基于FY-3D/MERSI-Ⅱ的积雪面积比例提取算法
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摘要
风云三号D星(FY-3D)是我国新一代极轨气象卫星。中分辨率光谱成像仪(MERSI-Ⅱ)是其携带的核心传感器之一,FY-3D对于全球数值天气预报、大气定量探测以及气候变化监测等具有重要意义。积雪面积比例产品是众多陆面产品之一,是水文模型和区域气候模型的主要输入参数。基于MERSI-Ⅱ数据发展了业务化提取积雪面积比例的算法,算法核心是混合像元分解。空间光谱端元提取(SSEE)的方法自动提取端元,全约束最小二乘法(FCLS)求解线性混合模型。解混结果叠合云掩膜得到FY-3D/MERSI-Ⅱ积雪面积比例数据(FY-FSC)。以Landsat 8的积雪面积比例数据(L-FSC)作为参考值对FY-FSC进行验证,同时将FY-FSC和MODIS积雪面积比例数据(M-FSC)进行比较。结果表明:FY-FSC的总体相关系数(R)为0.54,均方根误差(RMSE)为0.17,绝对平均误差(AME)为0.10;M-FSC总体R为0.41,RMSE为0.26,AME为0.29;利用积雪面积提取的精度评价因子K比较FY-FSC和M-FSC获取的总积雪面积的精度。结果表明:FYFSC和M-FSC数据的平均K值分别为88.51%和86.78%,FY-FSC精度高于M-FSC。FY-FSC将作为试验参数纳入FY-3D/MERSI-Ⅱ积雪覆盖业务产品中,可填补国产卫星业务化反演积雪面积比例参数的空白。
FY-3Dis a new generation of polar orbiting meteorological satellites in China.The Medium Resolution Spectral Imager(MERSI-Ⅱ)is one of the core sensors it carries.It is of great significance for global numerical weather prediction,atmospheric quantitative detection,and climate change monitoring.The snow area ratio product is one of many land surface products and is the main input parameter for hydrological models and regional climate models.based on MERSI-Ⅱ data,this paper develops an algorithm for extracting the proportion of snow cover area.The core of the algorithm is mixed pixel decomposition.The Spatial Spectral Endmember Extraction(SSEE)algorithm automatically extracts the endmembers,and the Fully Constrained Least Squares(FCLS)solves the linear mixed model.The unmixed results were superimposed on the cloud mask to obtain FY-3D/MERSI-Ⅱ snow area ratio data(FY-FSC).The FY-FSC was verified by using the Landsat 8snow area ratio data(L-FSC)as a reference value,and the FY-FSC and MODIS snow area ratio data(M-FSC)were compared.The results show that the overall root mean square error(RMSE)of FY-FSC is 0.17,the correlation coefficient(R)is 0.54,the Absolute Mean Error(AME)is0.10,the overall R of M-FSC is 0.41,RMSE is 0.26,and AME is 0.29.Using the accuracy evaluation factor K of the snow area extraction to compare the accuracy of the total snow area obtained by FY-FSC and M-FSC.The results show that the average K values of FY-FSC and M-FSC data are 88.51% and 86.78%,respectively,and the accuracy of FY-FSC is higher than that of M-FSC.FY-FSC will be included as a test parameter in the FY-3D/MERSI-Ⅱsnow cover business product,which can fill the blank of the domestic satellite operational inversion sub-pixel snow parameters.
FY-3Dis a new generation of polar orbiting meteorological satellites in China.The Medium Resolution Spectral Imager(MERSI-Ⅱ)is one of the core sensors it carries.It is of great significance for global numerical weather prediction,atmospheric quantitative detection,and climate change monitoring.The snow area ratio product is one of many land surface products and is the main input parameter for hydrological models and regional climate models.based on MERSI-Ⅱ data,this paper develops an algorithm for extracting the proportion of snow cover area.The core of the algorithm is mixed pixel decomposition.The Spatial Spectral Endmember Extraction(SSEE)algorithm automatically extracts the endmembers,and the Fully Constrained Least Squares(FCLS)solves the linear mixed model.The unmixed results were superimposed on the cloud mask to obtain FY-3D/MERSI-Ⅱ snow area ratio data(FY-FSC).The FY-FSC was verified by using the Landsat 8snow area ratio data(L-FSC)as a reference value,and the FY-FSC and MODIS snow area ratio data(M-FSC)were compared.The results show that the overall root mean square error(RMSE)of FY-FSC is 0.17,the correlation coefficient(R)is 0.54,the Absolute Mean Error(AME)is0.10,the overall R of M-FSC is 0.41,RMSE is 0.26,and AME is 0.29.Using the accuracy evaluation factor K of the snow area extraction to compare the accuracy of the total snow area obtained by FY-FSC and M-FSC.The results show that the average K values of FY-FSC and M-FSC data are 88.51% and 86.78%,respectively,and the accuracy of FY-FSC is higher than that of M-FSC.FY-FSC will be included as a test parameter in the FY-3D/MERSI-Ⅱsnow cover business product,which can fill the blank of the domestic satellite operational inversion sub-pixel snow parameters.
