Vol 2023, No 3 (2023)

Displacement velocity fields of the block-fault structure are constructed and the main geodynamic processes in the area of the East Anatolian fault are revealed based on the results of processing of 437 radar interferograms obtained from the Sentinel-1 radar in the period from the beginning of 2018 to disastrous seismic activity in February 2023 in Turkey by Stacking InSAR method. Anomalous block displacements along this fault have been identified, which are timed to the earthquake of January 24, 2020 (M = 6.7). Zones of stress-strain state of the main blocks in the period preceding the earthquake have been established using cluster analysis of time series of velocity fields. It is shown that the epicenters of February 2023 earthquakes are located in these zones. It is concluded that it is necessary to use such a technique to assess the stress-strain state in order to predict seismic activity.

The complexity of phytomass measurements on sample plots hinders the extrapolation of plot-related data to areas comparable to land use or landscape units. Vegetation indices calculated from satellite images are usually considered as indicators of green phytomass and are used for its areal estimates. The study solves the problem of establishing the information content of the normalized difference vegetation index NDVI depending on the fractional structure of living and dead aboveground phytomass, seasonal dynamics of the biological cycle, hydrothermal conditions and landscape position. We used the results of monthly measurements of aboveground phytomass fractions at 13 sites covered by Stipa zalesskii and Stipa lessingiana formations in the Burtinskaya steppe (Orenburgsky nature reserve) from May to September in 2015–2020. For each period, NDVI values were calculated from Landsat satellite images at all sites. Hypotheses about geobotanical, hydrothermal, phenological and landscape factors of NDVI informativity were tested by using the Spearman correlation coefficients, analysis of variance and multiple regression. The discrepancy between the seasonal peaks of NDVI and green phytomass is not consistent with the common opinion of a direct indicator value of NDVI. The total live biomass correlates more clearly with the index in June and July but weaker at the end of the season. NDVI turned out to be sensitive not so much to green phytomass as such, but to the mass and proportion of forbs and the ratio of live and dead phytomass. In late spring and early summer, NDVI is most closely associated with forbs while in July – with grasses. The hypothesis about the possibility of screening green mass with standing dead biomass was confirmed, which leads to a decrease in NDVI despite the absence of a decrease in green phytomass. NDVI may underestimate the real green phytomass if there is a sharp increase in the mass of dead biomass, usually in the second half of summer and early autumn. NDVI more adequately reflects the state of the aboveground phytomass of steppe communities that have not been exposed to fires for a long time, compared to burned communities and fallows.

The paper reports a new variant of the model of the morphological pattern for thermokarst plains with fluvial erosion involving numerous remote sensing data. This variant of the model takes into account different trends of the thermokarst processes for watershed plains and lowered bottoms of khasyreis (drained thermokarst lakes). The given model is tested at eight key sites located in different physiography, climate and geocryology. Testing was based on the high-resolution remote sensing data. The developed model is verified with a statistical analysis of lake area distributions within both watershed plain surface and lowered bottoms of khasyreis, demonstrating a state of dynamic balance in the course of emergence, growth and drainage of the thermokarst lakes. The analysis shows that the morphological pattern of the thermokarst plains with fluvial erosion changes evolutionarily, with reducing the area of the main watershed surface according to a dependence close to exponential; the specific type of the dependence results primarily from the ratio of the lake generation density and the location density of the fluvial sources.

The paper presents the method for cloud water path retrieval from daytime MSU-GS measurements on board the Russian hydrometeorological satellite Arktika-M No. 1. The presented technique based on the physical principles of the interaction of electromagnetic radiation with cloud particles at wavelengths of 0.55 and 4.0 μm. Cloud water path estimates obtained from the MSU-GS radiometer where compared with similar estimates from the AMSU/MHS and AHI radiometer data. Based on the results of the comparison, the required estimates of the cloud water path of drop clouds are within the permissible limits of the measurement error, not exceeding 50 g/m². At the same time, due to its design features, the MSU-GS radiometer does not allow retrieving the cloud water path of ice clouds with the required accuracy. On average, the cloud water path estimate of ice clouds according to the MSU-GS data is underestimated by 110 g/m², and the root-mean-square error is 158 g/m² compared to the AHI radiometer data. The obtained estimates of the cloud water path introduced into the geographic information system Arktika-M, which provides access to the Arktika-M No. 1 data and the results of their thematic processing in a near real time mode.