College of Resources and Environmental Engineering, Anhui University, Hefei 230601, China.
Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China.
Sensors (Basel). 2021 Feb 28;21(5):1662. doi: 10.3390/s21051662.
Research on the consistency of suspended particulate matter (SPM) concentration retrieved from multisource satellite sensors can serve as long-time monitoring of water quality. To explore the influence of the atmospheric correction (AC) algorithm and the retrieval model on the consistency of the SPM concentration values, Landsat 8 Operational Land Imager (OLI) and Sentinel 2 MultiSpectral Imager (MSI) images acquired on the same day are used to compare the remote sensing reflectance (Rrs) SPM retrieval values in two high-turbidity lakes. An SPM retrieval model for Shengjin Lake is established based on field measurements and applied to OLI and MSI images: two SPM concentration products are highly consistent ( = 0.93, Root Mean Squared Error (RMSE) = 20.67 mg/L, Mean Absolute Percentage Error (MAPE) = 6.59%), and the desired results are also obtained in Chaohu Lake. Among the four AC algorithms (Management Unit of the North Seas Mathematical Models (MUMM), Atmospheric Correction for OLI'lite'(ACOLITE), Second Simulation of Satellite Signal in the Solar Spectrum (6S), Landsat 8 Surface Reflectance Code & Sen2cor (LaSRC & Sen2cor)), the two Rrs products, as well as the final SPM concentration products retrieved from OLI and MSI images, have the best consistency when using the MUMM algorithm in SeaWIFS Data Analyst System (SeaDAS) software. The consistency of SPM concentration values retrieved from OLI and MSI images using the same model or same form of models is significantly better than that retrieved by applying the optimal models with different forms.
对多源卫星传感器获取的悬浮颗粒物（SPM）浓度的一致性进行研究，可用于水质的长期监测。为了探究大气校正（AC）算法和反演模型对 SPM 浓度值一致性的影响，利用同一天获取的 Landsat 8 陆地成像仪（OLI）和 Sentinel 2 多光谱成像仪（MSI）图像，对比了两个高浊度湖泊的遥感反射率（Rrs）SPM 反演值。基于现场测量，建立了盛金湖的 SPM 反演模型，并将其应用于 OLI 和 MSI 图像：两个 SPM 浓度产品高度一致（=0.93，均方根误差（RMSE）=20.67mg/L，平均绝对百分比误差（MAPE）=6.59%），在巢湖也得到了理想的结果。在四种 AC 算法（北海数学模型管理单元（MUMM）、OLI'Lite'（ACOLITE）大气校正、太阳光谱第二模拟卫星信号（6S）、Landsat 8 表面反射率代码和 Sen2cor（LaSRC 和 Sen2cor））中，使用 SeaDAS 软件中的 MUMM 算法时，两个 Rrs 产品以及从 OLI 和 MSI 图像反演得到的最终 SPM 浓度产品的一致性最好。使用相同模型或相同形式模型反演的 OLI 和 MSI 图像的 SPM 浓度值的一致性明显优于应用不同形式的最优模型反演的一致性。
