## 1 Introduction
Adopting publicly available satellite data and an automated system that includes an effective algorithm suited for the inversion of regional water quality can provide reliable and effective information for regional lake management. Yunnan Plateau lakes are one of the five lake districts in China, possessing 31 lakes with areas greater than 1 km\({}^{2}\)[29]. In situ data from most of these lakes are absent. Thus, the development of a reliable and cost-effective approach based on the remote-sensing inversion of water quality as the main objective is critical for the management of lakes in the Yunnan Plateau. Here, we selected Landsat Operational Land Imager (OLI) imagery as a remote-sensing data source, which can provide sufficient multispectral channels centered in the red, green, and blue regions that are sensitive and effective for SD inversion [30; 31; 32]. Moreover, the longer time series of Landsat satellites compared to other satellites can provide the possibility to trace the water quality of lakes with missing in situ data. Additionally, because of the relatively small area of Yunnan Plateau lakes, of which 64.5% are smaller than 10 km\({}^{2}\), the spatial resolution of OLI imagery is more suitable than other moderate spatial resolution imagery, such as the moderate-resolution spectroradiometer (MODIS) and medium-resolution imaging spectrometer instrument (MERIS). The ACOLITE algorithm has been regarded as one of the mainstream atmospheric correction algorithms by scholars and is frequently used in related studies [33; 34; 35]. However, so far, there are still difficulties in combining ACOLITE with GEE, which means that the application of the ACOLITE algorithm requires the support of local image data. The combination of ACOLITE and GEE will bring significant application value for the online processing of image data. The matching window between in situ data and satellite data is generally \(\pm 1\) day, but due to the lack of in situ data of lakes and the mismatch between sampling time and satellite imaging time, researchers have to expand the matching time window to obtain more experimental data. Although expanding the matching time window will reduce the accuracy of the empirical model, high accuracy is still observed for the model within the \(\pm 7\)-day time window [16; 36; 37]. Due to the inconsistency of the time windows used in the relevant studies of machine-learning methods, it is difficult to determine an effective time window for the training of machine-learning models [19; 20; 21; 22]. At the same time, due to the existence of long water exchange cycles and stable aquatic ecosystems in the Yunnan lakes, lake transparency will not change significantly in a short period of time, and it is worth discussing whether it is suitable to use a longer window. To obtain remote-sensing transparency information of lakes more effectively, we (1) built a bridge between the ACOLITE algorithm and the GEE platform to achieve online processing of remote-sensing images using the ACOLITE method. Additionally, using four lakes from Yunnan Province, China, as an example, we (2) evaluated the applicability of empirical algorithms and machine-learning algorithms for lakes in the Yunnan Province under different time windows to obtain more suitable algorithms and time windows for the application of lake management in Yunnan. We hope that the algorithmic model obtained in this study and the combination of GEE and ACOLITE algorithms will help complete the online deducing function of lake transparency distribution in the region in the future, as well as expand the supported time series to the initial imaging time of Landsat series satellites, using remote sensing to fill in the missing data from past water resources surveys.