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 Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called W_SPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the W_SPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.

Unmanned Aerial Vehicles (UAVs) have become powerful tools for high-resolution, quantitative remote sensing in ecological and environmental studies. In this study, we present a novel approach to accurately mapping and estimating the biomass of Suaeda salsa using UAV-based visible-light imagery combined with hue angle inversion modeling. By integrating diffuse reflectance standard plates into the flight protocol, we converted RGB pixel values into reflectance and derived hue angle metrics with enhanced radiometric accuracy. A hue angle cutoff threshold of 249.01° was identified as the optimal cutoff to distinguish Suaeda salsa from the surrounding land cover types with high confidence. To estimate biomass, we developed an exponential inversion model based on hue angle data calibrated through extensive field measurements. The resulting model—Biomass = 3.57639 × 10⁻¹⁵ × e^0.12201×α—achieved exceptional performance (R² = 0.99696; MAPE = 3.616%; RMSE = 0.02183 kg/m²), indicating strong predictive accuracy and robustness. This study highlights a cost-effective, non-destructive, and scalable method for the real-time monitoring of coastal vegetation, offering a significant advancement in remote sensing applications for wetland ecosystem management.

Nuclear energy plays a crucial role in global carbon reduction. However, thermal discharges from nuclear power plants can potentially impact marine ecosystems. This study investigates the long-term thermal impact of the Haiyang Nuclear Power Plant on the adjacent marine environment using a decade-long Landsat thermal infrared dataset. Spatial and temporal patterns of thermal discharge were analyzed, focusing on the temperature difference between intake and outlet water, the warming trend in the thermal mixing zone, and the spatial distribution of the thermal plume. Our results indicate the following: (1) Seasonal Variation in Thermal Discharge: The temperature difference between intake and outlet water exhibited significant seasonal variability, with higher values in winter and lower values in summer. The spatial distribution of the thermal plume was influenced by tidal currents, leading to a cyclical pattern. (2) Long-Term Warming Trend: Prolonged thermal discharge resulted in a notable warming trend in the thermal mixing zone, with an average annual increase of 0.3 °C. This warming effect was most pronounced in winter and least in summer. (3) Spatial Distribution of Thermal Plume: The spatial extent and intensity of the thermal plume varied seasonally. Summer exhibited a larger influence range but with lower temperature rises, while winter showed a smaller influence range but with higher temperature rises. In winter, the 4 °C temperature rise area exceeded the designated environmental functional zone boundary in some instances. These findings provide valuable insights into the thermal impact of nuclear power plants and highlight the importance of considering seasonal variations and long-term monitoring to ensure environmental sustainability.