A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes

As particulate organic carbon (POC) from lakes plays an important role in lake ecosystem sustainability and cycle, the estimation of its concentration using satellite remote sensing is great interest. However, high complexity variability water composition pose major challenges to algorithm POC Class II water. This study aimed formulate a machine-learning predict compare their modeling performance. A Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) based on spectral time sequences was proposed construct model Sentinel 2 images surface sample data Chaohu Lake China. comparison, performances Backpropagation Network (BP), Generalized Regression (GRNN), (CNN) models were evaluated for inversion concentration. The results show that CNN–LSTM obtained higher prediction precision than BP, GRNN, CNN models, with coefficient determination (R2) 0.88, root mean square error (RMSE) 3.66, residual deviation (RPD) 3.03, which are 6.02%, 22.13%, 28.4% better model, respectively. indicates effectively combines spatial temporal information, quickly captures time-series features, strengthens learning ability multi-scale conducive improving offers good support source monitoring assessment lakes.