基于时空融合的NDVI时序生成技术在冬小麦监测中的应用
|
摘要
高时空分辨率归一化植被指数(normalized difference vegetation index,NDVI)数据对于冬小麦的动态监测具有重要意义,而高分一号卫星的不足之处是无法获得时间序列数据。为了解决上述问题,以河南省东北部为实验研究区,以高分一号卫星16m分辨率的多光谱宽覆盖GF-1/WFV(Gaofen-1satellite/wide field of view)数据与MODIS地表反射率产品MOD09Q1数据为数据源,采用STARFM (spatial and temporal adaptive reflectance fusion model)时空融合算法,对冬小麦出苗生长期、越冬期、返青-拔节期、抽穗期、成熟期等5个不同物候期的数据进行分析,并最终生成步长为8d的GF-1/WFV NDVI时间序列数据(即预测NDVI).结果显示:5个不同物候期的预测GF-1/WFV NDVI与实际GF-1/WFV NDVI的相关系数分别为0.695 9,0.840 4,0.892 1,0.897 0,0.632 9;预测GF-1/WFV NDVI时间序列数据与实际MOD09Q1NDVI数据具有高度的一致性。
Normalized difference vegetation index(NDVI)with high spatial and temporal resolution is of great significance for dynamic monitoring of winter wheat,however,Gaofen-1satellite can not get time-series data.With the aim to solve this problem,northeast Henan province was taken as the experimental area,the GF-1/WFV and MODIS surface reflectance product MOD09Q1 data of 5different phenological periods of winter wheat were analyzed by STARFM spatial-temporal algorithm.The 5different phenological periods include emergence and growth,over-wintering,returning green,jointing,and maturation.Finally,8dstep NDVI time-series data of GF-1/WFV was generated.The results show that correlation coefficients between predicted NDVI and actual NDVI of 5different phenological periods are 0.695 9,0.840 4,0.892 1,0.897 0,0.632 9,generated NDVI time-series data of GF-1/WFV has high consistency with corresponding MOD09Q1 NDVI data.
Normalized difference vegetation index(NDVI)with high spatial and temporal resolution is of great significance for dynamic monitoring of winter wheat,however,Gaofen-1satellite can not get time-series data.With the aim to solve this problem,northeast Henan province was taken as the experimental area,the GF-1/WFV and MODIS surface reflectance product MOD09Q1 data of 5different phenological periods of winter wheat were analyzed by STARFM spatial-temporal algorithm.The 5different phenological periods include emergence and growth,over-wintering,returning green,jointing,and maturation.Finally,8dstep NDVI time-series data of GF-1/WFV was generated.The results show that correlation coefficients between predicted NDVI and actual NDVI of 5different phenological periods are 0.695 9,0.840 4,0.892 1,0.897 0,0.632 9,generated NDVI time-series data of GF-1/WFV has high consistency with corresponding MOD09Q1 NDVI data.
引文
[1] DUNCAN J,STOW D,FRANKLIN J,et al.Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin,New Mexico[J].International Journal of Remote Sensing,1993,14(18):3395-3416.
[2] GALLO K P,FLESCH T K.Large-area crop monitoring with the NOAA AVHRR:Estimating the silking stage of corn development[J].Remote Sensing of Environment,1989,27(1):73-80.
[3] XIANG G,HUETE A R,NI W,et al.Optical-biophysical relationships of vegetation spectra without background contamination[J].Remote Sensing of Environment,2000,74(3):609-620.
[4] GEVAERT C M,GARCA-HARO F J.A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion[J].Remote Sensing of Environment,2015,156:34-44.
[5] COOPS N C,JOHNSON M,WULDER M A,et al.Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation[J].Remote Sensing of Environment,2006,103(1):67-80.
[6] HOLBEN B.Characteristics of maximum-value composite images from temporal AVHRR data[J].International Journal of Remote Sensing,1986,7(11):1417-1434.
[7] SHEN H,WU P,LIU Y,et al.spatial and temporal reflectance fusion model considering sensor observation differences[J].International Journal of Remote Sensing,2013,34(12):4367-4383.
[8] WENG Q,FU P,GAO F.Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data[J].Remote Sensing of Environment,2014,145(8):55-67.
[9] ANDERSON M C,KUSTAS W P,Norman J M,et al.Mapping daily evapotranspiration at field to global scales using geostationary and polar orbiting satellite imagery[J].Hydrology&Earth System Sciences,2011,15(1):223-239.
[10] WATTS J D,POWELL S L,LAWRENCE R L,et al.Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery[J].Remote Sensing of Environment,2011,115(1):66-75.
[11] LIU H,WENG Q.Enhancing temporal resolution of satellite imagery for public health studies:a case study of West Nile Virus outbreak in Los Angeles in 2007[J].Remote Sensing of Environment,2012,117(2):57-71.
[12] HUANG B,WANG J,SONG H,et al.Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring[J].IEEE Geoscience&Remote Sensing Letters,2013,10(5):1011-1015.
[13] ZHU X L,JIN C,FENG G,et al.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions[J].Remote Sensing of Environment,2010,114(11):2610-2623.
