Anhui Province is rich in geothermal water resources, making the study of its hydrochemical evolution and genetic models essential for scientific development and sustainable utilization. This study combines hydrochemical and hydrogen–oxygen isotopic data from different regions of Anhui Province to analyze the hydrogeochemical evolution characteristics and recharge mechanisms of basin-type and mountainous-type geothermal waters. The results show that basin-type geothermal water is predominantly of the Cl–Na type, with water–rock interactions mainly including halite dissolution, gypsum dissolution, dedolomitization, and silicate hydrolysis. The groundwater system is relatively closed off, with slow flow rates. In contrast, mountainous geothermal water is mainly of the HCO₃–Na·Ca, SO₄–Na·Ca, and SO₄–Na types, with water–rock interactions primarily involving calcite dissolution, dolomite dissolution, and gypsum dissolution. Enhanced precipitation infiltration due to fault structures leads to stronger recharge and an open-system characteristic. The genetic models of the two types of geothermal water reveal the structural and recharge mechanisms of thermal reservoirs under different geological settings, highlighting the significant control of geological background on geothermal water formation.

To reduce downstream ecological damage, it is crucial to analyze water and salt sources in saline–alkali farmland drainage and optimize soil salt discharge. This study employs statistical, hydrochemical, and isotope methods to identify controlling factors and characteristics in water bodies during winter irrigation. The results show average TDS values of 0.59, 6.40, and 4.14 g/L for irrigation, phreatic, and drainage water. Irrigation and phreatic water mainly belong to the HCO₃-Ca·Mg·Na and Cl·SO₄-Na·Mg types. Na⁺/(Na⁺ + Ca²⁺) and Cl⁻/(Cl⁻ + HCO₃⁻) values suggest the rock–water interaction and evaporation influences in irrigation water, while evaporation primarily controls phreatic water. The main salt types include NaCl, MgSO₄, Na₂CO₃, and CaCO₃ for irrigation water and NaCl, MgSO₄, Na₂SO₄, and CaCO₃ for phreatic water. These findings lay a research basis for analyzing water and salt sources in farmland drainage during winter irrigation in saline–alkali land.

Surface soil moisture (SSM) plays a pivotal role various fields, including agriculture, hydrology, water environment, and meteorology. To investigate the impact of land use types and fractional vegetation cover (FVC) on the accuracy of SSM estimation, this study conducted a comprehensive analysis of SSM estimation performance across diverse land use scenarios (e.g., multiple land use combinations and cropland) and varying FVC conditions. Sentinel-2 NDVI and MOD09A1 NDVI were fused by the Enhanced Spatial and Temporal Adaptive Reflection Fusion Model (ESTARFM) to obtain NDVI with a temporal resolution better than 8 d and a spatial resolution of 20 m, which improved the matching degree between NDVI and the Sentinel-1 backscattering coefficient (σ⁰). Based on the σ⁰, NDVI, and in situ SSM, combined with the water cloud model (WCM), the SSM estimation model is established, and the model of each land use and FVC is validated. The model has been applied in Handan. The results are as follows: (1) Compared with vertical–horizontal (VH) polarization, vertical–vertical (VV) polarization is more sensitive to soil backscattering (${\mathsf{\sigma }}_{\mathrm{soil}}^0$). In the model for multiple land use combinations (Multiple-Model) and the model for the cropland (Cropland-Model), the R² increases by 0.084 and 0.041, respectively. (2) The estimation accuracy of SSM for the Multiple-Model and Cropland-Model is satisfactory (Multiple-Model, RMSE = 0.024 cm³/cm³, MAE = 0.019 cm³/cm³, R² = 0.891; Cropland-Model, RMSE = 0.023 cm³/cm³, MAE = 0.018 cm³/cm³, R² = 0.886). (3) When the FVC > 0.75, the accuracy of SSM by the WCM is low. It is suggested the model should be applied to the cropland where the FVC ≤ 0.75. This study clarified the applicability of SSM estimation by microwave remote sensing (RS) in different land uses and FVCs, which can provide scientific reference for regional agricultural irrigation and agricultural water resources management. The findings highlight that the VV polarization-based model significantly improves SSM estimation accuracy, particularly in croplands with FVC ≤ 0.75, offering a reliable tool for optimizing irrigation schedules and enhancing water use efficiency in agriculture. These results can aid in better water resource management, especially in regions with limited water availability, by providing precise soil moisture data for informed decision-making.