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

Abstract

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

A Fully Automatic Burnt Area Mapping Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor

“…Channel degradation over time causes decreases in measurement precision [17]. However, problems regarding orbital drift and channel degradation could be minimized within the TIMELINE project [18][19][20].…”

“…The developed burnt area mapping algorithm is part of the German Aerospace Center's (DLR) TIMELINE project, which aims to develop an operational processing and data management environment to reprocess 30 years of NOAA-AVHRR raw data into L1b, L2, and L3 products on the basis of 1.1-km High-Resolution Picture Transmission (HRPT) and Local Area Coverage (LAC) data of the European continent, as well as offer these products online to a wider community using a free and open data policy. Therefore, an enhanced preprocessing by taking into account geometric distortions due to rotation and satellite clock errors, varying spectral responses of different AVHRR sensors, calibration drift, orbit drift, sensor degradation, and atmospheric correction is performed [18][19][20]. The consistency of calibrated reflectance and thermal information is highly required for time series analysis as planned within TIMELINE [35,36].…”

“…Moreover, enhanced cloud, water, snow masks, and land use/land cover products are developed based on accurate and continuous L1b products. Currently, preprocessed (top of atmosphere reflectances) map-projected L1b AVHRR data are used as input for the burnt area mapping processor [18][19][20]. However, the project is ongoing, and the preprocessing and derivation of L2 and L3 products is not yet finished.…”

A Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor

“…The developed hotspot detection algorithm is part of the German Aerospace Center's (DLR) TIMELINE project, which aims to develop an operational processing and data management environment to reprocess 30 years of NOAA-AVHRR raw data into L1b, L2 and L3 products on the basis of 1.1 km High Resolution Picture Transmission (HRPT) and Local Area Coverage (LAC) data of the European continent and offer them online to a wider community using a free and open data policy. Therefore, an enhanced pre-processing by taking geometric distortions due to rotation and satellite clock errors, varying spectral responses of different AVHRR sensors, calibration drift, orbit drift, sensor degradation and atmospheric correction into account is performed [23][24][25]. Consistency of calibrated reflectance and thermal information is highly required for time series analysis as planned within TIMELINE [71,72].…”

“…Pre-processed (top of atmosphere reflectances and brightness temperatures) L1b AVHRR data is used as input for the hotspot detection processor [23][24][25]. The first AVHRR sensor was launched with TIROS-N in 1978.…”