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Application of Sentinel 3 OLCI for chl-a retrieval over small inland water targets: Successes and challenges
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2020-02-01 , DOI: 10.1016/j.rse.2019.111562
Jeremy Kravitz , Mark Matthews , Stewart Bernard , Derek Griffith

Abstract Eutrophication and increasing prevalence of potentially toxic cyanobacterial blooms among global inland water bodies is becoming a major concern and requires direct attention. The European Space Agency recently launched the Ocean and Land Color Instrument (OLCI) aboard the Sentinel 3 satellite. The success of the mission will depend on extensive validation efforts for the development of accurate and robust in-water algorithms. In this study, four full atmospheric correction methods are assessed over four inland water reservoirs in South Africa, along with a suite of red/NIR based semi-analytic and band difference models for chl-a estimation which were applied to both full and partial atmospherically corrected data. In addition, we tested a novel duplicate pixel correction method to account for duplicate pixels induced by high observation zenith angles. Radiometric errors associated with OLCI Top of Atmosphere (TOA) radiances over small water targets were also investigated by modeling in situ reflectance measurements to at-sensor radiances using MODTRAN. Of the four atmospheric corrections, the 6SV1 radiative transfer code showed the most promise for producing reasonable reflectances when compared to in-situ measurements. Empirically derived band difference models outperformed all other chl-a retrieval methods on both partially and fully corrected reflectances. The Maximum Peak Height (MPH) algorithm applied to Bottom of Rayleigh Reflectance (BRR) performed best overall (R2 = 0.55, RMSE(%) = 99), while the Maximum Chlorophyll Index (MCI) performed best on atmospherically corrected data using 6SV1 (R2 = 0.35, RMSE(%) = 107). Semi-analytic chl-a retrieval methods proved very successful when applied to in situ Rrs, however, are not reliable when applied to low quality reflectance data. The SIMilarity Environment Correction (SIMEC), an adjacency correction applied in conjunction with the image correction for atmospheric effects (iCOR) processor, did not improve retrieval results for these small water targets.

中文翻译:

Sentinel 3 OLCI 在小型内陆水域目标 chl-a 检索中的应用:成功与挑战

摘要 全球内陆水体富营养化和潜在有毒蓝藻水华日益流行正成为一个主要问题,需要直接关注。欧洲航天局最近在哨兵 3 号卫星上发射了海洋和陆地颜色仪器 (OLCI)。任务的成功将取决于为开发准确而强大的水中算法而进行的广泛验证工作。在这项研究中,评估了南非四个内陆水库的四种完整大气校正方法,以及一套基于红色/NIR 的半解析和波段差异模型,用于 chl-a 估计,这些模型应用于完整和部分大气更正的数据。此外,我们测试了一种新的重复像素校正方法,以解决由高观测天顶角引起的重复像素。还通过使用 MODTRAN 对传感器处辐射度的原位反射测量建模,研究了与 OLCI 大气顶 (TOA) 辐射度在小型水域目标上相关的辐射测量误差。在四次大气校正中,与原位测量相比,6SV1 辐射传输代码最有希望产生合理的反射率。经验得出的带差模型在部分和完全校正的反射率方面均优于所有其他 chl-a 检索方法。应用于瑞利底反射 (BRR) 的最大峰高 (MPH) 算法总体上表现最佳(R2 = 0.55,RMSE(%) = 99),而最大叶绿素指数 (MCI) 在使用 6SV1 的大气校正数据上表现最佳(R2 = 0.35,RMSE(%) = 107)。半解析 chl-a 反演方法在应用于原位 Rrs 时被证明是非常成功的,但是当应用于低质量反射率数据时并不可靠。SIMilarity Environment Correction (SIMEC) 是一种与大气效应图像校正 (iCOR) 处理器一起应用的邻接校正,并没有改善这些小水目标的检索结果。
更新日期:2020-02-01
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