Basin ecohydrological processes are essential for informing policymaking and social development in response to growing environmental problems. In this paper, we review watershed ecohydrology, focusing on the interaction between watershed ecological and hydrological processes. Climate change and human activities are the most important factors influencing water quantity and quality, and there is a need to integrate watershed socioeconomic activities into the paradigm of watershed ecohydrological process studies. Then, we propose a new framework for integrated watershed management. It includes (1) data collection: building an integrated observation network; (2) theoretical basis: attribution analysis; (3) integrated modeling: medium- and long-term prediction of ecohydrological processes by human–nature interactions; and (4) policy orientation. The paper was a potential solution to overcome challenges in the context of frequent climate extremes and rapid land-use change.

Fine particulate matter (PM_2.5) is a major pollutant in Guanzhong Urban Agglomeration (GUA) during the winter, and GUA is one of China’s regions with the highest concentrations of PM_2.5. Daily surface PM_2.5 maps with a spatial resolution of 1 km × 1 km can aid in the control of PM_2.5 pollution. Thus, the Random Forest and eXtreme Gradient Boosting (RF-XGBoost) model was proposed to fill the missing aerosol optical depth (AOD) at the station scale before accurately estimating ground-level PM_2.5 using the recently released MODIS AOD product derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC), high density meteorological and topographic conditions, land-use, population density, and air pollutions. The RF-XGBoost model was evaluated using an out-of-sample test, revealing excellent performance with a coefficient of determination (R²) of 0.93, root-mean-square error (RMSE) of 12.49 μg/m³, and mean absolution error (MAE) of 8.42 μg/m³. The result derived from the RF-XGBoost model indicates that the GUA had the most severe pollution in the winter of 2018 and 2019, owing to the burning of coal for heating and unfavorable meteorological circumstances. Over 90% of the GUA had an annual average PM_2.5 concentrations decrease of 3 to 7 μg/m³ in 2019 compared to the previous year. Nevertheless, the air pollution situation remained grim in the winter of 2019, with more than 65% of the study area meeting the mean PM_2.5 values higher than 35 μg/m³ and the maximum reaching 95.57 μg/m³. This research would be valuable for policymakers, environmentalists, and epidemiologists, especially in urban areas.

Eutrophication is a significant factor that damages the water ecosystem’s species balance. The total phosphorus (TP) concentration is a vital water quality indicator in assessing surface water eutrophication. This paper predicts the spatial distribution of TP concentration using remote sensing, measured data, and the partial least squares regression (PLSR) method. Based on the correlation analysis, the models were built and tested using the TP concentration and Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) image spectra. The results demonstrated that the best technique based on band combinations of the Sentinel-2 and Landsat-8 images achieved good precision. The coefficient of determination (R²), root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) were 0.771, 0.023 mg/L, and 2.086 for Sentinel-2 images and 0.630, 0.032 mg/L, and 1.644 for Landsat-8 images, respectively. The TP concentration maps were interpolated using the inverse distance weighting method, and the inversion results obtained from the images were in good agreement. The western and northwestern regions of Taihu Lake, where significant cyanobacterial blooms occurred, had TP concentrations greater than 0.20 mg/L; nevertheless, the central and eastern regions had amounts ranging from 0.05 to 0.20 mg/L. In order to prove the extensibility of the model, the optimal algorithm was applied to the Sentinel-2 and Landsat-8 images in 2017. The optimal algorithm based on Landsat-8 images has a better verification effect (RMSEP = 0.027 mg/L, and R = 0.879 for one Landsat-8 image), and the optimal algorithm based on Sentinel-2 images has moderate verification effect (RMSEP = 0.054 mg/L and 0.045 mg/L, and R = 0.771 and 0.787 for two Sentinel-2 images). The interpolation and inversion maps are in good agreement, indicating that the model is suitable for the Landsat-8 and Sentinel-2 images, which can be complementary for higher temporal resolutions. Monitoring water quality using multiple remote sensing images can provide the scientific basis for water quality dynamic monitoring and prevention in China.

The contamination of farmlands with hazardous metals from mining puts the safety of agricultural commodities at risk. For remediation, it is crucial to map the spatial distribution of contaminated soil. Typical sampling-based procedures are time-consuming and labor-intensive. The use of visible, near-infrared, and short-wave infrared reflectance (VNIR-SWIR) spectroscopy to detect soil heavy metal pollution is an alternative. With the aim of investigating a methodology of detecting the most sensitive bands using VNIR-SWIR spectra to find lead (Pb) anomalies in agriculture soil near mining activities, the area in Xiaoqinling Mountain, downstream from a series of active gold mines, was selected to test the feasibility of utilizing VNIR-SWIR spectroscopy to map soil Pb. A total of 115 soil samples were collected for laboratory Pb analysis and spectral measurement. Partial least squares regression (PLSR) was adopted to estimate the soil Pb content by building the prediction model, and the model was optimized by finding the optimal number of bands involved. The spatial distribution of Pb concentration was mapped using the ordinary kriging (OK) interpolation method. This study found that five spectral bands (522 nm, 1668 nm, 2207 nm, 2296 nm, and 2345 nm) were sensitive to soil Pb content. The optimized prediction model’s coefficient of determination (R²), residual prediction deviation (RPD), and root mean square error (RMSE) were 0.711, 1.860, and 0.711 ln(mg/kg), respectively. Additionally, the result of OK interpolation was convincing and accurate (R² = 0.775, RMSE = 0.328 ln(mg/kg)), comparing maps from estimated and ground truth data. This study proves that it is feasible to use VNIR-SWIR spectral data for in situ estimation of the soil Pb content.