Satellite Data For Monitoring Research Articles

Near Real-Time Monitoring of Large Gradient Nonlinear Subsidence in Mining Areas: A Hybrid SBAS-InSAR Method Integrating Robust Sequential Adjustment and Deep Learning

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results.

Assessing the impact of global carbon dioxide changes on atmospheric fluctuations in Iran through satellite data analysis

ABSTRACT Atmospheric Carbon Dioxide (CO2), a significant greenhouse gas, drives climate change, influencing temperature, rainfall, and the hydrologic cycle. This alters precipitation patterns, intensifies storms, and changes drought frequency and timing of floods, impacting ecosystems, agriculture, water resources, and societies globally. Understanding how global CO2 fluctuations impact regional atmospheric CO2 levels can inform mitigation strategies and Facilitate water resources management. The study investigates how global CO2 fluctuations affect atmospheric CO2 concentrations (XCO2) in Iran from 2015 to 2020, aiming to inform mitigation strategies against climate change. XCO2 data OCO-2 satellite and CO2 surface flux data from the Copernicus Atmosphere Monitoring Service (CAMS) were analyzed. Over the 6 years, XCO2 in Iran increased steadily by 12.66 ppm, mirroring global rises. However, Iran's CO2 surface flux decreased, with slight increases in anthropogenic emissions but decreased natural and total fluxes. Monthly patterns of XCO2 and surface flux exhibited variations, with XCO2 reaching its zenith in spring and dipping to its lowest point during summer, while surface flux peaked during the summer months. The results reveal a significant discrepancy between Iran's surface CO2 flux and atmospheric XCO2 trends. While Iran's anthropogenic emissions increased barely from 2015–2020, its natural and total CO2 fluxes decreased. However, XCO2 increased steadily over this period, indicating the dominant impact of global rather than local factors on Iran's XCO2. Curbing worldwide greenhouse gas output is imperative to disrupt the current trajectory of climate change. Reporting CO2 levels can inform climate mitigation plans, reducing emissions to combat global warming and minimize global impacts on the hydrologic cycle.

Accuracy of calculating carbon monoxide emissions from large forest fires using the balance technique based on satellite monitoring data

Accuracy of calculating carbon monoxide emissions from large forest fires using the balance technique based on satellite monitoring data

Inversion of soil moisture in Duolun County, Inner Mongolia, China using Sentinel-1 data

Soil moisture is an important physical quantity that reflects the surface conditions. Soil moisture retrieval from remote sensing satellite monitoring data is a common method at present and the crucial issue is how to eliminate the influence of other surface and reflect soil parameters like roughness and soil bulk density, and the interference of vegetated areas to electromagnetic wave. In this paper, the Sentinel-1 SAR data, optical data and other auxiliary data such as the Land Surface Data Assimilation System (CLDAS) of the China Meteorological Administration are used as data sources. By coupling the Advanced Integral equation (AIEM) model, a method suitable for the inversion of soil moisture content in the Duolun area is established, and the inverted soil moisture map is analyzed.

Feature Importance Ranking of Random Forest-Based End-to-End Learning Algorithm

Efficient land management and farming practices are critical to maintaining agricultural production, especially in Europe with limited arable land. It is very time consuming to rely on a manual field inspection of cultivated land to archive farm crops. But with the help of satellite monitoring data on the earth’s surface, it is a new vision to classify farmland based on deep learning. This article has studied the Sentinel 2 (S2) data, which are top-of-atmosphere (TOA) reflectance values at the processing level-1C (L1C) observed from some areas of Germany and France. Aiming at the problem that the interference of atmosphere and cloud coverage weakens the recognition accuracy of subsequent algorithms, a method of combining feature expansion and feature importance analysis is proposed to optimize the raw S2 data. Specifically, the new 13 spectral features are expanded based on the linear and nonlinear combination of the raw 13 spectral bands of S2. The random forest (RF) algorithm is used to score the importance of features, and the important features of each time series are selected to form a new dataset. Then, an end-to-end deep learning model has been used for training. The structure of the model is a two-layer unidirectional recurrent neural network with long short-term memory (LSTM) as the backbone. And two linear layers as the output, which form two decision-making heads, respectively, representing output classification probability and the stop decision. The results show that adding features and selecting features is beneficial for the model to improve classification accuracy and predict the classification without all of the input data. This end-to-end classification pattern with early prediction would support intelligent monitoring of farm crops with a great advantage to the implementation of various agricultural policies.

