Automated spectral transfer learning strategy for semi-supervised regression on Chlorophyll-a retrievals with Sentinel-2 imagery

Multispectral images make it possible to retrieve water quality parameters over a wide range and long time series by remote sensing. The robustness of traditional supervised machine learning models, which have been widely used, is affected in various regions, and these models are developed by in-situ measurements and limited satellite images. This study combined a spectral transfer learning strategy with a semi-supervised regression model to conduct data augmentation and developed a robust model for two inland lakes in Jilin Province, Northeast China, based on Sentinel-2 Multispectral Instruments images. Specifically, this study integrated an automated module for Balance Distribution Adaptation and a spectral features transfer method, and then applied it to Co-training Regressors for Chlorophyll-a retrievals to achieve the highest accuracy, which solved the selection of parameters and models. The Automated model was developed and achieved the best performance (R2 is 0.91, the root mean square error is 2.62 mu g L-1 and the mean absolute percentage error is 22.88%) compared with other models, and showed robustness in various lakes and months. Our results offer a reliable approach to provide an accurate Chlorophyll-a estimation of spatial-temporal variations.