Prediction of Water Carbon Fluxes and Emission Causes in Rice Paddies Using Two Tree-Based Ensemble Algorithms

Abstract

Quantification of water carbon fluxes in rice paddies and analysis of their causes are essential for agricultural water management and carbon budgets. In this regard, two tree-based machine learning models, which are extreme gradient boosting (XGBoost) and random forest (RF), were constructed to predict evapotranspiration (ET), net ecosystem carbon exchange (NEE), and methane flux (FCH4) in seven rice paddy sites. During the training process, the k-fold cross-validation algorithm by splitting the available data into multiple subsets or folds to avoid overfitting, and the XGBoost model was used to assess the importance of input factors. When predicting ET, the XGBoost model outperformed the RF model at all sites. Solar radiation was the most important input to ET predictions. Except for the KR-CRK site, the prediction for NEE was that the XGBoost models also performed better in the other six sites, and the root mean square error decreased by 0.90–11.21% compared to the RF models. Among all sites (except for the absence of net radiation (NETRAD) data at the JP-Mse site), NETRAD and normalized difference vegetation index (NDVI) performed well for predicting NEE. Air temperature, soil water content (SWC), and longwave radiation were particularly important at individual sites. Similarly, the XGBoost model was more capable of predicting FCH4 than the RF model, except for the IT-Cas site. FCH4 sensitivity to input factors varied from site to site. SWC, ecosystem respiration, NDVI, and soil temperature were important for FCH4 prediction. It is proposed to use the XGBoost model to model water carbon fluxes in rice paddies.