Quantifying the spatial and temporal influence of infrastructure on seasonal snow melt timing and its influence on vegetation productivity and early season surface water cover in the Prudhoe Bay Oilfields

2021 ◽  
Author(s):  
Helena Bergstedt ◽  
Benjamin Jones ◽  
Donald Walker ◽  
Jana Pierce ◽  
Annett Bartsch ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Natural Environment ◽  
Traffic Load ◽  
The Arctic ◽  
Snow Melt ◽  
Vegetation Productivity ◽  
Near Surface ◽  
Seasonal Snow ◽  
Prudhoe Bay ◽  
Sentinel 2

<p>Increased industrial development in the Arctic has led to a rapid expansion of infrastructure in the region. Past research shows that infrastructure in the form of roads, pipelines and various building types impacts the surrounding landscape directly and indirectly by changing vegetation patterns, locally increasing ground temperatures, changing the local hydrology, introducing road dust into the natural environment, and affecting the distribution and timing of seasonal snow cover. Localized impacts of infrastructure on snow distribution and snow melt timing and duration feedbacks into the coupled Arctic system causing a series of cascading effects that remain poorly understood.  In this study, we quantify spatial and temporal patterns of snow-off dates in the Prudhoe Bay Oilfields (PBO), North Slope, Alaska using multispectral remote sensing data from the Sentinel-2 constellation. The Sentinel-2 satellite constellation provides good spatial and temporal coverage of Arctic regions with adequate spatial resolution to quantify and monitor infrastructure impacts on the natural environment in polar regions. We derive the Normalized Difference Snow Index (NDSI) to quantify the presences and absences of snow on a pixel-by-pixel basis between 2015 and 2020. Additional indices, like the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) were derived to understand linkages between patterns in vegetation and surface hydrology, respectively, to patterns in snow-off dates that are influenced by the presence and type of infrastructure on a regional basis at PBO. Newly available infrastructure data sets derived from Sentinel-1 and 2 data were employed to quantify differences in snow melt patterns in relation to distance to roads and other types of infrastructure. Near-surface ground temperature measurements from multiple transects oriented in a perpendicular direction from the road up to 100 m provided ground-truth observations for snow-off timing derived from the remote sensing analysis. Our results from the regional remote sensing analysis show a relationship between snow-off date and distance to different types of infrastructure that vary by their use and traffic load during the snowmelt period as well as their orientation relative to the prevailing wind direction. Results from field data observations indicate that the early onset of snowmelt near heavily traveled infrastructure corridors impacts near-surface soil freezing degree days, vegetation productivity, and waterbody surface cover.</p>

scholarly journals Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4674
Author(s):  
Yuqing Qin ◽  
Jie Su ◽  
Mingfeng Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Relative Error ◽  
Large Scale ◽  
The Arctic ◽  
Landsat Data ◽  
Melt Pond ◽  
Melt Ponds ◽  
Very High ◽  
Sentinel 2

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

scholarly journals Microtopographic control on the ground thermal regime in ice wedge polygons

2018 ◽  
Vol 12 (6) ◽  
pp. 1957-1968 ◽  
Author(s):  
Charles J. Abolt ◽  
Michael H. Young ◽  
Adam L. Atchley ◽  
Dylan R. Harp
Keyword(s):  
Thermal Conditions ◽  
Ground Temperature ◽  
The Arctic ◽  
Near Surface ◽  
Hydrologic Processes ◽  
Ground Temperatures ◽  
Air Temperatures ◽  
Winter Temperatures ◽  
Ice Wedge ◽  
Prudhoe Bay

Abstract. The goal of this research is to constrain the influence of ice wedge polygon microtopography on near-surface ground temperatures. Ice wedge polygon microtopography is prone to rapid deformation in a changing climate, and cracking in the ice wedge depends on thermal conditions at the top of the permafrost; therefore, feedbacks between microtopography and ground temperature can shed light on the potential for future ice wedge cracking in the Arctic. We first report on a year of sub-daily ground temperature observations at 5 depths and 9 locations throughout a cluster of low-centered polygons near Prudhoe Bay, Alaska, and demonstrate that the rims become the coldest zone of the polygon during winter, due to thinner snowpack. We then calibrate a polygon-scale numerical model of coupled thermal and hydrologic processes against this dataset, achieving an RMSE of less than 1.1 ∘C between observed and simulated ground temperature. Finally, we conduct a sensitivity analysis of the model by systematically manipulating the height of the rims and the depth of the troughs and tracking the effects on ice wedge temperature. The results indicate that winter temperatures in the ice wedge are sensitive to both rim height and trough depth, but more sensitive to rim height. Rims act as preferential outlets of subsurface heat; increasing rim size decreases winter temperatures in the ice wedge. Deeper troughs lead to increased snow entrapment, promoting insulation of the ice wedge. The potential for ice wedge cracking is therefore reduced if rims are destroyed or if troughs subside, due to warmer conditions in the ice wedge. These findings can help explain the origins of secondary ice wedges in modern and ancient polygons. The findings also imply that the potential for re-establishing rims in modern thermokarst-affected terrain will be limited by reduced cracking activity in the ice wedges, even if regional air temperatures stabilize.

