Satellite-derived chlorophyll-a concentrations for Lake Harsha (USA) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery

This dataset contains satellite-derived chlorophyll-a data of Lake Harsha (USA) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery. Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following: Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860 Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em> Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604本数据集涵盖美国哈尔莎湖（Lake Harsha）2013年3月21日至2021年2月1日的卫星反演叶绿素-a（chlorophyll-a）数据。其中叶绿素-a浓度通过混合密度网络（Mixture Density Networks）结合Sentinel-2与Landsat 8遥感影像反演计算得到。混合密度网络是一类神经网络，通过构建高斯混合模型对目标变量的多模态分布进行建模，以此解决逆问题。更多详细信息可参考如下文献： Pahlevan, N.、Smith, B.、Alikas, K.、Anstee, J. 等. (2022). 从Landsat-8、Sentinel-2与Sentinel-3同步反演若干光学水质指标. 《环境遥感（Remote Sensing of Environment）》, 270, 112860 Smith, B.、Pahlevan, N.、Schalles, J. 等. (2021). 基于混合密度网络的Landsat-8叶绿素-a反演算法. 《遥感前沿（Frontiers in Remote Sensing）》, 1 Pahlevan, N.、Smith, B.、Schalles, J. 等. (2020). 内陆与近岸水体Sentinel-2（MSI）和Sentinel-3（OLCI）叶绿素-a无缝反演：一种机器学习方法. 《环境遥感（Remote Sensing of Environment）》, 240, 111604