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Recently, numerous bio-optical algorithms have been proposed to estimate chlorophyll-a (Chla) concentration in global surface waters. The surge of algorithms is mainly due to the heterogeneity of waters, especially in inland systems. To reduce redundant modeling work for inland waters and to enhance the usability of algorithms, it is urgent to evaluate the published algorithms for similar optical water types. However, approaches of the algorithm assessment in previous studies vary in error metrics usage or relative comparison, leading to the current lack of a standard or consensus in the community. Thus, we proposed a round-robin scoring method based on optically fuzzy clustering for algorithm assessment. An improved scoring approach (namely the sort-based method) was employed to aggregate the metrics of candidate algorithms. We generate a new set of optical water types using 1702 in situ samples, showing an improvement on our previous work [Bi et al. (2019)]. Then, an integrated Chla result was produced by blending the estimations of optimal algorithms weighted by the membership values, which showed better performance than that of any single algorithm. The blending framework presents more flexible and expandable than previous studies. We aim to illustrate the importance of assessment which considers key properties of algorithms (i.e., Accuracy, Precision, and Effectiveness) and to advocate more in-depth research on certain water types under the fuzzy clustering-based estimation framework (such as clean inland waters with low Chla). This article is accompanied by the open-source R package FCMm for implementation of the proposed method.

Chemical oxygen demand concentration (C

Tracking the spatiotemporal dynamics of particulate phosphorus concentration (C

Understanding the spatiotemporal dynamics of total dissolved phosphorus concentration (C

Particulate composition provides important information for understanding the changes in underwater light fields and primary productivity. In this study, a semianalytical algorithm, based on Rayleigh-corrected reflectance at 678 nm and 748 nm on Moderate Resolution Imaging Spectroradiometer (MODIS) images was used to estimate the ratio of chlorophyll a to total suspended solids (Chla/TSS), which characterizes the particulate composition of the Great Lakes. The long-term spatial and temporal characteristics of Chla/TSS in the Great Lakes from 2000 to 2020 were obtained. The results demonstrated that Lake Superior had the highest average Chla/TSS values (5.79±0.76 µg/mg), while Lake Erie had the lowest average Chla/TSS values (2.93±0.76 µg/mg). The Mann-Kendall test showed that the Chla/TSS of the Great Lakes all showed an increasing trend, notably in Lake Michigan, with 88.23% pixels showing significant increasing trend. Climatic and hydrological factors dominated the intra-annual variation of Chla/TSS, with contribution rates ranging from 71.47% to 92.54%. Through the annual Chla/TSS change pattern analysis, it was found that the contribution of wind speed to the annual variation in Chla/TSS was slight. Changes in temperature played a major role in the interannual variability of Chla/TSS in Lake Superior and Ontario; runoff and settlement were the major contributors in Lake Huron and Michigan, while cropland dominated the Chla/TSS interannual variability in Lake Erie. Furthermore, the significantly low values of Chla/TSS in spring had the potential to predict the occurrence of blooms in western Lake Erie, and the spatial distribution of Chla/TSS could help predict the location of blooms in the next few days.

Cyanobacterial blooms are one of the most severe ecological problems affecting lakes. The vertical migration of cyanobacteria in the water column increases the uncertainty in the formation and disappearance of blooms, which may be closely associated with light, temperature, and wind speed. However, it is difficult to quantitatively evaluate the influencing factors of cyanobacteria vertical movement in natural environment compared to the laboratory experimental environment. Besides, both field survey and laboratory experiment method have the difficulties in determining the diurnal vertical migration of cyanobacteria at the synoptic lake scale. In this study, based on the diurnal dynamics of cyanobacterial bloom intensity (CBI) observed by the Geostationary Ocean Color Imager (GOCI) from 2011 to 2019, the daily variations, floating rate, and sinking rate of Microcystis aeruginosa were calculated in the natural environment. Then, the effects of light, temperature, and wind speed on the vertical migration of M. aeruginosa were analysed from the perspectives of day, night, and season. The results are as follows: the records of three typical patterns of diurnal CBI exhibited strong seasonal variability from the 9-year statistics; at night, the buoyancy recovery rate of cyanobacterial colonies increased with temperature, so that at temperature15 °C and wind speed3 m s

