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In this paper we discuss results of a pilot study conducted by Ukrainian Space Research Institute of NASU-NSAU, in collaboration with the MARS team of the JRC, to explore the feasibility, cost-efficiency and specific difficulties of crop area estimation assisted by satellite remote sensing in Ukraine. The study compares the cost efficiency of several image types (MODIS, Landsat TM, AWiFS, LISS-III and RapidEye) combined with a field survey on a stratified sample of square segments. Additionally, field data were collected "along the road" as training data for image classification algorithms. The study shows that TM images from Landsat 5 yielded the best results, in spite of the old age of this sensor. Among the sensors that were tested, only MODIS and Landsat TM reach cost-efficiency thresholds.

This paper presents a technique for the assessment and mapping of land biodiversity by using remote sensing data. The proposed approach uses a fuzzy model that encapsulates different ecological factors influencing biodiversity. We implemented our approach as a web service for the Pre-Black Sea region of the Ukraine.

This paper presents a Grid infrastructure that is being developed at the Space Research Institute NASU NSAU, and integrates the resources of several geographically distributed organizations. The use of Grid technologies is motivated by the need to make computa tions in the near real time for fast response to natural disasters and to manage large volumes of satellite data. We use the Grid infra structure for a number of applications that heavily rely on Earth observation data. The applications include: weather prediction, flood monitoring, biodiversity assessment, crop yield prediction, and Earth land parameters estimation.