A Computation Framework for LISS-III Analysis Ready Data (ARD) Products for Indian Spatial Data Cube Generation

The velocity and volume of MultiSpectral (MS) remote sensing data have recently increased exponentially. In recent times, however, the absence of a spatial data cube to store analysis-ready data (ARD) products for the Indian sensors’ data delimits its ready use and depreciates its value. Establishing a framework for storing, managing, and providing online processing ARD products for different sensors is necessary. The current work proposes a framework to produce ARD products by radiometrically correcting the data using the 6 S atmospheric correction and Shepherd Diamond-based terrain correction method to provide normalised surface reflectance. The generated ARD product for LISS-III shows a good correlation with the Planet Lab’s surface reflectance ARD product and an excellent correlation with the SACRS2- a Scheme for Atmospheric Correction of ResourceSat-2 corrected product. A frequency-based geometric correction algorithm provides RMSE of less than half a pixel registration error compared to LANDSAT-8 OLI orthorectified imagery. Finally, A Spatial Data Cube (SDC) with CARD4L metadata standard stores the ARD products post ingestion. The paper explains the complete integrated software development with an end-to-end processing chain of LISS III, an Indian optical sensor data.