Wang Jialin, Chen Xiaoling
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China.
Sci Total Environ. 2024 Jan 1;906:167631. doi: 10.1016/j.scitotenv.2023.167631. Epub 2023 Oct 6.
Chlorophyll-a (Chl-a) concentration is a reliable indicator of phytoplankton biomass and eutrophication, especially in inland waters. Remote sensing provides a means for large-scale Chl-a estimation by linking the spectral water-leaving signal from the water surface with in situ measured Chl-a concentrations. Single-sensor images cannot meet the practical needs for long-term monitoring of Chl-a concentrations due to cloud cover and satellite operational lifetimes. However, quantifying long-term inland water Chl-a concentrations using multi-source remote sensing data remains a problem, as improper input of satellite reflectance products will affect the accuracy of Chl-a over inland waters, as well as existing models cannot meet the need for multi-source remote sensing data to retrieve high precision Chl-a. To explore these problems towards a solution, four reflectance data derived from Ocean and Land Colour Instrument (OLCI), MultiSpectral Instrument (MSI), and Operational Land Imager (OLI) were evaluated against in situ measurements of Erhai Lake. Reflectance data from these sensors were assessed to determine their consistency. Results indicate that R_rhos products (i.e., surface reflectance, a semi-atmospheric correction reflectance) that controlled for the atmospheric diffuse transmittance were highly correlated with the measured reflectance values. The in situ reflectance also confirmed the higher fidelity of satellite reflectance in the green-red band. Subsequently, a new extreme gradient boosting (XGB) model applied to multi-source remote sensing data is proposed to estimate long-term inland water Chl-a concentrations. Comparative experiments showed the XGB model with R_rhos products outperformed other solutions, providing accurate estimates for daily, monthly, and long-term trends in Erhai Lake. The XGB model was finally processed 3954 R_rhos reflectance data derived from OLCI, ENVISAT Medium Resolution Imaging Spectrometer (MERIS), MSI, and OLI sensors, mapping Chl-a concentrations in Erhai Lake over a 20-year period. This study could serve as a reference for the long-term Chl-a monitoring using multi-source remote sensing data to support inland lake management and future water quality evaluation.
叶绿素a（Chl-a）浓度是浮游植物生物量和富营养化的可靠指标，在内陆水域尤为如此。遥感通过将来自水面的光谱离水信号与现场测量的Chl-a浓度联系起来，为大规模Chl-a估算提供了一种手段。由于云层覆盖和卫星运行寿命，单传感器图像无法满足长期监测Chl-a浓度的实际需求。然而，利用多源遥感数据量化内陆水域长期Chl-a浓度仍然是一个问题，因为卫星反射率产品的不当输入会影响内陆水域Chl-a的准确性，而且现有的模型无法满足多源遥感数据反演高精度Chl-a的需求。为了探索这些问题的解决方案，对来自海洋和陆地颜色仪器（OLCI）、多光谱仪器（MSI）和业务陆地成像仪（OLI）的四种反射率数据与洱海的现场测量数据进行了评估。对这些传感器的反射率数据进行了评估，以确定它们的一致性。结果表明，控制了大气漫射透过率的R_rhos产品（即表面反射率，一种半大气校正反射率）与测量的反射率值高度相关。现场反射率也证实了卫星反射率在绿红波段具有更高的保真度。随后，提出了一种应用于多源遥感数据的新的极端梯度提升（XGB）模型，以估算内陆水域长期Chl-a浓度。对比实验表明，使用R_rhos产品的XGB模型优于其他解决方案，能够准确估算洱海的每日、每月和长期趋势。最终，XGB模型处理了来自OLCI、ENVISAT中分辨率成像光谱仪（MERIS）、MSI和OLI传感器的3954个R_rhos反射率数据，绘制了洱海20年期间的Chl-a浓度图。本研究可为利用多源遥感数据进行长期Chl-a监测提供参考，以支持内陆湖泊管理和未来水质评估。