SENTINEL-2 DERIVED WATERLINES FOR COASTAL MONITORING APPLICATIONS: A NEW APPROACH FOR QUANTIFYING VERTICAL AND HORIZONTAL ACCURACIES

Abstract. Accurate and consistent mapping of the boundary between land and water (the ‘waterline’) is critical for tracking coastal change and coastal management. Earth Observation satellite remote sensing provides a unique cost-effective alternative to traditional methods. Waterlines from satellites are often derived by methods based on spectral indices that lead to the separation between land and water. The validation strategy for these products requires a complex approach from accuracy assessment (quantifying error) to verification of its suitability for monitoring applications. Traditionally the accuracy of EO products is reduced and simplified to the resolution of the sensor or satellite that collects the data. However, environmental variables (sea conditions, weather, vegetation, anthropic) that may have a direct effect on the sensor and on the coastline that we are trying to monitor are not taken into consideration. Segments of Sentinel-2-derived waterlines were selected in North Bull Island for further analysis in the creation of a new benchmark dataset for understanding the waterline models of eastern Ireland. In our novel approach, we propose that horizontal accuracy assessment is performed by using the mean absolute distance between the GNSS reference line and the Sentinel-2-derived waterline. The vertical accuracy assessment was then calculated by the difference between the attributed waterline height compared with the mean GNSS elevation at the intersection points. Results were then compared with Dublin Port tide gauge height record. The development of reference validation models can allow more efficient application of satellite data for monitoring, and understanding how environmental variables affect each case study.

ASSESSMENT OF ENVIRONMENTAL RISKS AND ECONOMIC LOSSES TO GEOLOGICAL ENVIRONMENT AND MINERAL RESOURCES CAUSED BY RUSSIA’S MILITARY AGGRESSION AGAINST UKRAINE. UPDATED DATA

Beyond the enormous losses of priceless human lives, extreme economic losses, tremendous impacts on our country’s geological environment have been caused by russia’s military aggression since 2014. In particular, as a result of hostilities and missile attacks, the entire network of large metallurgical and chemical facilities have been completely destroyed, nearly a hundred of oil depots and other fuel storage facilities throughout the country have been damaged, resulting in the contamination of soil, surface and groundwater, vegetation, etc. A number of hydrotechnical facilities were damaged, leading to large-scale flooding, etc. Shelling damaged a number of mining facilities, mineral deposits and other geological sites, such as unique stratotypic cross-sections, geological landmarks, and landscape parks. The objective of this paper is to provide an assessment of environmental damage and economic losses to the geological environment and mineral resource base of the country based on updated satellite monitoring data using remote sensing. Based on the analysis of the map of operating deposits and the updated map of hostilities (impact of shells, missiles, etc.), it was found that as of 13.04.2023, out of 2,164 total mineral deposits in Ukraine (excluding water resources), only 490 deposits (22.6%) are located in the temporarily occupied territories or within 25 km of the frontline. According to the results of the assessments, the following deposits are located in the territories not controlled by Ukraine: 139 hydrocarbon deposits (24%); 175 coal deposits (81%); 67 non-metallic mineral deposits (5.3%); 5 metallic mineral deposits (7%). Thus, the use of geological environment monitoring and remote sensing using GIS tools (in the conditions of lack of access to such objects) plays a key role in obtaining reliable and internationally acceptable data as proof for financial compensation. In this regard, there is a critical need for immediate actions in order to revitalise and reform the geological monitoring system in Ukraine.

How does air pollution affect household energy expenditure: A micro-empirical study based on avoidance behavior

China is one of the world's most highly-pollutant countries, and over the last few years has experienced severe and persistent haze pollution. Exploring the effects of air pollution on household energy expenditure will provide a more comprehensive and accurate understanding of the economic effects of environmental pollution. It is an important question but remains unanswered because of an estimation endogeneity. Household non-clean energy consumption would increase air pollution. A major challenge in estimates is how to accurately and cleanly identify the unwatched effect of air pollution given the endogeneity. Using the global satellite monitoring data and unique micro-household survey data, we try to construct an instrumental variable to identify the net effect of air pollution on Chinese household energy expenditure. We find that the increase in air pollution has a significantly positive effect on household energy expenditure. The results remain robust after a series of pivotal checks. Our findings highlight that stay-home-avoidance behaviors as a potential mechanism behind the energy-associated effects of air pollution on household energy expenditure. The well-educated, high-income, and urban households in southern China are more likely to engage in stay-home-avoidance behaviors. These findings can provide some useful policy enlightenments for the government in terms of environmental regulation and promotion of household clean energy.

