SBIR Phase II: A Novel Method for Atmospheric Correction of Earth Observation Satellite Data

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to improve satellite-based agricultural imaging to allow monitoring and managing of broad regions for enhanced sustainability and food security. The proposed algorithms address noise in satellite data that is caused by the atmosphere. The method can be used for many types of satellites, providing advantages in efforts to reduce weight and space to control launch cost. This SBIR Phase II project proposes to address a major issue in modern remote sensing, atmospherically induced noise in the data. EOS systems look through the atmosphere that distorts spectral relationships of reflectance (ratio of EOS-measured reflected light to the sunlight that would be received were there no atmosphere). The variable atmosphere distorts the data variably so must be corrected to accurately interpret highly useful measures such as agricultural production/yield, photosynthesis, plant water use, plant disease/insect infestations, etc., rendering EOS data unreliable unless corrected. The technical tasks are to: (1) study European Space Agency Sentinel 2 EOS data, (2) migrate the method to NASA/USGS Landsat 8, (3) adapt the method for calibration of the several decades-long Landsat Multispectral Scanner record that cannot otherwise be corrected to surface reflectance, (4) develop a calibration tool for multiple commercial EOS, and (5) develop a fully integrated software system. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.