆面数据同化研究。Email:wanghw_ucas@163.com。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
王宏伟
黄春林
侯金亮

引用本文:

王宏伟,黄春林,侯金亮. 一种融合遥感和地面观测资料的雪深空间插值方法[J]. 遥感技术与应用, 2014, 29(6): 993-1000.

Wang Hongwei,Huang Chunlin,Hou Jinliang. A Novel Spatial Interpolation Method for Snow Depth by Integrating Satellite and Ground Observations. Remote Sensing Technology and Application, 2014, 29(6): 993-1000.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2014.6.0993        http://www.rsta.ac.cn/CN/Y2014/V29/I6/993

[1]Foster J L,Sun C,Walker J P,et al.Quantifying the Uncertainty in Passive Microwave Snow Water Equivalent Observations[J].Remote Sensing of Environment,2005,94(2):187-203.

[2]Qin Dahe,Xiao Cunde,Ding Yongjian,et al.Progress on Cryospheric Studies by International and Chinese Communities and Perspectives[J].Journal of Applied Meteorological Science,2006,17(6):649-656.[秦大河,效存德,丁永建,等.国际冰冻圈研究动态和我国冰冻圈研究的现状与展望[J].应用气象学报,2006,17(6):649-656.]

[3]Huang Xiaodong,Hao Xiaohua,Wang Wei,et al.Algorithms for Cloud Removal in MODIS Daily Snow Products[J].Journal of Glaciology and Geocryology,2012,34(5):1118-1126.[黄晓东,郝晓华,王玮,等.MODIS 逐日积雪产品去云算法研究[J].冰川冻土,2012,34(5):1118-1126.]

[4]Cao Meisheng,Li Xin,Chen Xianzhang,et al.Remote Sensing of Cryosphere[M].Beijing:Science Press,2006.[曹梅盛,李新,陈贤章,等.冰冻圈遥感[M].北京:科学出版社,2006.][5]He Yongqi,Huang Xiaodong,Fang Jin,et al.Snow Cover Mapping Algorithm based on HJ-1B Satellite Data[J].Journal of Glaciology and Geocryology,2013,35(1):65-73.[何咏琪,黄晓东,方金,等.基于HJ-1B卫星数据的积雪面积制图算法研究[J].冰川冻土,2013,35(1):65-73.]

[6]Wang X,Xie H,Liang T,et al.Comparison and Validation of MODIS Standard and New Combination of Terra and Aqua Snow Cover Products in Northern Xinjiang,China[J].Hydrological Processes,2009,23(3):419-429.

[7]Hall D K,Riggs G A,Foster J L,et al.Development and Evaluation of a Cloud-gap-filled MODIS Daily Snow-cover Product [J].Remote Sensing of Environment,2010,114(3):496-503.

[8]Liang T,Zhang X,Xie H,et al.Toward Improved Daily Snow Cover Mapping with Advanced Combination of MODIS and AMSR-E Measurements[J].Remote Sensing of Environment,2008,112(10):3750-3761.

[9]Tang Zhiguang,Wang Jian,Li Hongyi,et al.Accuracy Validation and Cloud Obscuration Removal of MODIS Fractional Snow Cover Products over Tibetan Plateau[J].Remote Sensing Technology and Application,2013,28(3):423-430.[唐志光,王建,李弘毅,等.青藏高原 MODIS 积雪面积比例产品的精度验证与去云研究[J].遥感技术与应用,2013,28(3):423-430.]

[10]Salomonson V V,Appel I.Estimating Fractional Snow Cover from MODIS Using the Normalized Difference Snow Index[J].Remote Sensing of Environment,2004,89(3):351-360.

[11]Li Xin,Che Tao.A Review on Passive Microwave Remote Sensing of Snow Cover[J].Journal of Glaciology and Geocryology,2007,29(3):487-496.[李新,车涛.积雪被动微波遥感研究进展[J].冰川冻土,2007,29(3):487-496.]

[12]Erxleben J,Elder K,Davis R.Comparison of Spatial Interpolation Methods for Estimating Snow Distribution in the Colorado Rocky Mountains[J].Hydrological Processes,2002,16(18):3627-3649.

