| Popis: |
Total Suspended Matter is the core parameter of water color remote sensing and the important indicator for water quality evaluation of lakes. Rapid and high-precision monitoring of TSM is an important guarantee for water quality remote-sensing applications. China has launched many broad-bandwidth remote sensing satellites, all of which have similar bandwidth. The coordinated observation of multiple satellites can effectively meet the large-scale and high-frequency dynamic monitoring requirements of TSM concentration in lakes. This study proposed a machine-learning model to retrieve the TSM concentration from broad bandwidth satellites. The reliability and accuracy of various retrieve models (i.e., linear regression model, support vector regression model, random forest model, and back propagation neural networks model) were evaluated through the in-situ datasets of TSM concentration in lakes. The RF model was selected as the retrieved model of TSM concentration using broad bandwidth satellites. The results showed that 1) Compared with four machine learning models, the RF model can provide better performance (R2=0.88, Mean Absolute Percentage Error (MAPE) = 22.5%). Similarly, compared with the documented six TSM retrieve model, the RF retrieve model also has substantial advantages. 2) the Forel-Ule Index (FUI) can effectively enhance the precision and accuracy of the TSM retrieve model. 3) The RF model has good generalization ability and accuracy in the validation datasets (Lake Chagan: MAPE = 3.7%, Lake Changdang: MAPE = 4.3%). 4) The RF model was applied to the broad bandwidth satellites retrieve of TSM concentrations in Lake Bosten, Lake Chagan, and Lake Changdang, and the MAPEs were 5.3%, 8.1%, and 12.1%, respectively. This study showed that the RF model could effectively improve the retrieve performance and generalization ability of the broad bandwidth satellite’s TSM concentration, which meets the accuracy requirements of high-frequency dynamic monitoring of TSM concentration. |
