The nitrogen-to-phosphorus ratio (TN:TP) is a key indicator influencing phytoplankton nutrient limitation and growth dynamics, directly regulating algal growth rates, abundance, and community structure, thereby affecting the process of water eutrophication. This study aims to evaluate the modeling performance of integrated machine learning approaches for lake total nitrogen to total phosphorus ratios (TN:TP), utilizing Zhuhai-1 hyperspectral satellite imagery to develop a CNN-SVR ensemble model integrating convolutional neural networks and support vector regression for remote sensing inversion of lake TN:TP ratios. Performance is evaluated against random forest (RF) and convolutional neural network (CNN) models, systematically analyzing spatial distribution patterns and primary drivers. Results indicate that the CNN-SVR model demonstrated superior performance among the tested models, with R², RMSE, MAPD, and RPD values of 0.856, 2.675, 9.516%, and 2.390, respectively. Spatially, the nitrogen-to-phosphorus ratio in lakes during the growing season exhibits an increasing trend from the western to the eastern half of the lake, progressing from northwest to southeast. When TN:TP falls below 9, algal growth becomes nitrogen-limited, indicating a higher degree of eutrophication; when TN:TP exceeds 22.6, phosphorus becomes the limiting factor, indicating lower eutrophication levels. A similar distribution pattern is observed during the non-growing season. Regarding driving mechanisms, the nitrogen-to-phosphorus ratio during the growing season is primarily influenced by TN accumulation and shows significant correlations with dissolved oxygen (DO) and pH. During the non-growing season, while still affected by TN input, its association with other water quality parameters is weaker. The results indicate that the combined use of CNN and SVR improves feature extraction and model fitting in nitrogen-to-phosphorus ratio inversion and helps clarify its ecological significance as an indicator of algal growth. This provides methodologies and evidence for precise diagnosis and ecological management of lake eutrophication.

Land use patterns are a critical driver of changes in carbon emissions, making it essential to elucidate the relationship between regional carbon emissions and land use types. As a nationally designated economic strategic zone, the Yangtze River Basin encompasses megacities, rapidly developing medium-sized cities, and relatively underdeveloped regions. However, the mechanism underlying the interaction between landscape patterns and carbon emissions across such gradients remains inadequately understood. This study utilizes nighttime light, land use and carbon emissions datasets, employing XGBoost, CatBoost, LightGBM and a stacking ensemble model to analyze the impacts and driving factors of land use changes on carbon emissions in the Yangtze River Basin from 2002 to 2022. The results showed: (1) The stacking ensemble learning model demonstrated the best predictive performance, with a coefficient of determination (R²) of 0.80, a residual prediction deviation (RPD) of 2.22, and a root mean square error (RMSE) of 4.46. Compared with the next-best models, these performance metrics represent improvements of 19.40% in R² and 28.32% in RPD, and a 22.16% reduction in RMSE. (2) Based on SHAP feature importance and Pearson correlation analysis, the primary drivers influencing CO₂ net emissions in the Yangtze River Basin are GDP per capita (GDPpc), population density (POD), Tertiary industry share (TI), land use degree comprehensive index (LUI), dynamic degree of water-body land use (K_water), Largest patch index (LPI), and number of patches (NP). These findings indicate that changes in regional landscape patterns exert a significant effect on carbon emissions in strategic economic regions, and that stacked ensemble models can effectively simulate and interpret this relationship with high predictive accuracy, thereby providing decision support for regional low-carbon development planning.

