Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this stu...Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this study,a novel method that combining Kriging interpolation and Cressman interpolation was proposed to generate high spatial and temporal resolution LAI products by fusing Moderate Resolution Imaging SpectroRadiometer(MODIS)characterized by coarse spatial resolution and high temporal resolution and Gaofen-1(GF-1)with fine spatial resolution and coarse temporal resolution.This method was applied to the Huangpu district of Guangzhou,Guangdong,China.The results showed that compared to field observation,the predicted values of LAI had an acceptable accuracy of 73.12%.Using Moran’s I index and Kolmogorov-Smirnov tests,it was found that the MODIS data were spatially auto-correlated and characterized by normal distributions.Scaling down the 1 km×1 km spatial resolution MODIS products to a spatial resolution of 30 m×30 m using point-Kriging resulted in a precision of 79.38%compared to the results at the same spatial resolution derived from an 8 m×8 m spatial resolution GF-1 image by scaling up using block-Kriging.Moreover,the regression models that accounts for the relationship between NDVI(Normalized Difference Vegetation Index)and LAI based on MODIS data obtained the determination coefficients ranging from 0.833 to 0.870.Finally,the data fusion and interpolation of MODIS and GF-1 data using Cressman method generated high spatial and temporal resolution LAI maps,which showed reasonably spatial and temporal variability.The results imply that the proposed method is a powerful tool to create high spatial and temporal resolution LAI products.展开更多
基金Science and Technology Program of Guangzhou,China(2014A050503060).
文摘Generation of high spatial and temporal resolution LAI(leaf area index)products is challenging because higher spatial resolution remotely sensed data usually have coarse temporal resolutions and vice versa.In this study,a novel method that combining Kriging interpolation and Cressman interpolation was proposed to generate high spatial and temporal resolution LAI products by fusing Moderate Resolution Imaging SpectroRadiometer(MODIS)characterized by coarse spatial resolution and high temporal resolution and Gaofen-1(GF-1)with fine spatial resolution and coarse temporal resolution.This method was applied to the Huangpu district of Guangzhou,Guangdong,China.The results showed that compared to field observation,the predicted values of LAI had an acceptable accuracy of 73.12%.Using Moran’s I index and Kolmogorov-Smirnov tests,it was found that the MODIS data were spatially auto-correlated and characterized by normal distributions.Scaling down the 1 km×1 km spatial resolution MODIS products to a spatial resolution of 30 m×30 m using point-Kriging resulted in a precision of 79.38%compared to the results at the same spatial resolution derived from an 8 m×8 m spatial resolution GF-1 image by scaling up using block-Kriging.Moreover,the regression models that accounts for the relationship between NDVI(Normalized Difference Vegetation Index)and LAI based on MODIS data obtained the determination coefficients ranging from 0.833 to 0.870.Finally,the data fusion and interpolation of MODIS and GF-1 data using Cressman method generated high spatial and temporal resolution LAI maps,which showed reasonably spatial and temporal variability.The results imply that the proposed method is a powerful tool to create high spatial and temporal resolution LAI products.