Explanatory models for tick-borne disease incidence (Astrakhan rickettsial fever and Crimean-Congo hemorrhagic fever)

Introduction. The study focuses on methods providing mathematical substantiation of discrepancies between actual incidence rates of Astrakhan rickettsial fever (ARF) and Crimean-Congo hemorrhagic fever (CCHF) and predicted rates due to the indirect impact of weather conditions during the current epidemic season.
 The purpose of the study was to develop explanatory models for ARF and CCHF incidence using satellite monitoring (remote sensing) data and to present the results of their practical evaluation in the Stavropol Territory and Astrakhan Region.
 Materials and methods. The materials included climate data provided by the Space Research Institute of the Russian Academy of Sciences as well as epidemiological data on CCHF and ARF incidence from 2005 to 2021. The explanatory models incorporated the Bayes theorem and Wald sequential analysis. All the calculations were completed using the Microsoft Excel 2010-based program developed by the authors.
 Results. It has been found that the greatest indirect effect on development of the CCHF epidemiological situation is produced by the normalized difference vegetation index and relative air humidity in June-July in the Stavropol Territory and by the maximum, minimum and average air temperature in October as well as the minimum air temperature in July in the Astrakhan Region. ARF incidence rates depend on the indirect effect of the annual average and average annual maximum temperature, maximum temperature and the normalized difference vegetation index in April-July. The match between explanatory model-based results and prediction model-based results ranged within 46.2-100%.
 Discussion. In addition to projecting incidence rates, which could be reached with the observed values of climatic factors in the current year, the explanatory models can be used for indirect verification of prediction models and for identification of factors causing differences in results.
 Conclusion. The practical evaluation of explanatory models confirms the prospects and benefits of the study that should be continued, involving other regions highly endemic for tick-borne infections.

Association of greenness exposure with waist circumference and central obesity in Chinese adults aged 65 years and over

Objective: To examine the association of greenness exposure with waist circumference (WC) and central obesity in older adults in China. Methods: Based on the cross-sectional data from the Chinese Longitudinal Healthy Longevity Survey in 2017-2018, 14 056 participants aged 65 years and over were included. Demographic characteristics, lifestyle, WC, and other information were collected through a questionnaire and physical examination. Based on the satellite monitoring data of moderate-resolution imaging spectroradiometer (MODIS) provided by NASA, the annual mean of normalized difference vegetation index (NDVI) within a radius of 1 000 meters was obtained as the measurement value of greenness exposure. Multivariate linear regression model, multivariate logistic regression model, and restricted cubic splines (RCS) model were used to analyze the association and dose-response relationship between greenness exposure and WC and central obesity in older adults in China. Results: A total of 14 056 participants were enrolled with a median age of 84.0 years [IQR: 75.0-94.0 years]. About 45.0% (6 330) of them were male and 48.6% (5 853) were illiterate. There were 10 964 (78.0%) participants from rural. The mean of WC was (84.4±10.8) cm. Central obesity accounted for 60.2% (8 465), and the NDVI range was (-0.06, 0.78). After adjusting for confounding factors, the multivariate linear regression model showed that the change value of WC in the urban group [β (95%CI):-0.49 (-0.93, -0.06)] was smaller than that in the rural [-0.78 (-0.98, -0.58)] for every 0.1 unit increase in NDVI (Pinteraction=0.022). Compared with the Q1 group in NDVI, WC of Q2 and Q3 groups in rural decreased, and the β (95%CI) values were-1.74 (-2.5, -0.98) and-2.78 (-3.55, -2.00), respectively. The multivariate logistic regression model showed that after adjusting for confounding factors, the risk of central obesity decreased for urban and rural older adults with an increase of 0.1 unit in NDVI, and the OR (95%CI) values were 0.87 (0.80, 0.95) and 0.86 (0.82, 0.89), respectively (Pinteraction=0.284). Compared with the Q1 group in NDVI, the risk of central obesity in the Q2 and Q3 groups in rural was lower, and the OR (95%CI) values were 0.68 (0.58, 0.80) and 0.57 (0.49, 0.68), respectively. The results of the multivariate regression model with RCS showed that there was a non-linear association of NDVI with WC (Pnonlinear=0.006) and central obesity (Pnonlinear=0.025). Conclusion: Greenness exposure is negatively associated with WC and central obesity in older adults in China.

