Harmonized Chlorophyll-a dataset from Landsat-8/9 OLI and Sentinel 2 MSI in lakes of the Yunnan-guizhou Plateau, China
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Moderate-high resolution satellite missions provide an opportunity to capture subtle spatial variability in lakes; however, the sparsity of time series for individual satellite instruments cannot monitor temporal variation in the lake environment. To date, studies on the joint observations of chlorophyll-a (Chl-a) in inland lakes from multiple missions have been poorly reported. Here, we generated a harmonized Chl-a dataset for the lakes in the Yunnan–Guizhou Plateau in China from 2013 to 2022 by the Landsat 8/9 and Sentinel-2A/B virtual constellation. This study first examined the performance of four atmospheric correction processors to derive remote sensing reflectance (R<sub>rs</sub>) from Landsat 8/9 Operational Land Imager (OLI) and Sentinel-2A/B multispectral instrument (MSI) images. We determined that the dark spectral fitting algorithm generated better R<sub>rs</sub> than the other processors, e.g., R<sub>rs</sub>(561) mean absolute percentage error (MAPE)=17.4%, R<sub>rs</sub>(665) MAPE=25.8%, and R<sub>rs</sub>(704) MAPE=25.8%. OLI derived R<sub>rs</sub> at five visible and near-infrared bands showed satisfactory agreement with MSI (slope=0.94, MAPE=11.8%). The mixed density network outperformed the six state-of-the-art algorithms and other two machine learning models in retrieving Chl-a [MSI: MAPE=41.0% (N=109), OLI: MAPE=37.4% (N=74)]. The satisfactory agreement of Chl-a retrievals between the synchronous MSI and OLI images (N=2,293,821, MAPE=38.4%) supported to establish the virtual constellation. MSI- and OLI derived Chl-a in nine major lakes in studied area exhibited apparent seasonal variability within the period of 2013–2022, particularly after 2017. Results highlight a method to establish the Landsat/Sentinel-2 virtual constellation for improving the spatial and temporal resolutions of a database of lake water quality.
