Year: 2025
Type: article
Abstract: Wheat is a critical economic and food crop in global agricultural production, with changes in wheat cultivation directly impacting the stability of the global food market. Therefore, developing a method capable of accurately estimating carbon flux in wheat is of significant importance for early warning agricultural production risks and guiding farming practices. This study constructs a multimodal model framework to estimate wheat carbon flux using MODIS data products, including the Leaf Area Index (LAI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and meteorological data products. The results demonstrate that the constructed carbon flux detection model effectively estimates carbon flux across different growth stages of wheat. Evaluation of the model, using comprehensive accuracy metrics, shows an average adjusted R2 of 0.88, an RMSE of 5.31 gC·m−2·8d−1, and nRMSE of 0.05 across four growth stages, indicating high accuracy with minimal error. Notably, the model performs more accurately at the green-up stage compared to other stages. Interpretability analysis further reveals key features influencing model estimations, with the top five ranked features being (1) LAI, (2) NDVI, (3) EVI, (4) vapor pressure (Vap), and (5) the Palmer Drought Severity Index (PDSI). Remote sensing indices exhibit a greater influence on carbon flux estimation throughout the whole growth stages compared to meteorological indices. Under water-limiting conditions, the importance of evapotranspiration, precipitation, and drought-related factors fluctuates significantly. This study not only provides an important reference for monitoring wheat carbon flux, but also offers novel insights into the crop carbon cycling mechanisms within agroecosystems under the current environmental context.
Source: Agronomy
Authors: Xiaohua Chen, Ying Du, Dong Han
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDGs: Zero hunger, Climate action
Open Access status: gold
Estimated APC paid: $3,137
Awards: G1422269082, G3945177617
OpenAlex ID: W4409328031