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Understanding crop types and their annual cycles is key to managing natural resources, especially when the pressures on these resources are attributable to climate change and social, environmental, and economic policies. In recent years, the space sector's development, with programs such as Copernicus, has enabled a greater availability of satellite data. This study uses a multi-sensor approach to retrieve crop information by developing a Proof of Concept for the integration of high-resolution SAR imagery and optical data. The main goal is to develop a pre-automatized processing chain that explores the temporal dimension of different crop. Results are related to the advantage of using a multi-sensor approach to retrieve vegetation biomass and vertical structure for the identification of phenological stages and different crops. The novelty consists of investigating the multi-temporal pattern of radiometric indices and radar backscatter to detect the different phenological stages of each crop, identifying the Day of the Year (DoY) in which the classes showed greater separability. The current study could be considered a benchmark for the exploitation of future multi-sensor missions in downstream services for the agricultural sector, strengthening the evolution of Copernicus services. [ABSTRACT FROM AUTHOR]

Understanding crop types and their annual cycles is key to managing natural resources, especially when the pressures on these resources are attributable to climate change and social, environmental, and economic policies. In recent years, the space sector’s development, with programs such as Copernicus, has enabled a greater availability of satellite data. This study uses a multi-sensor approach to retrieve crop information by developing a Proof of Concept for the integration of high-resolution SAR imagery and optical data. The main goal is to develop a pre-automatized processing chain that explores the temporal dimension of different crop. Results are related to the advantage of using a multi-sensor approach to retrieve vegetation biomass and vertical structure for the identification of phenological stages and different crops. The novelty consists of investigating the multi-temporal pattern of radiometric indices and radar backscatter to detect the different phenological stages of each crop, identifying the Day of the Year (DoY) in which the classes showed greater separability. The current study could be considered a benchmark for the exploitation of future multi-sensor missions in downstream services for the agricultural sector, strengthening the evolution of Copernicus services.

The emergence of new organizational and legal forms of management has created the preconditions for the merger of criminal offenses in the economy with general crime, in connection with which law enforcement agencies are brought to the forefront of organizational and tactical tasks to prevent crime in all sectors of the economy. objects and territories. Based on this, at this stage of economic development, the role and importance of operational maintenance of criminal police lines of work is growing rapidly. The article considers the existing scientific approaches to the definition of “operational service”, which allowed to formulate the author's position on the definition of “operational service of criminal police units of work lines” as a balanced organizational and managerial model of criminal police units on a particular line of work, which means the following: 1) operative overlapping of objects of operative attention by representatives of the agency network; 2) conducting control and supervision proceedings for the purpose of control, monitoring, analysis, assessment and forecasting of the operational situation along the line of work; 3) search and recording of factual data on illegal activities of individuals and groups; 4) use the possibilities of criminal analysis in order to create a single security space in all spheres and sectors of the economy and ensure an effective system of operational and investigative counteraction to criminal offenses that encroach on the functioning of the state economy. It is proved that effective organization of operative service of concrete subject of economic activity is a precondition for ensuring timely prevention and detection of criminal illegalities of economic orientation, and optimization of this direction of work is possible by creation of operative positions at the expense of secret workers. economic knowledge. Based on the analysis of the practical activities of criminal police units, the author identified ways to optimize the organization of operational maintenance of criminal police units work lines, including: 1) daily monitoring and analysis of the operational situation along the line of work; 2) determination for each object (branch) of economy of the corresponding mode of operative service (intensity of supervision) which needs constant operative attention, in particular by their operative overlapping; 3) preparation and determination of the most effective operational-search, operational-technical and agency-operational measures aimed at obtaining primary operational-search information along the line of work.

