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Due to depletion of natural resources, climate change and their impact on the land-production systems, farmers are facing more and more challenges related to the practical application of the sustainable development paradigm. These problems result in rapid development of precision agriculture as a management strategy, taking advantage of state-of-theart technologies. In precision agriculture, Variable Rate Application (VRA) technology is focused on the automated application of materials (such as fertilizers, herbicides, and irrigation water) to a given crop field. It involves different approaches, including sensor-based systems for monitoring and assessment of crop status and field environmental conditions. For operational success of VRA reliable data is needed to indicate the variety of processes taking place in the farm field. In the present research, we present spectral signature data for the status of winter wheat (Triticum aestivum L.) development in different growth stages. Spectral signatures vary depending on environmental conditions and related effects for the agroecosystems such as drought stress, crop diseases, and crop nutrient deficiencies. The generated spectral signature profiles are based on the Sentinel-2 satellite data, acquired in three consecutive growing seasons, distinguished with different ecological conditions. Spectral vegetation indices, indirectly representing the manifestation of biophysical processes and drought stress are calculated for each profile. Field climatic data is used for differentiation of the ecological conditions and validation of the results. The present research supports the creation of spectral library and can be used to create machine learning algorithms for monitoring of winter wheat status and application of variable rate technology.
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