Numerical Estimation of Shaft Stability and Surface Deformation Induced by Underground Mining Transferred from Open-Pit Mining in Jinfeng Gold Mine

In this study, a three-dimensional finite difference numerical model of the Jinfeng Gold Mine, including surface topography, ore body, shafts, and main faults, was built to estimate the shaft stability and surface deformation induced by underground mining transferred from open-pit mining. Satellite monitoring data of surface displacement at several points was used to calibrate the numerical model. The sequence of excavation and filling in the simulation was determined according to the mining schemes with appropriate simplification. The distribution of large deformations in simulation is consistent with the cracking areas on the slopes and surface. Besides, shaft deformation in the simulation is small, which is consistent with the reality that there are no large deformations of shafts in the underground mining activities above 30 m level. After the completion of simulated underground mining, the deformations of shafts and surface are generally far less than the critical deformation. Hence, we concluded that the shafts and surface of the Jinfeng Gold Mine can remain stable in the underground mining stage. Overall, the method in the study provides references for the estimation of shaft stability and surface deformation in the underground mining stage of mine transfer from open pit.

METHODOLOGICAL BASIS FOR BUILDING ENERGY EFFICIENT TERRITORIAL COMMUNITIES

The work is devoted to the topical issue of the energy security of the territorial community, namely, the identification of promising local resources for the widespread use of biogas reactors. The development of methodological foundations for the introduction of territorial energy complexes based on local raw materials for urbanized areas is the goal of the work. Traditional approaches to the analysis of possible raw materials are hardly acceptable for urbanized territories, therefore, a hypothesis was put forward about the expediency of its identification by indirect signs, namely, methane emissions, which were estimated from satellite monitoring data. The concentration of gases in the atmosphere was measured by continuous scanning with the TROPOMI spectrometer in the ultraviolet, visible, near and short-wave infrared spectra. Thanks to the use of the Google Earth Engine cloud platform for geospatial data analysis, it was possible to create a web application for displaying spatial and temporal changes in the content of greenhouse gases in the atmosphere with the ability to determine their concentration. For the first time, it was possible to identify the presence in the Kyiv region of significant seasonal methane emissions, the peak of which occurs in October. Based on the results of the experiment, which can be interpreted as indirect measurements of the amount of the above organic waste, there is a significant resource for the production on an industrial scale of specialized pellets for biogas reactors of an energy efficient community.

ГЕОИНФОРМАЦИОННАЯ СИСТЕМА ОЦЕНКИ ВОЗНИКНОВЕНИЯ ПОЖАРНОЙ ОПАСНОСТИ СОПРЯЖЕННЫХ УЧАСТКОВ РАСТИТЕЛЬНОСТИ ПО ПРИРОДНО-АНТРОПОГЕННЫМ УСЛОВИЯМ

On the basis of a deterministic-probabilistic model for assessing the occurrence of fires of interconnected vegetation areas according to natural and anthropogenic conditions, a geoinformation system has been developed, consisting of a database and blocks for access to open data and searching adjacent neighborhoods, calculating meteorological and anthropogenic fire danger, and for regulating forest protection measures. Databases contain information about vegetation fires, data from hydrometeorological stations and anthropogenic sources. To account for the influence of interconnected areas on fire danger, a complex with an algorithm for searching vegetation quarters was used and its integration with the blocks of the geoinformation system was carried out. The architecture of the geoinformation system is based on a three-tier distribution, including a database server with API access support, an application broker and an application for visualization of calculation results. The API server stores structured data from open sources accessible via application exchange protocols, the application broker contains functions for data collection, calculation of indicators and their visualization in third-party applications. To build electronic maps, open data from the OpenStreetMap resource for the districts of the Russian Federation in the WSG 84 cartographic projection was used. Individual vegetation fires were obtained according to MODIS satellite monitoring data from 2013 to 2022. The attribute data of fires contain the identifier, the coordinates of the center, the area of ignition, the date of detection and elimination, the forest and non-forest area. Verification of the system is planned to be carried out in the fire season 2023–2024.

