Harmonized Chlorophyll-a Retrievals in Inland Lakes From Landsat-8/9 and Sentinel 2A/B Virtual Constellation Through Machine Learning

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 (L8/L9) and Sentinel-2A/B (S2A/S2B) virtual constellation. This study first examined the performance of four atmospheric correction processors to derive the remote sensing reflectance ( Rrs) from L8/L9 Operational Land Imager (OLI) and S2A/S2B multispectral instrument (MSI) images. We determined that the dark spectral fitting algorithm generated better Rrs than the other processors, e.g., Rrs (561) mean absolute percentage error (MAPE) = 15.2%, Rrs (665) MAPE = 27.5%, and Rrs (704) MAPE = 25.7%. OLI-derived Rrs at five visible and near-infrared bands showed satisfactory agreement with MSI (slope = 0.94 and 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 = 31.4% ( N = 109) and OLI: MAPE = 38.0% ( N = 74)]. The satisfactory agreement of Chl-a retrievals between the synchronous MSI and OLI images ( N = 2 293 821 and MAPE = 34.6%) supported the establishment of the virtual constellation. MSI- and OLI-derived Chl-a in nine major lakes in the studied area exhibited apparent seasonal variability from 2013 to 2022, particularly after 2017. Results highlight a solution to establish the Landsat/Sentinel-2 virtual constellation for improving the spatial and temporal resolutions of a database of lake water quality.


Z. Cao et al., IEEE Transactions on Geoscience and Remote Sensing, 60,1-16, 2022, doi: 10.1109/TGRS.2022.3207345.