冬小麦叶面积指数真实性检验尺度扩展方法研究
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摘要
为在空间尺度上实现冬小麦LAI地面观测与遥感观测直接匹配,从1 m×1 m范围的实测LAI出发,通过优化采样方法扩展得到16 m×16 m范围的冬小麦LAI,然后利用空间分辨率为16 m的高分1号卫星的多光谱数据计算样本点的植被指数,建立其与冬小麦LAI的拟合模型,从四种植被指数的拟合模型中挑选表现最好的LAI估测模型,获得16 m×16 m尺度的LAI分布图,并经过重采样聚合为250 m×250 m尺度的LAI格点图,从而实现从地面点测量数据到卫星尺度数据的扩展。检验结果表明,16 m×16 m和250 m×250 m两个研究区域模拟点值和实测点值的相对误差分别为4.18%和3.64%,说明这种尺度扩展方法是科学可行的。
The high precision simulation of the leaf area index(LAI) of winter wheat and realization of the scale expansion from single point to region is of great significance to the validation of remote sensing LAI products and the assimilation of remote sensing and crop models. However,at present,ground-based observation LAI can not be directly matched with remote sensing observations at the spatial scale. In this study,based on the measured LAI of 1 m×1 m scale,the winter wheat LAI of 16 m*16 m scale was obtained by optimizing the sampling method. Then the vegetation index of the sample point was calculated using the multi spectral data of the GF-1 satellite with spatial resolution of 16 m. The fitting model of winter wheat LAI was established,and the best LAI estimation model was selected from the four vegetation index fitting model. The LAI distribution map of the research area of the 16 m*16 m scale was obtained,and the LAI map of the 250 m×250 m scale was aggregated by resampling,which achieved the expansion of the data from the ground point measurements to the satellite scale. The results show that the relative error of simulated point values and measured values in the two areas are 4.18% and 3.64%,and this scaling method is scientific and feasible.
The high precision simulation of the leaf area index(LAI) of winter wheat and realization of the scale expansion from single point to region is of great significance to the validation of remote sensing LAI products and the assimilation of remote sensing and crop models. However,at present,ground-based observation LAI can not be directly matched with remote sensing observations at the spatial scale. In this study,based on the measured LAI of 1 m×1 m scale,the winter wheat LAI of 16 m*16 m scale was obtained by optimizing the sampling method. Then the vegetation index of the sample point was calculated using the multi spectral data of the GF-1 satellite with spatial resolution of 16 m. The fitting model of winter wheat LAI was established,and the best LAI estimation model was selected from the four vegetation index fitting model. The LAI distribution map of the research area of the 16 m*16 m scale was obtained,and the LAI map of the 250 m×250 m scale was aggregated by resampling,which achieved the expansion of the data from the ground point measurements to the satellite scale. The results show that the relative error of simulated point values and measured values in the two areas are 4.18% and 3.64%,and this scaling method is scientific and feasible.
引文
[1]刘良云.叶面积指数遥感尺度效应与尺度纠正[J].遥感学报,2014,18(6):1158. LIU L Y.Simulation and correction of spatialscaling effects for leaf area index [J].Journal of Remote Sensing,2014,18(6):1158.
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[8] BARET F,WEISS M,LACAZE R,et al.GEOV1:LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products.Part1:Principles of development and production [J].Remote Sensing of Environment,2013,137:299.
[9] CAMACHO F,CERNICHARO J,LACAZE R,et al.GEOV1:LAI,FAPAR essential climate variables and FCOVER global time series capitalizing over existing products.Part 2:Validation and intercomparison with reference products [J].Remote Sensing of Environment,2013,137:310.
[10] WANG S G,LI X,GE Y,et al.Validation of regional-scale remote sensing products in China:From site to network [J].Remote Sensing,2016,8(12):980.
[11]李新,晋锐,刘绍民,等.黑河遥感试验中尺度上推研究的进展与前瞻[J].遥感学报,2016,20(5):921. LI X,JIN R,LIU S M,et al.Upscaling research in HiWATER:Progress and prospects [J].Journal of Remote Sensing,2016,20(5):921.
[12]徐保东,李静,柳钦火,等.地面站点观测数据代表性评价方法研究进展[J].遥感学报,2015,19(5):703. XU B D,LI J,LIU Q H,et al.Review of methods for evaluating representativeness of ground station observations [J].Remote Sensing,2015,19(5):703.
[13] CESCATTI A,MARCOLLA B,SANTHANA VANNAN S K,et al.Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network [J].Remote Sensing of Environment,2012,121:323.
[14] ROMáN M O,GATEBE C K,SHUAI Y M,et al.Use of in situ and airborne multiangle data to assess MODIS-and Landsat-Based estimates of directional reflectance and albedo [J].IEEE Transactions on Geoscience and Remote Sensing,2013,51(3):1393.
[15] WANG Z S,SCHAAF C B,STRAHLER A H,et al.Evaluation of MODIS albedo product(MCD43A) over grassland,agriculture and forest surface types during dormant and snow covered periods [J].Remote Sensing of Environment,2014,140:60.
