Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new consistent long-term normalized difference vegetation index (NDVI) time series at a 1-km 2  resolution for Southern Africa that is based on the data from Satellite Pour l'Observation de la Terre VEGETATION (VGT) (1998-2006) and the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) (1985-1998) has been produced for vegetation-dynamics monitoring purposes.  presents the evaluation of the newly processed AVHRR data set, as ll as the integration of this data set with the VGT archive. First, the AVHRR processing chain and the resulting AVHRR data set have been investigated with respect to calibration accuracy, cloud masking, and atmospheric and geometric correction. Second, different calibration approaches, spectral response (SR) functions, spatial resolutions, overpass times, and geometries of observation for the VGT and AVHRR data sets have been compared for a common observation period. The application of published correction functions accounting for the SR differences for both sensors considerably improved the consistency beten both data sets. An r 2  of 0.85 is obtained beten paired samples of the NDVI from the VGT and the newly processed AVHRR archive. After the application of the correction functions, the slope of the regression line beten the two NDVI data sets was much closer to the 1:1 line. The performance of the correction functions differed among vegetation types. The largest reduction in the root-mean-square error beten the NDVI of both sensors is obtained from areas with higher biomass. Large parts of the remaining variability are suggested to be attributed to the bidirectional reflectance distribution function effects, as demonstrated by the intersensor NDVI time-series variability versus the intrasensor NDVI time-series variability.
['Else Swinnen', 'Frank Veroustraete']
I am writing a research paper on a idea Extending the SPOT-VEGETATION NDVI Time Series (1998–2006) Back in Time With NOAA-AVHRR Data (1985–1998) for Southern Africa
[{"from": "human", "value": "I am writing a research paper on a idea Extending the SPOT-VEGETATION NDVI Time Series (1998\u20132006) Back in Time With NOAA-AVHRR Data (1985\u20131998) for Southern Africa"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new consistent long-term normalized difference vegetation index (NDVI) time series at a 1-km 2  resolution for Southern Africa that is based on the data from Satellite Pour l'Observation de la Terre VEGETATION (VGT) (1998-2006) and the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) (1985-1998) has been produced for vegetation-dynamics monitoring purposes.  presents the evaluation of the newly processed AVHRR data set, as ll as the integration of this data set with the VGT archive. First, the AVHRR processing chain and the resulting AVHRR data set have been investigated with respect to calibration accuracy, cloud masking, and atmospheric and geometric correction. Second, different calibration approaches, spectral response (SR) functions, spatial resolutions, overpass times, and geometries of observation for the VGT and AVHRR data sets have been compared for a common observation period. The application of published correction functions accounting for the SR differences for both sensors considerably improved the consistency beten both data sets. An r 2  of 0.85 is obtained beten paired samples of the NDVI from the VGT and the newly processed AVHRR archive. After the application of the correction functions, the slope of the regression line beten the two NDVI data sets was much closer to the 1:1 line. The performance of the correction functions differed among vegetation types. The largest reduction in the root-mean-square error beten the NDVI of both sensors is obtained from areas with higher biomass. Large parts of the remaining variability are suggested to be attributed to the bidirectional reflectance distribution function effects, as demonstrated by the intersensor NDVI time-series variability versus the intrasensor NDVI time-series variability."}]