## 1 Introduction
established a remote sensing-based single band algorithm for estimating the diffuse attenuation coefficient of the photosynthetically available radiation, \(K_{\mathrm{d}}\)(PAR), for Lake Taihu using Medium Resolution Imaging Spectrometer (MERIS) top-of-atmosphere (TOA) radiance data in the 753-nm band [27]. [PERSON] et al. developed an algorithm for deriving \(K_{\mathrm{d}}\)(490) for Dongting Lake from the ratio of Landsat data in the near-infrared (NIR) band to Landsat data in the red band [28]. [PERSON] et al. established a remote sensing-based algorithm for estimating \(K_{\mathrm{d}}\)(PAR) for lakes in Northeast China through a multiple stepwise regression analysis using Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat data [25]. These algorithms have significant spatial and seasonal limitations and differ significantly in satellite band selection. To our knowledge, no diffuse attenuation algorithm has been specifically developed for the Ocean and Land Color Imager (OLCI) sensor. Given the opportunities that this sensor presents, there is an urgent need to develop a simple, high-accuracy \(K_{\mathrm{d}}\)(490) estimation algorithm that is suitable for turbid shallow lakes. The OLCI sensor will be one of the main sensors for remote sensing-based monitoring of the color of inland waters for the next 15 years. The MERIS and MODIS sensors have been commonly used to monitor the water quality of turbid inland waters. However, the MERIS sensor stopped working in April 2012, and the MODIS sensor has been in operation for eight years longer than its planned service life and has started to suffer signal attenuation; consequently, it may soon stop operating. The OLCI, which is the optical payload on the Sentinel-3A satellite, part of the Copernicus Constellation, is a high-accuracy visible spectral imager [29]. As a sensor for observing ocean colors across the globe and the successor to the ENVISAT-MERIS, the OLCI is equipped with more spectral bands (21 bands in the range of 400-1020 nm) suitable for inland waters, especially full resolution (FR) data of 300 m, and has a higher stability ([[https://earth.esa.int/web/sentinel/user-guides/sentinel-3-olci/resolutions/radiometric](https://earth.esa.int/web/sentinel/user-guides/sentinel-3-olci/resolutions/radiometric)]([https://earth.esa.int/web/sentinel/user-guides/sentinel-3-olci/resolutions/radiometric](https://earth.esa.int/web/sentinel/user-guides/sentinel-3-olci/resolutions/radiometric))). OLCI data have been used since October 2016. A satellite of the same type as the Sentinel-3A, the Sentinel-3B, is planned for launch in 2018. The Sentinel-3B and Sentinel-3A will form a two-satellite network that will be capable of performing observations once every 1.5 days, which compares favorably with the MODIS and thus has better prospects for highly variable inland waters. This study examined the temporal and spatial distributions of \(K_{\mathrm{d}}(490)\) in Lake Taihu, which is a large shallow lake in China, based on data measured during seven surveys conducted between 2008 and 2017 in combination with the Sentinel-3A-OLCI dataset from 2016-2017 to determine the characteristics of the photoecological environment in the waters of Lake Taihu. The main goals were: (1) to develop an algorithm for estimating \(K_{\mathrm{d}}(490)\) for the waters of Lake Taihu based on OLCI sensor data and compare it with a MODIS product of the same type; and (2) to establish a long time-series \(K_{\mathrm{d}}(490)\) dataset for Lake Taihu and determine the patterns of and driving factors for the temporal and spatial variations of \(K_{\mathrm{d}}(490)\) in Lake Taihu. To our knowledge, this study is the first to investigate the temporal and spatial variations of \(K_{\mathrm{d}}(490)\) in an inland lake using OLCI data. It is hoped that this study will significantly advance the application of OLCI data in research on inland lakes. The monitoring and application of long time-series data can be achieved with OLCI data in combination with ENVISAT-MERIS data.