The normalized difference vegetation index(NDVI)is the most widely used vegetation index for monitoring vegetation vigor and cover.As NDVI time series are usually derived at coarse or medium spatial resolutions,pixel ...The normalized difference vegetation index(NDVI)is the most widely used vegetation index for monitoring vegetation vigor and cover.As NDVI time series are usually derived at coarse or medium spatial resolutions,pixel size often represents a mixture of vegetated and non-vegetated surfaces.In heterogeneous urban areas,mixed pixels impede the accurate estimation of gross primary productivity(GPP).To address the mixed pixel effect on'NDVI time series and GPP estimation,we proposed a framework to extract subpixel vegetation NDVI(NDVI_(vege))from Landsat OLI images in urban areas,using endmember fractions,mixed NDVI(NDVI_(mix)),and NDVI.of non-vegetation,endmembers.Results demonstrated that the NDVI_(vege) extracted by this framework agreed well with the true NDVI_(vege) cross seasons and vegetation fractions,with R^(2) ranging from 0.74 to 0.82 and RMSE ranging from 0.03 to 0.04.The NDVI_(vege) time series was applied to evaluate vegetation GP in Wuhan,China.The total annual GPp estimated with NDVI_(vege) was 28-35%higher than the total annual GPP estimated with NDVI_(mix) implying uncertainty in the GPP estimations caused by mixed pixels.This study showed the potential of the proposed framework to resolve NDVI_(vege) for characterizing vegetation dynamics in heterogeneous areas.展开更多
基金supported by the National Key Research and Development Program of China(No..2022YFB3903405)National Natural Science Foundation of China(General Program:42171466 and 42171350)the Fundamental Research Funds for the Central Universities(2662021JC002).
文摘The normalized difference vegetation index(NDVI)is the most widely used vegetation index for monitoring vegetation vigor and cover.As NDVI time series are usually derived at coarse or medium spatial resolutions,pixel size often represents a mixture of vegetated and non-vegetated surfaces.In heterogeneous urban areas,mixed pixels impede the accurate estimation of gross primary productivity(GPP).To address the mixed pixel effect on'NDVI time series and GPP estimation,we proposed a framework to extract subpixel vegetation NDVI(NDVI_(vege))from Landsat OLI images in urban areas,using endmember fractions,mixed NDVI(NDVI_(mix)),and NDVI.of non-vegetation,endmembers.Results demonstrated that the NDVI_(vege) extracted by this framework agreed well with the true NDVI_(vege) cross seasons and vegetation fractions,with R^(2) ranging from 0.74 to 0.82 and RMSE ranging from 0.03 to 0.04.The NDVI_(vege) time series was applied to evaluate vegetation GP in Wuhan,China.The total annual GPp estimated with NDVI_(vege) was 28-35%higher than the total annual GPP estimated with NDVI_(mix) implying uncertainty in the GPP estimations caused by mixed pixels.This study showed the potential of the proposed framework to resolve NDVI_(vege) for characterizing vegetation dynamics in heterogeneous areas.
