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EN
The study included the analysis of changes in sea surface and water column temperature and air temperature in the years 1959–2019 in the southern Baltic Sea based on in situ measurement (CTD probe), satellite data, and model data (ERA5). SST increased on average by 0.6°C per decade. Analyses at different depths showed that the highest temperature increase per decade at 0.60–0.65°C characterised the layers from 0 to 20 m. The smallest increase (0.11°C) was recorded at a depth of 70 m, below which the temperature change per decade increases again to 0.24°C. The results from satellite observations covering 1982–2019 were consistent with measurement data. The most intense water warming occured in the spring – summer (0.8–1°C per decade); in the winter, the change did not exceed 0.2°C. In the offshore area, in 1951–2020, air temperature increased by approx. 2°C, with an average increase of 0.37°C per decade. The average increase in seawater temperature in the coastal zone was 0.2°C per decade. The most intense warming characterised March to May (0.25–0.27°C). The average annual air temperature increase on the coast from 1951 to 2020 was 0.34°C per decade. The results represent an important contribution to research and prediction of changes in the marine environment caused by global climate change.
EN
Coastal upwelling along the SE Baltic Sea coast is a common feature, especially during the warm season. It significantly lowers sea surface temperature (SST) in the coastal areas, and, therefore, may be responsible for modifying meteorological conditions in those coastal areas, where upwelling is most frequently observed. This study aims to assess the effect of coastal upwelling on the air temperature at the south-eastern coast of the Baltic Sea based on long-term period observations (2002–2021) from coastal hydrometeorological stations and satellite data. Overall, our study revealed that due to its high frequency and spatial extent, upwelling is responsible for lowering the mean summer season SST of the SE Baltic Sea coast by about 1°C. And even though upwelling is a short-term event, upwelling-induced SST drop results in cooling air temperatures in the coastal areas, i.e., the mean air temperatures during upwelling are typically 2−4°C lower than before. It was also observed that upwelling is favouring the development of advective fog. Thus, sudden changes in meteorological parameters during upwelling can have versatile effects on various socio-economic activities. The results of this study contribute to the understanding of upwelling feedback onto the lower atmosphere and, therefore, are important for advancing the accuracy of weather forecasts that are needed for coastal communities, including marine and coastal industries.
EN
With the expansion of science and technology worldwide, various satellite products of meteorological parameters have been developed, which can compensate for the lack of observational data. However, to use these products effectively, their accuracy needs to be verified. Therefore, this study investigates the precision of NASA POWER satellite minimum and maximum air temperature data in 70 synoptic stations in Iran from 1987 to 2018. The study examines the data on three-time scales, with 0.5 degree spatial resolution. The findings indicate that the degree of exactness in the NASA POWER temperature data was greater during the monthly interval. The performance of the minimum and maximum air temperature data of the NASA POWER database has been relatively constant in different latitudes. However, changes in the performance of NASA POWER products in stations with different altitudes indicate that the performance of maximum air temperature is better in stations with an altitude of > 1900 m.a.s.l. On the other hand, altitude greatly affects the accuracy of the performance of the satellite model's maximum air temperature. On a daily and monthly time scale, altitude does not significantly impact the minimum temperature data, but on an annual scale, NASA POWER products perform better at low-altitude stations. Hence, the RMSE error values are lower in stations with an altitude of 400-700 m.a.s.l (RMSE = 2.8 °C) than in stations with an altitude of > 1900 m.a.s.l (RMSE = 6.9 °C). NASA-POWER products perform better in summer than in winter. The RMSE error values in the hot months of the year were between 2.64 and 2.81 °C all over Iran, while the minimum temperature in the cold months of the year was between 6.26 and 7.48 °C, which is significantly different. The research also showed that NASA POWER data are more accurate at maximum temperature than at minimum temperature, and these products have less accuracy at minimum temperatures in cold regions. Additionally, the results showed that the error rate in minimum and maximum data in dry and hot regions was less than in other regions, and this satellite product had a more acceptable performance in detecting the temperature of hot and desert regions. The investigation of diverse return periods of minimum and maximum air temperatures elucidates that in arid and parched areas, the maximum air temperature escalates as the return period amplifies; conversely, in hilly and moist regions, the minimum air temperature heightens with the upsurge of the return period. The consequences garnered from this exploration can be employed in meteorological and climatologic analyses and drought inquiries.
