Mapping and reconstruct suspended sediment dynamics (1986–2021) in the source region of the Yangtze River, Qinghai-Tibet Plateau using Google Earth Engine

Abstract

Using remote sensing to measure suspended sediment concentration (SSC) in mountainous rivers can compensate for the scarcity of in situ sediment observations, providing valuable direct supplementation to observational records. However, for inland rivers, remote sensing SSC assessments face challenges such as data quality, long-term water body changes, environmental noise, flood events, and the transferability of local calibrations. Here, we introduce and apply remote sensing big data techniques using 12,445 cloud-free Landsat 5, 7, and 8 satellite images to calibrate SSC in the source region of the Yangtze River (SRYR). Utilizing Google Earth Engine, we implemented a series of image preprocessing techniques and water fraction methods to extract precise inland river water masks. Then we used unsupervised K-Means clustering and machine learning algorithms to model the relationship between water optical properties and SSC. By integrating these methodologies, we achieved an average relative calibration error of 0.26 for each optical cluster, and an average relative station deviation of 0.24 based on in situ measurements, minimizing SSC calibration to acceptable levels. Additionally, our results reveal that geomorphic patterns significantly influence sediment yield and transport by regulating sediment sources and sinks, fluvial morphology, and water-sediment connectivity. Over the past two decades, approximately 35.73 % of the sediment relative to the basin outlet discharge in the SRYR has been temporarily stored or confined within sediment sinks. These methods and findings hold significant implications for assessing and projecting fluvial sediment dynamics and the associated ecological and environmental issues in ungauged cold headwater regions.