[16]李新.陆地表层系统模拟和观测的不确定性及其控制[J].中国科学:地球科学,2013,43(11):1735. LI X.Characterization,controlling,and reduction of uncertainties in the modeling and observation of land-surface systems [J].Science China:Earth Sciences,2013,43(11):1735.
[17]卢伟,范文义.中小区域尺度时间序列林地 LAI 快速估测方法[J].农业工程学报,2016,32(5):188. LU W,FAN W Y.Fast estimation method for time-series forest LAI at small and medium regional scale [J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(5):188.
[18]刘艳,王锦地,周红敏,等.用地面点测量数据验证LAI产品中的尺度转换方法[J].遥感学报,2014,17(1):1189. LIU Y,WANG J D,ZHOU H M,et al.Upscaling approach for validation of LAI products derived from remote sensing observation [J].Remote Sensing,2014,17(1):1189.
[19] STROEVE J,BOX J E,GAO F,et al.Accuracy assessment of the MODIS 16-day albedo product for snow:Comparisons with Greenland in situ measurements [J].Remote Sensing of Environment,2005,94(1):46.
[20]ROMáN M O,SCHAAF C B,WOODCOCK C E,et al.The MODIS(Collection V005 ) BRDF /albedo product:Assessment of spatial representativeness over forested landscapes [J].Remote Sensing of Environment,2009,113(11):2476.
[21]CLEVERS J G P W & GITELSON A A.Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 [J].International Journal of Applied Earth Observation and Geoinformation,2013,23:344.
[22]LIANG S L,FANG H L,CHEN M Z,et al.Validating MODIS land surface reflectance and albedo products:Methods and preliminary results [J].Remote Sensing of Environment,2002,83(1-2):149.
[23]CHEN J M,PAVLIC G,BROWN L,et al.Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements [J].Remote Sensing of Environment,2002,80(1):165.
[24]GARRIGUES S,LACAZE R,BARET F,et al.Validation and intercomparison of global leaf area index products derived from remote sensing data [J].Journal of Geophysical Research:Biogeosciences(2005-2012),2008,113:G02028.
[25]YANG W Z,TAN B,HUANG D,et al.MODIS leaf area index products:From validation to algorithm improvement [J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(7):1885.
[26]刘轲,周清波,吴文斌,等.基于多光谱与高光谱遥感数据的冬小麦叶面积指数反演比较[J].农业工程学报,2016,32(3):155. LIU K,ZHOU Q B,WU W B,et al.Comparison between multispectral and hyperspectral remote sensing for LAI estimation [J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(3):155.
[27]付立哲,屈永华,王锦地.MODIS LAI产品真实性检验与偏差分析[J].遥感学报,2017,21(2):206. FU L Z,QU Y H,WANG J D.Bias analysis and validation method of the MODIS LAI product [J].Remote Sensing,2017,21(2):206.
[28]李颖,韦原原,刘荣花,等.河南麦区一次高温低湿型干热风灾害的遥感监测[J].中国农业气象,2014,35(5):593. LI Y,WEI Y Y,LIU R H,et al.Remote sensing monitoring of a dry-hot wind disaster in wheat-growing area of Henan Province [J].Chinese Journal of Agrometeorology,2014,35(5):593.
[29]曾也鲁,李静,柳钦火,等.基于NDVI先验知识的LAI地面采样方法[J].遥感学报,2013,17(1):109. ZENG Y L,LI J,LIU Q H,et al.A sampling strategy based on NDVI prior knowledge for LAI ground measurements [J].Remote Sensing,2013,17(1):109.
[2]李小文,王祎婷.定量遥感尺度效应刍议[J].地理学报,2013,68(9):1163. LI X W,WANG W T.Prospects on future developments of quantitative remote sensing[J].Acta Geographica Sinica,2013,68(9):1163.
[3]李小文.定量遥感的发展与创新[J].河南大学学报(自然科学版),2005,35(4):49. LI X W.Retrospect,prospect and innovation in quantitative remote sensing [J].Journal of Henan University(Natural Science),2005,35(4):49.
[4]WU H,LI Z L.Scale issues in remote sensing:A review on analysis,processing and modeling [J].Sensors,2009,9(3):1768.
[5]MORISETTE J T,PRIVETTE J L,JUSTICE C O.A framework for the validation of MODIS land products [J].Remote Sensing of Environment,2002,83:77.
[6]BARET F,MORISSETTE J T,FERNANDER R A,et al.Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products:Proposition of the CEOS-BELMANIP [J].IEEE Transactions on Geoscience and Remote Sensing, 2006,44:1794.
[7] TIAN Y H,WOODCOCK C E,WANG J,et al.Multiscale analysis and validation of the MODIS LAI product-II.Sampling strategy [J].Remote Sensing of Environment,2002,83:431.
[8] BARET F,WEISS M,LACAZE R,et al.GEOV1:LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products.Part1:Principles of development and production [J].Remote Sensing of Environment,2013,137:299.
[9] CAMACHO F,CERNICHARO J,LACAZE R,et al.GEOV1:LAI,FAPAR essential climate variables and FCOVER global time series capitalizing over existing products.Part 2:Validation and intercomparison with reference products [J].Remote Sensing of Environment,2013,137:310.
