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

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1840196
Agency Tracking Number: 1840196
Amount: $225,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: CT
Solicitation Number: N/A
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-02-01
Award End Date (Contract End Date): 2019-09-30
Small Business Information
407 N VANDEMARK AVE
HARTFORD, SD 57033
United States
DUNS: 081141576
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 David Groeneveld
 (505) 690-6864
 david@advancedremotesensing.com
Business Contact
 David Groeneveld
Phone: (505) 690-6864
Email: david@advancedremotesensing.com
Research Institution
N/A
Abstract

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.

* Information listed above is at the time of submission. *

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