The results of the work conducted by the institutes of the Far East Branch of the Russian Academy of Sciences (FEB RAS) to develop the IT infrastructure for solving general and specialized tasks for...

Kamchatka and the Kuril Islands are home to 36 active volcanoes with yearly explosive eruptions that eject ash to heights of 8 to 15 km above sea level, posing hazards to jet planes. In order to reduce the risk of planes colliding with ash clouds in the north Pacific, the KVERT team affiliated with the Institute of Volcanology and Seismology of the Far East Branch of the Russian Academy of Sciences (IV&S FEB RAS) has conducted daily satellite-based monitoring of Kamchatka volcanoes since 2002. Specialists at the IV&S FEB RAS, Space Research Institute of the Russian Academy of Sciences (SRI RAS), the Computing Center of the Far East Branch of the Russian Academy of Sciences (CC FEB RAS), and the Far East Planeta Center of Space Hydrometeorology Research (FEPC SHR) have developed, introduced into practice, and were continuing to refine the VolSatView information system for Monitoring of Volcanic Activity in Kamchatka and on the Kuril Islands during the 2011–2015 period. This system enables integrated processing of various satellite data, as well as of weather and land-based information for continuous monitoring and investigation of volcanic activity in the Kuril–Kamchatka region. No other information system worldwide offers the abilities that the Vol-SatView has for studies of volcanoes. This paper shows the main abilities of the application of VolSatView for routine monitoring and retrospective analysis of volcanic activity in Kamchatka and on the Kuril Islands

Crop yield modeling at the regional level is one of the most important methods to ensure the profitability of the agro-industrial economy and the solving of the food security problem. Due to a lack of information about crop distribution over large agricultural areas, as well as the crop separation problem (based on remote sensing data) caused by the similarity of phenological cycles, a question arises regarding the relevance of using data obtained from the arable land mask of the region to predict the yield of individual crops. This study aimed to develop a regression model for soybean crop yield monitoring in municipalities and was conducted in the Khabarovsk Territory, located in the Russian Far East. Moderate Resolution Imaging Spectroradiometer (MODIS) data, an arable land mask, the meteorological characteristics obtained using the VEGA-Science web service, and crop yield data for 2010–2019 were used. The structure of crop distribution in the Khabarovsk District was reproduced in experimental fields, and Normalized Difference Vegetation Index (NDVI) seasonal variation approximating functions were constructed (both for total district sown area and different crops). It was found that the approximating function graph for the experimental fields corresponds to a similar graph for arable land. The maximum NDVI forecast error on the 30th week in 2019 using the approximation parameters according to 2014–2018 did not exceed 0.5%. The root-mean-square error (RMSE) was 0.054. The maximum value of the NDVI, as well as the indicators characterizing the temperature regime, soil moisture, and photosynthetically active radiation in the region during the period from the 1st to the 30th calendar weeks of the year, were previously considered as parameters of the regression model for predicting soybean yield. As a result of the experiments, the NDVI and the duration of the growing season were included in the regression model as independent variables. According to 2010–2018, the mean absolute percentage error (MAPE) of the regression model was 6.2%, and the soybean yield prediction absolute percentage error (APE) for 2019 was 6.3%, while RMSE was 0.13 t/ha. This approach was evaluated with a leave-one-year-out cross-validation procedure. When the calculated maximum NDVI value was used in the regression equation for early forecasting, MAPE in the 28th–30th weeks was less than 10%.

<!-- *** Custom HTML *** --> We describe an algorithm based on a convolutional neural network that detects cloud formations and snow cover in satellite images using textures. Herein, multispectral satellite images, received from a multizone scanning instrument used for hydrometeorological support and installed on the Russian satellite Electro-L No. 2, are used as input data. The problem of snow and cloud classification in the absence of a spectral channel in the range of 1.4 to 1.8 μm, which is necessary for their accurate separation, is considered. The developed algorithm can produce cloud and snow cover masks for an area limited by the values of the solar zenith angle in the range of 0 deg to 80 deg for daytime. Algorithm accuracy was evaluated using machine learning metrics and comparing its results with ground truth masks segmented manually by an experienced interpreter. In addition, we compared the resulting masks with a similar cloud mask product from the European Organisation for the Exploitation of Meteorological Satellites based on the data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument installed on the Meteosat-8 satellite. According to the results of this comparison, we conclude that the cloud masks produced by the proposed convolutional neural network-based algorithm have a lower probability of false detection than products based on the SEVIRI data. The proposed algorithm is fully automatic, and it works in any season of the year during the daytime.