scholarly journals Near-surface thermal stratification during summer at Summit, Greenland, and its relation to MODIS-derived surface temperatures

2017 ◽  
Author(s):  
Alden C. Adolph ◽  
Mary R. Albert ◽  
Dorothy K. Hall
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
The Arctic ◽  
Cold Bias ◽  
Near Surface ◽  
Surface Temperatures ◽  
In Situ Ir

Abstract. As rapid warming of the Arctic occurs, it is imperative that climate indicators such as temperature be monitored over large areas to understand and predict the effects of climate changes. Temperatures are traditionally tracked using in situ 2 m air temperatures, but in remote locations where few ground-based measurements exist, such as on the Greenland Ice Sheet, temperatures over large areas are assessed using remote sensing techniques. Because of the presence of surface-based temperature inversions in ice-covered areas, differences between 2 m air temperature and the temperature of the actual snow surface (referred to as skin temperature) can be significant and are particularly relevant when considering validation and application of remote sensing temperature data. We present results from a field campaign extending from 8 June through 18 July 2015, near Summit Station in Greenland to study surface temperature using the following measurements: skin temperature measured by an infrared (IR) sensor, thermochrons, and thermocouples; 2 m air temperature measured by a NOAA meteorological station; and a MODerate-resolution Imaging Spectroradiometer (MODIS) surface temperature product. Our data indicate that 2 m air temperature is often significantly higher than snow skin temperature measured in-situ, and this finding may account for apparent biases in previous surface temperature studies of MODIS products that used 2 m air temperature for validation. This inversion is present during summer months when incoming solar radiation and wind speed are both low. As compared to our in-situ IR skin temperature measurements, after additional cloud masking, the MOD/MYD11 Collection 6 surface-temperature standard product has an RMSE of 1.0 °C, spanning a range of temperatures from −35 °C to −5 °C. For our study area and time series, MODIS surface temperature products agree with skin surface temperatures better than previous studies indicated, especially at temperatures below −20 °C where other studies found a significant cold bias. The apparent cold bias present in others’ comparison of 2 m air temperature and MODIS surface temperature is perhaps a result of the near-surface temperature inversion that our data demonstrate. Further investigation of how in-situ IR skin temperatures compare to MODIS surface temperature at lower temperatures (below −35 °C) is warranted to determine if this cold bias does indeed exist.

scholarly journals Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing

2021 ◽  
Vol 13 (8) ◽  
pp. 1597
Author(s):  
Shangharsha Thapa ◽  
Virginia E. Garcia Millan ◽  
Lars Eklundh
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Vegetation Phenology ◽  
Change Analysis ◽  
Near Surface ◽  
Holistic Understanding ◽  
Sentinel 2

The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of <3 days with GCC, while these metrics from VIgreen and NDVI resulted in a slightly higher bias of (3–10) days. The observed agreement between UAVNDVI vs. satelliteNDVI and PhenoCamGCC vs. satelliteGCC suggests that it is feasible to use PhenoCams and UAVs for satellite data validation and upscaling. Thus, a combination of these near-surface vegetation metrics is promising for a holistic understanding of vegetation phenology from canopy perspective and could serve as a good foundation for analysing the interoperability of different sensors for vegetation dynamics and change analysis.

scholarly journals Resolution of an important discrepancy between remote and in-situ measurements of tropospheric BrO during Antarctic enhancements

2012 ◽  
Vol 5 (4) ◽  
pp. 5419-5448 ◽  
Author(s):  
H. K. Roscoe ◽  
N. Brough ◽  
A. E. Jones ◽  
F. Wittrock ◽  
A. Richter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
The Arctic ◽  
Optical Absorption Spectroscopy ◽  
Free Troposphere ◽  
Aircraft Measurements ◽  
Near Surface ◽  
Remote Sensor ◽  
Ionisation Mass Spectrometry ◽  
Surface Technique

