A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring DOI Creative Commons
Xiaohua Chen,

Ying Du,

Dong Han

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 920 - 920

Published: April 9, 2025

Wheat is a critical economic and food crop in global agricultural production, with changes wheat cultivation directly impacting the stability of market. Therefore, developing method capable accurately estimating carbon flux significant importance for early warning production risks guiding farming practices. This study constructs multimodal model framework to estimate using MODIS data products, including Leaf Area Index (LAI), Normalized Difference Vegetation (NDVI), Enhanced (EVI), meteorological products. The results demonstrate that constructed detection effectively estimates across different growth stages wheat. Evaluation model, comprehensive accuracy metrics, shows an average adjusted R2 0.88, RMSE 5.31 gC·m−2·8d−1, nRMSE 0.05 four stages, indicating high minimal error. Notably, performs more at green-up stage compared other stages. Interpretability analysis further reveals key features influencing estimations, top five ranked being (1) LAI, (2) NDVI, (3) EVI, (4) vapor pressure (Vap), (5) Palmer Drought Severity (PDSI). Remote sensing indices exhibit greater influence on estimation throughout whole indices. Under water-limiting conditions, evapotranspiration, precipitation, drought-related factors fluctuates significantly. not only provides important reference monitoring flux, but also offers novel insights into cycling mechanisms within agroecosystems under current environmental context.

Language: Английский

A Multimodal Data-Driven Framework for Enhanced Wheat Carbon Flux Monitoring DOI Creative Commons
Xiaohua Chen,

Ying Du,

Dong Han

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 920 - 920

Published: April 9, 2025

Wheat is a critical economic and food crop in global agricultural production, with changes wheat cultivation directly impacting the stability of market. Therefore, developing method capable accurately estimating carbon flux significant importance for early warning production risks guiding farming practices. This study constructs multimodal model framework to estimate using MODIS data products, including Leaf Area Index (LAI), Normalized Difference Vegetation (NDVI), Enhanced (EVI), meteorological products. The results demonstrate that constructed detection effectively estimates across different growth stages wheat. Evaluation model, comprehensive accuracy metrics, shows an average adjusted R2 0.88, RMSE 5.31 gC·m−2·8d−1, nRMSE 0.05 four stages, indicating high minimal error. Notably, performs more at green-up stage compared other stages. Interpretability analysis further reveals key features influencing estimations, top five ranked being (1) LAI, (2) NDVI, (3) EVI, (4) vapor pressure (Vap), (5) Palmer Drought Severity (PDSI). Remote sensing indices exhibit greater influence on estimation throughout whole indices. Under water-limiting conditions, evapotranspiration, precipitation, drought-related factors fluctuates significantly. not only provides important reference monitoring flux, but also offers novel insights into cycling mechanisms within agroecosystems under current environmental context.

Language: Английский

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