PL
Monitorowanie upraw w trakcie sezonu wegetacyjnego stanowi podstawę planowania zabiegów agrotechnicznych w rolnictwie precyzyjnym. Opiera się ono zazwyczaj na wykorzystaniu multispektralnych danych satelitarnych, których dostępność jest często ograniczona przez występowanie chmur. Powoduje to potrzebę sięgnięcia po inne rozwiązania, a jednym z nich jest wykorzystanie niezależnych od zachmurzenia satelitarnych danych radarowych. Celem prezentowanego badania było opracowanie map aplikacyjnych zmiennego nawożenia azotem rzepaku ozimego, poprzez modelowanie wskaźnika pokrycia liściowego (Leaf Area Index-LAI) z wykorzystaniem danych Sentinel-1 (S-1) i Sentinel-2 (S-2). Użyte dane teledetekcyjne i dane in-situ zebrano podczas dwóch sezonów wegetacyjnych z różnych regionów w Polsce. Współczynnik wstecznego rozpraszania obliczony na podstawie S-1 został zastosowany jako dane wejściowe do modelowania wskaźnika LAI z wykorzystaniem kilku technik regresji. Ze względu na charakterystykę zobrazowań radarowych, LAI było szacowane jako wartość średnia dla pojedynczego pola osiągając najlepsze wyniki dla algorytmu Random Forest (R2=0.85; RMSE=0.41). W celu zwiększenia precyzji wymaganej przy zabiegach agrotechnicznych wykorzystano zależność pomiędzy LAI wyznaczonym na podstawie ostatniego dostępnego bezchmurnego zdjęcia S-2 i LAI modelowanym przy użyciu S-1. Pozwoliło to na uzyskanie przestrzennego zróżnicowania w obrębie pola do poziomu piksela 10 m×10 m dla okresu z zachmurzeniem. Przygotowana w procesie syntezy danych S-1 i S-2 mapa LAI pozwoliła oszacować dotychczas pobraną przez rzepak ilość azotu. Na tej podstawie dostosowano dawkę nawozu do aktualnych potrzeb roślin oraz opracowano mapę aplikacyjną. Badanie wykazało potencjał i użyteczność syntezy danych S-1 i S-2 do opracowywania map aplikacyjnych zmiennego nawożenia, gdyż umożliwia ich tworzenie również w okresie niedostępności aktualnych danych optycznych. Proponowana metoda może stanowić uzupełnienie dla rozwiązań stosowanych obecnie w rolnictwie precyzyjnym.
EN
Regular crop monitoring during a vegetation season is necessary to make right decisions in precision agriculture. It is usually based on multispectral satellite data but their use is often limited by cloud cover. This problem can be reduced by applying data from synthetic aperture radar (SAR) satellite sensors that operate independently of cloudiness. The aim of this study was to develop maps of variable nitrogen fertilization for winter oilseed rape, by modelling Leaf Area Index (LAI) using Sentinel-1 (S-1) and Sentinel-2 (S-2) data. Satellite and in-situ data were collected for several fields during two growing seasons in various regions of Poland. Backscattering coefficients derived from S-1 were used as input to the LAI estimation process using different regression techniques. Due to the characteristics of radar imagery, LAI was estimated as an average value for a single field achieving the best results with a Random Forest algorithm (R2=0.85; RMSE=0.41). In order to increase the precision required for agrotechnical treatments, the relationship between LAI calculated using the latest available cloudless S-2 image and LAI derived from S-1 was established. That allowed for spatial differentiation of LAI values within a field at the level of 10×10 m pixel for the clouded period. LAI map prepared in the process of synthesis allowed to estimate the amount of nitrogen taken up so far by winter oilseed rape. Using this information, the dose of fertilizer was adjusted to the current needs of plants in the prepared application maps of variable fertilization. This study showed the potential and usefulness of the S-1 and S-2 data synthesis for developing maps of variable fertilization, as it enabled their creation also in the period of unavailability of optical data. The method can become a complement to the current solutions in precision agriculture.
