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

SBIR第二阶段：地球观测卫星数据大气校正的新方法

• 批准号: 1950746
• 负责人: David Groeneveld
• 金额: $ 68.29万
• 依托单位: ADVANCED REMOTE SENSING, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1950746&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Novel; Method

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.

这一小型企业创新研究(SBIR)项目的更广泛影响/商业潜力是改进基于卫星的农业成像，以便能够监测和管理广大地区，以增强可持续性和粮食安全。拟议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，在努力减轻重量和空间以控制发射成本方面具有优势。这个SBIR第二阶段项目旨在解决现代遥感中的一个主要问题，即数据中的大气诱导噪声。EOS系统通过扭曲反射率光谱关系(EOS测量的反射光与阳光的比率，如果没有大气)的大气进行观测。变化的大气会使数据发生不同程度的扭曲，因此必须加以修正，以准确地解释高度有用的衡量标准，如农业生产/产量、光合作用、植物水分利用、植物病虫害等，这使得EOS数据不可靠，除非经过修正。技术任务是：(1)研究欧洲航天局哨兵2号状态方程数据，(2)将该方法移植到NASA/USGS陆地卫星8号，(3)将该方法用于校准几十年长的陆地卫星多光谱扫描仪记录，否则无法校正地面反射率，(4)开发用于多个商业状态的校准工具，以及(5)开发完全集成的软件系统。