# A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring
###### Abstract  
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 R\({}^{2}\) of 0.88, an RMSE of 5.31 gC\(\cdot\)m\({}^{-2}\cdot\)sd\({}^{-1}\), 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. remote sensing data; wheat crops; carbon flux monitoring; deep learning; interpretability 20252003  
assessing ecosystem carbon dynamics. Each of these indicators provides valuable insights into different components of the carbon cycle within ecosystems, and thus, they are frequently applied in ecological and environmental monitoring studies. Specifically, GPP quantifies the total carbon fixation by plants through photosynthesis, NPP represents the carbon remaining in plant biomass after autotrophic respiration, and NEE characterizes the net carbon exchange between the ecosystem and atmosphere, including both plant and soil microbial respiration. [PERSON] et al. [4] assessed the relationship between ecosystem net primary productivity (NPP) and climatic factors in Togo through regression analyses based on the MODIS NPP product (500 m resolution) combined with CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) precipitation data and ECMWF (European Centre for Medium-Range Weather Forecasts) ERA5 (fifth-generation ECMWF atmospheric reanalysis of the global climate) temperature data, and found that precipitation has a dominant effect on carbon sink capacity, whereas a warmer climate may shorten the growing season and reduce carbon fixation capacity. Compared to NPP and NEE, GPP has a distinct advantage in remote sensing applications as it aligns closely with vegetation indices derived directly from satellite observations, accurately reflecting photosynthetic activity without requiring complex additional measurements (such as respiration rates). High GPP values indicate greater carbon absorption and fixation, stronger photosynthetic capacity, and enhanced carbon storage potential within ecosystems. Therefore, measuring and modeling GPP is a vital approach to evaluating crop carbon flux dynamics [5]. For instance, [PERSON] et al. [6] offer significant empirical support for the application of remotely sensed vegetation indices in assessing regional vegetation productivity.