[14] HUANG B,SONG H.Spatiotemporal reflectance fusion via sparse representation[J].IEEE Transactions on Geoscience&Remote Sensing,2012,50(10):3707-3716.
[15]孙锐,荣媛,苏红波,等.MODIS和HJ-1CCD数据时空融合重构NDVI时间序列[J].遥感学报,2016,20(3):361-373.SUN R,RONG Y,SU H B,et al.NDVI time-series reconstruction based on MODIS and HJ-1CCD data spatial-temporal fusion[J].Journal of Remote Sensing,2016,20(3):361-373.
[16] MENG J H,WU B F,DU X,et al.Method to construct high spatial and temporal resolution NDVI DataSet-STAVFM[J].Journal of Remote Sensing,2011,15(1):44-59.
[17] MENG J H,DU X,WU B F.Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation[J].International Journal of Digital Earth,2013,6(3):1-16.
[18]徐磊,巫兆聪,罗飞,等.基于GF-1/WFV与MODIS时空融合的森林覆盖定量提取[J].农业机械学报,2017(7):150-157.XU L,WU Z C,LUO F,et al.Quantitative extraction of forest cover based on fusing of GF-1/WFV and MODIS data[J].Transactions of the Chinese Society for Agricultural Machinery,2017(7):150-157.
[19] JAKUBAUSKAS M E,LEGATES D R,KASTENS J H.Crop identification using harmonic analysis of time-series AVHRR NDVI data[J].Computers&Electronics in Agriculture,2002,37(1):127-139.
[2] GALLO K P,FLESCH T K.Large-area crop monitoring with the NOAA AVHRR:Estimating the silking stage of corn development[J].Remote Sensing of Environment,1989,27(1):73-80.
[3] XIANG G,HUETE A R,NI W,et al.Optical-biophysical relationships of vegetation spectra without background contamination[J].Remote Sensing of Environment,2000,74(3):609-620.
[4] GEVAERT C M,GARCA-HARO F J.A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion[J].Remote Sensing of Environment,2015,156:34-44.
[5] COOPS N C,JOHNSON M,WULDER M A,et al.Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation[J].Remote Sensing of Environment,2006,103(1):67-80.
[6] HOLBEN B.Characteristics of maximum-value composite images from temporal AVHRR data[J].International Journal of Remote Sensing,1986,7(11):1417-1434.
[7] SHEN H,WU P,LIU Y,et al.spatial and temporal reflectance fusion model considering sensor observation differences[J].International Journal of Remote Sensing,2013,34(12):4367-4383.
[8] WENG Q,FU P,GAO F.Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data[J].Remote Sensing of Environment,2014,145(8):55-67.
[9] ANDERSON M C,KUSTAS W P,Norman J M,et al.Mapping daily evapotranspiration at field to global scales using geostationary and polar orbiting satellite imagery[J].Hydrology&Earth System Sciences,2011,15(1):223-239.
[10] WATTS J D,POWELL S L,LAWRENCE R L,et al.Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery[J].Remote Sensing of Environment,2011,115(1):66-75.
[11] LIU H,WENG Q.Enhancing temporal resolution of satellite imagery for public health studies:a case study of West Nile Virus outbreak in Los Angeles in 2007[J].Remote Sensing of Environment,2012,117(2):57-71.
[12] HUANG B,WANG J,SONG H,et al.Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring[J].IEEE Geoscience&Remote Sensing Letters,2013,10(5):1011-1015.
[13] ZHU X L,JIN C,FENG G,et al.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions[J].Remote Sensing of Environment,2010,114(11):2610-2623.
[14] HUANG B,SONG H.Spatiotemporal reflectance fusion via sparse representation[J].IEEE Transactions on Geoscience&Remote Sensing,2012,50(10):3707-3716.
[15]孙锐,荣媛,苏红波,等.MODIS和HJ-1CCD数据时空融合重构NDVI时间序列[J].遥感学报,2016,20(3):361-373.SUN R,RONG Y,SU H B,et al.NDVI time-series reconstruction based on MODIS and HJ-1CCD data spatial-temporal fusion[J].Journal of Remote Sensing,2016,20(3):361-373.
[16] MENG J H,WU B F,DU X,et al.Method to construct high spatial and temporal resolution NDVI DataSet-STAVFM[J].Journal of Remote Sensing,2011,15(1):44-59.
[17] MENG J H,DU X,WU B F.Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation[J].International Journal of Digital Earth,2013,6(3):1-16.
[18]徐磊,巫兆聪,罗飞,等.基于GF-1/WFV与MODIS时空融合的森林覆盖定量提取[J].农业机械学报,2017(7):150-157.XU L,WU Z C,LUO F,et al.Quantitative extraction of forest cover based on fusing of GF-1/WFV and MODIS data[J].Transactions of the Chinese Society for Agricultural Machinery,2017(7):150-157.
[19] JAKUBAUSKAS M E,LEGATES D R,KASTENS J H.Crop identification using harmonic analysis of time-series AVHRR NDVI data[J].Computers&Electronics in Agriculture,2002,37(1):127-139.