Suspended particulate matter (SPM) is one of the dominant water constituents in inland and coastal waters, and SPM concnetration (C_SPM) is a key parameter describing water quality. This study, using in-situ spectral and C_SPM measurements as well as Sentinel 2 Multispectral Imager (MSI) images, aimed to develop C_SPM retrieval models and further to estimate the C_SPM values of Poyang Lake, China. Sixty-eight in-situ hyperspectral measurements and relative spectral response function were applied to simulate Sentinel 2 MIS spectra. Thirty-four samples were used to calibrate and the left samples were used to validate C_SPM retrieval models, respectively. The developed models were then applied to two Sentinel 2 MSI images captured in wet and dry seasons, and the derived C_SPM values were compared with those derived from MODIS B1 (λ = 645 nm). Results showed that the Sentinel 2 MSI B4–B8b models achieved acceptable to high fitting accuracies, which explained 81–93% of the variation of C_SPM. The validation results also showed the reliability of these six models, and the estimated C_SPM explained 77–93% of the variation of measured C_SPM with the mean absolute percentage error (MAPE) ranging from 36.87% to 21.54%. Among those, a model based on B7 (λ = 783 nm) appeared to be the most accurate one. The Sentinel 2 MSI-derived C_SPM values were generally consistent in spatial distribution and magnitude with those derived from MODIS. The C_SPM derived from Sentinel 2 MSI B7 showed the highest consistency with MODIS on 15 August 2016, while the Sentinel 2 MSI B4 (λ = 665 nm) produced the highest consistency with MODIS on 2 April 2017. Overall, this study demonstrated the applicability of Sentinel 2 MSI for C_SPM retrieval in Poyang Lake, and the Sentinel 2 MSI B4 and B7 are recommended for low and high loadings of SPM, respectively.

Significance: Torrential rains induced by tropical cyclones (TCs) are a major trigger of catastrophic flood hazards. Devastating TCs causing unprecedented floods in recent years were usually characterized with low translation speeds. We find that both observations and numerical simulations show a significant slowdown of TCs over the coast of China. Our analyses of long-term observations exhibit a significant increase in extreme rainfall amounts induced by TCs, and significant inverse relationships between TC translation speeds and local rainfall totals over the study period. Our probability analysis reveals the association of higher local rainfall totals and slow-moving TCs. We provide observational evidence that slowdown of TCs tends to elevate local rainfall totals and thus impose greater flood risks at the regional scale.