应用于哨兵-2 MSI 和哨兵-3 OLCI 数据的不同水内算法对富营养化和吸收性水域的适用性

Ave Ansper-Toomsalu, Mirjam Uusõue, Kersti Kangro, Martin Hieronymi, K. Alikas
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摘要

由于高浓度的光学活性物质(OASs)及其固有光学特性(IOPs)以及邻近效应,光学复杂水域给遥感工作带来了巨大挑战。OAS 和 IOP 可通过大气校正处理器的水内算法得出,该算法适用于哨兵-2 多光谱仪器(S2 MSI)和哨兵-3 海洋与陆地色彩仪器(S3 OLCI)的数据。这项研究将针对 Case-2 水域的 S3 OLCI Level-2 水内产品与来自 ACOLITE、POLYMER、C2RCC 和 A4O 的其他水内算法进行了比较。使用来自湖泊和沿海地区的大量匹配数据集对 50 种水中算法进行了评估,尤其侧重于在 442 nm 波长具有高彩色溶解有机物吸收(高达 48 m-1)的小湖泊。Gons 等人(2022 年)引入的 Chl a 波段比应用于 ACOLITE 处理的数据,在 S3 OLCI Chl a 检索中表现最佳(分散度 = 23%,偏差 = 10%)。Gons 等人(2022 年)的波段比也显示出 S3 OLCI 与 S2 MSI 重采样数据之间的一致性(截距为 6.27,斜率为 0.83,接近 1:1 线);但 Chl a 值较低 ( 59%,偏差 < -29%)。
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Suitability of different in-water algorithms for eutrophic and absorbing waters applied to Sentinel-2 MSI and Sentinel-3 OLCI data
Optically complex waters present significant challenges for remote sensing due to high concentrations of optically active substances (OASs) and their inherent optical properties (IOPs), as well as the adjacency effect. OASs and IOPs can be derived from atmospheric correction processors’ in-water algorithms applied to data from Sentinel-2 MultiSpectral Instrument (S2 MSI) and Sentinel-3 Ocean and Land Color Instrument (S3 OLCI). This study compared S3 OLCI Level-2 in-water products for Case-2 waters with alternative in-water algorithms derived from ACOLITE, POLYMER, C2RCC, and A4O. Fifty in-water algorithms were evaluated using an extensive match-up dataset from lakes and coastal areas, focusing particularly on small lakes with high colored dissolved organic matter absorption at 442 nm (up to 48 m-1). The Chl a band ratio introduced by Gons et al. (2022) applied to data processed by ACOLITE performed best for S3 OLCI Chl a retrieval (dispersion = 23%, bias = 10%). Gons et al. (2022) band ratio also showed consistent agreement between S3 OLCI and S2 MSI resampled data (intercept of 6.27 and slope of 0.83, close to the 1:1 line); however, lower Chl a values (<20 mg/m3) were overestimated by S2 MSI. When estimating errors associated with proximity to land, S2 MSI Chl a in-water algorithms had higher errors close to the shore (on average 315%) compared to S3 OLCI (on average 150%). Chl a retrieved with POLYMER had the lowest errors close to the shore for both S2 MSI and S3 OLCI data (on average 70%). Total suspended matter (TSM) retrieval with C2RCC performed well for S2 MSI (dispersion 24% and bias −12%). Total absorption was most accurately derived from C2RCC applied to S3 OLCI L1 data (dispersion < 43% and bias < −39%), and it was better estimated than its individual components: phytoplankton, mineral particles, and colored dissolved organic matter absorption. However, none of the colored dissolved organic matter absorption in-water algorithms performed well (dispersion > 59% and bias < −29%).
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