SLSTR Level 1 processing produces calibrated, ortho-geolocated data on a regular quasi-Cartesian grid. The processing is separated into Level-1A and Level-1B processing stages.
The first process, Level-1A processing, aims to compute ortho-geolocation of each instrument pixel and to compute radiometric calibration coefficients. It contains five main steps:
- Source packet processing: unpacking, validating and converting source packet data and ancillary parameters.
- IR channel calibration coefficients: calculating the calibration offset and slope that describes the linear relationship between numerical counts and radiance (focusing on thermal and fire channels).
- Visible/NIR/SWIR channel calibration coefficients: calculating the visible/NIR/SWIR channel calibration using the VISCAL target. The numerical counts measured towards the VISCAL target during its illumination by the Sun (once per orbit near the South Pole) are extracted and used to calculate gain and offset.
- Time calibration: computing the time stamp of each pixel.
- Geolocation: computing the longitude/latitude/altitude and the corresponding (x,y) coordinates of each instrument pixel.
The second process, Level-1B processing, aims to calibrate and re-grid the SLSTR measurements, and test each pixel for cloud presence. It contains nine main steps:
- Signal calibration for thermal and solar channels using the calibration coefficients derived from Level-1A processing.
- Re-gridding: determining, for each instrument pixel, its indices in across and along track directions taking into account of a 1 km grid for thermal IR channels or a 0.5 km grid for visible, NIR and SWIR channels.
- Cosmetic filling: filling missing image pixels as well as some parameters needed for further sections.
- Surface classification and cloud flagging are applied to each image pixel.
- Reflectance to radiance conversion.
- Tie point interpolation: delimitate rectangular SLSTR L1B image and increase the tie point resolution in along-track direction (1 km instead of 16 km).
- Meteorological annotation computation providing ECMWF variables on a sub-grid of tie-points.
- ayesian and Probabilisitic cloud mask computation using the Meterological annotation data.
- Product formatting: aiming to identify the variables included in SLSTR product files and metadata.