基于累积分布函数匹配的多源遥感土壤水分数据连续融合算法
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摘要
数据融合是解决不同来源遥感数据无法直接对比分析这一瓶颈的有效方法。实时更新的SMOS土壤水分数据(soil moisture and ocean salinity)可开展实时干旱评价(2010年至今),但由于序列短无法开展频率及演变分析。CCI(climate change initiative)土壤水分数据是联合了多种主被动遥感数据合成的长序列数据产品(1979—2013年)。为提高不同来源遥感数据的融合精度,该研究基于累积分布匹配原理构建了多源遥感土壤水分连续融合算法,将SMOS和CCI融合成长序列、近实时的遥感土壤水分数据。经验证分析,累积概率曲线相关性中表征干旱的低值区纳什效率系数由0.52提高到0.99,且融合后土壤水分数据可以较准确地反映当地的干旱事件。该研究提出的多源遥感土壤水分连续融合算法显著提高了现有融合算法的融合精度。
As an important grain-producing region, Songnen Plain located in Northeast China has been significantly affected by drought in recent years. Remote sensing soil moisture is one of the important indices for monitoring agricultural drought in large-scale farmland area. The time series length and update speed of remote sensing data are 2 important factors affecting its application. In 2009, satellite called SMOS(soil moisture and ocean salinity) was launched. As the first satellite dedicated to monitoring soil moisture of earth, daily updated SMOS soil moisture data have been proven to be suitable for the application in real-time drought monitoring and evaluation in many researches. In the field of agricultural drought management, drought characteristics and frequency analysis are basic contents of these researches. However, it is impossible to analyze the drought frequency and characteristic evolution by SMOS data, due to their short time series. CCI(climate change initiative) soil moisture data, which have a long time series(1979-2013), was combined with a variety of C-band scattered data and multi-frequency radiometer data. As a kind of historical data, CCI soil moisture product can make up for SMOS data to analyze the agricultural drought characteristics. Because of the difference of the sensors and the inversion methods, remote sensing data from different sources cannot be directly compared and analyzed. Therefore, data fusion becomes a hotspot and key issue in the application research of remote sensing data nowadays. Based on cumulative distribution matching principle, the key of data fusion is to establish the correlation between cumulative probability curves of different data. The work amount of traditional piecewise linear fusion method is proportional to the fusion accuracy. This linear method is difficult to process a number of data in batches with high precision. Unary interpolation can establish this correlation between any quantile on different cumulative probability distribution curves. Therefore, a continuous fusion algorithm of multi-source remote sensing soil moisture was built in this study. Using this continuous fusion method, SMOS and CCI data were fused to real-time remote sensing soil moisture data product with long time series characteristics with the Songnen Plain as the case. This study compared the fusion accuracy between this continuous fusion and piecewise linear fusion method. And the time series of original SMOS data and fused SMOS data was also analyzed. The analysis results indicate that this unary interpolation continuous fusion method can improve the fusion accuracy of multi-source remote sensing soil moisture significantly. Data segment of the cumulative probability distribution curve with low water content can characterize agricultural drought. By the piecewise linear fusion method, data segment of the cumulative probability distribution curve with low water content yet has some errors, which will lead to the inaccuracy of drought evaluation. By this new continuous fusion method, fused SMOS data and CCI data are completely coincident at each quantile in the low-value region of the curve. Through the accurate evaluation of drought events, the fused SMOS data can reflect local drought conditions. Through time series analysis, the range of fused SMOS data is closer to the CCI data, and the relative change pattern of original SMOS data still remains. This remote sensing fusion data combining the advantages of CCI and SMOS data can provide reliable data support for the next study of agricultural drought evaluation.