引文
[1] King M D.EOS Science Plan:The State of Science in the EOS Program[M].Washington,D.C.:National Aeronautics and Space Administration,1999.
[2] Kirnbauer R,Bloschl G,Gutknecht D.Entering the Era of Distributed Snow Models[J].Nordic Hydrology,1994,25(1-2):1-24.
[3] Cline D W,Bales R C,Dozier J.Estimating the Spatial Distribution of Snow in Mountain Basins Using Remote Sensing and Energy Balance Modeling[J].Water Resources Research,1998,34(5):1275-1285.
[4] Dozier J,Painter T H.Multispectral and Hyperspectral Remote Sensing of Alpine Snow Properties[J].Annual Review of Earth and Planetary Sciences,2004,32:465-494.
[5] Hall D K,Benson C S,Field W O.Changes of Glaciers in Glacier Bay,Alaska,Using Ground and Satellite Measurements[J].Physical Geography,1995,16(1):27-41.
[6] Wang J,Li H X,Hao X H,et al.Remote Sensing for Snow Hydrology in China:Challenges and Perspectives[J].Journal of Applied Remote Sensing,2014,8:1-25.
[7] Luce C H,Tarboton D G,Cooley R R.The Influence of the Spatial Distribution of Snow on Basin-averaged Snowmelt[J].Hydrological Processes,1998,12(10-11):1671-1683.
[8] Bloschl G,Kirnbauer R,Gutknecht D.A Spatially Distributed Snowmelt Model for Application in Alpine Terrain[J].Snow,Hydrology and Forests in High Alpine Areas,1991,205:51-60.
[9] Luce C H,Tarboton D G,Cooley K R.Sub-grid Parameterization of Snow Distribution for an Energy and Mass Balance Snow Cover Model[J].Hydrological Processes,1999,13(12-13):1921-1933.
[10] Roesch A,Wild M,Gilgen H,et al.ANew Snow Cover Fraction Parametrization for the ECHAM4GCM[J].Climate Dynamics,2001,17(12):933-946.
[11] Li Hongyi,Wangjian.Key Research Topics and Their Advance on Modeling Snow Hydrological Process[J].Journal of Glaciology and Geocryology,2013,35(2):430-437.[李弘毅,王建.积雪水文模拟中的关键问题及其研究进展[J].冰川冻土,2013,35(2):430-437.]
[12] Hall D H,Riggs.In:National Snow and Ice Data Center(NSIDC)(Ed.),MODIS/Terra Snow Cover Daily L3Global500m Grid,Version 6.NASA,Boulder,Colorado USA[J].Quaternary Science Reviews,2016,32(1):6-15.
[13] Salomonson V V,Appel I.Development of the Aqua MODIS NDSIFractional Snow Cover Algorithm and Validation Results[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(7):1747-1756.
[14] 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.
[15] Painter T H,Rittger K,Mckenzie C,et al.Retrieval of Subpixel Snow Covered Area,Grain Size,and Albedo from MODIS[J].Remote Sensing of Environment,2009,113(4):868-879.
[16] Shi J.AnAutomatic Algorithm on Estimating Sub-Pixel Snow Cover from MODIS[J].Quaternary Science Reviews,2012,32(1):6-15.
[17] Vikhamar D,Solberg R.Snow-cover Mapping in Forests by Constrained Linear Spectral Unmixing of MODIS Data[J].Remote Sensing of Environment,2003,88(3):309-323.