[10] WANG S G,LI X,GE Y,et al.Validation of regional-scale remote sensing products in China:From site to network [J].Remote Sensing,2016,8(12):980.
[11]李新,晋锐,刘绍民,等.黑河遥感试验中尺度上推研究的进展与前瞻[J].遥感学报,2016,20(5):921. LI X,JIN R,LIU S M,et al.Upscaling research in HiWATER:Progress and prospects [J].Journal of Remote Sensing,2016,20(5):921.
[12]徐保东,李静,柳钦火,等.地面站点观测数据代表性评价方法研究进展[J].遥感学报,2015,19(5):703. XU B D,LI J,LIU Q H,et al.Review of methods for evaluating representativeness of ground station observations [J].Remote Sensing,2015,19(5):703.
[13] CESCATTI A,MARCOLLA B,SANTHANA VANNAN S K,et al.Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network [J].Remote Sensing of Environment,2012,121:323.
[14] ROMáN M O,GATEBE C K,SHUAI Y M,et al.Use of in situ and airborne multiangle data to assess MODIS-and Landsat-Based estimates of directional reflectance and albedo [J].IEEE Transactions on Geoscience and Remote Sensing,2013,51(3):1393.
[15] WANG Z S,SCHAAF C B,STRAHLER A H,et al.Evaluation of MODIS albedo product(MCD43A) over grassland,agriculture and forest surface types during dormant and snow covered periods [J].Remote Sensing of Environment,2014,140:60.
[16]李新.陆地表层系统模拟和观测的不确定性及其控制[J].中国科学:地球科学,2013,43(11):1735. LI X.Characterization,controlling,and reduction of uncertainties in the modeling and observation of land-surface systems [J].Science China:Earth Sciences,2013,43(11):1735.
[17]卢伟,范文义.中小区域尺度时间序列林地 LAI 快速估测方法[J].农业工程学报,2016,32(5):188. LU W,FAN W Y.Fast estimation method for time-series forest LAI at small and medium regional scale [J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(5):188.
[18]刘艳,王锦地,周红敏,等.用地面点测量数据验证LAI产品中的尺度转换方法[J].遥感学报,2014,17(1):1189. LIU Y,WANG J D,ZHOU H M,et al.Upscaling approach for validation of LAI products derived from remote sensing observation [J].Remote Sensing,2014,17(1):1189.
[19] STROEVE J,BOX J E,GAO F,et al.Accuracy assessment of the MODIS 16-day albedo product for snow:Comparisons with Greenland in situ measurements [J].Remote Sensing of Environment,2005,94(1):46.
[20]ROMáN M O,SCHAAF C B,WOODCOCK C E,et al.The MODIS(Collection V005 ) BRDF /albedo product:Assessment of spatial representativeness over forested landscapes [J].Remote Sensing of Environment,2009,113(11):2476.
[21]CLEVERS J G P W & GITELSON A A.Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 [J].International Journal of Applied Earth Observation and Geoinformation,2013,23:344.
[22]LIANG S L,FANG H L,CHEN M Z,et al.Validating MODIS land surface reflectance and albedo products:Methods and preliminary results [J].Remote Sensing of Environment,2002,83(1-2):149.
[23]CHEN J M,PAVLIC G,BROWN L,et al.Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements [J].Remote Sensing of Environment,2002,80(1):165.
[24]GARRIGUES S,LACAZE R,BARET F,et al.Validation and intercomparison of global leaf area index products derived from remote sensing data [J].Journal of Geophysical Research:Biogeosciences(2005-2012),2008,113:G02028.
[25]YANG W Z,TAN B,HUANG D,et al.MODIS leaf area index products:From validation to algorithm improvement [J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(7):1885.
[26]刘轲,周清波,吴文斌,等.基于多光谱与高光谱遥感数据的冬小麦叶面积指数反演比较[J].农业工程学报,2016,32(3):155. LIU K,ZHOU Q B,WU W B,et al.Comparison between multispectral and hyperspectral remote sensing for LAI estimation [J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(3):155.
[27]付立哲,屈永华,王锦地.MODIS LAI产品真实性检验与偏差分析[J].遥感学报,2017,21(2):206. FU L Z,QU Y H,WANG J D.Bias analysis and validation method of the MODIS LAI product [J].Remote Sensing,2017,21(2):206.
[28]李颖,韦原原,刘荣花,等.河南麦区一次高温低湿型干热风灾害的遥感监测[J].中国农业气象,2014,35(5):593. LI Y,WEI Y Y,LIU R H,et al.Remote sensing monitoring of a dry-hot wind disaster in wheat-growing area of Henan Province [J].Chinese Journal of Agrometeorology,2014,35(5):593.
[29]曾也鲁,李静,柳钦火,等.基于NDVI先验知识的LAI地面采样方法[J].遥感学报,2013,17(1):109. ZENG Y L,LI J,LIU Q H,et al.A sampling strategy based on NDVI prior knowledge for LAI ground measurements [J].Remote Sensing,2013,17(1):109.