Evapotranspiration is a key driver of water and energy exchanges between terrestrial surfaces and the atmosphere, significantly influencing ecosystem balances. This study focuses on the Yellow River Basin (YRB), where evapotranspiration impacts both ecological dynamics and human activities. By analyzing actual evapotranspiration data from 1982 to 2017, this research provides insights into its spatial and temporal patterns within the YRB. Furthermore, a comprehensive assessment and comparative analysis were performed on four distinct evapotranspiration product datasets: GLDAS-Noah, ERA5-Land, GLEAM v3.8a, and MOD16A2. Employing the Geodetector model, the research identified seven key influencing factors—the digital elevation model (DEM), slope, aspect, precipitation, temperature, soil moisture, and normalized difference vegetation index (NDVI)—and analyzed their impact on evapotranspiration variations, yielding the following insights: (1) Based on the monthly-scale actual evapotranspiration dataset from 1982 to 2017, the annual average evapotranspiration in the YRB fluctuated between 375 and 473 mm, with an average value of 425 mm. A declining trend in the region’s overall evapotranspiration was discerned using the Theil–Sen median slope estimator and Mann–Kendall trend test. (2) The datasets from GLDAS-Noah, ERA5-Land, and GLEAM exhibited the highest correlation with the observed datasets, all exceeding a correlation coefficient of 0.96. In contrast, the MOD16A2 dataset showed the least favorable performance. The ERA5-Land dataset was particularly noteworthy for its close alignment with observational benchmarks, as evidenced by the lowest recorded root mean square error (RMSE) of 5.09 mm, indicative of its outstanding precision. (3) Employing the Geodetector model, a thorough analysis was conducted of the interactions between evapotranspiration and seven critical determinants. The findings revealed that precipitation and the NDVI were the most significant factors influencing evapotranspiration, with q-values of 0.59 and 0.42 in 2010, and 0.71 and 0.59 in 2015, respectively. These results underscore their pivotal role as the main drivers of evapotranspiration variability within the YRB. Conversely, the q-values for slope in 2010 and 2015 were only 0.01 and nearly zero, respectively, indicating their minimal impact on the dynamics of evapotranspiration in the YRB.

Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data.

Surface soil moisture (SSM) is a critical indicator of crop growth conditions, and its accurate retrieval is essential for agricultural monitoring. Integrating multispectral and microwave remote sensing data can enhance SSM estimation, but discrepancies among platforms often reduce accuracy at local scales. In this study, we fused Sentinel-2 and UAV multispectral images through resampling to generate fusion data, which were then combined with miniature synthetic aperture radar (MiniSAR) data. A modified water cloud model (WCM) was applied to mitigate vegetation effects on radar backscattering coefficients. Three machine learning algorithms—random forest (RF), extreme gradient boosting (XGBoost), and extreme learning machine (ELM)—were employed to retrieve SSM. Field measurements at two depths (0–10 cm and 0–20 cm) over winter wheat fields in Xunxian, Hebi City, Henan Province, China, were used for validation. Results showed the following: (1) Fused multispectral data improved retrieval accuracy compared with single-satellite data, with the best configuration (fused data + VV + RF) achieving an R² of 0.85 and an RMSE of 1.51% at 0–10 cm. (2) At 0–20 cm, the fused data combined with VV polarization and XGBoost achieved the best performance (R² = 0.67, RMSE = 2.61%). (3) ELM exhibited the largest accuracy improvement after incorporating fused data, with R² increases up to 0.40 and RMSE reductions up to 18.24%. These results demonstrate the strong potential of multi-platform multispectral fusion combined with MiniSAR data for improving field-scale SSM retrieval in winter wheat regions.

With the accelerated urbanization process, cities are suffering from extremely heavy rain and urban storm water logging disasters in recent years. To provide reliable and effective information for urban management and emergency decision-making, the accuracy of basic data must be guaranteed in any urban rainwater model. This paper presents a novel MKFCM-MRF (Multiple Kernel Fuzzy C Means-Markov Random Field) model to segment high-resolution Unmanned Aerial Vehicle (UAV) images. The core ideas of MKFCM-MRF model are as follows. Firstly, in order to increase the correlation information between pixels, multiple-kernel functions are introduced into Fuzzy C Means (FCM) clustering algorithm, which automatically filters out the optimal weight combination among kernel functions according to the distribution characteristics of pixels in feature space. Secondly, in order to better segment the texture and edge of the image, the clustering results of multiple-kernel FCM clustering algorithm are introduced into Markov Random Field (MRF) model, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw clustering results are regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of MKFCM-MRF is performed by high-resolution UAV images data. The experimental results indicate MKFCM-MRF can refine the classification map in homogeneous areas, while reducing most of the edge blurring artifact, and improving the classification accuracy compared with FCM clustering algorithm. In addition, the validation of the urban storm flood model shows that the trend of the two clustering results is the same, but the runoff producing time and the peak time of FCM clustering results are advanced, the peak flow and the total runoff are larger; the simulation results corresponding to MKFCM-MRF clustering results are more realistic.

Water is a crucial and fundamental resource. It is well known that agricultural cultivation, industrial production, and human daily life are not possible without water. Efficiently utilizing water resources is of great significance for achieving global Sustainable Development Goals (SDGs). In order to improve water use efficiency in various industries and promote water-saving development, China has been implementing water-saving society construction since 2002. Henan Province is the main grain-producing area in China, with wheat production accounting for a quarter of the country’s total. As the core area of “Central Plains Agricultural Valley” in Henan Province, Xinxiang City plays an important role in agricultural technology innovation and agricultural production. However, Xinxiang City is facing problems of water scarcity and pollution, which constrain the sustainability of agricultural production. Therefore, building a water-saving society can solve the current water problems faced by Xinxiang City and ensure the sustainable development of the economy and society. This study built an evaluation index system for water-saving society construction in Xinxiang, Henan Province, China. The proposed evaluation index system includes 20 evaluation indices from six aspects—integrated, agricultural water, industrial water, domestic water, water ecology and environment, and water-saving management—and then divides its development level into several stages. The Analytical Hierarchy Process (AHP) was adopted to calculate the index weight. Then, a comprehensive evaluation model for water-saving society construction in Xinxiang City was established by combining it with grey relative analysis (GRA). The results showed that the overall level of water-saving society construction in Xinxiang City is in the excellent stage, whereas water consumption per CNY 10,000 of GDP, the effective utilization coefficient of irrigation water, the reuse rate of industrial water, and the leakage rate of urban water supply network are all in the good stage. However, the urban recycled water utilization rate is still in the poor stage. These research results can effectively and reasonably reflect the development level of water-saving society construction in Xinxiang City and guide the continued implementation of water-saving society construction. At the same time, the comprehensive evaluation of water-saving society construction helps to formulate and adjust water resource management policies and measures; it also holds significant value for sustainable water management and combating water scarcity.

Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R² of 0.855, lowest MAE of 0.0212 cm³/cm³, and lowest RMSE of 0.0253 cm³/cm³. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.