Improve Carbon Budget Assessment in Floodplain Wetlands Using Hydrodynamic and Integrated Machine Learning Models

Abstract Floodplain wetlands play a crucial role in the global carbon cycle, yet the spatiotemporal variation of carbon flux in floodplain wetlands remains poorly understood due to complex hydrological processes. Here, we present an integrated framework to upscale net ecosystem CO 2 exchange (NEE) in floodplain wetlands by combining eddy covariance measurements, object‐based image analysis (OBIA), hydrodynamic models, and integrated machine learning (ML) techniques. The proposed framework was instantiated in the Poyang Lake floodplain wetland, China. Results showed that OBIA could effectively capture the heterogeneous surface of wetlands through multi‐scale segmentation, thereby providing suitable space units for NEE upscaling. Hydrological regimes obtained by the hydrodynamic model, that is, maximum flood level (MFL), mean water level (WL) and water level fluctuation (WLF), were the key drivers to the NEE variability. The integrated ML model could effectively upscale NEE with higher R 2 and superior robustness than commonly used individual ML model, benefiting from an optimized integration of outputs from multiple ML model through Powell optimization. The spatiotemporal distribution of the upscaled NEE results indicated that permanently inundated areas in floodplain wetlands mostly functioned as carbon sources or weak carbon sinks, while littoral zones functioned as carbon sinks. Hydrological regime changes lead to a shift between carbon sources and sinks in floodplain wetlands. The proposed framework can provide a feasible way to analyze the spatiotemporal changes of NEE and is of great benefit to achieving carbon sequestration in floodplain wetlands.