Remote Sensing of Infrasound Signals of the “Voice of the Sea” during the Evolution of Typhoons

The article presents summarized results of the study of registered periods of microseisms of the “voice of the sea” infrasound oscillations, generated due to the influence of wind of tropical cyclones (typhoons) passing in the Sea of Japan. We compared the recorded signals and how they correspond to the microseismic background in the wind wave range. Using satellite monitoring data, we verified and identified the groups of cyclones that excite the “voice of the sea” microseisms with approximately the same characteristics. We found that the bandwidth of the “voice of the sea” range and its central frequency, with the maximum amplitude in this frequency range, are approximately the same for typhoons (cyclones) propagating along the same routes. Wind speed also determines the central frequency of the “voice of the sea” range for a particular typhoon (tropical cyclone) propagation route.

Improving the accuracy of nonpoint-source pollution estimates in inland waters with coupled satellite-UAV data

Quantitatively and accurately analyzing nonpoint-source (NPS) pollution is essential for efficiently preventing the input of NPS loads into inland waters. However, the accuracy of previous NPS pollution models is limited by the accuracy of ground parameter data. In addition, there are few effective methods that thoroughly verify modeling results at large scales. This paper presents a framework for accurate NPS pollution estimation by coupling satellite and unmanned aerial vehicle (UAV) monitoring data, and the results are verified by both field sampling and a newly developed inlet NPS pollution “observation” simulation method. Fractional vegetation coverage (FVC) data obtained by satellite were used to improve the accuracy of the runoff module of the framework. Satellite and UAV data were coupled to acquire livestock data, determine inlets, and identify reservoir buffer zones and vegetation types. These new data were then used to improve the accuracy of the livestock and runoff modules in the framework. The results show that the estimation accuracy of total nitrogen, total phosphorus, ammonia nitrogen, and chemical oxygen demand with FVC were improved by 39.96%, 69.29%, 54.05% and 47.22% (in relative error), respectively. The high-resolution livestock data acquisition improved the estimation accuracy of the NPS pollution load by 7–53%. The high-resolution inlet extraction improved the accuracy by 3–24%. The high-resolution buffer zone identification improved the accuracy with the estimated NPS pollutant concentration into reservoir decreasing by 60–99%. Finally, the high-resolution vegetation type identification improved the accuracy by 10–72%. The framework performs satisfactorily, which was verified based on the simulated NPS observations with an average relative error of 11.54–24.31%. We found that the FVC, livestock number, and inlet number are key parameters for NPS pollution modeling; the introduction of monthly variation in the FVC makes the modeled NPS pollution load much higher in areas with mature complex forested ecosystems or densely distributed vegetation but much lower in areas with sparsely distributed vegetation. The above methods provide a scientific reference for high-efficiency NPS pollution prevention in inland waters, laying a solid basis for decision-making regarding water quality management in data-scarce regions around the world.

Characteristics of fishery in open sea and conventional areas of the World ocean in 2020 according to satellite monitoring data and prospects for the development of the Russian fishery in these areas

One of the strategic goals of the Russian fishing industry in modern conditions is to expand the activities of the fishing fleet to the open ocean areas. Based on modern data from satellite monitoring of fishing vessels in the World Ocean for 2020, the paper provides a characteristic of the state of oceanic fisheries. The most active fishing zones in the open part of the World Ocean outside the exclusive economic zones of coastal states, which may be of interest for the development of domestic oceanic fishing, have been identified and described. The most active fishing takes place in the open areas of the World Ocean, which are not covered by the regulation of regional fisheries organizations, and especially active is the fishing for species that are not regulated by them. First of all, squid are such commercial species. Tunas and other pelagic predators are also promising species in open areas of the World Ocean. The main types of fishing vessels in the open ocean are longline fishing vessels, jigging vessels and purse seiners. The information provided will be in demand when organizing the fishing of biological resources in areas beyond national jurisdiction, as well as preparing exploratory research expeditions to promising fishing areas.

Is the sky of smart city bluer? Evidence from satellite monitoring data.

How to win the "Blue Sky Protection Campaign" is becoming the focus all over the world, especially in developing economies, while the implementation of the smart cities initiative (SCI) is seen to be a feasible program to address the negative environmental externalities through Information and Communication Technologies (ICTs), but it lacks the quantitative evidence so far. This study aims to examine the impacts and potential mechanisms of SCI on air pollution governance from the objective satellite monitoring data within a quasi-natural experiment framework. We find that SCI directly reduces the air pollutants concentration such as PM2.5, SO2, NO2, and smog in urban China and improves the air quality very well, which also has significant and positive spillovers on air pollution governance in adjacent cities. This encouraging phenomenon can also be achieved through contributing to green technological innovation, industrial structure upgrading, and decentralizing urban spatial structure, such that most of can be attributed to the technological effect. Heterogeneity analyses demonstrate that the governance effect of air pollution is more obvious in large smart cities, and increases with the expansion of city size. Additionally, the effect performs better in resource-based smart cities and smart cities with stronger financing capacity and air pollution pressure.

