# A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring
###### Abstract
The findings indicate that satellite-derived vegetation indices, such as NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and LSWI (Land Surface Water Index), are strongly correlated with GPP. These indices reflect seasonal variations in vegetation growth and provide valuable insights into how climate factors, including soil moisture and precipitation, influence GPP. Since then, multi-source remote sensing datasets have been incorporated into carbon flux monitoring to improve the accuracy of carbon flux estimation. [PERSON] et al. [7] combined MODIS GPP products (8-day interval) with Sentinel-2 SIF (Solar-Induced Chlorophyll Fluorescence) data to optimize carbon flux estimation using an optical-thermal infrared fusion model and Support Vector Regression (SVR), and found that rain-fed cotton was more carbon-efficient than irrigated cotton (15% increase in photosynthetic efficiency), with the correlation coefficient between SIF and GPP reaching 0.89. In the early stages of research, scholars employed basic linear statistical models to predict agricultural crop carbon flux. [PERSON] et al. [8] developed the SIF-CO\({}_{2}\)-GPP model, which integrates SIF and atmospheric CO\({}_{2}\) to estimate GPP, outperforming the nonlinear SIF-GPP model, especially by reducing seasonal over- and underestimation. [PERSON] et al. [9] employed hyperspectral data and PLRR (Partial Least Squares Regression) to estimate GPP, showing that multivariate methods like PLRR offer more accurate predictions than traditional reflectance-based and vegetation index approaches. This highlights the progression from basic linear models to more sophisticated statistical methods in GPP estimation. However, early simple linear models for carbon flux estimation had performance limitations. In contrast, research increasingly focuses on crop growth models, which simulate soil, climate, and biological interactions based on ecological mechanisms. By integrating remote sensing data, these models enable large-scale carbon flux estimation and capture the ecological processes affecting crop growth and carbon dynamics [10]. [PERSON] et al. [11] developed the SAFY-CO\({}_{2}\) model, using high-resolution optical data to simulate winter wheat biomass, yield, and carbon budget, with excellent performance across sites in southwestern France. [PERSON] et al. [12] introduced the crop data-model assimilation (CDMA) framework, improving regional-scale GPP and yield estimates for winter wheat by assimilating satellite-derived GPP into the WOFOST (World Food Studies) model (post-assimulation R\({}^{2}\) = 0.87). Although ecological process models, such as crop growth models, provide more accurate results than linear models, challenges like parameter acquisition, model calibration, input uncertainties, climate sensitivity, and heterogeneous validation data still lead to inaccuracies. Recently, the Eddy Covariance (EC) technique has become standard for measuring carbon fluxes, particularly for optimizing terrestrial vegetation GPP models. [PERSON] et al. [13] employed the EC technique to monitor farmland carbon balance, comparing the magnitude and temporal dynamics of winter wheat carbon flux using EVI and NDVI. [PERSON] et al. [14] investigated the integration of NDVI data into the Eddy Covariance-Light Use Efficiency (EC-LUE) model to enhance carbon flux estimates. Their study demonstrated that incorporating high-resolution NDVI significantly improved model performance, particularly in heterogeneous landscapes such as savannas and croplands. In contrast to crop growth models, the EC technique relies on small-scale, site-specific observations, which makes it difficult to extrapolate results to larger regions. Therefore, a novel model is needed to overcome the limitations of both approaches and provide more scalable, accurate carbon flux predictions. With advancements in computational power, the emergence of big data analytics has facilitated the development of increasingly complex algorithms.