Issledovanie Zemli iz Kosmosa

Long-term changes in thermal fields were studied before and during strong earthquakes with magnitudes from 5.1 to 5.6 that occurred in the region of the Baikal rift zone in 2008–2022. Satellite data were used for these studies. For the analysis we used the values of land surface temperature, temperature of the near-surface layer of the atmosphere, outgoing long-wave radiation, and relative humidity recorded using the AIRS instrument mounted on the Aqua satellite. During the periods of preparation and occurrence of these seismic events, anomalous variations in the parameters of thermal fields registered with satellite were revealed. They exceeded the average long-term values: for land surface temperature and temperature of the near-surface layer of the atmosphere by 5–10%, for outgoing long-wave radiation by 11–15%, and for relative humidity by 6–10%. A strong negative correlation was found between changes in the temperature of the near-surface layer of the atmosphere and relative humidity (correlation coefficient of –0.75), as well as antiphase oscillations between the values of the outgoing long-wave radiation and relative humidity. The obtained results can be used for studies of the precursor variability of thermal fields during monitoring of seismic hazard zones.

The paper presents the results of a study of upwelling in the Black Sea in three marine areas: in the northeastern part of the sea, near the Tendrovskaya Spit and the Western Crimea, and off the coast of Turkey. They are based on the use of multi-sensor remote sensing data, namely ocean color scanners (MODIS, VIIRS, OLCI-a and OLCI-b), infrared radiometers (TIRS and AVHRR), as well as synthetic aperture radar (SAR) images acquired by spaceborne SARs. An integrated approach using practically only remote sensing data makes it possible to quite fully characterize the observed upwellings in the sea. In the active phase, upwelling, in addition to sea surface temperature (SST), is usually displayed both in the phytoplankton chlorophyll-a concentration (chlor-a) and in the sea surface roughness on the SAR images. In the analyzed cases, the duration of upwellings varied from 6 to 10 days, the SST differences in the upwelling zone were up to 8°С, and the concentrations of chlor-a were 5–6 times higher than the background values of 0.5–0.7 mg/m³. The maximum SST anomalies, which are about 8°C, were observed off the Turkish coast. As a result of the analysis, a dynamic relationship was revealed between the areas of low SST in the upwelling zone (compared to the sea waters surrounding this zone), sea surface roughness and chlor-a concentration. It is shown that in the case of using the full set of available remote sensing data, the observation, monitoring and study of upwelling does not present any fundamental difficulties.

This paper presents a description of an approach that makes it possible to control the quality of the MTVZA-GYa georeferencing and determine the optimal values of the corrective parameters. The analysis of the data of this instrument showed that the main contribution to the georeferencing errors are made by the angles of roll, pitch and yaw, which determine the mismatch between the instrumental coordinate system and the spacecraft coordinate system. In this regard, an iterative algorithm for detecting these angles was proposed, where the difference in measurements on the ascending and descending orbit half-passes of the MTVZA-GYa was used as the minimized function. As a result of applying this algorithm to the results of measurements of the MTVZA-GYa for 2020, the average values of the correcting roll, pitch and yaw angles of this instrument were calculated. The following values were found: (–0.84 ± 0.15)° for yaw angle, (–0.44 ± 0.14)° for roll angle and (+1.13 ± 0.05)° for the pitch angle. It was shown that the introduction of these angles into the MTVZA-GYa georeferencing procedure can significantly reduce its errors. Thus, the average discrepancy between coastlines borrowed from high-precision geographic databases and reconstructed from radiometric portraits is 4.5 km when georeferencing is performed using this correction angles.

In recent years, research has been conducted in scientific institutions of the Ministry of Agriculture of the Russian Federation and the Russian Academy of Sciences on the introduction into practice of new technologies for the use of aerospace information in agriculture. The article, using the example of the Stavropol Territory, considers the possibility of using cloud services such as google earth engine (GEE) and Kaggle machine learning systems for mapping agricultural (agricultural) fields using deep learning methods based on remote sensing data. Median images of the Sentinel 2 space system for the 2022 growing season were used as data for the selection of training and validation samples. The total volume of the prepared training and training samples was 3998 images. One of the problems for researchers and manufacturers in the field of agricultural is the lack of centralized and verified sources of geospatial data. Deep learning methods are able to solve this problem by automating the task of digitizing the geometries of agricultural fields based on remote sensing data. One of the limitations in the widespread use of deep learning is its high demand for computing resources, which are not yet always available to a researcher or manufacturer in the field of agricultural. The paper describes the process of preparing the necessary data for working with a neural network, including correction and obtaining satellite images using the Google earth engine platform, their further standardization for training a neural network in the Kaggle service, and its further use locally. As part of the study, a neural network of the U-net architecture was used. The final classification quality was 97%. The threshold of division into classes according to the classification results was established empirically and amounted to 0.62. The proposed approach made it possible to significantly reduce the requirements for the local use of PC computing power. All the most resource-intensive processes related to the processing of satellite images were performed in the GEE system, and the learning process was transferred to the resources of the Kaggle system. The proposed combination of cloud services and deep learning methods can contribute to a wider spread of the use of modern technologies in agricultural production and scientific research.

The response of the lower and upper ionosphere to the passage of several powerful typhoons during 2014–2016 years was studied using regional network of VLF radio stations and measurements of electron density disturbances by satellites of the SWARM mission in the Russian Far East. It was found that the disturbances of the amplitude and phase of the VLF signal, as well as the electron density during the active stage of typhoons, correspond to the passage of atmospheric internal gravity waves and their dissipation. A mechanism for the action of internal gravity waves upon the ionosphere is proposed, which allows to interpret the observed variations in the phase of the VLF signal and variations in the electron density in the upper ionosphere.

Based on the analysis of satellite data from 1982 to 2021 with a spatial resolution of about 0.05° × 0.05°, the total increase in the Black Sea surface temperature was confirmed. Annual temperature averaged over the entire Black Sea rises with the rate of about 0.6°C/10 years. The annual temperature increment due to the linear trend is at a maximum in May–June. In these months of the hydrological spring, the rate of increase in sea surface temperature (SST) is about one and a half times greater than in October–November. For most of the year, the general warming of the surface water layer is not accompanied by a significant increase in the intra-monthly SST variance. Such an increase is observed only in some months of the transition seasons, especially during the hydrological spring, when the absolute magnitude of extreme thermal anomalies and their area significantly increases. The maximum amplitudes of interannual variations of SST are confined to the northwestern part of the Black Sea. Changes in atmospheric pressure and wind fields significantly impact on the spatiotemporal SST structure of the. Long-term trends of driving pressure above the Black Sea indicate an intensification of regional cyclonic activity in the atmosphere (especially pronounced since 2009), which leads to increased generation of the negative SST anomalies of significant amplitude. Such anomalies occur mainly in the warm half-year (especially in May and October) due to the development of wind-driven upwelling. The May and October negative SST anomalies from the range of –(6–5)°C are characterized by maximum areas. Warm anomalies are also most often recorded in May and (to a lesser extent) in October. They are generated by abnormal heat fluxes on the sea surface, including in shallow areas of the shelf and spread to open areas of the Black Sea due to horizontal advection of mainly wind origin. The described patterns of spatio-temporal SST variability and their causes are illustrated by a case-study of extreme thermal anomalies using comprehensive analysis of wind and SST fields of high spatial resolution.