Remote sensing of total suspended matter of inland waters: Past, current status, and future directions

Abstract

Total suspended matter (TSM) serves as an important water quality parameter, often carrying nutrients, micro-pollutants, and heavy metals, thereby closely influencing the ecological health of aquatic ecosystems. With the recent advancements in remote sensing technology, artificial intelligence algorithms, and cloud platforms, understanding remote sensing is crucial for TSM monitoring, especially in water resources management and decision-making. This review aims to summarize research advancements in TSM remote sensing of inland waters while addressing current conditions' limitations, outlining future directions, and providing recommendations. The technology for remote sensing utilized to capture inland TSM has its origins in the 1970s. In the last five decades, approximately eight hundred pertinent studies have been carried out by researchers, progressing from the development of algorithms to their applications in science. The band ratio algorithm, bio-optical model, and machine learning algorithm are increasingly recognized as the predominant methodologies. The red band, near-infrared band, and their combinations are typically chosen as sensitive bands for detecting TSM in turbid waters, whereas the blue and green bands are generally utilized for clear waters. Analysis of bibliometric data indicates that empirical and semi-empirical algorithms comprise the largest share at 72 %, with semi-analytical algorithms following at 9 %. The TSM is co-influenced by the composition, particle size, and refractive index. Considering these parameters to develop high-precision TSM inversion algorithms remains a challenge. Researchers frequently utilize the Landsat series sensors and MODIS for retrieving TSM concentrations across regional, national, and global scales, representing28 % and 16 % of the total publications, respectively, while Sentinel follows closely with 8 %. The Taihu Lake, Poyang Lake, Ebinur Lake, Vembanad Lake, Amazon River, Yangtze River, and Mississippi River have emerged as hot spot regions for research on TSM by scholars form various countries. However, for complex and variable inland water bodies, the atmospheric corrections, adjacency effects and limited resolutions of current sensors, as well as model transferability remain challenges, and many attempts should be made in the future.