No 4 (2024)

The paper presents a neural network method for precipitation estimation using microwave measurements from ATMS radiometer on board Suomi NPP and NOAA-20/21 satellites. The algorithms based on two fully-connected neural networks, the first one is used to detect precipitation clouds and the other one is used to quantify precipitation rate. When training the neural networks, the reference source of information was an array of measurements simulated using the fast radiation transfer model RTTOV in the bands of ATMS instrument and the corresponding precipitation rates were taken from ECMWF ERA5 reanalysis data. Validation of the obtained precipitation estimates was carried out using the results of the MIRS and GPROF algorithms for satellite radiometer ATMS, as well as ground-based radar observations from NIMROD. The results of the validation showed a high accuracy level consistent with many others works in this research field. The validation was carried out for land and water surface separately. The comparison with MIRS algorithm showed the correlation coefficient was more 0.9, and the RMSE error was approximately 0.78 mm/h for water and 0.84 mm/h for land surface. The same metrics for GPROF algorithm showed the correlation coefficient was ~0.8, and the RMSE error was approximately 1.27 mm/h and 0.9 for water and land surface, respectively. When compared with ground-based NIMROD radar data, the correlation and the RMSE were 0.47 and 1.37 mm/h, respectively. The results of the validation confirm the performance of the presented neural network method for precipitation estimation. In addition, further minor refinement of the presented algorithm will make it possible to apply it to measurements of other microwave satellite instruments, including Russian ones, such as MTVZA-GY, installed on Meteor-M satellites.

Geoinformation assessment of deflation processes in the Arctic conditions makes it possible to move to a new technological level in the planning of forest reclamation of the landscapes of the Arctic zone. The use of forest reclamation and phytomelioration of accumulative forms makes it possible to control desertification processes. To achieve the purpose of the study – to assess the spatial distribution of deflated surface areas on the territory of the Nenets Autonomous Okrug, a geoinformation analysis of current space sensing data was carried out and the degree of degradation (deflation and anthropogenic transformation) of the territory in controlled areas was revealed, on the basis of which the necessary measures are proposed to prevent land deflation and it is planned to create an information system for monitoring and forecasting the state of soil and vegetation cover. The decoding of satellite images of deflation sites in the research area made it possible to develop vector cartographic GIS layers, which show selected coastal, continental not grown and overgrown massifs. The conducted geomorphological differentiation of deflation sites makes it possible to effectively use such parameters as tiering, exposure, meso- and microclimatic differences, as well as plan anti-deflation measures. Vector cartographic layers of the spatial distribution of sandy accumulative forms have been developed and their morphometric characteristics have been determined, the features of the development of continental and coastal deflation have been established, the areas of which are 31.51 and 20.86 thousand hectares, respectively, the total number of sites allocated by vector contours exceeds 166 thousand, and their sizes vary from 0.001 hectares to more than 5.5 thousand hectares. As a result of a spatial assessment of 68 large sandy massifs overgrown with vegetation, their area of 543.85 thousand hectares has been established.

The paper discusses the use of a new version of the regression technique for derivation the total content of carbon dioxide in the atmosphere XCO2 (column-averaged dry-air mole fraction) from measurements of the infrared Fourier-transform spectrometer IKFS–2 installed on board Russian meteorological satellite Meteor-M No. 2. To evaluate the accuracy of satellite-based XCO2 estimates the retrospective comparison was made with data from ground-based spectroscopic measurements at Peterhof site of St. Petersburg State University as well as with aircraft measurements of the V. E. Zuev Institute of Atmospheric Optics (IOA) in the area of the Novosibirsk Reservoir conducted in 2019-2022. A brief description of the regression technique modifications is given made to improve the accuracy of satellite – based XCO2 estimates. In particular, to compensate for the effect of changes in the IKFS-2 characteristics during a long flight, the XCO2 estimates calibration is realized using ground - based XCO2 measurements at the NOAA Observatory on Mauna Loa volcano (island of Hawaii). After calibration and cloud scenes filtering, the discrepancy between satellite estimates and ground-based / aircraft measurements is characterized by root mean square deviation of ~4 ppm or 1% of the CO2 total content. In order to accelerate the adjustment of the regression technique, used for estimating XCO2, to IKFS-2 data on new satellites, it is reasonable to use XCO2 observations at the TCCON terrestrial network in addition to conventional contact measurements of CO₂ concentrations. Along with this, it seems rational to use the cryogenic film thickness on the glass of the IKFS-2 photodetector, characterizing the state of the instrument, as additional predictor in the regression model.

The brief communication demonstrates the potential for quantitative assessment of forest canopy height dynamics in mature and young pine forests on a plain using the method of weighted summing of time series of unwrapped interferometric phases. The latter were obtained using a modern approach based on cloud computations. By comparing the rates of canopy height growth for the years 2017, 2018, and 2019, it has been confirmed that the growth rate is influenced by the amount of precipitation in May-July of the respective year.

The underestimation of the integrated application of geomorphological, morphotectonic information and materials of space remote data during a geological research, the geological survey and predictive-prospecting work significantly reduces their effectiveness. The imperfection of the geomorphological researches methodology focused on the study of territories relief as a collection of surfaces of geological bodies is one of the possible reasons for this. Perspectives opens the transition to the research of landforms and geological structures, bodies in their unity, as three-dimensional, volumetric objects. Fundamentally new opportunities for studying the geology of the seas and oceans bottom are associated, in particular, with the visualization of digital 3D relief models created on the basis of the global bathymetric database GEBCO 2014 (http ://Ocean3dproects...), regional echo-depth-sounder metering’s maps, altimetry materials and remote sensing of the Earth from space. The topicality of such investigations is also due to the fact determined that plate tectonics cannot explain many features of the structure of the Earth and individual regions relief. A number of examples show that modern geomorphology combined with methods of remote sensing from space and computerization should be considered as one of the necessary disciplines for geological mapping and work aimed at solving various geological problems. The further common development of the planet relief sciences (geomorphology + morphotectonics) determines the need to train geomorphologists at both geographical and geological faculties of universities.