Remote sensing technology is widely used to obtain information on floating green tides, and thresholding methods based on indices such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI) play an important role in such studies. However, as the methods are influenced by many factors, the threshold values vary greatly; in particular, the error of data extraction clearly increases in situations of high-turbidity water (HTW) (NDVI > 0). In this study, high spatial resolution, multispectral images from the Sentinel-2 MSI mission were used as the data source. It was found that the International Commission on Illumination (CIE) hue angle calculated using remotely sensed equivalent multispectral reflectance data and the RGB method is extremely effective in distinguishing floating green tides from areas of HTW. Statistical analysis of Sentinel-2 MSI images showed that the threshold value of the hue angle that can effectively eliminate the effect of HTW is 218.94°. A test demonstration of the method for identifying the floating green tide in HTW in a Sentinel-2 MSI image was carried out using the identified threshold values of NDVI > 0 and CIE hue angle < 218.94°. The demonstration showed that the method effectively eliminates misidentification caused by HTW pixels (NDVI > 0), resulting in better consistency of the identification of the floating green tide and its distribution in the true color image. The method enables rapid and accurate extraction of information on floating green tide in HTW, and offers a new solution for the monitoring and tracking of green tides in coastal areas.

The precise correction of water and energy balance is a significant difficulty in studying turbulence energy balance and water flow for agricultural purposes. The need for efficient water and energy management is growing as the world struggles to keep up with rising water and energy demands. This research examines artificial intelligence (AI)'s impact on the water flow and energy balance confluence subnetwork of sensing elements from all the original network's nodes. The study proposed an AI-based optimized sensor energy balance model (AI-SEBM) that uses pressure data to maintain energy balance in turbines and save water with the optimized energy source for agriculture usage. This research explores the potential for installing Kalpan hydraulic turbines, which are most effective during half-load operation, and to forecast all loads with little computing effort to balance energy in turbulence. To anticipate daily pressure readings and energy consumption across all nodes in the network, an AI-based optimization wireless sensor network is designed for communication and linked to an energy balance system. Sensors are strategically deployed at the network's nerve centres. The maximum flow algorithm is used for a grid representing the water and energy balance to determine the positions of the virtual nodes.