6
Content available Nasze plejady
EN
This study concerns a Saharan wetland of southern Morocco, the Imlili Sebkha, located south of the Dakhla city. Considered among the rare permanent saharan sebkhas, it is recharged by episodic surface water supplies from an endorheic hydrographic network and by the unconfined aquifer, which emerges permanently through tens of shallow natural cavities. Using satellite data (DEM and rainfall), supplemented by field observations, an analysis of surface water supplies is carried out in this article. Due to the low slopes and the almost generalized silting of the catchment area, most of the rainwater is evaporated or recovered by the phreatic aquifer. Only a small proportion would arrive to the wetland, which would come from the surroundings of the sebkha. Nevertheless, these low inputs can flood a large part of the wetland, including the groundwater cavities, especially during the biggest autumn storms.
EN
This paper presents the evolution of the mesoscale convection system as seen on satellite images during all stages: pre-convection, initiation, and maturity. The evolution of any atmospheric phenomenon can be monitored effectively only when the data available have adequate temporal and spatial resolution. In case of convective storms the resolution should be minutes and kilometers. Therefore, data from the METEOSAT geostationary satellite, with 5-minute and 15-minute intervals were used operationally to monitor the storm of 11 August 2017; this was a most destructive storms, concentrated in several districts of the Pomeranian, Greater Poland, and Kuyavian-Pomeranian voivodeships. Analysis demonstrated that some alarming features, like cold rings or cold U/V shapes, can be visible on the single channel satellite images, without even referring to specific convective products. However, the nowcasting of the convective phenomena requires careful analysis of several dedicated products, including stability indices and water vapor content in the troposphere. It has been shown that with comprehensive analysis of the information provided by the different satellite images and satellite derived products, it is possible to draw conclusions about the severity of the observed storms as well as the probability of the occurrence of the extreme weather at the ground.
EN
The aim of the study was to diagnose the main trends of changes in land cover in selected communes of Polish metropolitan areas. Detailed studies were conducted in deliberately selected housing estates located in the core of metropolitan area (at least one housing estate) and communes located directly at the border of cities and located on the outskirts of metropolitan areas. The examined communes also differed in the quality of natural conditions of agricultural production. The study used LANDSAT 5 TM and RapidEye satellite images from three limited-time registrations (1996/1999, 2011, 2016/2017). On the basis of remote sensing data, changes in land use were specified by presenting them in a graphic form as compilation of numerical maps. The analyses were performed on processed images (colour compositions), which were subjected to supervised classification using the maximum-likelihood technique. The quality control of supervised classification showed accuracy of 89.3% for LANDSAT 5 TM scene analyses and 91.8% for RapidEye images. Kappa coefficient for the discussed classification was: 0.84 (LANDSAT TM) and 0.89 (Rapid Eye). The results obtained for individual metropolitan areas allow to identify the directions of changes (Land Use Change Cover) taking place in them, with consideration to specificity of each of them.
EN
This study reports the propagation dynamics of the Kara Sea surface desalinated layer (SDL) during the summer and autumn seasons. We analysed shipboard measurements data collected in 2013-2018 and MODIS ocean colour data that correlated with the shipboard ones. We formulated a comparatively strict criterion to determine the SDL border based on satellite data. For that, we analyzed the shipboard flow-through measuring system data obtained while crossing the surface desalinated layer border. Further, we used a regional algorithm to process the satellite data and estimate the coloured dissolved organic matter absorption coefficient for the Kara Sea. The results demonstrate a significant effect of the wind regime on the interseasonal and interannual variability of the transformation of the SDL boundaries. The positions of the surface desalinated layer boundaries at different times during 2013-2018 are given. The obtained results are important for calculating the heat balance and analyzing the Kara Sea bio-productivity.
EN
This study was designed to presents concise review of a novel subject regarding the use of large data sets (Big Data) which generates the functioning of the power system and their use to improve the operation and economic benefits of Smart Grids. Thanks to smart metering, we have current access to the data on the use of resources, which then using SCADA system and servers that support large data sets such as Apache Hadoop or Spark can be stored. Afterwards, these data are used for predictive calculations that are extremely important from an economic point of view. At the end of the paper, an interesting proposition of research is given by Author, namely to use, as ancillary information, the satellite data obtained from the Copernicus Programme provided by the European Space Agency ESA related for example with temperature to forecast energy consumption in electricity transmission and distribution networks.
