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The geolocation of Advanced Very High Resolution Radiometer (AVHRR) data is known\udto be imprecise due to minor satellite position and orbit uncertainties. These uncertainties lead\udto distortions once the data are projected based on the provided orbit parameters. This can cause\udgeolocation errors of up to 10 km per pixel which is an obstacle for applications such as time series\udanalysis, compositing/mosaicking of images, or the combination with other satellite data. Therefore,\uda fusion of two techniques to match the data in orbit projection has been developed to overcome this\udlimitation, even without the precise knowledge of the orbit parameters. Both techniques attempt to\udfind the best match between small image chips taken from a reference water mask in the first, and from\uda median Normalized Difference Vegetation Index (NDVI) mask in the second round. This match is\uddetermined shifting around the small image chips until the best correlation between reference and\udsatellite data source is found for each respective image part. Only if both attempts result in the same\udshift in any direction, the position in the orbit is included in a third order polynomial warping process\udthat will ultimately improve the geolocation accuracy of the AVHRR data. The warping updates the\udlatitude and longitude layers and the contents of the data layers itself remain untouched. As such,\udoriginal sensor measurements are preserved. An included automated quality assessment generates\uda quality layer that informs about the reliability of the matching

Automated Improvement of Geolocation Accuracy in AVHRR Data Using a Two-Step Chip Matching Approach—A Part of the TIMELINE Preprocessor