A model-data fusion approach for quantifying the carbon budget in cotton agroecosystems across the United States

作者：Rongzhu Qin, Kaiyu Guan, Bin Peng, Feng Zhang, Wang Zhou, Jinyun Tang, Tongxi Hu, R. F. Grant, Benjamin R. K. Runkle, Michele L. Reba, Xiaocui Wu · 发表于：Agricultural and Forest Meteorology · 年份：2025 · DOI：10.1016/j.agrformet.2025.110407 · 被引用次数：4 · 研究领域：Soil Carbon and Nitrogen Dynamics、Forest Management and Policy、Fire effects on ecosystems

• Developed a framework combining process-based modeling with model-data fusion (MDF). • The MDF uses deep learning to integrate satellite and survey data for model calibration. • Our framework accurately quantifies US cotton carbon fluxes and lint yield. • High vapor pressure deficit limits productivity, especially for rainfed cotton. Cotton ( Gossypium hirsutum L.) cultivation contributes to economic development, particularly in the Cotton Belt of the Southern United States (U.S.). As one of the world's largest exporters of cotton, the U.S. cotton industry plays a pivotal role in both the domestic and international markets. Accurate quantification of carbon budgets and their responses to the environment is thus crucial for the sustainable production of cotton, but such quantification at the regional scale remains unclear. Here we use a framework that combines an advanced process-based model, ecosys , and a deep learning-based Model-Data Fusion (MDF) approach to quantify the magnitude and patterns of carbon flux and cotton lint yield under both rainfed and irrigated conditions in the U.S. We first evaluate the performance of the process-based model in simulating carbon budgets of cotton agroecosystems using eddy-covariance (EC) values at production-scale farm sites. We then apply MDF to use satellite-based gross primary production (GPP) and survey-based cotton lint yield data as constraints of the ecosys model to generate the holistic carbon budget of cotton cropland at the ...