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

这个小企业创新研究项目的更广泛影响/商业潜力是改进基于卫星的农业成像，以便对广大地区进行监测和管理，以增强可持续性和粮食安全。提出的算法解决了卫星数据中由大气引起的噪声。该方法可用于多种类型的卫星，在减轻重量和空间以控制发射成本方面具有优势。该SBIR二期项目旨在解决现代遥感中的一个主要问题，即数据中的大气噪声。EOS系统透过大气层观察，大气层扭曲了反射率的光谱关系（在没有大气层的情况下，EOS测量的反射光与接收到的阳光的比率）。多变的大气会对数据造成不同程度的扭曲，因此必须进行校正才能准确地解释非常有用的指标，如农业生产/产量、光合作用、植物水分利用、植物病害/虫害等，除非进行校正，否则将导致EOS数据不可靠。技术任务是：(1)研究欧洲航天局Sentinel 2 EOS数据，(2)将该方法迁移到NASA/USGS Landsat 8，(3)采用该方法校准长达数十年的Landsat多光谱扫描仪记录，否则无法校正地表反射率，(4)开发用于多个商业EOS的校准工具，(5)开发完全集成的软件系统。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.