[13]Che Tao,Li Xin,Gao Feng.Estimation of Snow Water Equivation in the Tibetan Plateau Using Passive Microwave Remote Sensing Data(SSM/I)[J].Journal of Glaciology and Geocryology,2004,26(3):363-368.[车涛,李新,高峰.青藏高原积雪深度和雪水当量的被动微波遥感反演[J].冰川冻土,2004,26(3):363-368.]

[14]Feng Qisheng,Zhang Xuetong,Liang Tiangang.Dynamic Monitoring of Snow Cover based on MOD10A1 and AMSR-E in the North of Xinjiang Province,China[J].Acta Prataculturae Sinica,2009,18(1):125-133.[冯琦胜,张学通,梁天刚.基于MOD10A1和AMSR-E的北疆牧区积雪动态监测研究[J].草业学报,2009,18(1):125-133.]

[15]Li Xin,Cheng Guodong,Lu Ling.Comparison of Spatial Intertolation Methods[J].Advance in Earth Sciences,2000,15(3):260-265.[李新,程国栋,卢玲.空间内插方法比较[J].地球科学进展,2000,15(3):260-265.]

[16]Tang Guodong,Ke Changqing.Comparison of Spatial Interpolation Methods of Snow Depth in the West of China[J].Remote Sensing Technology and Application,2007,22(1):39-44.[唐国栋,柯长青.中国西部地区积雪深度的空间插值比较[J].遥感技术与应用,2007,22(1):39-44.]

[17]Feng Xuezhi,Bo Yanchen,Shi Zhengtao,et al.Snow Depth in North Xinjiang Region Estimated by Kriging Interpolation[J].Journal of Glaciology and Geocryology,2000,22(4):358-361.[冯学智,柏延臣,史正涛,等.北疆地区积雪深度的克里格内插估计[J].冰川冻土,2000,22(4):358-361.]

[18]Liu Yan,Ruan Huihua,Zhang Pu,et al.Kriging Interpolation of Snow Depth at the North of Tianshan Mountains Assisted by MODIS Data[J].Geomatics and Information Science of Wuhan University,2012,37(4):403-405.[刘艳,阮惠华,张璞,等.利用MODIS数据研究天山北麓Kriging雪深插值[J].武汉大学学报(信息科学版),2012,37(4):403-405.]

[19]Yang Yuting,Shang Songhao,Li Chao.Correcting in Smoothing Effect of Ordinary Kriging Estimates in Soil Moisture Interpolation[J].Advances in Water Science,2010,21(2):208-213.[杨雨亭,尚松浩,李超.土壤水分空间插值的克里金平滑效应修正方法[J].水科学进展,2010,21(2):208-213.]

[20]Yamamoto J K.On Unbiased Backtransform of Lognormal Kriging Estimates[J].Computational Geosciences,2007,11(3):219-234.

[21]Yamamoto J K.An Alternative Measure of the Reliability of Ordinary Kriging Estimates[J].Mathematical Geology,2000,32(4):489-509.

[22]Yamamoto J K.Correcting the Dmooth Rffect of Ordinary Kriging Estimates[J].Mathematical Geology,2005,37(11):69-94.

[23]Feng Yiming.Spatial Statistics Theory and Its Application in Forestry[M].Beijing:China Forsetry Publishing House,2008.[冯益明.空间统计学理论及其在林业中的应用[M].北京:中国林业出版社,2008.]

[24]Wang Zhengquan.Geostatistics and Applications in Ecology[M].Beijing:Science Press,1999.[王政权.地统计学及在生态学中的应用[M].北京:科学出版社,1999.]

[25]Goovaerts P.Geostatistics for Natural Resources Evaluation[M].New York:Oxford University Press,1997.

[26]Yang Gongliu,Zhang Guimin,Li Shixin.Application of Universal Kriging Interpolation in Geomagnetic Map[J].Journal of Chinese Inertial Technology,2008,16(2):162-166.[杨功流,张桂敏,李士心.泛克里金插值法在地磁图中的应用[J].中国惯性技术学报,2008,16(2):162-166.]

