The spectral variability problem is a big challenge in hyperspectral unmixing. Endmember bundles have been used to address the spectral variability problem by adopting a bundle of endmember spectra to represent one kind of endmember class. Existing endmember bundle extraction (EBE) algorithms mainly rely on the convex geometry assumption and integrate endmembers from image subsets as endmember bundles. On the one hand, they suffer from high risk of bad performance for real hyperspectral scene where the convex geometry assumption is not satisfied. On the other hand, endmember variabilities within image subsets are neglected, which may lose representative endmembers. In this article, we propose a novel EBE framework to capture endmember variability by introducing a dynamic optimization mechanism. Endmember bundles are obtained by dynamically minimizing the root-mean-square error (RMSE) between original pixels and reconstructed pixels through an iteration process, and a particle swarm optimization (PSO) method is introduced to find the optimal endmember combination in each iteration. The proposed EBE framework imposes no assumption on the hyperspectral data distribution and has great potential to be used in complex hyperspectral scenes. Experimental results on two real hyperspectral datasets demonstrate that the proposed algorithm is able to obtain endmember bundles that well express the spectral variability, and the performance of the proposed algorithm is competitive with the state-of-the-art algorithms.

With the planning and construction of marine ranching in China, water quality has become one of the critical limiting factors for the development of marine ranching. Due to geographical differences, marine ranches exhibit varying water quality conditions under the influence of the continental shelf. To the best of our knowledge, there is limited research on satellite-based water quality monitoring for marine ranching and the spatiotemporal variations in marine ranches in different geographical locations. Chlorophyll-a (Chl-a) is a key indicator of the ecological health and disaster prevention capacity of marine ranching, as it reflects the conditions of eutrophication and is crucial for the high-quality, sustainable operation of marine ranching. Using a physically based model, this study focuses on the retrieval of Chl-a concentration in Daya Bay. The coefficient of determination (R²) between the model retrieval values and the in situ Chl-a data is 0.69, with a root mean square error (RMSE) of 1.52 μg/L and a mean absolute percentage error (MAPE) of 44.25%. Seasonal variations in Chl-a concentration are observed in Daya Bay and are higher in spring–summer and lower in autumn–winter. In the YangMeikeng waters, Chl-a concentration shows a declining trend with the development of marine ranching. A comparison between the YangMeikeng (nearshore) and XiaoXingshan (offshore) marine ranches suggests that offshore ranching may be less impacted by terrestrial pollutants. The primary sources of Chl-a input in Daya Bay are the Dan’ao River and the aquaculture areas in the northeastern part of the bay. This study can provide valuable information for the protection and management of marine ranching.

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most urbanized and industrialized coastal regions in China, where intense human activities contribute to substantial terrestrial sediment discharge into the adjacent marine environment. However, complex hydrodynamic conditions and high spatiotemporal variability pose challenges for accurate suspended sediment concentration (SSC) retrieval. Developing water quality retrieval models based on different classifications of water bodies could enhance the accuracy of SSC inversion in coastal waters. Therefore, this study classified the coastal waters of the GBA into clear and turbid zones based on Hue angle α, and established retrieval models for SSC using a single-scattering approximation model for clear zones and a secondary-scattering approximation model for turbid zones based on radiative transfer processes. Model validation with in-situ data shows a coefficient of determination (R²) of 0.73, a root mean square error (RMSE) of 8.30, and a mean absolute percentage error (MAPE) of 42.00%. Spatial analysis further reveals higher SSC in the waters around Qi’ao Island in the Pearl River Estuary (PRE) and along the coastline of Guanghai Bay, identifying these two areas as priorities for attention. This study aims to offer valuable insights for SSC management in the coastal waters of the GBA.

In recent years, the introduction of multiobjective evolutionary algorithms (MOEAs) into the field of endmember extraction (EE) in hyperspectral unmixing has demonstrated a breadth of results that surpass those derived from single-objective-based methodologies. Despite these advancements, the adaptation of MOEAs to EE and the attainment of globally optimal solutions represent unresolved challenges meriting continued exploration. This study addresses two principal obstacles in MOEA-based EE: the notorious “curse of dimensionality” in high-dimensional optimization, and the difficulty in striking a balance between convergence and population diversity. We propose a two-stage, evolutionary-based EE algorithm, referred to as TSEA, designed to confront these issues. A novel solution space splitting strategy is incorporated into TSEA that efficiently mitigates the curse of dimensionality by strategically contracting the search space. This advantage is largely attributed to the significant reduction of invalid solutions achieved through the simple application of a clustering procedure. Furthermore, a two-stage optimization approach is employed to meticulously uphold the convergence and diversity of the population, aiming to attain the optimal solution within the realm of high-dimensional optimization. Empirical evidence from four real hyperspectral images demonstrates that the proposed TSEA outperforms other comparison multiobjective optimization algorithms. Thus, this study contributes to the ongoing discourse on the optimization and applicability of MOEAs in the context of EE.

Surface water fraction mapping is an essential preprocessing step for the subpixel mapping (SPM) of surface water, providing valuable prior knowledge about surface water distribution at the subpixel level. In recent years, spectral mixture analysis (SMA) has been extensively applied to estimate surface water fractions in multispectral images by decomposing each mixed pixel into endmembers and their corresponding fractions using linear or nonlinear spectral mixture models. However, challenges emerge when introducing existing surface water fraction mapping methods to hyperspectral images (HSIs) due to insufficient exploration of spectral information. Additionally, inaccurate extraction of endmembers can result in unsatisfactory water fraction estimations. To address these issues, this paper proposes an adaptive unmixing method based on iterative multi-objective optimization for surface water fraction mapping (IMOSWFM) using Zhuhai-1 HSIs. In IMOSWFM, a modified normalized difference water fraction index (MNDWFI) was developed to fully exploit the spectral information. Furthermore, an iterative unmixing framework was adopted to dynamically extract high-quality endmembers and estimate their corresponding water fractions. Experimental results on the Zhuhai-1 HSIs from three test sites around Nanyi Lake indicate that water fraction maps obtained by IMOSWFM are closest to the reference maps compared with the other three SMA-based surface water fraction estimation methods, with the highest overall accuracy (OA) of 91.74%, 93.12%, and 89.73% in terms of pure water extraction and the lowest root-mean-square errors (RMSE) of 0.2506, 0.2403, and 0.2265 in terms of water fraction estimation. This research provides a reference for adapting existing surface water fraction mapping methods to HSIs.

Organic pollution poses a significant threat to water security, making the monitoring of organic pollutants in water environments essential for the protection of water resources. Remote sensing technology, with its wide coverage, continuous monitoring capability, and cost-efficiency, overcomes the limitations of traditional methods, which are often time-consuming, labor-intensive, and spatially restricted. As a result, it has become an effective tool for monitoring organic pollution in water environments. In this study, we propose a physically constrained remote sensing algorithm for the quantitative estimation of organic pollution in inland waters based on radiative transfer theory. The algorithm was applied to the Feilaixia Basin using Sentinel-2 data. Accuracy assessment results demonstrate good performance in the quantitative assessment of organic pollution, with a coefficient of determination (R²) of 0.79, a mean absolute percentage error (MAPE) of 13.03%, and a root mean square error (RMSE) of 0.39 mg/L. Additionally, a seasonal variation map of organic pollutant concentrations in the Feilaixia Basin was generated, providing valuable scientific support for regional water quality monitoring and management.