Analysis of the influence of electromagnetic interference of transmission lines on the data quality and positioning accuracy of Beidou deformation monitoring

In this paper, aiming at the application of geological disaster monitoring of transmission lines, by analyzing the data quality and positioning accuracy of Beidou original observation data, the influence of the electromagnetic environment of transmission lines on Beidou satellite monitoring data is obtained. According to the Beidou monitoring data of 500 kV transmission line for 23 days, the analysis results show that: in the case of strong electromagnetic interference/no electromagnetic interference, the difference of data integrity is less than 1%, the difference of multipath effect is less than 0.1 m, the difference of signal-to-noise ratio is less than 0.3 dB Hz, and the difference of cycle hop ratio is less than 150. The relative difference in positioning accuracy is less than 5%. In general, the electromagnetic environment of transmission lines has no significant impact on the data quality and positioning accuracy of Beidou deformation monitoring.

Predicting incidence of Crimean-Congo hemorrhagic fever using satellite monitoring (remote sensing) data in the Stavropol Territory

Introduction. With the epidemiological situation for Crimean-Congo hemorrhagic fever (CCHF) remaining tense in many countries worldwide, special attention should be focused on development and improvement of risk-based epidemiological prediction methods.The aim of the study was to build a prediction model for CCHF incidence dynamics (based on the Stavropol Territory) using satellite monitoring (remote sensing) data.Materials and methods. We analyzed the climate data obtained from the Space Research Institute of the Russian Academy of Sciences as well as the data of public statistics reports on CCHF incidence from 2005 to 2021. The prediction model incorporated the Bayes theorem and Wald sequential analysis. The information content of the factors was assessed using the Kullback method.Results. Predictions for each of 26 districts were made stepwise (compared to threshold levels) to predict whether there will be at least one case of CCHF, whether the relative incidence per 100,000 population will exceed the median level (0.9 cases) or the average rate (3.5 cases) or the third quartile rate (4.7 cases). The highest values of information coefficients were obtained for soil temperature and moisture content (at depths of 10 and 40 cm), normalized relative vegetation index, relative humidity, maximum and average air temperature, relative air humidity. During the testing of the model in 2021, false-negative (erroneous) prediction was made for 2 districts.Discussion. The model proved to be most efficient in prediction of occurrence or absence of cases. More accurate quantitative prediction may be difficult due to subjective factors (including misdiagnosing CCHF cases without hemorrhagic manifestations and administering treatment for other conditions with similar symptoms).Conclusion. The tests of the model demonstrate its potential. The practical application of the prediction will make healthcare workers more alert when screening and detecting CCHF cases.

Monitoring of climate change and its impact on grain production in the Saratov region using remote sensing data

The relevance of the topic of the article is due to the increase in the number of adverse climatic phenomena and the strengthening of their influence on the basic branch of agriculture - crop production. The differential manifestation of negative weather factors in the most productive regions leads to fluctuations in yields and agricultural production, disruption of food chains and a decrease in food stocks, thereby jeopardizing sustainable agricultural production and creating risks for ensuring the food security of the Russian Federation. This necessitates the study of regional features of the manifestation of dangerous weather phenomena for the subsequent substantiation of organizational and economic measures to mitigate the consequences and adapt agriculture to global climate change. As an example, we took the Saratov region, as a region with a significant amount of sown (planting) areas and a high share of crop production in the total crop production of the Russian Federation. Using satellite monitoring data, a number of adverse meteorological (weather) phenomena have been established that manifested themselves in the period from 1991 to 2019 in the Saratov region. The tendency to their growth and increase in duration in the territory of the given region is revealed. The dependence of the main indicators of grain production in the Saratov region on the growing influence of negative climatic factors has been studied. The necessity of taking into account the impact of hazardous meteorological (weather) phenomena in the system of strategic priorities, as well as in the preparation of forecasts for the socio-economic development of the region, modeling the impact of negative climatic factors on the structural balance of the agro-industrial complex, is substantiated.