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 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.