Abstract
RapidEye is a constellation of 5 identical remote sensing satellites imaging up to 6 mio. sqkm. of the earth surface every day. Among others important application fields of the satellite data are forestry and agriculture. The inter-calibration of the five RapidEye satellites is achieved with a statistical approach supported by vicarious absolute calibration.
The data analysis for forestry and agricultural applications sometimes requires an even better temporal resolution of remote sensing data than available from a single satellite system like RapidEye despite it's very high imaging capacity and repetition rate. As the relative spectral response curves of systems like Landsat 8, Sentinel 2, SPOT and RapidEye differ to some extent the data of these different sensors need to be cross calibrated to a common standard before they can be used together within such individual applications. Although cross calibration between different sensors is limited regarding its achievable accuracy due to different reflectance properties of the surface and also due to changing atmospheric conditions, it is a mandatory precondition to minimize potential differences due to sensor effects.
This presentation shows the results of the performed analysis and the determined surface dependent spectral band adjustment factors for the different sensors and is additionally showcasing the improvements in a forestry application which was achieved by the application of spectral band adjustment factors.
Inter-Calibration of the RapidEye Sensors with Landsat 8, Sentinel and SPOT
RapidEye is a constellation of 5 identical remote sensing satellites imaging up to 6 mio. sqkm. of the earth surface every day. Among others important application fields of the satellite data are forestry and agriculture. The inter-calibration of the five RapidEye satellites is achieved with a statistical approach supported by vicarious absolute calibration.
The data analysis for forestry and agricultural applications sometimes requires an even better temporal resolution of remote sensing data than available from a single satellite system like RapidEye despite it's very high imaging capacity and repetition rate. As the relative spectral response curves of systems like Landsat 8, Sentinel 2, SPOT and RapidEye differ to some extent the data of these different sensors need to be cross calibrated to a common standard before they can be used together within such individual applications. Although cross calibration between different sensors is limited regarding its achievable accuracy due to different reflectance properties of the surface and also due to changing atmospheric conditions, it is a mandatory precondition to minimize potential differences due to sensor effects.
This presentation shows the results of the performed analysis and the determined surface dependent spectral band adjustment factors for the different sensors and is additionally showcasing the improvements in a forestry application which was achieved by the application of spectral band adjustment factors.