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.

这项小型企业创新研究（SBIR）项目的更广泛的影响/商业潜力是提供软件服务，以纠正地球观察卫星（EOS）数据，以将其纠正到地面反射率。 EOS必须通过地球的大气层来诱发系统的误差，以通过散射和衰减来测量地面目标的实际反射率。大气引起的错误会影响数据实用性，因为大气气溶胶含量，即湿度，灰尘，花粉，烟雾颗粒等，都大大波动，影响了全球监测，国防和农业的应用。如果成功满足规格，数据的价值可能会使这是一个巨大而不断增长的市场机会。 该SBIR I期项目提议通过逆转效果来纠正EOS数据中的气溶胶诱导的误差，该效应在经验上是结构化的，与气溶胶类型无关，并且可以通过测量暗目标反射能力来预测？水体清除了水生植被，夹带的沉积物和风孔波的镜面反射率。研究方法是对Landsat 8数据的提取和统计分析，这是所有其他EOS平台校准和验证数据的标准参考。 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比较载后反射率与通过便携式光谱法测量的AT-OFFROMER反射率。残留误差可能是由于与黑暗目标相关的不确定性。一旦开发和证明，该算法将被带到Landsat 8 Cal/Val团队进行验证。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的审查标准，被认为值得通过评估来获得支持。