Particulate phosphorus (PP) is a major contributor to lakes developing eutrophic. It also serves as one of the most significant sources of phosphorus for primary productivity, serving as a possible source of soluble reactive phosphorus, and contributing a sizable amount of the total phosphorus (TP), so monitoring the spatial and temporal variability of PP is crucial for understanding eutrophication in water bodies. This study aims to propose an algorithm to accurately predict the PP concentration and to assess the performance of the model. Considering Chaohu Lake as a case study, we proposed a random forest algorithm based on the convolutional neural network (CNN‐RF) to investigate the spatial and temporal patterns of PP concentration in the lake using HJ‐2 hyperspectral satellite images. The applicability of backpropagation (BP) neural network, random forest (RF), convolutional neural network (CNN), and CNN‐RF models for remote sensing inversion of PP concentration is assessed through model comparison. The results indicate that the CNN‐RF model has the best prediction performance, with a coefficient of determination ( R ² ) of 0.91, a residual prediction deviation (RPD) of 3.42, a root mean square error (RMSE) of 0.0155 mg/L, and a percentage bias (PBIAS) of 5.01%, which is better than the BP model by 42%, 106%, 51%, and 81%, the RF model by 25%, 77%, 44%, and 59%, and the CNN model by 9.6%, 42%, 30%, and 49%, respectively, and 59% better than the CNN model. The inversion findings suggest that the PP of Chaohu Lake is substantially high in summer and autumn but comparatively low in spring and winter. The spatial distribution demonstrates an arrangement of highest in the western part of the lake, second in the eastern region, and lowest in the central, with obvious spatial and temporal distribution characteristics, and the trend is closely related to the quarterly mean air temperature and mean precipitation. This indicates that based on the differences in phosphorescence scattering signals of different morphologies in water bodies, the use of hyperspectral remote sensing and the CNN‐RF model can effectively extract PP spatiotemporal information, strengthen the learning capability of multiscale characteristics, and contribute to the improvement of the precision of estimating PP concentration, which could provide an innovative approach for determining the degree of eutrophication of lake water bodies.

Urban Form (UF), as a synthesis of urban functions and socioeconomic elements, is closely associated with Anthropogenic Carbon Emissions (ACE) and has important implications for low-carbon urban planning. As a key national economic strategy region, the Yangtze River Economic Belt (YREB) exhibits pronounced heterogeneity in urban development, highlighting the urgent need to elucidate the interaction mechanisms between UF and ACE to support carbon reduction strategies. This study employs nighttime light data and carbon emission records from 2002 to 2022 in the YREB. By integrating Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT), we developed a neural network ensemble model (RSG-Net) to analyze the impacts and driving mechanisms of UF on ACE. The results indicate the following: (1) Over the past two decades, total ACE in the YREB increased by 196%, displaying a three-phase trajectory of rapid growth, deceleration, and rebound. (2) The RSG-Net model achieved superior predictive performance, with an R² of 0.93, an RMSE of 1.96 × 10⁶ t, an RPD of 3.69, and a PBIAS of 4.53%. (3) Based on Pearson correlation analysis and SHAP (Shapley Additive Explanations) feature importance, beyond economic and demographic indicators, the most influential UF indicators are ranked as Number of Urban Patches (NP), Normalized Difference Vegetation Index (NDVI), and Construction Land Concentration (CLC). These findings demonstrate that the RSG-Net model can not only predict ACE but also identify key UF factors and explain their interrelationships, thereby providing technical support for the formulation of urban carbon reduction strategies.

Dissolved organic carbon (DOC) maintains the ecological balance of inland lake systems and contributes significantly to the global carbon cycle. This study aims to develop a novel deep learning algorithm to predict DOC concentrations and explore its modeling performance in nonlinear relationships. We used hyperspectral imagery (HSI) from the Chinese Ziyuan-1 satellite series alongside in situ water sample data to construct a Convolutional Multi-Head Attention Fusion Network (CMAF-Net) for prediction of DOC in Chaohu Lake, China. For comparison, we tested its performance against support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models. The spatial distribution patterns of the DOC were analyzed to explore the primary environmental drivers. The results demonstrate that CMAF-Net significantly outperforms the best-performing baseline CNN model, achieving an R² of 0.88, RMSE of 0.29 mg/L, and RPD of 2.79. Furthermore, environmental factor analysis reveals strong correlations between DOC concentrations and water temperature, total nitrogen (TN), and total phosphorus (TP), identifying them as dominant drivers of the spatial variability of DOC. Hyperspectral remote sensing integrated with CMAF-Net, under the synergistic optimization of local band feature extraction and global band-dependency modeling to screen characteristic water spectra, significantly improves DOC prediction accuracy and enhances multidimensional feature learning. The proposed approach establishes a novel pathway for the quantitative monitoring of DOC in inland aquatic lakes.