Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.

The main goal of this research was to estimate heavy metals (HMs) (molybdenum (Mo), copper (Cu), nickel (Ni), cadmium (Cd)) contents in crop leaves through multispectral satellite imagery. During the acquisition of a SPOT 7 satellite image (28 July 2017) in situ sampling (38 samples) was done from the leaves of potatoes and beans growing close to the mining town of Kajaran (Armenia). To estimate HMs contents, multivariate regression (multiple linear regression (MLR), partial least squares regression (PLSR)), and artificial neural network (ANN) were used. As input data for the models raw, atmospherically corrected (Dark Object Subtraction (DOS)) and hyperspherical direction cosines (HSDC) normalized values of SPOT 7 spectral data in combination with one or combined log10, multiplicative scatter correction (MSC), standard normal variate transform (SNV) preprocessing methods were utilized. The best results were obtained for Cu using MLR (R² _cal. = 0.79, R² _CV = 0.70, RMSE_cal. = 11.27, RMSE_CV = 13.47) and ANN (R² _Train ≈ 0.80, R² _Test ≈ 0.72, RMSE_Train ≈ 11, RMSE_Test ≈ 13) models in case of bean leaves. The results are quite optimistic, however, further research with the use of high spatial/spectral resolution satellite images is needed to improve the accuracy of models.

Lake Sevan being Armenia’s largest freshwater reservoir has a vital economic, recreational and cultural importance to both the catchment area and the nation as a whole. At present the Sevan which has seen the dramatic - some 20m drop - in water level entailing grave ecological consequences to the whole of its ecosystem, is at the stage of recovery. Hence, it is very important to study basic parameters describing the ecological status of the lake, and their annual and seasonal dynamics. The Sevan water surface temperature (WST) is a key parameter which influences all ecological processes that occur in the Lake. Declining lake level has brought to reduction of water volume and consequently to earlier warming of lake water in spring and its earlier cooling in the fall. Besides, more frequent becomes the complete surface freezing of Lake Sevan. Remotely sensed imagery makes it possible to get immediate information on a regular basis about WST across the entire surface of lakes. The purpose of this particular research was to study the space and time dynamics of Lake Sevan WST using Landsat 8 satellite imagery. The advantage of Landsat8 images is a regular frequency of capturing and availability of another thermal band that helps reduce the atmospheric refraction-induced errors/deviations. This research involved Landsat imagery for 2000-2018. The images underwent preprocessing steps (radiometric calibration, atmospheric correction, normalization etc) and then Lake Sevan WSTs and their monthly and annual changes over the mentioned periods were derived using both thermal bands (b10, b11). The research confirmed the fact, that Lake Sevan surface completely or partly freezing with periodicity of 2-3 years, whereas before the water drop the periodicity was 15-20 years. The study of spatial distribution of WST data derived from remote sensing shows that the temperature data corresponds to the overall general picture of temperature for Lake Sevan. This research has indicated that remotely sensed images and Landsat 8 imagery in particular allow derive both WST data on a regular basis and retrospective data (since 2013).