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Currently, the best practice to support land planning calls for the development of Spatial Data Infrastructures (SDI) capable of integrating both geospatial datasets and time series information from multiple sources, e.g., multitemporal satellite data and Volunteered Geographic Information (VGI). This paper describes an original OGC standard interoperable SDI architecture and a geospatial data and metadata workflow for creating and managing multisource heterogeneous geospatial datasets and time series, and discusses it in the framework of the Space4Agri project study case developed to support the agricultural sector in Lombardy region, Northern Italy. The main novel contributions go beyond the application domain for which the SDI has been developed and are the following: the ingestion within an a-centric SDI, potentially distributed in several nodes on the Internet to support scalability, of products derived by processing remote sensing images, authoritative data, georeferenced in-situ measurements and voluntary information (VGI) created by farmers and agronomists using an original Smart App; the workflow automation for publishing sets and time series of heterogeneous multisource geospatial data and relative web services; and, finally, the project geoportal, that can ease the analysis of the geospatial datasets and time series by providing complex intelligent spatio-temporal query and answering facilities.

This paper investigates the causes of imprecision of the observations and uncertainty of the authors who create Volunteer Geographic Information (VGI), i.e., georeferenced contents generated by volunteers when participating in some citizen science project. Specifically, various aspects of imprecision and uncertainty of VGI are outlined and, to cope with them, a knowledge-based approach is suggested based on the creation and management of "contextualized VGI". A case study example in agriculture is reported where contextualized VGI can be created about in situ crops observations by the use of a smart app that supports volunteers by means of both an ontology and the representation of the context of the geo-localization. Furthermore, an approach to cope with both ill-defined knowledge and volunteer's uncertainty or imprecise observations is defined based on a fuzzy ontology with uncertainty level-based approximate reasoning. By representing uncertainty and imprecision of VGI, users, i.e., consumers, can exploit quality checking mechanisms to filter VGI based on their needs.

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