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

TL;DR

This paper presents a geostatistical framework integrating satellite, survey, and census data to produce accurate small-area estimates of agricultural carbon footprint, addressing spatial misalignment issues.

Contribution

It introduces a novel method for combining satellite-derived emission data with traditional datasets to improve environmental indicator estimates at fine spatial scales.

Findings

Satellite data significantly improved estimate accuracy.

The framework effectively addresses spatial misalignment.

Uncertainty propagation enhances estimate reliability.

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.