Due to its long-term and high-frequency observation capabilities, remote sensing is widely recognized as an indispensable and preferred technology for large-scale and cross-regional water quality monitoring. This paper comprehensively reviews the recent progress of remote sensing for water environment monitoring, predominantly focusing on remote sensing data sources, inversion indices, and inversion models. Specifically, we summarize the inversion methods for commonly monitored water quality parameters, including optically active constituents (such as chlorophyll-a, colored dissolved organic matter, total suspended solids, and water clarity) and non-optically active constituents (including total nitrogen, total phosphorus, and chemical oxygen demand). Furthermore, the applications of remote sensing in the field of environmental sciences such as spatiotemporal evolution and driver factor analysis of water quality, carbon budget research, and pollution source identification are also systematically reviewed. Finally, we propose that atmospheric correction algorithm improvement, multi-source data fusion, and high-precision large-scale inversion algorithms should be further developed to reduce the current dependence on empirical observation algorithms in remote sensing and overcome the limitations imposed by temporal and spatial scales and that more inversion models for non-optically active parameters should be explored to realize accurate remote sensing monitoring of these components in the future. This review not only enhances our understanding of the critical role of remote sensing in inland water quality monitoring but also provides a scientific basis for water environment management.

Cottonseed is the second major product of cotton (Gossypium spp.) crops after fiber. Thus, the characterization and valorization of cottonseed are important parts of cotton utilization research. In this work, the nonpolar and polar fractions of glanded (Gd) cottonseed were sequentially extracted by 100% hexane and 80% ethanol aqueous solutions and subjected to ¹³C and ¹H nuclear magnetic resonance (NMR) spectroscopy and Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), respectively. The nonpolar (crude oil) extracts showed the characteristic NMR peak features of edible plant oils with the absence of ω-3 linolenic acid. Quantitative analysis revealed the percentage of polyunsaturated, monounsaturated, and saturated fatty acids as 48.7%, 16.9%, and 34.4%, respectively. Both general unsaturated fatty acid features and some specific olefinic compounds (e.g., oleic, linolenic, and gondonic acids) were found in the nonpolar fraction. In the polar extracts, FT-ICR MS detected 1673 formulas, with approximately 1/3 being potential phenolic compounds. Both the total and phenolic formulas fell mainly in the categories of lipid, peptide-like, carbohydrate, and lignin. A literature search and comparison further identifies some of these formulas as potential bioactive compounds. For example, one compound [2,5-dihydroxy-N′-(2,3,4-trihydroxybenzylidene) benzohydrazide] identified in the polar extracts is likely responsible for the anticancer function observed when used on human breast cancer cell lines. The chemical profile of the polar extracts provides a formulary for the exploration of bioactive component candidates derived from cottonseed for nutritive, health, and medical applications.

Lakes on the Inner Mongolia–Xinjiang Plateau (IMXP) are increasingly vulnerable to eutrophication under climate change and human pressure, yet long-term monitoring remains limited by sparse field sampling. Here, we reconstruct multi-decadal trophic dynamics across the IMXP using Landsat time series and temporally transferable machine-learning models and further quantify the underlying natural and anthropogenic drivers. We compiled monthly in situ water-quality observations (chlorophyll-a, Chl-a; total phosphorus, TP; total nitrogen, TN; Secchi depth, SD; and permanganate index, COD_Mn;) and calculated the trophic level index (TLI). After rigorous quality control and monthly aggregation, we compiled a dataset of 1345 matched lake–month samples spanning 2000–2024, and divided it into a training set (n = 1076; ≤2019) and an independent test set (n = 269; 2020–2024) to evaluate temporal transferability. We utilized Google Earth Engine to generate monthly surface reflectance composites from Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2. Four supervised regression algorithms—ridge regression (RR), support vector regression (SVR), random forest (RF), and eXtreme Gradient Boosting (XGBoost)—were trained to estimate TLI. On the independent test period, XGBoost performed best (R² = 0.780, RMSE = 3.290, MAE = 1.779), followed by RF (R² = 0.770, RMSE = 3.364), SVR (R² = 0.700, RMSE = 3.842), and RR (R² = 0.630, RMSE = 4.267); we then used XGBoost to reconstruct monthly and yearly TLI for 610 perennial grassland lakes from 2000 to 2024. From 2000 to 2024, the annual mean TLI (48–49) across the IMXP exhibited a statistically significant upward trend (slope = 0.0158 TLI yr⁻¹; 95% confidence interval (CI) = 0.0050–0.0267; p = 0.006). Meanwhile, spatial heterogeneity was distinct (TLI: 41.51–59.70). High values concentrated in endorheic and desert–oasis basins (e.g., Eastern Inner Mongolia Plateau, >51), whereas lower values characterized high-altitude regions (e.g., Yarkant River, <45). Overall, trends ranged from −0.49 to 0.51 yr⁻¹, increasing in 54% of lakes (15.6% significantly) and decreasing in 46% (15.4% significantly). Attribution analyses identified NDVI (33.92%) and temperature (21.67%) as dominant drivers (55.59% combined), followed by precipitation (13.99%) and human proxies (30.42% combined: population 10.66%, grazing 10.31%, built-up 9.45%). Across 53 sub-basins, NDVI was the primary driver in 28, followed by temperature (11), population (7), precipitation (3), grazing (3), and built-up land (1); notably, the top two drivers explained 56.6–87.1% of variations. TWFE estimates revealed bidirectional NDVI effects (significant in 31/53): positive associations in 22 basins were linked to nutrient retention, contrasting with negative effects in nine basins associated with agricultural return flows. Temperature effects were significant in 15 basins and predominantly negative (14/15), except for the Qiangtang Plateau. Overall, eutrophication risk across the IMXP lake region reflects the combined influences of climatic conditions, vegetation conditions, and human activities, with their relative contributions varying among basins.

Emerging organic pollutants (EOPs) in aquatic environments have attracted increasing attention because many occur at trace levels, undergo transformation during environmental transport, and contribute to poorly resolved mixture risks. Traditional targeted analysis is inherently restricted to predefined compounds, whereas high-resolution mass spectrometry (HRMS)-based full-scan workflows provide broader opportunities for discovering known unknowns and previously unrecognized contaminants. This review critically synthesizes an omics-based analytical framework for aquatic environments, covering sample digitalization, instrumental analysis and acquisition modes, chemical fingerprint/non-target screening, suspect screening, effect-directed analysis, and confidence-based structural identification. Particular emphasis is placed on practical decision points and trade-offs, including dissolved versus particulate-associated analytes, LC-HRMS versus GC-HRMS coverage, hard versus soft ionization, DDA- versus DIA-type acquisition, database dependence, and the persistent difficulty of linking analytical features to toxicological relevance. The review also discusses emerging directions involving artificial intelligence, chemometrics, organometallic contaminants, and microplastic-associated chemicals. By clarifying conceptual boundaries and highlighting current limitations, this article aims to support the development of more critical, transparent, and risk-oriented workflows for the discovery and prioritization of emerging pollutants in aquatic environments.