On the use of satellite information to estimate agricultural carbon footprint in a small area framework

2026-04-28

AI Summary

This study addresses the challenges of data scarcity and spatial scale mismatch in small-area assessment of agricultural carbon footprints by proposing an integrated modeling framework that combines survey, census, and satellite-based ammonia emission data to achieve sub-regional estimation accuracy in Italy’s Po Valley. The approach innovatively resolves the spatial misalignment between gridded remote sensing data and administrative boundaries and effectively propagates uncertainty from the covariate construction stage to the final estimates through geostatistical downscaling and parametric bootstrapping. This methodology significantly enhances estimation precision and robustness while reducing reliance on large volumes of heterogeneous auxiliary data, thereby demonstrating the potential of Earth observation data for environmental small-area statistics.

Abstract

The agricultural sector is undergoing rapid change due to climate pressures, demographic shifts, and uneven economic development, increasing the demand for reliable environmental indicators at fine spatial scales. However, limited data availability often constrains subregional analyses. This study develops a model-based framework for producing reliable small-area estimates for assessing the agricultural carbon footprint in the Po Valley (Northern Italy), a region characterized by intensive livestock farming and high environmental pressure. We integrate survey, census, and satellite-derived emission data into a unified framework and produce estimates at the level of Agrarian Subregions, defined as agriculturally homogeneous municipalities by the Italian National Institute of Statistics. Satellite-based ammonia emission data are incorporated as auxiliary covariates to improve precision and spatial coherence. A key methodological contribution is the treatment of spatial misalignment between gridded satellite data and administrative boundaries. This issue is addressed through a geostatistical upscaling procedure combined with a parametric bootstrap that propagates uncertainty from the covariate construction stage to the final small-area estimates. The results show that satellite-derived information substantially improves the accuracy and stability of carbon footprint estimates while reducing reliance on large, heterogeneous auxiliary datasets, illustrating the potential of Earth observation data in model-based environmental statistics.

Problem

• agricultural carbon footprint
• small-area estimation
• satellite data
• spatial misalignment
• environmental indicators

Innovation

• small-area estimation
• satellite-derived emissions
• spatial misalignment
• geostatistical upscaling
• agricultural carbon footprint