PL
Praca ta zawiera zwięzły przegląd bardzo świeżej tematyki dotyczącej zagadnień wykorzystania dużych zbiorów danych (Big Data) jakie generuje funkcjonowanie systemu elektroenergetycznego i użycie ich do ulepszania działania i ekonomicznych korzyści w tychże systemach typu Smart Grids. Dzięki inteligentnemu opomiarowaniu mamy bieżący dostęp do danych dotyczących wykorzystania zasobów, które następnie za pomocą systemu SCADA oraz serwerów obsługujących duże zbiory danych jak np. Apache Hadoop czy Spark mogą zostać składowane i następnie wykorzystane do obliczeń predykcyjnych niezmiernie istotnych chociażby z ekonomicznego punktu widzenia. Ponadto ciekawą propozycją Autora jest wykorzystanie jako informacji pomocniczych danych satelitarnych z Programu Copernicus udostępnianych przez Europejską Agencję Kosmiczną ESA związanych przykładowo z temperaturą do prognoz zużycia energii w sieci energetycznej.
EN
The objective of the study was to compare the sum of actual sunshine duration in Poland, based on satellite and ground-based measurements during the period of 1983-2015. Results from the first group of data were derived from sunshine duration measurements from 44 surface synoptic stations belonging to the Polish Institute of Meteorology and Water Management (IMGW-PIB). The second group of data includes values from observations of Meteosat geostationary satellites (SARAH-2 climate data record), provided by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). The study showed that in Poland, values of linear correlation coefficients (r) between both datasets are high, and range between 0.80 and 0.95. Differences in daily sums of sunshine duration are low, with the prevalence of positive deviations, i.e. slightly higher values for satellite data. The largest positive deviations were found in Tarnów, Zielona Góra, and Racibórz (+0.3 h), with equivalent negative deviations in Warsaw and on Kasprowy Wierch (−0.4 h). Moreover, minor discrepancies were found for the long-term variability of the mean annual sums of actual sunshine duration. However, after 1995, the deviations were insignificant, and averaged 4 hours. Differences between both data series are caused by several factors, including an underestimation of aerosols optical depth (AOD), as well as the failure to consider the type of clouds covering the Sun’s disc. With its high spatial resolution (0.05° × 0.05°), the satellite data can be a valuable source of information, particularly in regional studies of the spatial variation of sunshine duration.
EN
The studies on agricultural droughts require long-term atmospheric, hydrological and meteorological data. On the other hand, today, the possibilities of using spectral data in environmental studies are indicated. The development of remote sensing techniques, increasing the spectral and spatial resolution of data allows using remote sensing data in the study of water content in the environment. The paper presents the results of the analysis of moisture content of soil-plant environment in the lowland areas of river valley using the spectral data from Sentinel-2. The analyses were conducted between February and November 2016. The spectral data were used to calculate the Normalize Differential Vegetation Index (NDVI) which provided the information about the moisture content of the soil-plant environment. The analyses were performed only on grasslands, on 22 objects located in the research area in the Oder river valley between Malczyce and Brzeg Dolny, Poland. The NDVI values were correlated with the hydrological and meteorological parameters. The analyses showed spatial and temporal variability of the moisture conditions in the soil-plant environment showed by the NDVI variability and existence some relationships between the climatic and spectral indices characterizing the moisture content in the environment.
EN
The techniques of converting stereo-pair aerial photographs or satellite images are used to prepare the digital surface models (DSM), digital elevation models (DEM) or to obtain the height of the objects. Recently, the Copernicus Land Monitoring service released a product presenting the building heights for the major – capital cities in Europe. The Building Height 2012 layer was derived based on the stereo images acquired by the IRS-5 satellite close to the defined reference year 2012. The main aim of the study was to examine the accuracy of the Copernicus Building Height 2012 layer in comparison with the building height derived from airborne laser scanning system. The study was carried out over the city of Warsaw (the capital of Poland). In general, data from both datasets are compatible, however the overestimation of the height was observed. The comparison carried out in two ways produced similar results. On average, the overestimation of the satellite-based building height for the study area reached 1.08 m.
