Improving rapeseed carbon footprint evaluation via the integration of remote sensing technology into an LCA approach

Agricultural carbon footprint (CF) evaluation plays an important role in climate change mitigation and national food security. Many studies have been conducted worldwide to evaluate the CF of rapeseed and its byproducts; however, only a few of these studies have considered finer-scale spatial-temporal heterogeneity. Considering the advantages of using detailed crop information extracted by remote sensing (RS) techniques, we attempted to integrate RS into life cycle assessments to improve rapeseed CF evaluation. A case study was conducted from 2021 to 2023 in one of the most important grain- and rapeseed-producing areas in Southwest China, namely, the Chengdu Plain, covering an area of 18,810.00 km². The results of our study suggest that: (1) the proposed approach is applicable for high-resolution (10 m ∗ 10 m) rapeseed distribution mapping; (2) the farm-based CFs of rapeseed in the studied region range from 3333.08 to 4572.82 kgCO₂-eq ha^-1, while the product-based CFs (PCFs) vary from 1316.23 to 2443.95 kgCO₂-eq t^-1. Nitrogen fertilizer processing and its application are identified as the dominant contributors to upstream and downstream greenhouse emissions (GHGs), respectively; (3) the significant role of soil properties and soil organic carbon in influencing crop PCFs indicates good GHG offsets. The method used in the current study has strong adaptability and universality in different areas with various climatic conditions and can provide a solid basis for policymakers to formulate differentiated agricultural carbon reduction policies.