Accurate estimation of winter wheat yield is essential for ensuring food security. Recent studies on winter wheat yield estimation based on deep learning methods rarely explore the interpretability of the model from the perspective of crop growth mechanism. In this study, a multiscale winter wheat yield estimation framework (called MultiScaleWheatNet model) was proposed, which was based on time series remote sensing data and further takes into account meteorological and soil factors that affect wheat growth. The model integrated multimodal data from different temporal and spatial scales, extracting growth characteristics specific to particular growth stage based on the growth pattern of wheat phenological phase. It focuses on enhancing model accuracy and interpretability from the perspective of crop growth mechanisms. The results showed that, compared to mainstream deep learning architectures, the MultiScaleWheatNet model had good estimation accuracy in both rain-fed and irrigated farmlands, with higher accuracy in rain-fed farmlands (R² = 0.86, RMSE = 0.15 t·ha⁻¹). At the county scale, the accuracy of the model in estimating winter wheat yield was stable across three years (from 2021 to 2023, R² ≥ 0.35, RMSE ≤ 0.73 t·ha⁻¹, nRMSE ≤ 20.4%). Model interpretability results showed that, taking all growth stages together, the remotely sensed indices had relatively high contribution to wheat yield, with roughly equal contributions from meteorological and soil variables. From the perspective of the growth stages, the contribution of LAI in remote sensing factors demonstrated greater stability throughout the growth stages, particularly during the jointing, heading-filling and milky maturity stage; the combined impact of meteorological factors exhibited a discernible temporal sequence, initially dominated by water availability and subsequently transitioning to temperature and sunlight in the middle and late stages; soil factors demonstrated a close correlation with soil pH and cation exchange capacity in the early and late stages, and with organic carbon content in the middle stage. By deeply combining remote sensing, meteorological and soil data, the framework not only achieves high accuracy in winter wheat yield estimation, but also effectively interprets the dynamic influence mechanism of remote sensing data on yield from the perspective of crop growth, providing a scientific basis for precise field water and fertiliser management and agricultural decision-making.

Wheat is a critical economic and food crop in global agricultural production, with changes in wheat cultivation directly impacting the stability of the global food market. Therefore, developing a method capable of accurately estimating carbon flux in wheat is of significant importance for early warning agricultural production risks and guiding farming practices. This study constructs a multimodal model framework to estimate wheat carbon flux using MODIS data products, including the Leaf Area Index (LAI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and meteorological data products. The results demonstrate that the constructed carbon flux detection model effectively estimates carbon flux across different growth stages of wheat. Evaluation of the model, using comprehensive accuracy metrics, shows an average adjusted R² of 0.88, an RMSE of 5.31 gC·m⁻²·8d⁻¹, and nRMSE of 0.05 across four growth stages, indicating high accuracy with minimal error. Notably, the model performs more accurately at the green-up stage compared to other stages. Interpretability analysis further reveals key features influencing model estimations, with the top five ranked features being (1) LAI, (2) NDVI, (3) EVI, (4) vapor pressure (Vap), and (5) the Palmer Drought Severity Index (PDSI). Remote sensing indices exhibit a greater influence on carbon flux estimation throughout the whole growth stages compared to meteorological indices. Under water-limiting conditions, the importance of evapotranspiration, precipitation, and drought-related factors fluctuates significantly. This study not only provides an important reference for monitoring wheat carbon flux, but also offers novel insights into the crop carbon cycling mechanisms within agroecosystems under the current environmental context.