[18] Zhu J,Shi J C,Wang Y H.Subpixel Snow Mapping of the Qinghai-Tibet Plateau Using MODIS Data[J].International Journal of Applied Earth Observation and Geoinformation,2012,18:251-262.
[19] Dobreva I D,Klein A G.Fractional Snow Cover Mapping through Artificial Neural Network Analysis of MODIS Surface Reflectance[J].Remote Sensing of Environment,2011,115(12):3355-3366.
[20] Metsamaki S,Mattila O P,Pulliainen J,et al.An Optical Reflectance Model-based Method for Fractional Snow Cover Mapping Applicable to Continental Scale[J].Remote Sensing of Environment,2012,123:508-521.
[21] Bioucas-Dias J M,Plaza A.An Overview on Hyperspectral Unmixing:Geometrical,Statistical,and Sparse Regression based Approaches[J].2011IEEE International Geoscience and Remote Sensing Symposium(Igarss),2011:1135-1138.
[22] Sharma S,Sharma S,Buddhiraju K M.Sub-pixel Mapping of Hyperspectral Imagery Using Super-resoloution[J].Multispectral,Hyperspectral,and Ultraspectral Remote Sensing Technology,Techniques and Applications Vi,2016,(6):9880.
[23] Marsh S E,Switzer P,Kowalik W S.Resolving the Percentage of Component Terrains within Single Resolution Elements[J].Photogrammetric Engineering and Remote Sensing,1980,46(8):1079-1086.
[24] Veganzones M A,Grana M.Endmember extraction methods:A short review[J].Knowledge-based Intelligent Information and Engineering Systems,Pt 3,Proceedings,2008,5179:400-407.
[25] Bioucas-Dias J M.A Variable Splitting Augmented Lagrangian Approach to Linear Spectral Unmixing[C]∥2009 First Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing,2009:1-4.
[26] Plaza A,Chang C I.FastImplementation of Pixel Purity Index Algorithm[J].Algorithms and Technologies for Multispectral,Hyperspectral,and Ultraspectral Imagery XI,2005,5806:307-317.
[27] Winter M E.N-FINDR:An Algorithm for Fast Autonomous Spectral End-Member Determination in Hyperspectral Data[J].Imaging Spectrometry V,1999,3753:266-275.
[28] Chang C I,Wu C C,Liu W M,et al.A New Growing Method for Simplex-based Endmember Extraction Algorithm[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(10):2804-2819.
[29] Zhao L Y,Lv Y L,Zhang K,et al.A New Scheme for Decomposition of Mixed Pixels based on Modified Nonnegative Matrix Factorization and Genetic-Algorithm[J].International Conference on Artificial Intelligence and Computational Intelligence,Vol Iii,Proceedings,2009,3:457-461.
[30] Rogge D M,Rivard B,Zhang J,et al.Integration of Spatial-Spectral Information for the Improved Extraction of Endmembers[J].Remote Sensing of Environment,2007,110(3):287-303.
[31] Shimabukuro Y E,Smith J A.The Least-Squares Mixing Models to Generate Fraction Images derived from Remote-Sensing Multispectral Data[J].IEEE Transactions on Geoscience and Remote Sensing,1991,29(1):16-20.
[32] Li Chunfang,Bai Songzhu.Strong Precipitation Climate Features and Weather PatternsForm May to September in Aletai[C]∥Chinese Meteorological Society 2008 Annual Conference Weather Forecast Accuracy and Public Meteorological Services Sub-Venue,2008:7.[李春芳,白松竹.阿勒泰5~9月大降水天气气候特征与形式分析[C]∥中国气象学会2008年年会天气预报准确率与公共气象服务分会场,2008:7.]
[33] Zhuang Xiaocui,Guo Cheng,Zhao Zhenbo,et al.Snow Cover Variation Analysis Altay Area of Xinjiang[J].Journal of Arid Meteorology,2010,28(2):190-197.[庄晓翠,郭城,赵正波等.新疆阿勒泰地区积雪变化分析[J].干旱气象,2010,28(2):190-197.]
[34] Hollstein A,Segl K,Guanter L,et al.Ready-to-Use Methods for the Detection of Clouds,Cirrus,Snow,Shadow,Water and Clear Sky Pixels in Sentinel-2 MSI Images[J].Remote Sensing,2016,8(8):666-684.
