Livestock manure management, especially related to soil-fertilisation practice, is responsible for most of the emissions in agriculture, and in particular the ammonia emissions (NH₃), which play a key role in environmental problems, affecting water, soil and air quality. Within the digital agricultural framework, EO data contribute to agricultural-practices monitoring such as manure spreading, to mitigate pollutant emissions. This study presents a GIS-based tool on an open-source platform, developed for susceptibility estimation of sewage spreading occurrence in agricultural areas of Italy. The tool is based on the analysis of multispectral and hyperspectral satellite time series in synergy with field data and ancillary information collected from regional repositories, to produce a series of classified and prioritised spatially explicit information. Spectral analysis of satellite acquisitions enabled the identification of manure spreading with the precision of about 83%. Field campaigns from October to March were carried out to validate the spreading event. The case study demonstrates the ability of the proposed GIS-based tool in supporting the monitoring of manure spreading.

The European Union (EU) and national governments have set clear targets to reduce agricultural emissions, including ammonia from manure spreading practice, with regulations such as the Ambient Air Quality (AQ) and Clean Air Directives, the Common Agricultural Policy (CAP), and the Green Deal, with implication for ecosystem services and landscape planning, reflecting broader environmental sustainability objectives including those addressed by the Sustainable Development Goals (SDGs). Informative Inventory Reports (IIRs) are critical tools within the EMEP/EEA framework for monitoring long-range transboundary air pollution. They utilize three distinct methodological tiers (Tiers 1, 2, and 3) to estimate emission data across Europe. Despite the availability of Earth Observation (EO) data and products from the Copernicus Programme current estimation methods still rarely integrate EO information to produce spatially explicit estimates. This paper reviews current methodologies for estimating ammonia in IIRs and in scientific literature, including advanced methods not yet implemented in official inventories but potentially capable of supporting more spatially explicit and process-oriented estimation. A Medium Effort Methodology (MEM) is identified among those reviewed as a representative methodological pathway for integrating EO information with Tier 3 approaches. Building on this, the paper explores the association between specific EO data and Copernicus products, and input variables required by MEM, identifying opportunities and barriers for environmental monitoring with potential relevance to sustainable agriculture.