[27]Han Yan,Tao Fengmei,Xia Lixian.Matrix Analysis of Universal Co-kriging[J].Journal of Biomathematics,2001,16(3):374-378.[韩燕,陶凤梅,夏立显.泛协克里格方法的矩阵分析[J].生物数学学报,2001,16(3):374-378.]

[28]Kuhlman K L,Pardo I E.Universal Cokriging of Hydraulic Heads Accounting for Boundary Conditions[J].Journal of Hydrology,2010,384(1):14-25.

[29]Che Tao,Li Xin.Retrieval of Snow Depth in China by Passive Microwave Remote Sensing Data and Its Accuracy Assessment[J].Remote Sensing Technology and Application,2004,19(5):301- 306.[车涛,李新.利用被动微波遥感数据反演我国积雪深度及其精度评价[J].遥感技术与应用,2004,19(5):301-306.]

[30]Wang Jinfeng,Liao Yilan.Liu Xin.Spatial Data Analysis Tutorial[M].Beijing:Science Press,2010.[王劲峰,廖一兰,刘鑫.空间数据分析教程[M].北京:科学出版社,2010.]


 

[1] 王卷乐, 程凯, 边玲玲, 韩雪华, 王明明. 面向SDGs和美丽中国评价的地球大数据集成框架与关键技术[J]. 遥感技术与应用, 2018, 33(5): 775-783.
[2] 王恺宁,王修信,黄凤荣,罗涟玲. 喀斯特城市地表温度遥感反演算法比较[J]. 遥感技术与应用, 2018, 33(5): 803-810.
[3] 金点点,宫兆宁. 基于Landsat 系列数据地表温度反演算法对比分析—以齐齐哈尔市辖区为例[J]. 遥感技术与应用, 2018, 33(5): 830-841.
[4] 张晓峰,吕晓琪,张信雪,张继凯,王月明,谷宇,樊宇. 多时刻海色遥感数据融合及其可视化[J]. 遥感技术与应用, 2018, 33(5): 873-880.
[5] 冯姣姣,王维真,李净,刘雯雯. 基于BP神经网络的华东地区太阳辐射模拟及时空变化分析[J]. 遥感技术与应用, 2018, 33(5): 881-889.
[6] 谢旭,陈芸芝. 基于PSO-RBF神经网络模型反演闽江下游水体悬浮物浓度[J]. 遥感技术与应用, 2018, 33(5): 900-907.
[7] 迟文峰,匡文慧,贾静,刘正佳. 京津风沙源治理工程区LUCC及土壤风蚀强度动态遥感监测研究[J]. 遥感技术与应用, 2018, 33(5): 965-974.
[8] 胡云锋,商令杰,张千力,王召海. 基于GEE平台的1990年以来北京市土地变化格局及驱动机制分析[J]. 遥感技术与应用, 2018, 33(4): 573-583.
[9] 李晨伟,张瑞丝,张竹桐,曾敏 . 基于多源遥感数据的构造解译与分析—以西藏察隅吉太曲流域为例[J]. 遥感技术与应用, 2018, 33(4): 657-665.
[10] 李生生,王广军,梁四海,彭红明,董高峰,罗银飞. 基于Landsat-8 OLI数据的青海湖水体边界自动提取[J]. 遥感技术与应用, 2018, 33(4): 666-675.
[11] 汪航,师茁. 基于MODIS时间序列数据的春尺蠖虫害遥感监测方法研究—以新疆巴楚胡杨为例[J]. 遥感技术与应用, 2018, 33(4): 686-695.
[12] 廖凯涛,齐述华,王成,王点. 结合GLAS和TM卫星数据的江西省森林高度和生物量制图[J]. 遥感技术与应用, 2018, 33(4): 713-720.
[13] 张震,刘时银,魏俊锋,蒋宗立. 1974~2012年珠穆朗玛峰地区冰川物质平衡遥感监测研究[J]. 遥感技术与应用, 2018, 33(4): 731-740.
[14] 王琳,徐涵秋,李胜. 重钢重工业区迁移对区域生态的影响研究[J]. 遥感技术与应用, 2018, 33(3): 387-397.
[15] 任浙豪,周坚华. 增大特征空间复杂度的方法——以城镇下垫面遥感分类为[J]. 遥感技术与应用, 2018, 33(3): 408-417.