[35] Theys C,Dobigeon N,Tourneret J Y,et al.Linear Unmixing of Hyperspectral Images Using a Scaled Gradient Method[C]∥2009IEEE/Sp 15th Workshop on Statistical Signal Processing,Vols 1and 2,2009:728-732.
[36] Heinz D C,Chang C I.Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(3):529-545.
[37] Nielsen A A.Spectral mixture analysis:Linear and Semi-Parametric Full and Iterated Partial Unmixing in Multi-and Hyperspectral Image Data[J].Journal of Mathematical Imaging and Vision,2001,15(1-2):17-37.
[2] Kirnbauer R,Bloschl G,Gutknecht D.Entering the Era of Distributed Snow Models[J].Nordic Hydrology,1994,25(1-2):1-24.
[3] Cline D W,Bales R C,Dozier J.Estimating the Spatial Distribution of Snow in Mountain Basins Using Remote Sensing and Energy Balance Modeling[J].Water Resources Research,1998,34(5):1275-1285.
[4] Dozier J,Painter T H.Multispectral and Hyperspectral Remote Sensing of Alpine Snow Properties[J].Annual Review of Earth and Planetary Sciences,2004,32:465-494.
[5] Hall D K,Benson C S,Field W O.Changes of Glaciers in Glacier Bay,Alaska,Using Ground and Satellite Measurements[J].Physical Geography,1995,16(1):27-41.
[6] Wang J,Li H X,Hao X H,et al.Remote Sensing for Snow Hydrology in China:Challenges and Perspectives[J].Journal of Applied Remote Sensing,2014,8:1-25.
[7] Luce C H,Tarboton D G,Cooley R R.The Influence of the Spatial Distribution of Snow on Basin-averaged Snowmelt[J].Hydrological Processes,1998,12(10-11):1671-1683.
[8] Bloschl G,Kirnbauer R,Gutknecht D.A Spatially Distributed Snowmelt Model for Application in Alpine Terrain[J].Snow,Hydrology and Forests in High Alpine Areas,1991,205:51-60.
[9] Luce C H,Tarboton D G,Cooley K R.Sub-grid Parameterization of Snow Distribution for an Energy and Mass Balance Snow Cover Model[J].Hydrological Processes,1999,13(12-13):1921-1933.
[10] Roesch A,Wild M,Gilgen H,et al.ANew Snow Cover Fraction Parametrization for the ECHAM4GCM[J].Climate Dynamics,2001,17(12):933-946.
[11] Li Hongyi,Wangjian.Key Research Topics and Their Advance on Modeling Snow Hydrological Process[J].Journal of Glaciology and Geocryology,2013,35(2):430-437.[李弘毅,王建.积雪水文模拟中的关键问题及其研究进展[J].冰川冻土,2013,35(2):430-437.]
[12] Hall D H,Riggs.In:National Snow and Ice Data Center(NSIDC)(Ed.),MODIS/Terra Snow Cover Daily L3Global500m Grid,Version 6.NASA,Boulder,Colorado USA[J].Quaternary Science Reviews,2016,32(1):6-15.
[13] Salomonson V V,Appel I.Development of the Aqua MODIS NDSIFractional Snow Cover Algorithm and Validation Results[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(7):1747-1756.
[14] 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.
[15] Painter T H,Rittger K,Mckenzie C,et al.Retrieval of Subpixel Snow Covered Area,Grain Size,and Albedo from MODIS[J].Remote Sensing of Environment,2009,113(4):868-879.
[16] Shi J.AnAutomatic Algorithm on Estimating Sub-Pixel Snow Cover from MODIS[J].Quaternary Science Reviews,2012,32(1):6-15.
[17] Vikhamar D,Solberg R.Snow-cover Mapping in Forests by Constrained Linear Spectral Unmixing of MODIS Data[J].Remote Sensing of Environment,2003,88(3):309-323.
[18] Zhu J,Shi J C,Wang Y H.Subpixel Snow Mapping of the Qinghai-Tibet Plateau Using MODIS Data[J].International Journal of Applied Earth Observation and Geoinformation,2012,18:251-262.
[19] Dobreva I D,Klein A G.Fractional Snow Cover Mapping through Artificial Neural Network Analysis of MODIS Surface Reflectance[J].Remote Sensing of Environment,2011,115(12):3355-3366.
[20] Metsamaki S,Mattila O P,Pulliainen J,et al.An Optical Reflectance Model-based Method for Fractional Snow Cover Mapping Applicable to Continental Scale[J].Remote Sensing of Environment,2012,123:508-521.