Due to eutrophication and water quality deterioration in clear reservoirs, it is necessary to monitor and manage the main water parameters: concentration of total phosphorus (CTP), chemical oxygen demand (CCOD), chlorophyll-a (CChla), total suspended matter (CTSM), and Secchi disk depth (SDD). Five random forest (RF) models are developed to estimate these parameters in Xin’anjiang Reservoir, which is a clear drinking water resource in Zhejiang, China. Then, the spatio-temporal distributions of the parameters over 7 years (2013–2019) are mapped using GaoFen-1 (GF-1) images and the relationships with driving factors are analyzed. Our study demonstrates that the parameters’ distributions exhibited a significant spatio-temporal difference in Xin’anjiang Reservoir. Spatially, relatively high CTP, CCOD, CChla, and CTSM but low SDD appear in riverine areas, showing strong evidence of impact from the incoming rivers. Temporally, CChla and CTSM reached high values in summer and winter, whereas SDD and CTP were higher in the summer and autumn, respectively. In contrast, no significant seasonal variations of CCOD could be observed. This may be why CCOD is not sensitive to hydrological or meteorological factors. However, precipitation had a significant impact on CChla, CTP, SDD, and CTSM in riverine areas, though these parameters were less sensitive to meteorological factors. Moreover, the geomorphology of the reservoir and anthropogenic interference (e.g., tourism activities) also have a significant impact on the water quality parameters. This study demonstrates that coupling long-term GF-1 images and RF models could provide strong evidence and new insights to understand long-term dynamics in water quality and therefore support the development of corresponding management strategies for freshwater reservoirs.

With the acceleration of urbanization, increasing water pollution means that monitoring and evaluating urban water quality are of great importance. Although highly accurate, traditional evaluation methods are time consuming, laborious, and vastly insufficient in terms of the continuity of spatiotemporal coverage. In this study, a water quality assessment method based on remote sensing reflectance optical classification and the traditional grading principle is proposed. In this method, an optical water type (OWT) library was first constructed using the measured in situ remote sensing reflectance dataset based on fuzzy clustering technology. Then, comprehensive scoring rules were established by combining OWTs and 12 water quality parameters, and water quality was graded into different urban water quality levels (UWQLs) based on the scoring results. Using the proposed method, the relative water quality of urban waterbodies was qualitatively evaluated at the macro level based on images from the multispectral imager of Sentinel-2. In addition, there was a significant positive correlation between the UWQLs and the water quality index (WQI). These results indicate the potential of this method for quantitative assessment of urban water quality, providing a new way to evaluate water quality using remote sensing algorithms in the future.

This study investigated the effect of different bond primers (BP) and the thickness of BP on bond performance. The mechanical and microstructural properties of BP were studied systematically using compressive strength, drying shrinkage, X-ray diffraction, and other methods; bond strength was evaluated using the slant shear test, and the failure modes were observed. Further, kernel density estimation was performed to qualify the crack width of the repair interface statistically characterised by analysing images obtained using the backscattered-electron method. The results indicated that when the BP was modified using silica fume, nano-silica, and Portland cement after ball milling, the modified BP pores were refined because of the combined filler, packing, and pozzolanic effects. The drying shrinkage of BP modified by an expansive agent was reduced; the BP could narrow the repair interface gap or even disappear, and the modified BP made the interfacial zone denser and improved bond strength. The results of the microscopic characterisation method of the repair interface verify the multi-scale characterisation of the new and old concrete interface.

The diffuse attenuation coefficient Kd(λ) is an important optical parameter that can be determined remotely. It plays a meaningful role in inland and coastal biogeochemical processes and aids in determining water quality. Four data-gathering expeditions were conducted in Lake Hongze (HZL), China, between 2015 and 2017. Based on the in situ optical and biological data, we constructed and evaluated a band ratio model for retrieving Kd(490) of HZL from MODIS/Aqua images between 2002 and 2016. Our results are as follows: (1) The developed algorithm, using the visible bands (Rrs(red)/Rrs(green)), performs robustly for Kd(490) estimation, with R2 of 0.82 and p