Fehér, Zsolt ZoltánNagy, AttilaAli, Mehmood2022-11-102022-11-102022-10-21https://hdl.handle.net/2437/339355Automatic data acquisition from two open satellite sources, Sentinel-2 and Landsat-8 were done using specifically designed Python script. For processing sentinel-2 imagery A SNAP-based graph was designed for this purpose which resulted in the generation of 28 different indices related to agriculture and environmental research. Important Indices like NDVI, NDWI, SAVI, FAPAR and LAI generated using the designed graph were analyzed visually. Indices for two different time instances (June 2020 and June 2022) of the same area were analyzed. The impact of climatic events; high precipitation in the year 2020 and drought in 2022 were more evident in the generated indices. The generation of time series (Oct. 2021 to Oct. 2022) using Landsat-8 imagery of different indices derived on pre-defined subsets in the study area were generated. Five different land-use categories were digitized, subsets created, converted to point features. Pixel values generated were used for evaluating statistical distribution of co-temporal distributions of vegetation indices. Spectral indices created using the method used in current study enables the readily provision of cost-effective and near real time input for continuously running environmental models.83entime seriessemi-automaticmulti spectral analysisspatailindicesDEENK Témalista::Mezőgazdaságtudomány::Víz- és környezetgazdálkodás