[21] Bioucas-Dias J M,Plaza A.An Overview on Hyperspectral Unmixing:Geometrical,Statistical,and Sparse Regression based Approaches[J].2011IEEE International Geoscience and Remote Sensing Symposium(Igarss),2011:1135-1138.
[22] Sharma S,Sharma S,Buddhiraju K M.Sub-pixel Mapping of Hyperspectral Imagery Using Super-resoloution[J].Multispectral,Hyperspectral,and Ultraspectral Remote Sensing Technology,Techniques and Applications Vi,2016,(6):9880.
[23] Marsh S E,Switzer P,Kowalik W S.Resolving the Percentage of Component Terrains within Single Resolution Elements[J].Photogrammetric Engineering and Remote Sensing,1980,46(8):1079-1086.
[24] Veganzones M A,Grana M.Endmember extraction methods:A short review[J].Knowledge-based Intelligent Information and Engineering Systems,Pt 3,Proceedings,2008,5179:400-407.
[25] Bioucas-Dias J M.A Variable Splitting Augmented Lagrangian Approach to Linear Spectral Unmixing[C]∥2009 First Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing,2009:1-4.
[26] Plaza A,Chang C I.FastImplementation of Pixel Purity Index Algorithm[J].Algorithms and Technologies for Multispectral,Hyperspectral,and Ultraspectral Imagery XI,2005,5806:307-317.
[27] Winter M E.N-FINDR:An Algorithm for Fast Autonomous Spectral End-Member Determination in Hyperspectral Data[J].Imaging Spectrometry V,1999,3753:266-275.
[28] Chang C I,Wu C C,Liu W M,et al.A New Growing Method for Simplex-based Endmember Extraction Algorithm[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(10):2804-2819.
[29] Zhao L Y,Lv Y L,Zhang K,et al.A New Scheme for Decomposition of Mixed Pixels based on Modified Nonnegative Matrix Factorization and Genetic-Algorithm[J].International Conference on Artificial Intelligence and Computational Intelligence,Vol Iii,Proceedings,2009,3:457-461.
[30] Rogge D M,Rivard B,Zhang J,et al.Integration of Spatial-Spectral Information for the Improved Extraction of Endmembers[J].Remote Sensing of Environment,2007,110(3):287-303.
[31] Shimabukuro Y E,Smith J A.The Least-Squares Mixing Models to Generate Fraction Images derived from Remote-Sensing Multispectral Data[J].IEEE Transactions on Geoscience and Remote Sensing,1991,29(1):16-20.
[32] Li Chunfang,Bai Songzhu.Strong Precipitation Climate Features and Weather PatternsForm May to September in Aletai[C]∥Chinese Meteorological Society 2008 Annual Conference Weather Forecast Accuracy and Public Meteorological Services Sub-Venue,2008:7.[李春芳,白松竹.阿勒泰5~9月大降水天气气候特征与形式分析[C]∥中国气象学会2008年年会天气预报准确率与公共气象服务分会场,2008:7.]
[33] Zhuang Xiaocui,Guo Cheng,Zhao Zhenbo,et al.Snow Cover Variation Analysis Altay Area of Xinjiang[J].Journal of Arid Meteorology,2010,28(2):190-197.[庄晓翠,郭城,赵正波等.新疆阿勒泰地区积雪变化分析[J].干旱气象,2010,28(2):190-197.]
[34] Hollstein A,Segl K,Guanter L,et al.Ready-to-Use Methods for the Detection of Clouds,Cirrus,Snow,Shadow,Water and Clear Sky Pixels in Sentinel-2 MSI Images[J].Remote Sensing,2016,8(8):666-684.
[35] Theys C,Dobigeon N,Tourneret J Y,et al.Linear Unmixing of Hyperspectral Images Using a Scaled Gradient Method[C]∥2009IEEE/Sp 15th Workshop on Statistical Signal Processing,Vols 1and 2,2009:728-732.
[36] Heinz D C,Chang C I.Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(3):529-545.
[37] Nielsen A A.Spectral mixture analysis:Linear and Semi-Parametric Full and Iterated Partial Unmixing in Multi-and Hyperspectral Image Data[J].Journal of Mathematical Imaging and Vision,2001,15(1-2):17-37.