The project DEMETER (DEMonstration of Earth observation TEchnologies in Routine
irrigation advisory services) was designed to assess and demonstrate improvements introduced by
Earth observation (EO) and Information and Communication Technologies (ICT) in farm and
Irrigation Advisory Service (IAS) day-to-day operations. The DEMETER concept of near-real-time
delivery of EO-based irrigation scheduling information to IAS and farmers has proven to be valid. The
operationality of the space segment was demonstrated in three different pilot zones in South Europe
during the 2005 irrigation campaigns. Extra-fast image delivery and quality controlled operational
processing make the EO-based crop coefficient maps available at the same speed and quality as
ground-based data (point samples), while significantly extending the spatial coverage and reducing
service cost. The new online Space-Assisted Irrigation Advisory Service (e-SAIAS) is the central
outcome of the project. Its key feature is the operational generation of irrigation scheduling
information products from a virtual constellation of high-resolution EO satellites and their delivery to
farmers in near-real-time using leading-edge on-line analysis and visualization tools. First feedback of
users at IAS and farmer level is encouraging. The paper gives an overview of the project and its main
Irrigation Advisory Services (IAS) are the natural management instruments to achieve a better efficiency in the use of water for irrigation. IAS help farmers to apply water according to the actual crop water requirements and thus, to optimize production and cost-effectiveness. The project DEMETER (DEMonstration of Earth observation TEchnologies in Routine irrigation advisory services) aims at assessing and demonstrating how the performance and cost-effectiveness of IAS is substantially improved by the incorporation of Earth observation (EO) techniques and Information Society Technology (IT) into their day-to-day operations. EO allows for efficiently monitoring crop water requirements of each field in extended areas. The incorporation of IT in the generation and distribution of information makes that information easily available to IAS and to its associated farmers (the end-users) in a personalized way. This paper describes the methodology and selected results.
Several satellite sensor systems useful in Earth observation and monitoring have recently been launched and their derived products are being used in regional and global vegetation studies. The joint use of these multi-resolution sensors offers many opportunities for vegetation studies. Spectral vegetation indices obtained from Landsat, Spot, IRS and other sensors are now widely available for monitoring ecosystem dynamics. However, the joint use of data from different satellites requires inter-satellite cross-calibration. We will use a multi-temporal data synthesising procedure for this purpose.
In this paper we analyze the broadband reflectance and NDVI relationships among the various relevant sensors. The key to the method is in using synchronous or nearsynchronous imagery from different sensors.
Comparison between reflectances for different bands shows that a linear function fits well to describe the relation between different sensors. Observations made from different sensors at different spatial scales can be reliably compared only if they are spatially aggregated to an adequate grid size. This minimum spatial aggregation size depends on the spatial resolution of the sensors involved in the comparison. In any case, it must be at least 3 x 3 pixels of the coarser resolution sensor.
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