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Urban agriculture (UA) has been acknowledged for several positive effects such as access to fresh food, human activities moderation, agro-biodiversity and social and cultural relationships. In the city of Rome due to social and economic trends this kind of activity is spreading, but so far very few attempts have been addressed to inventorying the UA areas (e.g., community gardens, residential gardens, school gardens, illegal vegetable gardens, urban farms, etc.). Some mapping experiences have been carried out but all lack of a well-established methodology to be applied for a complete inventory. In this paper we propose a conceptual framework and methodology for mapping the UA in Rome through Earth Observation techniques by using the concepts of photointerpretation and the features of the most used web-mapping services. Google Earth has been used as a basic tool for visual interpretation and polygon digitalization, while other services have been used for the availability of additional features (e.g., the oblique view of Microsoft Bing Maps). The integration of the web-mapping services combined with ancillary data improved the UA site detection, allowing to define both the land use and the UA typologies. We argue that the integration of the web-mapping services for building urban agricultural land use datasets is cost-effective compared to processing commercial remote sensing images. The inventory can enable administrators to perform analysis about the interactions between agricultural activities and the urban environment, and to enact informed policies for green and vacant urban spaces management. [ABSTRACT FROM AUTHOR]

The occurrence of water shortages ascribed to projected climate change, especially in the Mediterranean region, fosters the interest in remote sensing (RS) applications to optimize water use in agriculture. Remote sensing evapotranspiration and water demand estimation over large cultivated areas were used to manage irrigation to minimize losses during the crop growing cycle. The research aimed to explore the potential of the MultiSpectral Instrument (MSI) sensor on board Sentinel-2A to estimate crop parameters, mainly surface albedo (α) and Leaf Area Index (LAI) that influence the dynamics of potential evapotranspiration (ET p ) and Irrigation Water Requirements (IWR) of processing tomato crop ( Solanum lycopersicum L.). Maximum tomato ET p was calculated according to the FAO Penman-Monteith equation (FAO-56 PM) using appropriate values of canopy parameters derived by processing Sentinel-2A data in combination with daily weather information. For comparison, we used the actual crop evapotranspiration (ET a ) derived from the soil water balance (SWB) module in the Environmental Policy Integrated Climate (EPIC) model and calibrated with in-situ Root Zone Soil Moisture (RZSM). The experiment was set up in a privately-owned farm located in the Tarquinia irrigation district (Central Italy) during two growing seasons, within the framework of the EU Project FATIMA (FArming Tools for external nutrient Inputs and water Management). The results showed that canopy growth, maximum evapotranspiration (ET p ) and IWR were accurately inferred from satellite observations following seasonal rainfall and air temperature patterns. The net estimated IWR from satellite observations for the two-growing seasons was about 272 and 338 mm in 2016 and 2017, respectively. Such estimated requirement was lower compared with the actual amount supplied by the farmer with sprinkler and drip micro-irrigation system in both growing seasons resulting in 364 (276 mm drip micro-irrigation, and 88 mm sprinkler) and 662 (574 mm drip micro-irrigation, and 88 mm sprinkler) mm, respectively. Our findings indicated the suitability of Sentinel-2A to predict tomato water demand at field level, providing useful information for optimizing the irrigation over extended farmland. [ABSTRACT FROM AUTHOR]