# A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring
###### Abstract
In particular, research on farmland carbon flux estimation based on machine learning or deep learning methods has progressively become a major research focus [15; 16; 17]. [PERSON] et al. [18] estimated the carbon fluxes of moso bamboo forests based on the Bayesian improved BP neural network method (B-BPNN) using measured data from the flux tower and data on latent heat fluxes, incident radiation, soil temperatures, wind speed, and other climatic factors. The results showed that the correlation between the carbon flux estimation results and the measured values reached 0.93 (higher than that of the traditional BPNN), and the RMSE (root mean-square error) was lower, which proved that the B-BPNN could effectively reduce the estimation uncertainty and significantly improve the carbon flux prediction accuracy. Convolutional neural networks have been widely applied in carbon flux estimation due to their unique advantages in image feature extraction. [PERSON] et al. [19] applied machine learning algorithms, including convolutional neural networks (CNNs), Artificial Neural Networks (ANNs), Random Forests (RFs), and eXtreme Gradient Boosting (XGBoost), to estimate GPP. The CNN model outperformed the others, achieving an average R\({}^{2}\) of 0.93, significantly improving GPP estimation. However, when applying long-term sequence remote sensing images for monitoring crop growth, CNN-based models have certain limitations in learning image features. Recurrent neural networks (RNNs), on the other hand, have a greater advantage in learning the nonlinear features of sequential data [20]. Therefore, the combination of CNN and RNN in a network architecture can better extract features from long-term sequence remote sensing images, thereby improving crop growth estimation accuracy. [PERSON] et al. [21] applied a convolutional long short-term memory (ConvLSTM) model, combining convolutional neural networks and recurrent neural networks, to forecast crop growth through NDVI estimation. [PERSON] et al. [22] developed a novel CNN and gated recurrent unit combined (CNN-GRU) model to estimate crop growth using remotely sensed data, including Vegetation Temperature Condition Index (VTCI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR). The model effectively captured the time-series cumulative effects of crop growth, demonstrating its potential for accurate crop growth estimation. Despite this, the explanatory potential of such models is limited due to their \"black-box\" nature [23]. This leads to the presence of uncertainties in the application of deep learning models, which may stem from factors such as the internal structure of the model,data selection during the training process, and parameter initialization [24]. As a result, the interpretability analysis of deep learning models has become an important research direction for improving model reliability and promoting their application. Shapley additive explanation (SHAP) values are a popular method for explaining machine learning model predictions [25]. [PERSON] et al. [26] explored how SHAP values were used to quantify feature importance across various deep learning-based model modules, improving both the accuracy and transparency of the predictions. By applying SHAP at both the model and cell levels, they provided clear insights into the contribution of each feature, leading to better feature selection and a more interpretable model. [PERSON] et al. [27] conducted an annual crop yield forecasting based on the XGBoost model and SHAP methodology, and evaluated the model to identify key features. The results showed that the model has an R\({}^{2}\) value of 0.98, which is better than the existing models. [PERSON] [28] employed the SHAP value to interpret XGBoost models, effectively extracting spatial effects and enhancing the understanding of complex geographical phenomena. [PERSON] et al.