As an important grain-producing region, Songnen Plain located in Northeast China has been significantly affected by drought in recent years. Remote sensing soil moisture is one of the important indices for monitoring agricultural drought in large-scale farmland area. The time series length and update speed of remote sensing data are 2 important factors affecting its application. In 2009, satellite called SMOS(soil moisture and ocean salinity) was launched. As the first satellite dedicated to monitoring soil moisture of earth, daily updated SMOS soil moisture data have been proven to be suitable for the application in real-time drought monitoring and evaluation in many researches. In the field of agricultural drought management, drought characteristics and frequency analysis are basic contents of these researches. However, it is impossible to analyze the drought frequency and characteristic evolution by SMOS data, due to their short time series. CCI(climate change initiative) soil moisture data, which have a long time series(1979-2013), was combined with a variety of C-band scattered data and multi-frequency radiometer data. As a kind of historical data, CCI soil moisture product can make up for SMOS data to analyze the agricultural drought characteristics. Because of the difference of the sensors and the inversion methods, remote sensing data from different sources cannot be directly compared and analyzed. Therefore, data fusion becomes a hotspot and key issue in the application research of remote sensing data nowadays. Based on cumulative distribution matching principle, the key of data fusion is to establish the correlation between cumulative probability curves of different data. The work amount of traditional piecewise linear fusion method is proportional to the fusion accuracy. This linear method is difficult to process a number of data in batches with high precision. Unary interpolation can establish this correlation between any quantile on different cumulative probability distribution curves. Therefore, a continuous fusion algorithm of multi-source remote sensing soil moisture was built in this study. Using this continuous fusion method, SMOS and CCI data were fused to real-time remote sensing soil moisture data product with long time series characteristics with the Songnen Plain as the case. This study compared the fusion accuracy between this continuous fusion and piecewise linear fusion method. And the time series of original SMOS data and fused SMOS data was also analyzed. The analysis results indicate that this unary interpolation continuous fusion method can improve the fusion accuracy of multi-source remote sensing soil moisture significantly. Data segment of the cumulative probability distribution curve with low water content can characterize agricultural drought. By the piecewise linear fusion method, data segment of the cumulative probability distribution curve with low water content yet has some errors, which will lead to the inaccuracy of drought evaluation. By this new continuous fusion method, fused SMOS data and CCI data are completely coincident at each quantile in the low-value region of the curve. Through the accurate evaluation of drought events, the fused SMOS data can reflect local drought conditions. Through time series analysis, the range of fused SMOS data is closer to the CCI data, and the relative change pattern of original SMOS data still remains. This remote sensing fusion data combining the advantages of CCI and SMOS data can provide reliable data support for the next study of agricultural drought evaluation.
引文
[1]林雨准,张保明,徐俊峰,等.多特征多尺度相结合的高分辨率遥感影像建筑物提取[J].测绘通报,2017(12):53-57.Lin Yuzhun,Zhang Baoming,Xu Junfeng,et al.Building extraction from high resolution remote sensing imagery with multi-feature and multi-scale[J].Bulletin of Surveying&Mapping,2017(12):53-57.(in Chinese with English abstract)
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[4]Calheiros R V,Zawadzki I.Reflectivity-rain rate relationships for radar hydrology in Brazil[J].Journal of Applied Meteorology,2010,26(1):118-132.
[5]Atlas D,Rosenfeld D,Wolff D B.Climatologically tuned reflectivity-rain rate relations and links to area-time integrals[J].Journal of Applied Meteorology,1990,29(11):1120-1135.
[6]Anagnostou E N,Negri A J,Adler R F.Statistical adjustment of satellite microwave monthly rainfall estimates over Amazonia[J].Journal of Applied Meteorology,2010,38(11):1590-1598.
[7]余凡,赵英时.基于主被动遥感数据融合的土壤水分信息提取[J].农业工程学报,2011,27(6):187-192.Yu Fan,Zhao Yingshi.Soil moisture information extraction based on integration of active and passive remote sensing data[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2011,27(6):187-192.(in Chinese with English abstract)
[8]Reichle R H,Koster R D.Bias reduction in short records of satellite soil moisture[J].Geophysical Research Letters,2004,31(19):187-206.
[9]Liu Y Y,Van Dijk A I J M,De Jeu R A M,et al.An analysis of spatiotemporal variations of soil and vegetation moisture from a 29-year satellite-derived data set over mainland Australia[J].Water Resources Research,2009,45(7):4542-4548.
[10]Liu Y Y,Parinussa R M,Dorigo W A,et al.Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals[J].Hydrology&Earth System Sciences Discussions,2010,15(2):425-436.
[11]庄媛.中国区域多源主被动微波遥感土壤湿度产品融合研究[D].南京:南京信息工程大学,2014.Zhuang Yuan.Research on the Fusion of Soil Moisture Products by Multi-source Active Passive Microwave Remote Sensing in China[D].Nanjing:Nanjing University of Information Science&Technology,2014.(in Chinese with English abstract)
[12]Calvet J C,Wigneron J P,Walker J,et al.Sensitivity of passive microwave observations to soil moisture and vegetation water content:L-band to W-band[J].IEEETransactions on Geoscience&Remote Sensing,2011,49(4):1190-1199.
