Hyperspectral remote sensing technology has been widely used in water quality monitoring. However, while it provides more detailed spectral information for water quality monitoring, it also gives rise to issues such as data redundancy, complex data processing, and low spatial resolution. In this study, a novel approach was proposed to determine the characteristic spectral band of water quality parameters based on satellite hyperspectral data, aiming to improve data utilization of hyperspectral data and to achieve the same precision monitoring of multispectral data. This paper first introduces the data matching method of satellite hyperspectral data and water quality based on space–time information for guidance in collecting research data. Secondly, the customizable and fixed spectral bands of the existing multispectral camera products were studied and used for the preprocessing of hyperspectral data. Then, the determination approach of characteristic spectral bands of water quality parameters is proposed based on the correlation between the reflectance of different bands and regression modeling. Next, the model performance for retrieval of various water quality parameters was compared between the typical empirical method and artificial neural network (ANN) method of different spectral band sets with different band numbers. Finally, taking the adjusted determination coefficient $\overline{R^2}$ as an evaluation index for the models, the results show that the ANN method has obvious advantages over the empirical method, and band set providing more band options improves the model performance. There is an optimal band number for the characteristic spectral bands of water quality parameters. For permanganate index (COD_Mn), dissolved oxygen (DO), and conductivity (EC), the $\overline{R^2}$ of the optimal ANN model with three bands can reach about 0.68, 0.43, and 0.49, respectively, whose mean absolute percentage error (MAPE) values are 14.02%, 16.26%, and 17.52%, respectively. This paper provides technical guidance for efficient utilization of hyperspectral data by determination of characteristic spectral bands, the theoretical basis for customization of multispectral cameras, and the subsequent water quality monitoring through remote sensing using a multispectral drone.

The purpose of this paper is to provide a numerical simulation, taking into account the collisional interactions of droplets in an airless rotary spray coating process. The hydrodynamics of gas and droplets are simulated using the CFD-discrete element method (DEM) with the JKR contact model in an airless rotary spray coating process of a horizontal square duct. The surface energy parameter used in the JKR model is calibrated using a virtual accumulation angle test in the funnel device. Based on the distribution of accumulation angles, a suitable surface energy for wall droplets is proposed. A rational gas RNG k-ε model is suggested in accordance with the comparisons of velocities, standard deviations, and the skewness of droplet number fractions from three turbulence models. The simulations of droplet film thicknesses agree with measurements from the literature regarding the film thickness along a vertical panel. The correlations of the exit gas and droplet velocities of sprayer holes are proposed with a discharge coefficient of 0.85 for gas and 5.87 for droplets. A number index of droplets is introduced in order to measure the uniformity of droplet distributions. A low droplet number index is found at low rotational speeds, representing a more uniform distribution of droplets as the rotation speeds reduce within the square duct. The normal force between the droplet and the wall is approximately an order of magnitude larger than the droplet–wall tangential force of collisions.

The particle absorption coefficient of pigments (a_ph(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of a_ph(λ) retrieval in complex aquatic environments, this study proposes a novel two-stage framework integrating optical classification and machine learning regression. Focusing on inland waters—key areas for eutrophication monitoring—we first developed an intelligent clustering method combining Kernel Principal Angle-based Component (KPAC) dimensionality reduction and Chameleon Swarm Algorithm (CSA)-optimized k-medoids to classify water bodies into four optical types based on hyperspectral reflectance features. Subsequently, an XGBoost regression model with L1-norm feature selection was applied to inversely derive a_ph(440), a_ph(555), a_ph(675), and a_ph(709) for each class. Experimental results demonstrated that optical classification significantly improved inversion accuracy: the determination coefficients R² all exceeded 0.9 in classified datasets, with RMSE reduced by up to 93.1% compared to unclassified scenarios. This indicates that the strategy based on optical classification and regression inversion can effectively enhance the accuracy of pigment particle absorption coefficient inversions. In summary, this study, with the central objective of accurately measuring the pigment particle absorption coefficient, successfully developed a comprehensive set of optical classification and regression inversion methods applicable to various aquatic environments. This new scientific approach and powerful tool provide a means for monitoring and interpreting the pigment particle absorption characteristics in water bodies using remote sensing technology.

Developing high-performance passive radiative cooling coatings (PRCC) with high thermal conductivity for fast heat dissipation from self-heating objects remains a significant challenge. In this study, a nanocellulose-mediated Pickering emulsion strategy was employed to construct a uniform hexagonal boron nitride (BN) thermal network within PRCC. Benefiting from the Pickering emulsion template stabilized by TEMPO-oxidized cellulose nanofibrils (TOCNF), BN nanosheets and other functional inorganic nanoparticles were uniformly dispersed in the PRCC matrix, forming a continuous and homogeneous thermally conductive network. This structural design endowed the PRCC with excellent thermal conductivity, high solar reflectance in the solar spectrum (0.3–2.5 μm), and strong emissivity in the “atmospheric window” (ATSW, 8–13 μm). Specifically, the thermal conductivity of the TOCNF-BN-modified PRCC reached 2.28 ± 0.15 W·m⁻¹·K⁻¹, which was significantly higher than that of the pristine PRCC. The coating exhibited a solar reflectivity of ~87% and an emissivity of ~93% in the ATSW. Outdoor model temperature tests indicated that the internal temperature of the PRCC-TOCNF-BN-coated metal cabinet was 9.6 °C lower than that of the bare cabinet and 6.7 °C lower than the cabinet coated with pristine PRCC. This novel approach of using Pickering emulsion templates to fabricate highly thermally conductive radiative cooling coatings provides a new strategy for the development of high-performance and multifunctional radiative cooling materials for thermal management applications.