Accurate crop yield prediction is crucial for optimizing cultivation practices and informing breeding decisions. Integrating UAV-acquired multispectral datasets with advanced machine learning methodologies has markedly refined the accuracy of crop yield forecasting. This study aimed to construct a robust and versatile yield prediction model for multi-genotyped oat varieties by investigating 14 modeling scenarios that combine multispectral data from four key growth stages. An ensemble learning framework, StackReg, was constructed by stacking four base algorithms—ridge regression (RR), support vector machines (SVM), Cubist, and extreme gradient boosting (XGBoost)—to predict oat yield. The results show that, for single growth stages, base models achieved R² values within the interval of 0.02 to 0.60 and RMSEs ranging from 391.50 to 620.49 kg/ha. By comparison, the StackReg improved performance, with R² values extending from 0.25 to 0.61 and RMSEs narrowing to 385.33 and 542.02 kg/ha. In dual-stage and multi-stage settings, the StackReg consistently surpassed the base models, reaching R² values of up to 0.65 and RMSE values as low as 371.77 kg/ha. These findings underscored the potential of combining UAV-derived multispectral imagery with ensemble learning for high-throughput phenotyping and yield forecasting, advancing precision agriculture in oat cultivation.

The accurate mapping of crop types and rotation patterns is essential for promoting sustainable agricultural development, particularly in ecologically fragile regions such as the farming–pastoral ecotone of China (FPEC). This study focuses on Zhangjiakou, a representative area of the FPEC, to develop a multi-sensor remote sensing framework for monitoring crop distribution and analyzing rotation dynamics. After cloud removal and Savitzky–Golay filtering were applied to correct noise, we selected vegetation index features with maximum inter-class separability during the optimal classification window (June 15–August 20) and generated quarterly Sentinel-1 SAR composites. A Random Forest classifier was employed to perform crop classification based on these optimized features, enabling 10 m resolution crop mapping from 2019 to 2023. The proposed method achieved high classification accuracy (overall accuracy and Kappa > 0.90), with strong agreement between mapped and statistical crop areas (R²: 0.85–0.88; RMSE: 0.42–0.58 × 10⁴ ha). Spatial analysis revealed distinct distribution patterns: oats, potato, sesame, and vegetables were predominantly cultivated in northern Zhangjiakou, while maize dominated southern regions. We observed significant annual variations in crop area proportions and identified specific altitudinal preferences: maize, potato, and sesame were mainly grown at 480–520 m, while oats and other crops at 520–600 m. Slope analysis showed that most crops were cultivated on gentle slopes of 0–5°, with sesame extending to 4–10° slopes. Temporal analysis from 2019 to 2023 indicated that sesame, oats, and potato predominantly followed rotation patterns, while maize cultivation was primarily monoculture. Key drivers of rotation change included water scarcity, economic incentives, and continuous cropping constraints. These findings provide critical insights for optimizing crop rotation strategies, enhancing agricultural sustainability, and improving land-use efficiency in ecologically fragile regions.

Balancing food production, water conservation, and carbon emissions (CEs) is critical in Northeast China (NEC), yet food–carbon–water (FCW) interactions remain poorly quantified at pixel scale. Conceptually, we move beyond administrative-unit nexus assessments by providing a crop-explicit, grid-based FCW diagnosis that identifies where crop-specific bottlenecks emerge and supports zoning-oriented interventions. We fused multi-source datasets with process models to estimate CEs, water use efficiency (WUE), and yield for maize, rice, and soybean at 500 m resolution during 2001–2020 and evaluated synergies/trade-offs based on Sen’s slope trends and nexus performance using coupling coordination degree (CCD). Annual mean CE (230.8–37,300 kg CO₂-eq ha⁻¹), yield (0–10,031 kg ha⁻¹), and WUE (0–6 kg C m⁻³) exhibited pronounced spatial heterogeneity. Higher CEs and yield concentrated in the central–southern plains, whereas WUE showed a patchier pattern with localized high values. Temporally, CEs increased for all crops, with rice consistently exhibiting the highest CEs. Soybean showed the most pronounced WUE improvement, reaching >2.0 kg C m⁻³ after the early 2010s. Pixel-wise correlations revealed a robust CE–WUE antagonism for all crops (r = −0.33 to −0.60), while CE–yield coupling was crop-dependent (soybean positive, maize weakly negative, rice non-significant). Trend-based coupling further showed that synchronized CE and yield increases dominated 45.7% of croplands, whereas trade-offs were more common when WUE was involved (CE–WUE: 38.0%; WUE–yield: 41.8%), peaking in rice systems (61.8% and 54.0%, respectively). CCD mapping indicated widespread basic coordination but strong crop contrasts. Rice had the lowest coordination (mean CCD = 0.36 ± 0.17) and the largest shares of moderate-to-severe imbalance, identifying rice as the primary FCW bottleneck, whereas maize and soybean more frequently achieved good-to-high coordination. These results support a zoned strategy that consolidates coordinated maize/soybean areas, prioritizes paddy water-saving and low-emission upgrades, and limits further rice expansion in water-constrained zones.