Abstract

One of the largest uncertainties in remote sensing data comes from atmospheric influence. This research aims to explain the uncertainties emanating from atmospheric correction (AC) product selection and how they influence chlorophyll α concentration retrieval in lakes in eastern Lithuania. We tested seven products from six AC processors (Acolite, Acolite Rayleigh, iCOR, Sen2Cor, C2RCC, C2X, and POLYMER) and 10 chlorophyll α retrieval algorithms with different architectures. The uncertainty of AC products transferred to chlorophyll α concentrations, and large differences in the chlorophyll α concentrations retrieved using different AC products were observed. The match-up analysis showed that chlorophyll α algorithms based on band difference performed best in terms of a high coefficient of determination and the lowest median bias when used with image-based, Sen2Cor, and TOA data. The results of this study highlight the uncertainties of AC products as well as how the selection of the chlorophyll α retrieval algorithm can mitigate the influence of AC selection.

摘要

遥感数据中最大的不确定性之一来自大气影响。本研究旨在解释大气校正 (AC) 产品选择产生的不确定性，以及它们如何影响立陶宛东部湖泊中叶绿素 α 浓度的反演。我们测试了来自六种 AC 处理器（Acolite、Acolite Rayleigh、iCOR、Sen2Cor、C2RCC、C2X 和 POLYMER）的七种产品和 10 种不同架构的叶绿素 α 检索算法。观察到 AC 产品转移到叶绿素 α 浓度的不确定性，并且观察到使用不同 AC 产品检索的叶绿素 α 浓度存在很大差异。匹配分析表明，当与基于图像、Sen2Cor 和 TOA 数据一起使用时，基于带差异的叶绿素 α 算法在高确定系数和最低中值偏差方面表现最佳。本研究的结果突出了 AC 产品的不确定性以及叶绿素 α 检索算法的选择如何减轻 AC 选择的影响。