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

SBIR 第一阶段：地球观测卫星数据大气校正的新方法

• 批准号: 1840196
• 负责人: David Groeneveld
• 金额: $ 22.5万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1840196&HistoricalAwards=false
• 关键词: SBIR; Phase; Novel; Method; Atmospheric

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.

该小型企业创新研究 (SBIR) 项目更广泛的影响/商业潜力是提供软件服务，以将地球观测卫星 (EOS) 数据校正为地面反射率。 EOS 必须透过地球大气层，通过光的散射和衰减来测量地面目标的实际反射率，从而导致系统误差。大气引起的误差影响数据效用，因为大气气溶胶含量，即湿度、灰尘、花粉、烟雾颗粒等波动很大，影响全球监测、国防和农业的应用。如果成功满足规范，数据的价值可能会使其成为一个重要且不断增长的市场机会。 该 SBIR 第一阶段项目建议通过逆转效应来纠正 EOS 数据中气溶胶引起的误差，根据经验发现该效应是结构化的，与气溶胶类型无关，并且可以通过测量暗目标反射率进行预测？水体中没有水生植被、夹带沉积物和风吹波浪的镜面反射。研究方法是对 Landsat 8 数据进行提取和统计分析，这是所有其他 EOS 平台数据校准和验证的标准参考。这个问题是通过一系列启发式调查来解决的，目的是 (1) 重建蓝色、绿色和红色波段与最初使用 Landsat 5 和 7 数据拟合的近红外 (NIR) 的关系（较长的波长可能无法解决，因为它们对大气有抵抗力） (2) 使用这些关系来扭转误差，(3) 开发方法来选择、验证和应用校准校正的暗目标，以及 (4) 通过将算法后反射率与地面反射率进行比较来测量残余误差通过便携式光谱仪测量反射率。残余误差可能是由于与暗目标相关的不确定性造成的。一旦开发并经过验证，该算法将提交给 Landsat 8 Cal/Val 团队进行验证。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。