Total phosphorus (TP) concentration is high in countless small inland waterbodies in Hubei province, middle China, which is threating the water environment. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, because the cost of time, labor, and money is high and it does not meet the needs of spatiotemporal dynamic monitoring. Remote sensing provides an effective tool for TP concentration monitoring spatiotemporally. However, monitoring the TP concentration of small inland waterbodies is challenging for satellite remote sensing due to the inadequate spatial resolution. Recently, unmanned aerial vehicles (UAV) have been applied to quantitatively retrieve the spatiotemporal distribution of TP concentration without the challenges of cloud cover and atmospheric effects. Although state-of-the-art algorithms to retrieve TP concentration have been improved, specific models are only used for specific water quality parameters or regions, and there are no robust and reliable TP retrieval models for small inland waterbodies at this time. To address this issue, six machine learning methods optimized by intelligent optimization algorithms (IOA-ML models) have been developed to quantitatively retrieve TP concentration combined with the reflectance of original bands and selected band combinations of UAV multispectral images. We evaluated the performances of models in terms of coefficient of determination (R²), root mean squared error (RMSE), and residual prediction deviation (RPD). The results showed that the R² of the six IOA-ML models for training, validation, and test sets were 0.8856–0.984, 0.8054–0.8929, and 0.7462–0.9045, respectively, indicating the methods had high precision and transferability. The extreme gradient boosting optimized by genetic algorithm (GA-XGB) performed best, with the highest precision for the validation and test sets. The spatial distribution of TP concentration of each flight derived from different models had similar distribution characteristics. This paper provides a reference for promoting the intelligent and automatic level of water environment monitoring in small inland waterbodies.

Biochar’s benign effects on agricultural production have been demonstrated. Still, no consistent conclusions have been drawn on the impact of biochar-amended paddy fields on carbon sequestration, gas emission reduction, and efficiency enhancement in typical cropping areas in the middle Yangtze River. A field experiment using five dosages of biochar (CK, BC1.5, BC3, BC4.5, and BC6) at 0, 1.5, 3, 4.5, and 6 kg·m⁻² was conducted at the Hubei Irrigation Experiment Center Station, Jingmen City, Hubei Province, China, to investigate the effects of biochar on carbon sequestration, greenhouse gas emissions, and agricultural efficiency in paddy in the middle Yangtze River Region. This study showed that the optimal biochar dosage was 4.5 kg·m⁻² (BC4.5). Biochar significantly improved soil properties, increased rice yield by 26.4–61.4%, and enhanced water use efficiency (WUE) and economic profit (EP) by 32.0–83.7% and −8.0–48.6%, respectively. Biochar increased soil carbon sequestration (SCS) and carbon pool management index (CPMI) by 23.0–198.3% and 22.9–71.5%, respectively. Biochar also reduced greenhouse gas emission intensity (GHGI), global warming potential (GWP), and emissions of CO₂, CH₄, and N₂O. Furthermore, structural equation modeling (SEM) indicated that soil organic carbon (SOC), in addition to the “biochar” influence factor, was a key positive influence factor for SCS, CPMI, and EP. Another major positive factor for GWP was silt, and for WUE it was saturated hydraulic conductivity, while TN and SOC were the major negative variables for GHGI. In summary, biochar demonstrated outstanding carbon sequestration and emission reduction impacts while ensuring crop production growth and efficiency improvement. The results provide a research basis for safeguarding food security and mitigating climate warming in the middle Yangtze River region.

Within a river catchment, the relationship between pollutant load migration and its related factors is nonlinear generally. When neural network models are used to identify the nonlinear relationship, data scarcity and random weight initialization might result in overfitting and instability. In this paper, we propose an averaged weight initialization neural network (AWINN) to realize the multi-index integrated prediction of a pollutant load under data scarcity. The results show that (1) compared with the particle swarm optimization neural network (PSONN) and AdaboostR models that prevent overfitting, AWINN improved simulation accuracy significantly. The R² in test sets of different pollutant load models reached 0.51–0.80. (2) AWINN is effective in overcoming instability. With more hidden layers, the stability of the models’ outputs was stronger. (3) Sobol sensitivity analysis explained that the main influencing factors of the whole process were the flows of the catchment inlet and outlet, and main factors changed across seasons. The algorithm proposed in this paper can realize stably integrated prediction of pollutant load in the catchment under data scarcity and help to understand the mechanism that influences pollutant load migration.