[13]Ramirez-Beltran N D,Calderon-Arteaga C,Harmsen E,et al.An algorithm to estimate soil moisture over vegetated areas based on in situ and remote sensing information[J].International Journal of Remote Sensing,2010,31(10):2655-2679.
[14]李小英,段争虎.基于SMOS的黄土高原区域尺度表层土壤水分时空变化[J].中国沙漠,2014,34(1):133-139.Li Xiaoying,Duan Zhenghu.Spatial variability of surface soil moisture at a regional scale in the Loess Plateau based on SMOS[J].Journal of Desert Research,2014,34(1):133-139.(in Chinese with English abstract)
[15]杨娜,崔慧珍,向峰.SMOS L2土壤水分数据产品在我国农区的验证[J].河南理工大学学报,2015,34(2):287-291.Yang Na,Cui Huizhen,Xiang Feng.Validation study on SMOSL2 soil moisture product in agricultural area of China[J].Journal of Henan Polytechnic University,2015,34(2):287-291.(in Chinese with English abstract)
[16]Al-Yaari A,Wigneron J P,Ducharne A,et al.Global-scale comparison of passive(SMOS)and active(ASCAT)satellite based microwave soil moisture retrievals with soil moisture simulations(MERRA-Land)[J].Remote Sensing of Environment,2014,152:614-626.
[17]闫利爽.基于SMOS数据的松辽平原西部地表土壤水分时空变化研究[D].长春:东北师范大学,2015.Yan Lishuang.Spatial and Temporal Variation of Surface Soil Moisture in the Western Songliao Plain Based on SMOSData[D].Changchun:Northeast Normal University,2015.(in Chinese with English abstract)
[18]Hollmann R,Merchant C J,Saunders R,et al.The ESAclimate change initiative:Satellite data records for essential climate variables[J].Bulletin of the American Meteorological Society,2013,94(10):1541-1552.
[19]张喆,丁建丽,鄢雪英,等.基于温度植被干旱指数的土库曼斯坦典型绿洲干旱遥感监测[J].生态学杂志,2013,32(8):2172-2178.Zhang Zhe,Ding Jianli,Yan Xueying,et al.Remote sensing monitoring of drought in Turkmenistan oasis based on temperature/vegetation drought index[J].Chinese Journal of Ecology,2013,32(8):2172-2178.(in Chinese with English abstract)
[20]á.González-Zamora,Sánchez N,Pablos M,et al.CCI soil moisture assessment with SMOS soil moisture and in situ,data under different environmental conditions and spatial scales in Spain[J].Remote Sensing of Environment,2018.doi.org/10.1016/j.rse.2018.02.010(in press).
[21]申晓骥,安如.欧空局主、被动微波土壤湿度产品的比较验证[J].遥感信息,2017,32(2):89-93.Shen Xiaoji,An Ru.Comparative evaluation of ESA CCIactive and passive microwave soil moisture products[J].Remote Sensing Information,2017,32(2):89-93.(in Chinese with English abstract)
[22]Dorigo W,Wagner W,Albergel C,et al.ESA CCI soil moisture for improved earth system understanding:state-of-the art and future directions[J].Remote Sensing of Environment,2017,203:185-215.
