Izvestiya Atmospheric and Oceanic Physics最新文献

Abstract—

Determining the patterns of changes in the productivity of agricultural land in different growing areas in the zone of influence of State Protective Forest Belts (SPFB) is relevant due to the need to assess the future crop yield in fields with differences in geomorphological, soil, and climatic conditions in the research area. The object was to study the sowing of winter grain crops in fields mixed within the influence of State Protective Forest Belts. Materials and methods involved a research methodology based on the geoinformation analysis of the results of the decryption of actual satellite images, both to identify the distribution of cultivated fields located in the zone of influence of SPFB, and the state of crops on them. At the same time, the soil zonality of the research area was taken into account in view of the considerable length of forest strips. The assessment of the condition of winter grain crops as they move away from the planting was carried out using the NDVI vegetation index calculated from the high-resolution spectral channels of satellite images. Results and conclusions: based on the results of the research, a database of spatial data of the processed fields has been compiled. The grouping of fields was carried out both according to the similarity of the conditions of the places of cultivation of crops and by agricultural crops. Their placement and geomorphological parameters have been established. With the use of geoinformation technologies for groups of fields using statistical processing tools, the average values of the width and area of the selected zones of influence, as well as terrain parameters, were determined. During geoinformation mapping, data on the state of crops at the end of May were obtained based on the change in the NDVI index by field groupings in the zone of SPFB impact. These data are the basis for the forecast of crop yields, taking into account the spatial location of fields.

Abstract

Currently, more and more attention is being paid to the development of technologies for the satellite monitoring of land use and the state of agricultural landscapes. The lack of up-to-date information about the boundaries of individual agricultural fields does not allow us to fully assess the state of arable land and take them into account. The available statistical sources have discrepancies and do not have information about the spatial distribution of used and unused agricultural fields. The purpose of this work is to establish the spatial distribution of cultivated and uncultivated arable lands of the Volgograd region according to remote sensing data. This paper presents the results of mapping the actual boundaries of arable lands of the Volgograd region as of 2021. High-resolution Sentinel-2 and Google Earth PRO data in the geographic information program are used to decrypt arable land. As a result, 6.05 million ha of arable land are mapped. The data are compared with official statistics for 2021, and an excess of 12% is noted in comparison with the results of decryption. It is noted that, over the past 20 years, according to statistical data, the areas of arable land and deposits have practically not changed. When comparing the decryption results with the data on arable lands of the Vega service, a difference of 4% was noted, which is quite high accuracy. According to the All-Russian Agricultural Census of 2016, the area of arable land used was exceeded by 8%. According to the SRTM digital terrain model, morphometric parameters of arable land were calculated throughout the region. It is determined that agricultural fields are located mainly on the slopes of the western exposure (37%), which is due to the predominance of the general slope of the relief to the west. Most (78%) of the field areas are on slopes with a steepness of up to 1°, and about 2% occupy areas of more than 3°. Water erosion is noted on steep slopes. The smoothest relief in the Volga region is on the territory of the Caspian lowland. Using remote methods, the assessment of the areas of fallow lands was carried out: about 960 000 ha. According to various sources, from 4800 to 891 000 ha of unused arable land are noted. The resulting geoinformation basis will make it possible to fully account for and assess the condition of cultivated and uncultivated arable lands, as well as develop projects for the rational use of land resources to increase yields and prevent the degradation of agricultural landscapes.

