Data Fusion, De-noising, and Filtering to Produce Cloud-Free High Quality Temporal Composites Employing Parallel Temporal Map Algebra

Abstract

Remotely sensed images from satellite sensors such as MODIS Aqua and Terra provide high temporal resolution and wide area coverage. Unfortunately, these images frequently include undesired cloud and water cover. Areas of cloud or water cover preclude analysis and interpretation of terrestrial land cover, vegetation vigor, and/or analysis of change. Cross platform multi-temporal image compositing techniques may be employed to create daily synthetic cloud free images using fused images from Aqua and Terra MODIS satellite images, and then creating a composite that includes representative values derived from a set of possibly cloudy satellite images collected during a given longer time period of interest. Spatio-temporal analytical processing methods that utilize moderate spatial resolution satellite imagery with high temporal resolution to create multi-temporal composites are data intensive and computationally intensive. Therefore, a study of the strategies using high performance parallel solutions is required. This research focuses on analyzing the fusion, de-noising, filtering, and compositing strategies for vegetation indices using parallel temporal map algebra. The report provides objective findings on methods and the relative benefits observed from various analysis methods and parallelization strategies.