[23]杨飞,姚作芳,刘兴土,等.松嫩平原的粮食生产潜力分析及建议[J].干旱地区农业研究,2013,31(3):207-212.Yang Fei,Yao Zuofang,Liu Xingtu,et al.Assessment of grain production potential in Songnen Plain and relevant suggestions[J].Agricultural Research in the Arid Areas,2013,31(3):207-212.(in Chinese with English abstract)
[24]郑盛华,覃志豪,张文博.松嫩平原干旱变化特征及其对气候变化的响应[J].中国农业气象,2015,36(5):640-649.Zheng Shenghua,Qin Zhihao,Zhang Wenbo.Drought variation in Songnen Plain and its response to climate change[J].Chinese Journal of Agrometeorology,2015,36(5):640-649.(in Chinese with English abstract)
[25]符琳.双参数有理插值[D].淮南:安徽理工大学,2014.Fu Lin.Two Parameter Rational Interpolation[D].Huainan:Anhui University of Science and Technology,2014.(in Chinese with English abstract)
[26]李波涛,杨长春,陈雨红,等.基于B样条函数的散乱数据曲面拟合和数据压缩[J].地球物理学进展,2009,24(3):936-943.Li Botao,Yang Changchun,Chen Yuhong,et al.Surface fitting and scattered data compressing with B-spline smoothing function[J].Process in Geophys,2009,24(3):936-943.(in Chinese with English abstract)
[27]陈朋.传感器网络数据插值算法研究[D].长沙:湖南大学,2011.Chen Peng.Research of Data Interpolation Algorithm for Sensor Networks[D].Changsha:Hunan University,2011.(in Chinese with English abstract)
[28]Grayson R B,Western A W.Towards areal estimation of soil water content from point measurements:Time and space stability of mean response[J].Journal of Hydrology,1998,207(1/2):68-82.
[2]刘金梅.多源遥感影像融合及其应用研究[D].青岛:中国海洋大学,2014.Liu Jinmei.The Research on Methods and Application of Multi-source Remote Sensing Imagery Fusion[D].Qingdao:Ocean University of China,2014.(in Chinese with English abstract)
[3]Waltz E,Llinas J.Multisensor Data Fusion[M].Artech House Inc.Norwood,Massachusetts,United States of America,1990.245-253.
[4]Calheiros R V,Zawadzki I.Reflectivity-rain rate relationships for radar hydrology in Brazil[J].Journal of Applied Meteorology,2010,26(1):118-132.
[5]Atlas D,Rosenfeld D,Wolff D B.Climatologically tuned reflectivity-rain rate relations and links to area-time integrals[J].Journal of Applied Meteorology,1990,29(11):1120-1135.
[6]Anagnostou E N,Negri A J,Adler R F.Statistical adjustment of satellite microwave monthly rainfall estimates over Amazonia[J].Journal of Applied Meteorology,2010,38(11):1590-1598.
[7]余凡,赵英时.基于主被动遥感数据融合的土壤水分信息提取[J].农业工程学报,2011,27(6):187-192.Yu Fan,Zhao Yingshi.Soil moisture information extraction based on integration of active and passive remote sensing data[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2011,27(6):187-192.(in Chinese with English abstract)
[8]Reichle R H,Koster R D.Bias reduction in short records of satellite soil moisture[J].Geophysical Research Letters,2004,31(19):187-206.
[9]Liu Y Y,Van Dijk A I J M,De Jeu R A M,et al.An analysis of spatiotemporal variations of soil and vegetation moisture from a 29-year satellite-derived data set over mainland Australia[J].Water Resources Research,2009,45(7):4542-4548.
[10]Liu Y Y,Parinussa R M,Dorigo W A,et al.Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals[J].Hydrology&Earth System Sciences Discussions,2010,15(2):425-436.
[11]庄媛.中国区域多源主被动微波遥感土壤湿度产品融合研究[D].南京:南京信息工程大学,2014.Zhuang Yuan.Research on the Fusion of Soil Moisture Products by Multi-source Active Passive Microwave Remote Sensing in China[D].Nanjing:Nanjing University of Information Science&Technology,2014.(in Chinese with English abstract)
[12]Calvet J C,Wigneron J P,Walker J,et al.Sensitivity of passive microwave observations to soil moisture and vegetation water content:L-band to W-band[J].IEEETransactions on Geoscience&Remote Sensing,2011,49(4):1190-1199.
[13]Ramirez-Beltran N D,Calderon-Arteaga C,Harmsen E,et al.An algorithm to estimate soil moisture over vegetated areas based on in situ and remote sensing information[J].International Journal of Remote Sensing,2010,31(10):2655-2679.
