Sentinel-2A/MSI (S2A) and Landsat-8/OLI (L8) data products present a new frontier for the assessment and retrieval of optically active water quality parameters including chlorophyll-a (Chl-a), suspended particulate matter (TSS), and turbidity in reservoirs. However, because of their differences in spatial and spectral samplings, it is critical to evaluate how well the sensors are suited for the seamless generation of the water quality parameters (WQPs). This study presents results from the retrieval of the WQP in a reservoir from L8 and S2A optical sensors, after atmospheric correction and standardization through band adjustment. An empirical multivariate regression model (EMRM) algorithmic approach is proposed for the estimation of the water quality parameters in correlation with in situ laboratory measurements. From the results, both sensors estimated Chl-a concentrations with of greater than 70% from the visible green band for L8 and a combination of green and SWIR-1 bands for S2A. While the NMSE% was nearly the same for both sensors in Chl-a estimation, the RMSE was <10 μg/L and >10 μg/L for L8 and S2A estimations of Chl-a, respectively. For TSS retrieval, L8 outperformed S2A by 31% in accuracy with from L8’s red, blue, and green bands, as compared to from S2A’s red and NIR bands. The RMSE were the same as for Chl-a, and the NMSE% were both in the same range. Both sensors retrieved turbidity with high and nearly equal accuracy of from the visible and NIR bands, with equal RMSE at <10% NTU and NMAE% from S2A being higher by more than 30% as compared to L8’s NMAE% at 15%. The study concluded that the higher performance accuracy of L8 is attributed to its higher SNR and spectral bandwidth placement as compared to S2A bands. Comparatively, S2A overestimated Chl-a and turbidity but performed equally well compared to OLI in the estimation of TSS. The results show that while absolute accuracy of retrieval of the WQPs still requires improvements, the developed algorithms are broadly able to discern the biooptical water quality in reservoirs.

中文翻译：

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使用Sentinel-2A MSI和Landsat-8 OLI卫星传感器通过经验多元回归对储层叶绿素a，TSS和浊度进行建模

Sentinel-2A / MSI（S2A）和Landsat-8 / OLI（L8）数据产品为评估和检索旋光性水质参数（包括叶绿素a（Chl- a），悬浮颗粒物（TSS））提供了一个新领域。和水库中的浊度。但是，由于它们在空间和频谱采样上的差异，因此评估传感器适用于无缝生成水质参数（WQP）的能力至关重要。这项研究提出了从大气层校正和通过波段调整进行标准化之后，从L8和S2A光学传感器中检索储层中WQP的结果。提出了一种经验多元回归模型（EMRM）算法，用于估算与水质相关的水质参数。原位实验室测量。从结果来看，两个传感器估计Chl-一个与浓度大于70％从L8可见绿光波段和绿色和SWIR-1频带为S2A的组合。虽然NMSE％几乎为在Chl-两个传感器相同的一个估算，RMSE <10  μ g / L和> 10  μ为Chl-的L8和S2A估计g / L的一个，分别。在TSS检索中，L8的精度比S2A高出31％， 来自L8的红色，蓝色和绿色频段 来自S2A的红色和NIR波段。的RMSE均同为Chl-一个，并且NMSE％均在相同的范围。两种传感器都能以很高的精度和几乎相等的精度获得浊度NTU小于10％时的均方根误差（RMSE）和S2A的NMAE％时的均方根均等值高于15％的L8的NMAE％。研究得出的结论是，与S2A频段相比，L8的更高性能精度归因于其更高的SNR和频谱带宽位置。相比之下，在TSS的估算中，S2A高估了Chl- a和浊度，但与OLI相比表现良好。结果表明，尽管仍需要提高WQP的绝对精度，但开发的算法广泛地能够识别储层中的生物光学水质。