Ms. Galya Cvetanova Profile

Galya Cvetanova

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Proceedings Article | 12 September 2021 Poster + Paper

Spectral signature profiles of winter wheat in different growth stages under various environmental conditions

Daniela Avetisyan, Galya Cvetanova

Proceedings Volume 11856, 118561A (2021) https://doi.org/10.1117/12.2599674

KEYWORDS: Vegetation, Near infrared, Agriculture, Reflectivity, Multispectral imaging, Sensors, Satellites, Short wave infrared radiation, Satellite imaging, Climatology

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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.

Proceedings Article | 21 October 2019 Paper

Application of spectral indices and spectral transformation methods for assessment of winter wheat state and functioning

Daniela Avetisyan, Roumen Nedkov, Denitsa Borisova, Galya Cvetanova

Proceedings Volume 11149, 1114929 (2019) https://doi.org/10.1117/12.2538117

KEYWORDS: Vegetation, Soil science, Near infrared, Agriculture, Ecosystems, Climatology, Reflectivity, Climate change, Multispectral imaging, Short wave infrared radiation

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As a commercial activity, agriculture is aimed primarily at production and relies on the availability of natural resources. The development of commercial activities has brought new environmental pressures on the natural capital stock. Technological progress and the desire to maximize returns and minimize costs have produced a marked intensification in agriculture over the last 40 years. Intensification can lead to degradation of soil, water and air. Water scarcity and related with it droughts have now emerged as a major challenge – and climate change is expected to make matters worse. In the last decades, Bulgarian agricultural sector is also negatively impacted by climate changes and water scarcity. Vegetation growing is limited by water scarcity and it is necessary to figure out the vegetation dynamic changes and responses to climate change to estimate the quality of ecosystems and maintain optimal ecosystem functioning. Water status can be effectively monitored by utilizing spectral indices and spectral transformation methods. Vegetation, water stress, and soil moisture indices are important to assess the crop state and its response of changing environmental conditions and to determine irrigation scheduling. The spectral transformation methods are very effective for interpretation and analysis of phenomena and processes related to the dynamics of change of the main components of the Earth surface. In the study Tasselled Cap model and obtained from its application Normalized Difference Greenness Index (NDGI) and Normalized Difference Wetness Index (NDWnI) will be applied. Microwave and optical satellite data, acquired by the sensors Sentinel 1 and Sentinel 2 of the European Space Agency Program for Earth Observation “Copernicus”, as well as climate data will be used.

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