A Fast Ice Prediction System (FIPS) was constructed and is the first regional land-fast sea-ice forecasting system for the Antarctic. FIPS had two components: (1) near-real-time information on the ice-covered area from MODIS and SAR imagery that revealed, tidal cracks, ridged and rafted ice regions; (2) a high-resolution 1-D thermodynamic snow and ice model (HIGHTSI) that was extended to perform a 2-D simulation on snow and ice evolution using atmospheric forcing from ECMWF: either using ERA-Interim reanalysis (in hindcast mode) or HERS operational 10-day predictions (in forecast mode). A hindcast experiment for the 2015 season was in good agreement with field observations, with a mean bias of 0.14 ± 0.07 m and a correlation coefficient of 0.98 for modeled ice thickness. The errors are largely caused by a cold bias in the atmospheric forcing. The thick snow cover during the 2015 season led to modeled formation of extensive snow ice and superimposed ice. The first FIPS operational service was performed during the 2017/18 season. The system predicted a realistic ice thickness and onset of snow surface melt as well as the area of internal ice melt. The model results on the snow and ice properties were considered by the captain of R/V Xuelong when optimizing a low-risk route for on-ice transportation through fast ice to the coastal Zhongshan Station.

In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the early and advanced melt seasons. Here, we employ an artificial neural network (ANN)-based sea ice type classification algorithm on a comprehensive data set of ALOS-2 PALSAR-2 fully polarimetric images acquired with a range of incidence angles and during different environmental conditions. The variability within the data set means that it is ideal for making a novel assessment of the robustness of the sea ice classification, investigating the intraclass variability, the seasonal variations, and the incidence angle effect on the sea ice classification results. The images coincide with two different Arctic campaigns in 2015: the Norwegian Young Sea Ice Cruise 2015 (N-ICE2015) and the Polarstern’s (PS92) Transitions in the Arctic Seasonal Sea Ice Zone (TRANSSIZ). We find that it is essential to take into account seasonality and intraclass variability when establishing training data for machine learning-based algorithms though moderate differences in incidence angle are possible to accommodate by the classifier during the dry and cold winter season. We also conclude that the incidence angle dependence of backscatter for a given ice type is consistent for different Arctic regions. [ABSTRACT FROM AUTHOR]

In recent years, the remote sensing based on meteorological satellite observations has become an important tool for assessing global ecological conditions. Since the early 2000, Fengyun (FY) satellite data have been widely used to derive the key parameters of ecological environment in China. An integrated earth-observation system has been developed in China through using FY satellite data, including retrievals the key ecological parameters as well as to constructions of long-term data records of vegetation index, land surface temperature, net primary production, vegetation health index, and so on. Considerable progress has thus been made in the application and service for prevention of air pollution, management and control of ecological redline, ecological monitoring for the Belt and Road Initiative, and assessment of ecological environment for human settlement. In order to monitor the ecological parameters in real time and with a full dynamic coverage, it is necessary to improve the technology in application of ecological remote sensing from meteorological satellites, and further enhance the ecological meteorological service. [ABSTRACT FROM AUTHOR]

In Italy, three different operational continuous monitoring experiences based on the exploitation of Multi Temporal Synthetic Aperture Radar data (MTInSAR) Sentinel-1 data are here depicted, and the results obtained in one year have been analysed. Tuscany region (Central Italy) has been the first region to implement such service, followed by Valle d’Aosta and Veneto regions (North-West and North-East Italy, respectively). In detail, the services benefit from regularly updated deformation maps (every 12 days) to promptly detect anomalies of deformation, i.e., trend variations in the time series of displacement. In this work, anomalies detected between September 2019 and September 2020 are thus correlated with several types of factors, either related to the environment, intrinsic of the data or derived from ancillary data. A statistical analysis has been performed on the three regions, and are discretized into five macro-areas, namely: i) spatial and temporal statistics, related to the geographic setting and the temporal distribution of the anomalies; ii) parametric, i.e., related to the interferometric processing; iii) triggering factors; iv) environmental and geological factors; v) urban setting. The results derived from the analysis of this work show the obvious differences between the three regions, highlighting distinct distributions of the anomalies according to the different settings of each study area. Furthermore, results were analyzed, to provide a summary of the main findings obtained, giving a first evaluation of the services and hypothesizing future further improvements and applications.