Abstract

The spatial distribution and seasonal variability of mesoscale eddies in the Sea of Japan have been investigated based on the regional database created from the AVISO Mesoscale Eddies Trajectory Atlas (1993–2020). The database contains information about the trajectories and parameters of mesoscale eddies in the Sea of Japan. The eddy detection method is based on the analysis of altimetric maps of absolute dynamic topography. A total of 578 eddies with a lifetime of more than 90 days have been identified (273 anticyclonic and 305 cyclonic). The average lifetime of eddies is 202 days for anticyclonic and 143 days for cyclonic and mean radius of 58 km for anticyclonic and 61 km for cyclonic. The mean speed of anticyclones and cyclones along their trajectories is 2.8 and 3.7 cm/s; the mean orbital velocities of geostrophic currents are 19.0 and 15.1 cm/s, respectively. The maximum number of cases of formation and destruction of anticyclones falls in July–September during the period with high values of water inflow through the Korea Strait. Most of the cyclonic eddies are generated between January and June and decay during the cold half of the year (October–March). A joint analysis of maps of the mean surface circulation in the Sea of Japan (satellite altimetry data) and the spatial distribution of mesoscale eddy shows that the stable eddies of the Sea of Japan are associated with the quasi-stationary meanders of the East Korea East Korea Warm Curent, Subpolar Front, and Tsushima current. The position of meanders is mainly determined by the interaction of the currents with the bottom topography.

Abstract

Spaceborne merged multisensory OC CCI (ocean Colour Climate Initiative) data were employed to reveal changes in primary production (PP) in the Arctic Ocean (AO) from 2003 to 2022. The assessments were performed making use of the algorithm by Behrenfeld and Falkowski (1997) that assured, according to previous investigations, the coefficient of correlation between the retrieved and shipborne PP values equal to 0.8 and 0.75 for the deep and coastal ocean zones, respectively. The applied methodology of the satellite ocean color data processing permitted to account for the effect of cloud masking and determine the phytoplankton concentration within both overcast areas and coastal waters that are subject to significant influences of land- and river run-off. The results obtained indicate that since 2003 the PP over the entire AO has increased by +18.5%. This increase in PP was mostly due to the PP steady rise in the pelagic basin whereas within the AO coastal zone the PP level remained rather steady with only a slight negative tendency (–1.6%). In the marginal seas, the PP change proved to be differently directed, ranging between +32% (Laptev Sea) and –13.6% (Chukchi Sea) and exhibiting a rather low reliability of statistical characteristics. The observed two-decadal variations/tendencies of PP are discussed in light of the AO climate warming phenomenon.

Abstract

In this paper, an algorithm for restoring the precipitation intensity over the ocean surface according to data from the MTVZA-GYa Russian microwave sounder is presented. The developed algorithm is based on the ALG’85 regression model in which the precipitation intensity is estimated using the scattering index on a high-frequency radiometric channel (~90 GHz). In this work, the scattering index is simulated based on MTVZA-GYa data and compared with GPM IMERG reanalysis data. To restore the precipitation intensity, it is proposed to use a fourth-degree polynomial. The quantitative estimates show that the RMS spread reaches 50%, and the correlation coefficient does not exceed 0.75. The qualitative comparison indicates a significant difference between the restored rain rate and the GPM IMERG data, as well as the presence of a shift of the precipitation area. As a result of the analysis, it is concluded that the incorrect convergence of the beams of the radiation patterns for different frequency channels of the MTVZA-GYa device might be one of the causes.

Abstract

This paper proposes a new method for mapping hayfields in floodplain landscapes based on the use of multitemporal spectral–zonal data of earth remote sensing (ERS) of high spatial resolution (Sentinel-2) using an expert threshold of spectral brightness coefficient (SBC) in the red channel (the maximum composite of values for the vegetation period) for freshly cut vegetation adjusted for the values of the maximum composite for the growing season of the normalized difference vegetation index (NDVI). The regularities of changes in the values of SBC in the sloping and nonsloped territories in the RGB and NIR channels, as well as the values of the NDVI and NDWI indices, are revealed. Annual sloping areas within the Volga-Akhtuba floodplain (VAF) in Volgograd oblast are mapped. Here, an average of 12 000 ha (8%) of the territory is mowed annually, while most of the area is mowed in August–September (more than 65% of the area). Most sloping areas are 1 to 10 ha. At the same time, over the past 6 years, there has been a tendency toward an increase in both the total annual mowed areas and the areas of hayfields. It is revealed that the main annually mowed areas are concentrated around infrastructure facilities: closer to consumers and transport routes.

