Total suspended matter (TSM) is an important water quality parameter, and the transport and deposition of TSM affect the evolution of coastal shorelines through sediment accumulation and erosion. Therefore, the accurate TSM mapping is essential for the management of estuarine systems.
The estimation of TSM concentration had already the high accuracy in the existing researches, however, these models with the high accuracy could only be applied to the specific estuaries, such as the Yangtze River or Yellow River estuaries in China. Furthermore, the original hybrid model led the high accuracy, but this method lacked reliability and continuity for TSM mapping.
A study from the Northeast Institute of Geography and Agroecology (IGA) of the Chinese Academy of Sciences was published in ISPRS Journal of Photogrammetry and Remote Sensing by Xiang Wang and Kaishan Song. This study focused on TSM mapping of China’s main estuaries, Liaohe River, Yellow River, Yangtze River, Hangzhou Bay, Min River and Pearl River estuaries.
The researchers proposed a stable hybrid model with a threshold of 100 mg/L for clear waters and turbid waters and a weight random forest (WRF) mapping method with the classification probability using Landsat images. This ensured a high prediction accuracy and a smoother and more continuous TSM mapping.
The red and near-infrared (NIR) bands are sensitive to the low and high TSM concentration estuaries, respectively. Hence, the hybrid model is more suitable than the single model for the large scale TSM mapping.
The results also revealed that the estuaries located in the city were clearer than that located in the cropland. The hybrid prediction model led the validation R2 of 0.90 and an RMSE of 0.56 mg/L, and the WRF method ensured the accuracy at the boundaries between different TSM concentrations.
The results provide a method reference for TSM mapping, and the hybrid model is an effective approach for large scale remote estimates of TSM concentrations.
Contact:
Song Kaishan
Northeast Institute of Geography and Agroecology (IGA) of the Chinese Academy of Sciences
Tel: 86-159-044-14023
E-mail: songkaishan@iga.ac.cn
Web: http://english.iga.cas.cn/
