LiDAR diffuse attenuation coefficient ( ${K}_{\text {lidar}}$ ) describes the laser attenuation degree in water, and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is expected to extend the observation of this parameter to satellites. However, the ICESat-2 observation is limited to the satellite trajectories, and it is hard to realize the coverage observation of inland water. Therefore, this study proposes a method fusing active and passive remote sensing data based on machine learning and uses ICESat-2 data and Sentinel-3 images to retrieve $K_{\text {lidar}}$ of inland waters. First, $K_{\text {lidar}}$ is retrieved from ICESat-2 water column photons. Second, the retrieved $K_{\text {lidar}}$ and Sentinel-3 images are used to train machine learning models to generate $K_{\text {lidar}}$ covering water bodies. Experiments were carried out in Xiaolangdi Reservoir using multitemporal data. The results show that the performance of CatBoost is better than that of random forest (RF) and XGBoost, and the consistency reaches 68.42%. Compared with in situ data, the root mean square error (RMSE) of the proposed method is $0.0555~\text {m}^{-1}$ , which is 61.05% higher than the RMSE of the Sentinel-3 product. By combining laser data with multispectral images, water quality observation close to field measurement can be retrieved from satellite platforms.