SBIR-STTR Award

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to provide software service to correct Earth observation satellite (EOS) data to at-ground reflectance. EOS must look through the Earth?s atmosphere that induces systematic error in measuring the actual reflectance of ground targets through scatter and attenuation of light. Atmospherically-induced error affects data utility because the atmospheric aerosol content, i.e., humidity, dust, pollen, smoke particles, etc., fluctuates greatly, impacting applications for global monitoring, defense and agriculture. The value of data could make this a significant and growing market opportunity if the specifications are met successfully. This SBIR Phase I project proposes to correct aerosol-induced error in EOS data by reversing the effect, found empirically to be structured, independent of aerosol type and potentially predictable through measurement of dark target-reflectance ? water bodies clear of aquatic vegetation, entrained sediment and specular reflectance from windblown waves. The method of study is extraction and statistical analysis of Landsat 8 data, the standard reference for calibration and validation of data from all other EOS platforms. This problem is approached through a series of heuristic investigations to (1) reconstruct relationships of blue, green and red bands to near infrared (NIR) originally fitted using Landsat 5 and 7 data (longer wavelengths may not be addressed because they are resistant to atmospheric affects), (2) use these relationships to reverse the error, (3) develop methods to select, proof and apply dark targets that calibrate the correction, and (4) measure residual error by comparing post-algorithm reflectance to at-ground reflectance measured by portable spectrometry. The residual error is likely due to uncertainty associated with dark targets. Once developed and proofed, the algorithm will be brought to the Landsat 8 Cal/Val team for validation. 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.

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.