Accurate and cost-effective quantification of the carbon cycle in agroecosystems is crucial for mitigating climate change and ensuring sustainable food production. Traditional modeling approaches often suffer from large prediction uncertainties due to complex biogeochemical processes and limited observational data. To address these challenges, the authors propose a Knowledge-Guided Machine Learning (KGML) framework that integrates process-based model knowledge, high-resolution remote sensing observations, and machine learning techniques. Using the U.S. Corn Belt as a testbed, the authors demonstrate that KGML outperforms conventional process-based and black-box machine learning models in quantifying carbon cycle dynamics. The high-resolution approach reveals 86% more spatial detail of soil organic carbon changes compared to conventional coarse-resolution methods. The study also outlines a protocol for improving KGML through various paths, which can be generalized to develop hybrid models for better predicting complex Earth system dynamics. The developed KGML framework, named KGML-ag-Carbon, combines a well-validated process-based model, *ecosys*, with a deep learning model to generate reliable predictions of agricultural carbon fluxes, crop yields, and changes in soil carbon stocks. The model's effectiveness is demonstrated through high-resolution predictions of carbon fluxes, crop yields, and soil carbon changes, providing valuable data for land managers. The study highlights the benefits of high-resolution carbon budgets and the potential of KGML in improving carbon sequestration assessments.Accurate and cost-effective quantification of the carbon cycle in agroecosystems is crucial for mitigating climate change and ensuring sustainable food production. Traditional modeling approaches often suffer from large prediction uncertainties due to complex biogeochemical processes and limited observational data. To address these challenges, the authors propose a Knowledge-Guided Machine Learning (KGML) framework that integrates process-based model knowledge, high-resolution remote sensing observations, and machine learning techniques. Using the U.S. Corn Belt as a testbed, the authors demonstrate that KGML outperforms conventional process-based and black-box machine learning models in quantifying carbon cycle dynamics. The high-resolution approach reveals 86% more spatial detail of soil organic carbon changes compared to conventional coarse-resolution methods. The study also outlines a protocol for improving KGML through various paths, which can be generalized to develop hybrid models for better predicting complex Earth system dynamics. The developed KGML framework, named KGML-ag-Carbon, combines a well-validated process-based model, *ecosys*, with a deep learning model to generate reliable predictions of agricultural carbon fluxes, crop yields, and changes in soil carbon stocks. The model's effectiveness is demonstrated through high-resolution predictions of carbon fluxes, crop yields, and soil carbon changes, providing valuable data for land managers. The study highlights the benefits of high-resolution carbon budgets and the potential of KGML in improving carbon sequestration assessments.