The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has demonstrated significant potential for soil moisture content (SMC) monitoring due to its high spatiotemporal resolution. However, GNSS-IR inversion experiments are notably influenced by vegetation and meteorological factors. To address these challenges, this study proposes a multi-factor SMC inversion method. Six GNSS stations from the Plate Boundary Observatory (PBO) were selected as study sites. A low-order polynomial was applied to separate the reflected signals, extracting parameters such as phase, frequency, amplitude, and effective reflector height. Auxiliary variables, including the Normalized Microwave Reflection Index (NMRI), cumulative rainfall, and daily average evaporation, were used to further improve inversion accuracy. A multi-factor SMC inversion dataset was constructed, and three machine learning models were selected to develop the SMC prediction model: Support Vector Regression (SVR), suitable for small and medium-sized regression tasks; Convolutional Neural Networks (CNN), with robust feature extraction capabilities; and NRBO-XGBoost, which supports automatic optimization. The multi-factor SMC inversion method achieved remarkable results. For instance, at the P038 station, the model attained an R² of 0.98, with an RMSE of 0.0074 and an MAE of 0.0038. Experimental results indicate that the multi-factor inversion model significantly outperformed the traditional univariate model, whose R² (RMSE, MAE) was only 0.88 (0.0179, 0.0136). Further analysis revealed that NRBO-XGBoost surpassed the other models, with its average R² outperforming SVR by 0.11 and CNN by 0.03. Additionally, the analysis of different surface types showed that the method achieved higher accuracy in grassland and open shrubland areas, with all models reaching R² values above 0.9. Therefore, the accuracy of the multi-factor SMC inversion model was validated, supporting the practical application of GNSS-IR technology in SMC inversion.

The value of ecosystem services (ESV) serves as a crucial metric for assessing the cost-effectiveness of ecosystems and evaluating their economic worth. Predicting the evolution of ESV across various land uses under different scenarios is essential for maintaining ecological stability and fostering sustainable developm0ent. Utilizing land use change data combined with the PLUS predictive model and ecosystem service value equivalence techniques, this study analyzes the spatiotemporal patterns and underlying drivers of ESV in Yuzhong County, China. The results indicate that the overall ESV distribution exhibits a “fragmented northeast, clustered southwest” pattern, dominated by high-high and low-low clustering. Among the driving factors, elevation exerts the greatest influence on ESV, followed by precipitation and population density, while slope contributes least. Under natural development scenarios, the ESV remains relatively stable compared to the base year of 2020. In contrast, the farmland protection scenario effectively preserves the ESV associated with cultivated land. However, the economic development scenario leads to a significant decline in the overall ESV, with a retraction of high-value areas and an expansion of low-value regions. These insights provide a fresh perspective for analyzing the factors influencing ESV and for conducting multi-scenario predictions, thereby aiding in the development of ecological resource conservation and landscape ecological risk prevention strategies in the study region.

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R² of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R² increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments.

Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of “time-series perception–dynamic simulation–feature identification–early prediction”, providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research.