[14]李小英,段争虎.基于SMOS的黄土高原区域尺度表层土壤水分时空变化[J].中国沙漠,2014,34(1):133-139.Li Xiaoying,Duan Zhenghu.Spatial variability of surface soil moisture at a regional scale in the Loess Plateau based on SMOS[J].Journal of Desert Research,2014,34(1):133-139.(in Chinese with English abstract)
[15]杨娜,崔慧珍,向峰.SMOS L2土壤水分数据产品在我国农区的验证[J].河南理工大学学报,2015,34(2):287-291.Yang Na,Cui Huizhen,Xiang Feng.Validation study on SMOSL2 soil moisture product in agricultural area of China[J].Journal of Henan Polytechnic University,2015,34(2):287-291.(in Chinese with English abstract)
[16]Al-Yaari A,Wigneron J P,Ducharne A,et al.Global-scale comparison of passive(SMOS)and active(ASCAT)satellite based microwave soil moisture retrievals with soil moisture simulations(MERRA-Land)[J].Remote Sensing of Environment,2014,152:614-626.
[17]闫利爽.基于SMOS数据的松辽平原西部地表土壤水分时空变化研究[D].长春:东北师范大学,2015.Yan Lishuang.Spatial and Temporal Variation of Surface Soil Moisture in the Western Songliao Plain Based on SMOSData[D].Changchun:Northeast Normal University,2015.(in Chinese with English abstract)
[18]Hollmann R,Merchant C J,Saunders R,et al.The ESAclimate change initiative:Satellite data records for essential climate variables[J].Bulletin of the American Meteorological Society,2013,94(10):1541-1552.
[19]张喆,丁建丽,鄢雪英,等.基于温度植被干旱指数的土库曼斯坦典型绿洲干旱遥感监测[J].生态学杂志,2013,32(8):2172-2178.Zhang Zhe,Ding Jianli,Yan Xueying,et al.Remote sensing monitoring of drought in Turkmenistan oasis based on temperature/vegetation drought index[J].Chinese Journal of Ecology,2013,32(8):2172-2178.(in Chinese with English abstract)
[20]á.González-Zamora,Sánchez N,Pablos M,et al.CCI soil moisture assessment with SMOS soil moisture and in situ,data under different environmental conditions and spatial scales in Spain[J].Remote Sensing of Environment,2018.doi.org/10.1016/j.rse.2018.02.010(in press).
[21]申晓骥,安如.欧空局主、被动微波土壤湿度产品的比较验证[J].遥感信息,2017,32(2):89-93.Shen Xiaoji,An Ru.Comparative evaluation of ESA CCIactive and passive microwave soil moisture products[J].Remote Sensing Information,2017,32(2):89-93.(in Chinese with English abstract)
[22]Dorigo W,Wagner W,Albergel C,et al.ESA CCI soil moisture for improved earth system understanding:state-of-the art and future directions[J].Remote Sensing of Environment,2017,203:185-215.
[23]杨飞,姚作芳,刘兴土,等.松嫩平原的粮食生产潜力分析及建议[J].干旱地区农业研究,2013,31(3):207-212.Yang Fei,Yao Zuofang,Liu Xingtu,et al.Assessment of grain production potential in Songnen Plain and relevant suggestions[J].Agricultural Research in the Arid Areas,2013,31(3):207-212.(in Chinese with English abstract)
[24]郑盛华,覃志豪,张文博.松嫩平原干旱变化特征及其对气候变化的响应[J].中国农业气象,2015,36(5):640-649.Zheng Shenghua,Qin Zhihao,Zhang Wenbo.Drought variation in Songnen Plain and its response to climate change[J].Chinese Journal of Agrometeorology,2015,36(5):640-649.(in Chinese with English abstract)
[25]符琳.双参数有理插值[D].淮南:安徽理工大学,2014.Fu Lin.Two Parameter Rational Interpolation[D].Huainan:Anhui University of Science and Technology,2014.(in Chinese with English abstract)
[26]李波涛,杨长春,陈雨红,等.基于B样条函数的散乱数据曲面拟合和数据压缩[J].地球物理学进展,2009,24(3):936-943.Li Botao,Yang Changchun,Chen Yuhong,et al.Surface fitting and scattered data compressing with B-spline smoothing function[J].Process in Geophys,2009,24(3):936-943.(in Chinese with English abstract)
[27]陈朋.传感器网络数据插值算法研究[D].长沙:湖南大学,2011.Chen Peng.Research of Data Interpolation Algorithm for Sensor Networks[D].Changsha:Hunan University,2011.(in Chinese with English abstract)
[28]Grayson R B,Western A W.Towards areal estimation of soil water content from point measurements:Time and space stability of mean response[J].Journal of Hydrology,1998,207(1/2):68-82.
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