Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R² values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area.

Since the advent of flush toilet systems, the aquatic environment has received a massive contaminant flow. Furthermore, the perception of human feces has changed from a useful nutrient source for agriculture to a harmful contaminant. In this study, we compared the nutritional quality of five samples: (1) human manure (HM), (2) human manure from a family mainly eating organic food (HMO), (3) cow manure (CM), (4) poultry manure (PM), and (5) commercial nursery media (CNM). Samples were analyzed in terms of organic and inorganic nutrient contents, molecular composition, seed germination, and chlorophyll concentration. Pyrolysis gas chromatography/mass spectrometry (GC/MS) was used to describe the differences in molecular composition. Three-dimensional excitation and emission matrix fluorescence spectroscopy characterized the organic composition of water extracts. From the results, CNM, PM, and HMO showed humic- and fluvic-like substance peaks, the highest values of potassium and sulfate ions, and of C/N ratios, indicating greater plant growth potential. This was confirmed by their higher chlorophyll concentrations and germination index values. These results contribute knowledge about the positive effects of manure, changing the negative perception of human excreta from waste to resource. This work provides a reference for reducing the wastewater loading rate in society.

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms.