Monitoring oil spill in Norilsk, Russia using satellite data

This paper studies the oil spill, which occurred in the Norilsk and Taimyr region of Russia due to the collapse of the fuel tank at the power station on May 29, 2020. We monitored the snow, ice, water, vegetation and wetland of the region using data from the Multi-Spectral Instruments (MSI) of Sentinel-2 satellite. We analyzed the spectral band absorptions of Sentinel-2 data acquired before, during and after the incident, developed true and false-color composites (FCC), decorrelated spectral bands and used the indices, i.e. Snow Water Index (SWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The results of decorrelated spectral bands 3, 8, and 11 of Sentinel-2 well confirmed the results of SWI, NDWI, NDVI, and FCC images showing the intensive snow and ice melt between May 21 and 31, 2020. We used Sentinel-2 results, field photographs, analysis of the 1980–2020 daily air temperature and precipitation data, permafrost observations and modeling to explore the hypothesis that either the long-term dynamics of the frozen ground, changing climate and environmental factors, or abnormal weather conditions may have caused or contributed to the collapse of the oil tank.

. Study area around Norilsk in Krasnoyarskiy Krai, the Federal district of Russia (the border is delineated in red), and distribution of permafrost zones: c-continuous (occupies more than 90% of land), ddiscontinuous (50% -90%), s-sporadic (10% -50%), p-isolated patches (less than 10%) [ Fig. 1) is centered at the HPP-3. The area falls into the forest-tundra vegetation zone and is characterized by the severe Arctic climate with very cold winter and short summer. Graphs in Fig. 2 illustrate air temperature and precipitation records at Norilsk weather station during 1980-2020; summary statistics of the climate data is given in Table 1. These data demonstrate an accelerating warming in all seasons in the recent decades, most of which is attributed to the rising daily minimum temperatures. Statistically significant at 95% level, the minimum temperature trends range from 0.6 °C/10a in the summer to 1.05 °C/10a in winter, while the daily maximum temperature and precipitation trends are small and statistically insignificant.

Study area. The study area (insert in
The region is located in the continuous permafrost zone, where frozen ground occupies more than 90% of the area. Below the depth of seasonal thawing, typically less than 1 m, ground has negative temperature all year round, except under the large water bodies, such as lakes and rivers, which do not freeze up to the bottom. Such unfrozen vertical "tunnels" are called taliks, they play important role in providing pathways for the transport of water, other liquid substances and gases between the surface and deeper soil layers 58 . The study area has many small (less than 1.0 km 2 ) and shallow (2-5 m) thermokarst lakes and ponds in local ground depressions with taliks underneath them. The tributary of the Ambarnaya River, the Daldykan River, flows northward into the Pyasino Lake. The riverbed consists of glacial tills, fluvial gravels and pebbles 59 . The Quaternary deposits are cemented by ice and clayey soil and peat. The presence of sandy, sandy loam, and gravel deposits are rare. The clayey soil constitutes 67% of the total area in the Norilsk region. The ice content of the frozen clayey soil varies in the range 15-45%, and the bearing capacity of the seasonally thawed soil is small, which complicates construction of the engineered structures.
Most structures in permafrost terrain are based on pile foundations. Metal piles of different lengths, typically between 6 and 12 m, are embedded into permafrost and anchored in soil by cementing ground ice. Adfreeze   8 . The data are acquired with a 20.6° field of view for providing an approximately 290 km swath, and the equatorial repeat cycle of each Sentinel-2 sensor is about ten days, and five days when combined 69 . MSI has high spatial (10 m, 20 m, and 60 m) and 12-bit radiometric resolutions. The sensor senses energy that comes from the earth surface in the spectral bands 1 to 9 (including band 8a) in the visible near-infrared (VNIR) wavelength (0.44 to 0.95 µm) and spectral bands 10 to 12 in the shortwave infrared (SWIR) wavelength (1.57 to 2.28 µm). Among the bands, the three bands 1 (443 nm), 9 (945 nm), and 10 (1375 nm) have 60 m spatial resolution and dedicated for aerosol correction, water vapor, and cirrus clouds detection, respectively. The four bands 2 (Blue, 490 nm), 3 (Green, 560 nm), 4 (Red, 665 nm), and 8 (Near Infra-Red, 842 nm) have 10 m spatial resolution and meet the basic requirements for land classification. Another four bands 5 (705 nm), 6 (740 nm), 7 (783 nm), and 8a (865 nm) have 20 m spatial resolution and are dedicated to vegetation detection. The bands 11 (1610 nm) and 12 (2190 nm) in the SWIR are useful for snow/ice/cloud detection ( Fig. 3; Table 2) 70,71 . Sentinel-2 data are freely downloadable from the Sentinel Hub developed by the European Space Agency (ESA) (https :// senti nel.esa.int/web/senti nel/senti nel-data-acces s). ESA provides satellite information analysis software 'Sentinel Application Platform (SNAP)' which reads all the information of the Sentinel series and provides and exports data to other relative analyses comparable with Quantum GIS (QGIS 3.14, https ://qgis.org/) and Environment for Visualizing Images (ENVI 5.5, Harris Geospatial Solutions, Broomfield, CO, USA; https:// www.harri sgeos patia l.com) software's. Sentinel-2 supports the continuity of the SPOT and Landsat missions and provides operational products for land-cover maps, land-cover change detection maps, and geochemical/physical variables 69 . Especially, the four bands at 10 m resolution ensure the compatibility with SPOT 4 and 5 and meet the users' requirements for land cover classification. Numerous studies demonstrated implications of Sentinel-2 data in agriculture 72,73 , vegetation 74 , geology 71,75 , water depth retrieval 76 , land use and land cover changes 77,78 . Our study utilizes the available Sentinel-2 data at ESA portal for the cloud-free conditions in the study area acquired before, during, and after the oil spill from May 18 to July 10, 2020 ( Table 3). The data were pre-processed and georeferenced to the WGS-84 ellipsoid and Universal Transverse Mercator (UTM) Zone 45 N projection and layers were staked using the QGIS 3.14 (https ://qgis.org/). Subsequently, the data were subset to the area of interest and processed for mapping the oil spill occurrence using ENVI 5.5 (https:// www.harri sgeos patia l.com) software.
Image processing. Visual imaging has been improved by the use of advanced image processing systems 79 .
Green and red light bands of remotely sensed data were used as optimal bands for detecting the oil spills of sea surface 3,25,49,70,80 . Different algorithms were used to correct aberrations, color, and light levels and to process images 39,40 . In this study, the occurrence of the oil spill is studied by developing a true color composite and decorrelated image, and the occurrence of snow and ice, water, vegetation, and wetlands are studied and monitored by developing false-color composites and band ratios indices images 3,49,54,81 . The visual interpretation of images has been considered as one of the most effective methods to study the occurrence of the oil spill and associated features. www.nature.com/scientificreports/ In this study true-color images are developed to detect the oil spill using the Sentinel-2 spectral bands red (B04), green (B03), and blue (B02) in the corresponding red, green and blue channels. In brief, true-color image is a good representation of the visual features as our eyes see the world, i.e. we see healthy vegetation in green.
Developing false-color composites (FCCs) using spectral bands of VNIR and SWIR regions have the potential to show image features from a combination of three spectral bands. The method develops images using three primary colors namely red, green, and blue (RGB) at a time as a color composite. The resulted image exhibits each pixel in a color determined by the combination of the RGB in different brightness and depicts features in different colors that differ from those of a true-color image. A standard FCC (examples; 8, 4, 3 of Sentinel Bands; 5, 4, 3 bands of OLI of Landsat 8; 3, 2, 1 of ASTER) shows denser plant-covered land in deep red, water in blue or black, and cities in gray. The images are commonly used to assess vegetation and plant density and health since they reflect near-infrared and green light while absorbing red. The band combinations of Sentinel-2 bands for different applications are found in the Sentinel Application Platform (SNAP) program.
Construction of band ratios and indices enhance spectral differences between bands and reduce the effects of topography. Several indices have been constructed and available at the Index database (IDB) specifically for Sentinel-2 satellite (https ://custo m-scrip ts.senti nel-hub.com/custo m-scrip ts/senti nel-2/index db/). In this study,  www.nature.com/scientificreports/ the indices namely, the Snow Water Index (SWI) 82 , Normalized Difference Water Index (NDWI) 83 , and Normalized Difference Vegetation Index (NDVI) [84][85][86] , were used for detecting the snow, water, vegetation and wetland and for evaluating their changes between May 18 to July 10, 2020. Snow Water Index (SWI): Snow is highly reflective in the visible parts of the electromagnetic spectrum and highly absorptive in the NIR or the short-wave infrared part of the spectrum. This study uses the new Snow Water Index (SWI) proposed by Dixit et al. (2019) 82 to study the occurrence and distribution of snow and ice in the study area. The index uses the combination of green, NIR, and SWIR bands and analyzes the spectral characteristics of snow and eliminates the impacts of cloud, soil, and vegetation. In the SWI, the water feature shows higher reflectance in the green wavelength and absorbs the maximum radiation in the NIR. The absorptive property of water in the NIR makes it possible to easily discriminate snow from water 82,87,88 . In this study we used the SWI to create a significant contrast between snow and other features, especially in the case of water 82 . Moreover, the SWI uses the snow and vegetation reflectance characters in the green and NIR wavelength to map the snow under vegetation. The snow water index (SWI) is expressed by Here, the ratio of (green/green + NIR) is used for reducing the impact of vegetation on snow and the ratio (NIR − SWIR/NIR + SWIR) works as a water mask.
Normalized Difference Water Index (NDWI): To understand the occurrence and distribution of water in the study area, this study uses the NDWI. The NDWI uses the green (band 3) and NIR (band 8) bands. The NDWI index is expressed by Normalized Difference Vegetation Index (NDVI): Occurrence and distribution of vegetation in the study area are studied using NDVI 89 . It normalizes the ratio between red (R, band4) and near-infrared (NIR, band 8) bands 84,90 . The NDVI is used to monitor vegetation health, hydrologic stress, and the amount of biomass. Strong and well-nourished vegetation will absorb most of the received visible wavelengths and will reflect a large proportion of the NIR light, whereas poor or thin vegetation will reflect more visible wavelength light and less near-infrared light.
The occurrence and distribution of wetlands are studied using the NDVI and NDWI, and by developing FCC images using spectral bands 3, 4, and 8 of Sentinel-2 85,86 .
In this study, we also used the decorrelation stretching method 91,92 to study the land and water resources and assess the impacts of the oil spill. The method is used to remove the high correlation commonly found in multispectral data sets. It produces a more colorful composite image by transforming the highly correlated data sets usually from a three bands input. The transformed channels can themselves be contrast stretched and arbitrarily assigned primary colors to display a color composite image [91][92][93] . The method has the potential to distinguish the spectral reflectance of different features of the earth surface and was utilized to map rock types using enhanced Landsat Thematic Mapper data 92,94 and ASTER data 95 . In this study, the Sentinel-2 spectral bands 3, 8, and 11 were decorrelated to optimize the display of snow and ice, water, vegetation, and wetland resources.
The outcome of the Sentinel-2 results could be validated with post-event field photographs available in the 'News and Press-Release' of the Norilsk Nickel (Nornickel) Company (https ://www.norni ckel.com/) 41 .

Results and Discussion
The spectra of the features collected from the images are given in Fig. 4. The spectra of snow and ice pixels (Fig. 4a, black and colored, respectively) were collected over their exposures found on the land and in lakes/ ponds, the Ambarnaya River, and the Pysaina Lake using the Sentinel-2 data acquired on May 18, 21 and 23, 2020. The plot shows that the spectra of snow are more reflective than ice in the visible bands and highly absorptive in the NIR and SWIR bands (Fig. 4a). The snow has spectral bands absorptions in the bands 3 (560 nm, red vertical line), 8a (865 nm, red dash-dot vertical line), and 11 (1610 nm, red dash vertical line) whereas, the ice exhibits absorptions in the bands 8a and 11. The bands could be best used to map the snow and ice of the study area. The spectra of water collected over the lakes/ponds, the Ambarnaya River, and the Pysaino Lake using the Sentinel-2 spectral bands acquired from May 31 to July 10, 2020, are given in Fig. 4b. The plot shows that water has strong absorption in the green and near-infrared (NIR) bands and low reflection in the range from visible to infrared wavelengths when compared to the spectra of snow and ice (compare reflectance values in Fig. 4a, b). The water showed absorptions in the bands 3 (red vertical line), 6 (740 nm, blue vertical line), 8a (red dash-dot vertical line), and 11 (red dash vertical line). The spectra collected over the oil spill and water of the Ambarnaya River using the Sentinel-2 spectral bands acquired during May 31, 2020, and June 01, 2020, are given in Fig. 4c. The spectra of oil spill showed strong absorptions in the green, NIR and SWIR bands and low reflection in the range from visible to infrared wavelengths when compared to the spectra of snow and ice and water (Fig. 4a, b). The spectra of the oil spill of the river showed strong absorptions in the spectral bands 3 (red vertical line), 4 (665 nm, blue vertical line), 6 (blue dash vertical line), 8a (red dash-dot vertical line) and 11 (red dash vertical line). The spectra of the river water collected during the dates showed poor absorption in the spectral band 3 (red vertical line) and strong absorptions in the bands 6 (blue dash vertical line), 8a (red dash-dot vertical line) and 11 (red dash vertical line). The water with oil spill showed strong absorptions in the bands 3 and 4 when compared to the river water, which showed more reflection and poor absorption in the bands 3 and 4. The oil spill exhibited more reflective in the NIR and SWIR bands when compared to the river water 49,54 . The study of spectra well distinguished the spectral band absorptions characteristics of the oil spill and water of the Ambarnaya River and the bands can be used to discriminate the oil spill.
The spectra collected over vegetation of the study area using the Sentinel-2 spectral bands acquired from June 08 to July 10, 2020 are given in Fig. 4d. The plot shows that the spectra of vegetation have strong absorptions in red and NIR bands. The vegetation showed absorptions in the bands 4 (red vertical line) and 8a (red dash-dot vertical line). The spectra collected over the wetland of the study area are given in Fig. 4e. The plot shows that the spectra of wetlands have strong absorptions in green, red, and NIR bands. The wetland showed absorptions in the bands 3 (red vertical line), 4 (blue vertical line), 5 (704 nm, blue dash vertical line), and 8a (red dash vertical www.nature.com/scientificreports/ line). Thus, the spectral bands 3, 4, 5, 6, 8a, and 11 of Sentinel-2 can be well utilized to discriminate the oil spill and the associated major features of the study area.
Sentinel-2 mapping of oil spill. o confirm the incident of the Norilsk diesel oil spill, show the spatial distribution of the spill and assess the impacts of the spillover the land, true color composites (R4; G:3; B:2) were developed and studied using Sentinel-2 data acquired before, during and after the incident (May 18 to July 10, 2020). Here, the bands 3 and 4 were chosen since the upper red to near-infrared spectral (between 660 and 760 nm) are the best for oil spill identification. The true color images of May 31 and June 01, 2020, acquired during the oil spill and the images of May 23 and June 08, 2020, acquired before and after the oil spill is given in Fig. 5. The true color images of May 31 and June 01, 2020, the spectral bands red (B4), green (B3) and blue (B2) projected in the corresponding red, green and blue channels, show the oil spill in crimson red with fine texture www.nature.com/scientificreports/ over the channel of Ambarnaya River 28 . The movement of oil is interpreted from their position over the images between May 31 and June 01, 2020 55 . The movement of oil from the Norilsk-Taimyr Energy Thermal and Power Plant is not studied since the images acquired before May 31, 2020, were affected by clouds over the area. The image acquired on May 23, 2020 shows the absence of the oil spill in the river channel. The image acquired on June 08, 2020 shows the presence of post-oil spill in the creeks and water bodies found parallel to the river channel. The causes of the spill are interpreted on both sides of the bank of the river channel in the shades of dark reddish-brown.
Sentinel-2 mapping of snow and ice. The occurrence and distribution of snow and ice of the study area were studied using Snow Water Index (SWI). The results of SWI obtained from May 18 to July 10, 2020, are given in Fig. 6. The images show the presence of snow and ice in bright pixels and appear as white warped patches and dots with fine texture in parts of the land and near the Pyasino Lake in the images acquired on May 18 and 21, 2020 and very few or none in the images acquired on the subsequent dates. For example, the occurrence of snow and ice in the image of May 21, 2020 (see red ellipticals) can be compared and interpreted with the images of subsequent dates. The water features of the land exhibit as white spots on the land, and the Pyasino Lake appears as massive large white patches in all the images. The image on May 18, 2020 shows the area of Pyasino Lake more exposed (see magenta Ellipticals) and the gradual increase of water in the area in the subsequent dates due to the melting of snow and ice and the movement of water from the upstream. This is consistent with the daily air temperature data (see Fig. 10b in below section), which demonstrates that May in 2020 was markedly warmer than the norm, with up to 15 °C temperatures at the daytime, while normally it is below 5 °C. Unusual atmospheric warmth and large quantities of incoming solar radiation due to long daytime at high latitudes favored rapid snow and ice melt, as seen at the images. Water streams may have drained over the surface surrounding the foundation of the oil tank, leading to local ground temperature rise and erosion. Taken together, these factors may have caused the failure of the construction. We used the Normalized Difference Water Index (NDWI) to understand the occurrence and distribution of water against the background of rapid snow and ice melt. NDWI over the period from May 18 to July 10, 2020 is illustrated in Fig. 7, water appears on these images as bright pixels. Images taken after May 31, look bright due to the melting of the snow and ice in the entire watershed. Interestingly enough, the Pyasino Lake and Ambarnaya River on the images of 18 and 21 May 2020, exhibit less water and appear as light grey suggesting that they were covered by snow or ice when compared with that of the images of subsequent dates (see magenta and green ellipticals). The results of NDWI support the detection of water using SWI.

Sentinel-2 mapping of vegetation and wetland.
Occurrence and distribution of vegetation in the study area were studied using the Normalized Difference Vegetation Index (NDVI). The results of NDVI obtained from May 18 to July 10, 2020, are given in Fig. 8. The images show the presence of vegetation in white pixels (high values) with a smooth texture and water in black pixels (low or negative values) with smooth texture from May 21 to July 10, 2020. The gradual increases of vegetation (white pixels) around the Daldykan River and along the Ambarnaya River are observed which may be due to pronounced warmth in the area before and during those dates (Fig. 2). The image of July 10, 2020 showed well established vegetation around the Pyasino Lake and water bodies, unlike the image of May 18, where these features are not pronounced. The image exhibits land in grey; changes in the tone are observed over the images that were acquired thereafter. The images of NDWI do not show the changes that interpreted over the images of NDVI. The changes represent that the land was wetter during May 18 and 21, 2020, and the wetness got decreased with the increase of vegetation thereafter due to the increase of temperature. The fumes of Thermal and Power Plant appear in a white and vertical linear pattern and can be distinguished in this image. The growth and increase of vegetation and changes in the wetness of land of the study area are further confirmed by developing FCC images using the spectral bands 8A, 4, and 3 (Fig. 9).
The FCC images (R:8; G:4; B:3) of May 18 to July 10, 2020 depict almost all the information studied from SWI, NDWI, and NDVI images. The images (Fig. 9) show the occurrence of snow as white warped patches and dots in parts of the land and near the Pyasino Lake (compare red elliptical in Fig. 6) in the images acquired on May 18 and 21, 2020. The snows have not appeared on the images acquired between May 31 and July 10, 2020. The water of the land appeared in light blue till May 21, 2020, and exhibited dark blue thereafter. The detailed interpretations show that the water features of the land have occurred as ice with water initially (until 31 May 2020, see SWI image) and later the features appeared with water due to the fact of ice melting. Additionally, the water of Pyasino Lake exhibited dark blue, blue, and light blue from May 18 to July 10, 2020 representing changes in the state of water due to an increase in temperature. The gradual increase of water in Pyasino Lake is well seen between May 31 and July 10, 2020. The increase of water in the lake is due to the melting of ice and the movement of water from the upstream of the watershed. The images show vegetation in red to dark pink with fine to medium texture on the images acquired from May 31 to July 10, 2020, and no or very poor vegetation is observed on the image of June 18 and 21, 2020. The increase of vegetation is interpreted along the Daldykan and Ambarnaya rivers and around the Pyasino Lake thereafter as interpreted on the NDVI images (Fig. 8). The increase of vegetation may be due to an increase in the temperature and water and changes in the climate of the region. The land features of the area in the images of May 18 and 21, 2020 appear in dark brown to brown, representing that the land is wetter due to presence of a high amount of water content in soil compared to the images of subsequent dates, which exhibit the land in brown, light brown and very light brown signifying that the land got decreased in wetness due to increase of temperature and change in the climate. The FCC images show the change in the wetness of land in the study area. www.nature.com/scientificreports/ www.nature.com/scientificreports/

Did permafrost thaw cause the Norilsk Oil Spill?
Official statement of Norilsk Nickel named the climate change and thawing permafrost as the main factors that caused sinking of supporting piles in the foundation of the storage tank 41,57 . We examined this hypothesis using the dynamical permafrost model 96 . We validated the model by comparing the calculated depth of seasonal thawing with the observations in Talnakh at R32 site ( Fig. 1) of the Circumplar Active Layer Monitoring (CALM) project in the period 2005-2019 (https ://www2.gwu.edu/~calm/data/north .htm). Figure 10a illustrates the high performance of the model, which replicated the observed interannual variations of the seasonal thaw depth with high accuracy. We calculated changes in the depth of seasonal thawing over the 1980-2019 period. Although we did not find positive trends, results demonstrate significant interannual variations of the seasonal thaw depth in Norilsk. Such variations in the past two decades (Fig. 10a) may have caused uneven ground settlement around the structure, which accumulated over time and favoured water ponding and thermokarst. Combination of these factors, markedly warmer than normal weather in May 2020 (Fig. 10b), and intensive meltwater streams eroding the surface may have weakened the pile foundation underneath the structure and ultimately led to the collapse of the oil tank.
Our conceptual model based on the analysis of the meteorological data and permafrost modelling is consistent with the results based on the Sentinel-2 data. The SWI images evidenced the melting of snow and ice between May 21 and 31, 2020, which is further confirmed by the NDWI images demonstrating the occurrence of more water in the area from May 31, 2020 due to ice and snow melt. These findings are consistent with the NDVI and FCC images, which showed wetland with no or very poor vegetation till May 21, 2020, and an increase of vegetation thereafter. However, all the images could not well discriminate the snow, ice, water, vegetation, and wetland clearly, and therefore a decorrelated image was developed using the spectral bands 3, 8 and 11 of Sentinel-2 acquired between May 18 to July 10, 2020, since these bands have characteristics absorptions of oil, snow, ice, water, vegetation, and wetland (see Sect. "Conclusions"). The resulted images are given in Fig. 11.
The images of May 18 and 21, 2020, show the occurrence of snow in light green to yellow as warped patches with fine texture. The existence of ice in water bodies appears as pink dots in parts of the land (circled in Fig. 11). The Pyasino Lake exhibits dark green to light green with fine texture, possibly due to the presence of snow, ice and wet soil in the lake. The land appears in dark purple and has no or few vegetation. Both images show the fumes of thermal and power plants in shades of white and linear features. The images between May 31 and July 10, 2020 show the disappearance of the snow. The Pyasino Lake appeared in dark green to light green turned to green-red to red with fine texture which may be due to the expansion of condensed cold water to water in the lake (see FCC images in Fig. 9). The significant increase in the amount of water was observed from May 31, 2020. The Sentinel-2 images best showed the changes in the snow to water from May 18 to 31, 2020 (for example yellow circled) as well as the increase in water quantity in the Pyasino Lake. The image discriminated vegetation of the area in light green and the increase in the growth of vegetation is interpreted along the Daldykan and Ambarnaya rivers and around the Pyasino Lake from May 31, 2020. The wetland that appeared in dark purple between May 18 and 21, 2020 turned to light purple, possibly due to an increase in temperature in the region. All features of the area are well discriminated in different tones over the images and the MSI sensor of Sentinel-2 has proved its capability in discriminating the features and monitor their changes during the dates.

Validation of Sentinel-2 mapping
In this study, the true-color images of Sentinel-2 dated May 31 and June 01, 2020, showed the occurrence of the oil spill in crimson red as a large patch over the Ambarnaya River (Figs. 5 and 12a) 41 . Monitoring of oil spill using the Sentinel-2 data available on June 06, 2020 acquired after six days from the date of the incident did not show a further spread of oil spill in the Ambarnaya River. It may be due to the activities of cleanup teams to stop the spread of fuel, further into the Ambarnaya River and prevent the oil spill movement towards the Pyasino Lake and Piyasino River which originates from the lake (Fig. 12b-d) 41,57-59 . The rates of the oil spill advance are not  www.nature.com/scientificreports/ addressed in this study because of the restricted temporal resolution of the satellite data. Norilsk Nickel took great measures, and engaged clean-up teams, which placed floating dam and boom structures (Figs. 12b-d), removed tons of contaminated soil ( Fig. 12e-g) and skimmed off oil and the toxic mix of water from the river using special containers (Fig. 12h, i) 41 . The company stated that they collected a total of 34,451 cubic meters of water and fuel mixture, and cleaned the land of 422,816 square meters (as of 28 September, 2020) 41,57 . It is believed that the rest of the spilled oil, if any, could have been removed in the subsequent dates, although it is difficult to remove them fully from the water and soil in the affected area. The post-oil spill activities improved the ecology condition of the area (Fig. 12j-l) 41 . The study of the stretch of Ambarnaya River using true-color composite images acquired during and after the oil spill showed the activities of the clean-up teams (Fig. 12). The MSI of Sentinel-2 has the potential to show the area before, during, and after the oil spill (Figs. 5, 6, 7, 8 and 9), and capable of monitoring the post-oil spill activities (Fig. 13).

Conclusions
This study analyses the Sentinel-2 satellite imagery data acquired before, during, and after the oil spill from the fuel tank in Norilsk on May 29, 2020, and explores the hypothesis that changing climatic, permafrost and weather conditions may have caused the collapse of the structure. In summary, the Sentinel-2 data show the following: 1) disappearance of snow and ice in ten days between May 21 and 31, 2020 due to unusual warmth in May 31, 2020; 2) the change of water state in the Pyasino Lake between May 21 and 31, 2020 and the subsequent increase of the water level in the lake after May 31, 2020; 3) development of vegetation between May 21 and 31, 2020 and increase in vegetation greenness after May 31, 2020, and 4) the change of wetland to vegetated land between May 18 and July 10, 2020. All these changes are consistent with the conceptual model suggesting that abnormally high temperature in May 2020 may have contributed to the collapse of the fuel tank and oil spill in Norilsk. This study demonstrated one of the applications of Sentinel-2 data.