Abstract

An analysis of water exchange between the Bering (Pacific Ocean) and Chukchi (Arctic Ocean) seas in the summer was carried out using satellite data on sea level, geostrophic currents, and measurement data of water transport in the Bering Strait. It is shown that there is good agreement (r = 0.85, July–October 1997−2019) between the velocities of geostrophic currents (satellite data) and measurements of water transport (buoy station data) through the Bering Strait. It has been established that the temporal variability of water transport through the Bering Strait is determined by sea level variations in the southern part of the Chukchi Sea (66°–68° N, 170°–172° W). Strengthening of the eastern (western) winds is accompanied by a decrease (increase) in the sea level in the southern part of the Chukchi Sea and, as a result, an increase (decrease) in the flow of water through the Bering Strait. An increase (decrease) in the flow of the Yukon River is accompanied by a rise (decrease) in sea level and changes in water circulation in the northern Bering Sea and the southern Chukchi Sea.

Abstract—The paper presents the results of a study of spatiotemporal variability of the characteristics of the wind field in the free atmosphere of the Northern Hemisphere in the SEVIRI radiometer field of view of European geostationary meteorological satellites of the second generation Meteosat 8–Meteosat 11 in the time interval 2007–2021. It is noted that the maximum wind speeds, as well as the maximum average monthly and seasonal anomalies of the wind speed modulus, are observed over the Atlantic. A feature of the temporal variability of the area-averaged wind speed modulus is revealed, which consists in a change in the sign of the trend at the turn of 2015–2017 from positive to negative. At the same time, positive linear trends in the time intervals from 2007 to the points of a change in the sign of the trend over the Atlantic, the entire region under consideration and Eurasia, including the European territory of the Russian Federation, are significantly different from zero with a probability of more than 95% and the negative trend is significant only over the Atlantic. A high correlation was noted in the area of seasonal wind speed variations with the area of Arctic sea ice and temperature characteristics of the troposphere at levels of 500 and 200 hPa. Based on the analysis of the relationship between wind speed variability and the main climatic characteristics and large-scale atmospheric processes, a scheme is proposed for the effect of the accelerating reduction in the area of Arctic sea ice associated with global warming on wind speed in the free atmosphere.

Abstract

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 introducing new technologies for the use of aerospace information in agriculture. This article, using the example of Stavropol krai, considers the possibility of using cloud services such as Google Earth Engine (GEE) and Kaggle machine learning systems for mapping agricultural fields using deep learning methods based on remote sensing data. Median images of the Sentinel 2 space system for the 2022 growing season are used as data for the selection of training and validation samples. The total volume of the prepared training samples is 3998 images. One problem for researchers and manufacturers in the field of agriculture is a 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 always available to a researcher or manufacturer in the field of agriculture. This paper describes the process of preparing the necessary data for working with a neural network, including correcting and obtaining satellite images using GEE, their standardization for training a neural network in Kaggle, and further use locally. A neural network of the U-net architecture is used as part of the study. The final classification quality is 97%. The threshold of division into classes according to the classification results is established empirically and amounts to 0.62. The proposed approach makes 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 are performed in the GEE system, and the learning process is 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.

Abstract

Long-term changes in thermal fields have been 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 are used for these studies. For analysis we use the values of land surface temperature, temperature of the near-surface layer of the atmosphere, outgoing longwave radiation (OLR), and relative humidity (RH) 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 are revealed. They exceed the average long-term values: for land surface temperature and temperature of the near-surface layer of the atmosphere by 5–10%, for OLR by 11–15%, and for RH by 6–10%. A strong negative correlation is found between changes in the temperature of the near-surface layer of the atmosphere and RH (correlation coefficient of –0.75), as well as antiphase oscillations between the values of the OLR and RH. The results can be used for studies of the precursor variability of thermal fields during monitoring of seismic hazard zones.