A Fast Ice Prediction System (FIPS) was constructed and is the first regional land-fast sea-ice forecasting system for the Antarctic. FIPS had two components: (1) near-real-time information on the ice-covered area from MODIS and SAR imagery that revealed, tidal cracks, ridged and rafted ice regions; (2) a high-resolution 1-D thermodynamic snow and ice model (HIGHTSI) that was extended to perform a 2-D simulation on snow and ice evolution using atmospheric forcing from ECMWF: either using ERA-Interim reanalysis (in hindcast mode) or HERS operational 10-day predictions (in forecast mode). A hindcast experiment for the 2015 season was in good agreement with field observations, with a mean bias of 0.14 ± 0.07 m and a correlation coefficient of 0.98 for modeled ice thickness. The errors are largely caused by a cold bias in the atmospheric forcing. The thick snow cover during the 2015 season led to modeled formation of extensive snow ice and superimposed ice. The first FIPS operational service was performed during the 2017/18 season. The system predicted a realistic ice thickness and onset of snow surface melt as well as the area of internal ice melt. The model results on the snow and ice properties were considered by the captain of R/V Xuelong when optimizing a low-risk route for on-ice transportation through fast ice to the coastal Zhongshan Station. [ABSTRACT FROM AUTHOR]

Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships, while limited by the capability of climate models. A bias correction methodology in this study was proposed and performed on raw products from two climate models, the First Institute Oceanography Earth System Model (FIOESM) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS), to improve 60 days predictions for Arctic sea ice. Both models were initialized on July 1, August 1, and September 1 in 2018. A 60-day forecast was conducted as a part of the official sea ice service, especially for the ninth Chinese National Arctic Research Expedition (CHINARE) and the China Ocean Shipping (Group) Company (COSCO) Northeast Passage voyages during the summer of 2018. The results indicated that raw products from FIOESM underestimated sea ice concentration (SIC) overall, with a mean bias of SIC up to 30%. Bias correction resulted in a 27% improvement in the Root Mean Square Error (RMSE) of SIC and a 10% improvement in the Integrated Ice Edge Error (IIEE) of sea ice edge (SIE). For the CFS, the SIE overestimation in the marginal ice zone was the dominant features of raw products. Bias correction provided a 7% reduction in the RMSE of SIC and a 17% reduction in the IIEE of SIE. In terms of sea ice extent, FIOESM projected a reasonable minimum time and amount in mid-September; however, CFS failed to project both. Additional comparison with subseasonal to seasonal (S2S) models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases. [ABSTRACT FROM AUTHOR]

The purpose of this article was to investigate the effects of the custody hearing of the National Council of Justice, in the operational service of the Military Police of Goiás, through a study carried out in the 30th Military Police Battalion. For this, field research was carried out by means of a questionnaire with the operational troop of the police unit, as well as data collection through a visit to the 7th criminal branch of Goiânia and Security Observatory of the Goiás Public Security Secretariat., while 62% of people arrested in flagrante delicto were released at these hearings, such as attempted murder, robbery, vehicle theft, trade theft, among others, have increased significantly. It was also possible to establish that custody hearings are considered by the police officers to be a factor that started to cause demotivation and fear of punishment in the performance of work, besides being considered as an obstacle to the reduction of criminal indices. The research is important because it demonstrates the need for the PM's administration to seek, more and more, the ability to manage the people who make up the corporation, as well as to discuss the topic of custody hearing, seeking adjustments to the reality of this institution in making public security. [ABSTRACT FROM AUTHOR]

The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data—GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data—GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement. [ABSTRACT FROM AUTHOR]

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires. [ABSTRACT FROM AUTHOR]