Lake Topography and Wind Waves Determining Seasonal-Spatial Dynamics of Total Suspended Matter in Turbid Lake Taihu, China: Assessment Using Long-Term High-Resolution MERIS Data

Abstract

Multiple comprehensive in situ bio-optical investigations were conducted from 2005 to 2010 and covered a large variability of total suspended matter (TSM) in Lake Taihu to calibrate and validate a TSM concentration estimation model based on Medium Resolution Imaging Spectrometer (MERIS) data. The estimation model of the TSM concentration in Lake Taihu was developed using top-of-atmosphere (TOA) radiance of MERIS image data at band 9 in combination with a regional empirical atmospheric correction model, which was strongly correlated with the in situ TSM concentration (r 2 = 0.720, p<0.001, and n = 73). The relative root mean square error (RRMSE) and mean relative error (MRE) were 36.9% and 31.6%, respectively, based on an independent validation dataset that produced reliable estimations of the TSM concentration. The developed algorithm was applied to 50 MERIS images from 2003 to 2011 to obtain a high spatial and temporal heterogeneity of TSM concentrations in Lake Taihu. Seasonally, the highest and lowest TSM concentrations were found in spring and autumn, respectively. Spatially, TSM concentrations were high in the southern part and center of the lake and low in Xukou Bay, East Lake Taihu. The lake topography, including the water depth and distance from the shore, had a significant effect on the TSM spatial distribution. A significant correlation was found between the daily average wind speed and TSM concentration (r 2 = 0.685, p<0.001, and n = 50), suggesting a critical role of wind speed in the TSM variations in Lake Taihu. In addition, a low TSM concentration was linked to the appearance of submerged aquatic vegetation (SAV). Therefore, TSM dynamics were controlled by the lake topography, wind-driven sediment resuspension and SAV distribution.

Mapping Aquatic Vegetation in a Large, Shallow Eutrophic Lake: A Frequency-Based Approach Using Multiple Years of MODIS Data

“…The optimum depth for the growth of the aquatic vegetation in Lake Taihu has been reported as less than 2.0 m [14,41]. Similar findings were obtained in other studies of coastal and shallow lakes [43,57].…”

“…The total phosphorus concentration in the water from the Yangtze River (~0.1 mg/L; [52]) is greater than that in the majority of the lake (~0.03-0.09 mg/L; [51]), except in Meiliang Bay and Zhushan Bay. More importantly, the concentration of total suspended matter in the lower Yangtze River is higher than that of Gonghu Bay [14,53]. The underwater light supply is a key factor determining the growth and distribution of aquatic vegetation; the increased nutrient loading is expected to increase the nutrient level in Gonghu Bay and promote the growth of algae, and the increased loading of total suspended matter is expected to increase the turbidity and thereby reduce the underwater light supply and decrease the area of aquatic vegetation [30].…”

“…A previous study in Lake Taihu found that the aquatic vegetation occurs at a water depth less than 2 m [14]. In our study, a water depth of < 2.2 m was used to improve the classification accuracy of aquatic vegetation considering findings from other similar published studies [43].…”

“…Generally, Lake Taihu can be divided into six areas based on the shoreline geometry, environmental factors, and human activities: Zhushan Bay, Meiliang Bay, Gonghu Bay, Xukou Bay, East Lake Taihu and the open area [14] (Figure 1). The average water depth in the six areas increases in the following order: East Lake Taihu (1.76 ± 0.37 m; average ± standard deviation), Xukou Bay (1.82 ± 0.50 m), Zhushan Bay (1.97 ± 0.54 m), Gonghu Bay (1.98 ± 0.44 m), Meiliang Bay (2.11 ± 0.63 m) and the open area (2.63 ± 0.38 m) (Figure 1 and 2).…”

“…Satellite remote sensing has many advantages in aquatic vegetation monitoring: (1) repeat coverage by remote sensing enables the detection of changes over time, which has proven beneficial for rapidly assessing the optical variables in coastal and inland water [13,14]; (2) satellite data are in a digital format and can be easily integrated into a geographic information system (GIS) for analysis; and (3) satellite remote sensing is more practical and economical than the other monitoring methods [12,15]. In comparison, airborne imagery is unsuitable for regular monitoring on large spatial and temporal scales because it is costly and time consuming to process [12].…”