Rapid and accurate leaf area index (LAI) determination is important for monitoring daylily growth, yield estimation, and field management. Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data as well as the random forest regression (RFR) importance score to select features. A high-precision LAI estimation model for daylilies was constructed by optimizing feature combinations. The RFR importance score screened the top five important features, including vegetation indices land surface water index (LSWI), generalized difference vegetation index (GDVI), normalized difference yellowness index (NDYI), and backscatter coefficients VV and VH. Vegetation index features characterized canopy moisture and the color of daylilies, and the backscatter coefficient reflected dielectric properties and geometric structure. The selected features were sensitive to daylily LAI. The RFR algorithm had good anti-noise performance and strong fitting ability; thus, its accuracy was better than the partial least squares regression and artificial neural network models. Synergistic optical and microwave data more comprehensively reflected the physical and chemical properties of daylilies, making the RFR-VI-BC05 model after feature selection better than the others ( r = 0.711, RMSE = 0.498, and NRMSE = 9.10%). This study expanded methods for estimating daylily LAI by combining optical and radar data, providing technical support for daylily management.

As global climate change intensifies and economic transformation progresses, the agro-pastoral ecotone of northern China faces dual challenges of stopping ecological degradation and enhancing farmers’ livelihoods. Yunzhou District in Shanxi Province represents a typical ecologically fragile area, where the daylily industry contributes significantly to improving livelihood resilience. This study categorized farmers into three types based on their dependence on daylily income: major-job farmers (50–90% income from daylily), sole agriculture farmers (≥90%), and side-job farmers (<50%). Using questionnaire survey data and the optimal parameter-based geographical detector method, we evaluated and compared the livelihood resilience levels of these farmer types and identified their key explanatory factors. The results showed that (1) major-job farmers exhibited the highest livelihood resilience index (0.165), followed by sole agriculture farmers (0.152), whereas side-job farmers exhibited the lowest (0.138); (2) significant differences in livelihood resilience existed across farmer types (p < 0.05); and (3) health status was a common key factor across all types, while factors such as traffic accessibility, policy awareness, social security, and information acquisition capability exhibited differential effects among groups. These findings provide empirical evidence to guide targeted livelihood interventions and sustainable transitions in the agro-pastoral ecotone.

Remote sensing spatiotemporal fusion technology can provide abundant data source information for assimilating crop growth model data, enhancing crop growth monitoring, and providing theoretical support for crop irrigation management. This study focused on the winter wheat planting area in the southeastern part of the Loess Plateau, a typical semi-arid region, specifically the Linfen Basin. The SEBAL and ESTARFM were used to obtain 8 d, 30 m evapotranspiration (ET) for the growth period of winter wheat. Then, based on the ‘localization’ of the CERES-Wheat model, the fused results were incorporated into the data assimilation process to further determine the optimal assimilation method. The results indicate that (1) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, p < 0.01). (2) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, p < 0.01). The calibrated CERES-Wheat ET characteristic curve effectively reflects the ET variation throughout the winter wheat growth period while being consistent with the trend and magnitude of ESTARFM ET variation. (3) The correlation between Ensemble Kalman filter (EnKF) ET and ESTARFM ET (R² = 0.7119, p < 0.01) was significantly higher than that of Four-Dimensional Variational data assimilation (4DVar) ET (R² = 0.5142, p < 0.01) and particle filter (PF) ET (R² = 0.5596, p < 0.01). The results of the study provide theoretical guidance to improve the yield and water use efficiency of winter wheat in the region, which will help promote sustainable agricultural development.

Accurate determination of Soil Organic Carbon (SOC), which is the foundation of soil health and safeguards ecological and food security, is crucial in local agricultural production. We aimed to investigate the influence of soil texture on hyperspectral models for predicting SOC content and to evaluate the role of different preprocessing methods and feature band selection algorithms in improving modeling efficiency. Laboratory-determined SOC content and hyperspectral reflectance data were obtained using soil samples from daylily cultivation areas in Yunzhou District, Datong City. Mathematical transformations, including Savitzky–Golay smoothing (SG), First Derivative (FD), Second Derivative (SD), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV), were applied to the spectral reflectance data. Feature bands extracted based on the successive projection algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to establish SOC content inversion models employing four algorithms: partial least-squares regression (PLSR), Random Forest (RF), Backpropagation Neural Network (BP), and Convolutional Neural Network (CNN). The results indicate the following: (1) Preprocessing can effectively increase the correlation between the soil spectral reflectance process and SOC content. (2) SPA and CARS effectively screened the characteristic bands of SOC in daylily cultivated soil from the spectral curves. The SPA algorithm and CARS selected 4–11 and 9–122 bands, respectively, and both algorithms facilitated model construction. (3) Among all the constructed models, the FD-CARS-PLSR performed most prominently, with coefficients of determination (R²) for the training and validation sets reaching 0.93 and 0.83, respectively, demonstrating high model stability and reliability. (4) Incorporating soil texture as an auxiliary variable into the PLSR inversion model improved the inversion accuracy, with accuracy gains ranging between 0.01 and 0.05.

Rapid and accurate leaf area index (LAI) determination is important to monitoring daylily growth, yield estimation, and field management. Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data, and the random forest regression (RFR) importance score to select features. A high-precision LAI estimation model for daylilies was constructed by optimizing feature combinations. The RFR importance score screened the top five important features, including vegetation indices land surface water index (LSWI), generalized difference vegetation index (GDVI), normalized difference yellowness index (NDYI) and backscatter coefficients VV and VH. Vegetation index features characterized canopy moisture and color of daylilies, and the backscatter coefficient reflected dielectric properties and geometric structure. Selected features were sensitive to daylily LAI. The RFR algorithm had good anti-noise performance and strong fitting ability; thus, its accuracy was better than the partial least squares regression and artificial neural network models. Synergistic optical and microwave data more comprehensively reflected the physical and chemical properties of daylilies, making the RFR-VI-BC05 model after feature selection better than the others. This study expanded methods for estimating daylily LAI by combining optical and radar data, providing technical support for daylily management.

Rapid and non-destructive monitoring of soil total nitrogen (STN) is important for precision nutrient management in ecologically fragile agricultural systems. This study established a controlled spectral resampling experiment to simulate the multispectral responses of Sentinel-2, WorldView-3, GF-6, and Landsat-9 from laboratory ASD hyperspectral measurements of Hemerocallis citrina fields. Two-dimensional (DI, RI, and NDI) and three-dimensional (TBI1–TBI5) spectral indices were constructed and evaluated using eight machine learning algorithms across five phenological stages. The results demonstrate that: (1) Three-dimensional spectral indices exhibited substantially higher sensitivity to STN than conventional two-dimensional indices, with TBI3 showing the strongest overall correlation; among the simulated sensor configurations, GF-6 delivered the best mean performance due to its dual red-edge bands. (2) Genetic algorithm-optimized backpropagation neural network (GA-BPNN) effectively addressed the local-minima limitation of standard backpropagation neural networks (BPNNs) and displayed robust generalization under multi-sensor and multi-phenological scenarios. (3) Phenology-specific modeling reduced spectral heterogeneity caused by pooling across growth stages, increasing R² by 20.28% and decreasing RMSE by 18.35%, with leaf expansion and bolting identified as optimal estimation windows. These findings elucidate the spectral response potential explanations of STN across phenological stages and provide a reference for applying multi-source simulated remote sensing data to nutrient monitoring in specialty agricultural systems.