PL
Techniki przetworzenia stereopar zdjęć lotniczych lub obrazów satelitarnych wykorzystywane są do tworzenia numerycznych modeli terenu, numerycznych modeli pokrycia terenu czy generowania wysokości budynków. W 2018 r., w ramach europejskiego programu monitorowania powierzchni Ziemi – Copernicus Land Monitoring została udostępniona warstwa przedstawiająca wysokości budynków obejmująca zasięgiem wszystkie Europejskie stolice. Warstwa wysokości budynków została opracowana na podstawie analizy stereopar obrazów satelitarnych z satelity IRS-5, zarejestrowanych około roku 2012. Głównym celem prowadzonych analiz było wykonanie oceny jakościowej warstwy wysokości budynków Building Height 2012 w odniesieniu do krajowych danych referencyjnych, którymi są dane z lotniczego skaningu laserowego uzyskane w ramach projektu ISOK. Analizami objęto obszar miasta Warszawy. Wyniki analizy pokazują, że jest całkiem duża zgodność pomiędzy dwoma zbiorami danych, jednakże zaobserwowano także przeszacowanie wartości wysokości budynków. Obie metody porównania wykorzystane w tej pracy przyniosły podobne wyniki. Średnia wartość przeszacowania w wysokościach uzyskanych z danych satelitarnych wynosi 1.08 m.
18
Content available Processing of satellite data in the cloud
EN
The dynamic development of digital technologies, especially those dedicated to devices generating large data streams, such as all kinds of measurement equipment (temperature and humidity sensors, cameras, radio-telescopes and satellites – Internet of Things) enables more in-depth analysis of the surrounding reality, including better understanding of various natural phenomenon, starting from atomic level reactions, through macroscopic processes (e.g. meteorology) to observation of the Earth and the outer space. On the other hand such a large quantitative improvement requires a great number of processing and storage resources, resulting in the recent rapid development of Big Data technologies. Since 2015, the European Space Agency (ESA) has been providing a great amount of data gathered by exploratory equipment: a collection of Sentinel satellites – which perform Earth observation using various measurement techniques. For example Sentinel-2 provides a stream of digital photos, including images of the Baltic Sea and the whole territory of Poland. This data is used in an experimental installation of a Big Data processing system based on the open source software at the Academic Computer Center in Gdansk. The center has one of the most powerful supercomputers in Poland – the Tryton computing cluster, consisting of 1600 nodes interconnected by a fast Infiniband network (56 Gbps) and over 6 PB of storage. Some of these nodes are used as a computational cloud supervised by an OpenStack platform, where the Sentinel-2 data is processed. A subsystem of the automatic, perpetual data download to object storage (based on Swift) is deployed, the required software libraries for the image processing are configured and the Apache Spark cluster has been set up. The above system enables gathering and analysis of the recorded satellite images and the associated metadata, benefiting from the parallel computation mechanisms. This paper describes the above solution including its technical aspects.
EN
This study aimed to estimate above-ground carbon sequestration of orchards using satellite data. The research methodology analyzed the relationship between the amount of above-ground carbon sequestration and vegetation indices from the data obtained from LANDSAT 8 OLI including (1) Difference Vegetation Index (DVI), (2) Green Vegetation Index (GVI), (3) Simple Ratio (SR), (4) Normalized Difference Vegetation Index (NDVI), and (5) Transformed Normalized Difference Vegetation Index (TNDVI) in order to find out the most appropriate equation to estimate above-ground carbon sequestration of the orchards in the study area at Sang Kho sub district, Phu Phan district, Sakon Nakhon province in northeast Thailand. The study results found that the relationship between the amount of above-ground carbon sequestration and the most appropriate index relating to vegetation was TNDVI. At any rate, TNDVI had the relationship equation y = 0.226e0.039x and coefficient of determination R2 = 0.877, which represented the amount of above-ground carbon sequestration in the study area in a total of 40.86 tons per hectare.
EN
Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G). In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data.
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