This study develops a model-based framework for producing reliable small-area estimates for assessing the agricultural carbon footprint in the Po Valley. Specifically, we integrate satellite information into small area models (i.e., Fay-Herriot) using geostatistical predictors (i.e., block kriging) in order to obtain accurate estimates of the target variable at a fine spatial scale. The results highlight the benefits of this procedure, which improves the direct estimators reducing the dependency from the large traditional datasets.
Pajno, R., Carillo, F., Maranzano, P., Schmid, T., Borgoni, R. (2026). Using Satellite Data to Estimate Agricultural Carbon Footprint in Northern Italy: A Small Area Estimation Approach. In Statistical Science: From Theory to Applied Research IV SIS-FENStatS 2026, Short Papers, Contributed Sessions 3 (pp.180-186). Springer [10.1007/978-3-032-30665-4_30].
Using Satellite Data to Estimate Agricultural Carbon Footprint in Northern Italy: A Small Area Estimation Approach
Pajno, Riccardo
;Maranzano, Paolo;Borgoni, Riccardo
2026
Abstract
This study develops a model-based framework for producing reliable small-area estimates for assessing the agricultural carbon footprint in the Po Valley. Specifically, we integrate satellite information into small area models (i.e., Fay-Herriot) using geostatistical predictors (i.e., block kriging) in order to obtain accurate estimates of the target variable at a fine spatial scale. The results highlight the benefits of this procedure, which improves the direct estimators reducing the dependency from the large traditional datasets.
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