Abstract. Tropospheric BrO was measured by a ground-based remote-sensing spectrometer at Halley in Antarctica, and BrO was measured by remote-sensing spectrometers in space using similar spectral regions and Differential Optical Absorption Spectroscopy (DOAS) analyses. Near-surface BrO was simultaneously measured at Halley by Chemical Ionisation Mass Spectrometry (CIMS), and in an earlier year near-surface BrO was measured at Halley over a long path by a DOAS spectrometer. During enhancement episodes, total amounts of tropospheric BrO from the ground-based remote-sensor were similar to those from space, but if we assume that the BrO was confined to the boundary layer they were very much larger than values measured by either near-surface technique. This large apparent discrepancy can be resolved if substantial amounts of BrO were in the free troposphere during most enhancement episodes. Amounts observed by the ground-based remote sensor at different elevation angles, and their formal inversions to vertical profiles, also show that much of the BrO was often in the free troposphere. This is consistent with the ~5 day lifetime of Bry, from the enhanced BrO observed during some Antarctic blizzards, and from aircraft measurements of BrO well above the surface in the Arctic.

scholarly journals Multi-Dimensional Remote Sensing Analysis Documents Beaver-Induced Permafrost Degradation, Seward Peninsula, Alaska

2021 ◽  
Vol 13 (23) ◽  
pp. 4863
Author(s):  
Benjamin M. Jones ◽  
Ken D. Tape ◽  
Jason A. Clark ◽  
Allen C. Bondurant ◽  
Melissa K. Ward Jones ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Unmanned Aircraft ◽  
The Arctic ◽  
Series Data ◽  
Landsat 8 ◽  
Permafrost Degradation ◽  
Channel Network ◽  
Seward Peninsula ◽  
Sentinel 2

Beavers have established themselves as a key component of low arctic ecosystems over the past several decades. Beavers are widely recognized as ecosystem engineers, but their effects on permafrost-dominated landscapes in the Arctic remain unclear. In this study, we document the occurrence, reconstruct the timing, and highlight the effects of beaver activity on a small creek valley confined by ice-rich permafrost on the Seward Peninsula, Alaska using multi-dimensional remote sensing analysis of satellite (Landsat-8, Sentinel-2, Planet CubeSat, and DigitalGlobe Inc./MAXAR) and unmanned aircraft systems (UAS) imagery. Beaver activity along the study reach of Swan Lake Creek appeared between 2006 and 2011 with the construction of three dams. Between 2011 and 2017, beaver dam numbers increased, with the peak occurring in 2017 (n = 9). Between 2017 and 2019, the number of dams decreased (n = 6), while the average length of the dams increased from 20 to 33 m. Between 4 and 20 August 2019, following a nine-day period of record rainfall (>125 mm), the well-established dam system failed, triggering the formation of a beaver-induced permafrost degradation feature. During the decade of beaver occupation between 2011 and 2021, the creek valley widened from 33 to 180 m (~450% increase) and the length of the stream channel network increased from ~0.6 km to more than 1.9 km (220% increase) as a result of beaver engineering and beaver-induced permafrost degradation. Comparing vegetation (NDVI) and snow (NDSI) derived indices from Sentinel-2 time-series data acquired between 2017 and 2021 for the beaver-induced permafrost degradation feature and a nearby unaffected control site, showed that peak growing season NDVI was lowered by 23% and that it extended the length of the snow-cover period by 19 days following the permafrost disturbance. Our analysis of multi-dimensional remote sensing data highlights several unique aspects of beaver engineering impacts on ice-rich permafrost landscapes. Our detailed reconstruction of the beaver-induced permafrost degradation event may also prove useful for identifying degradation of ice-rich permafrost in optical time-series datasets across regional scales. Future field- and remote sensing-based observations of this site, and others like it, will provide valuable information for the NSF-funded Arctic Beaver Observation Network (A-BON) and the third phase of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) Field Campaign.

scholarly journals The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multiplatform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification

2019 ◽  
Vol 100 (5) ◽  
pp. 841-871 ◽  
Author(s):  
Manfred Wendisch ◽  
Andreas Macke ◽  
André Ehrlich ◽  
Christof Lüpkes ◽  
Mario Mech ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aerosol Particles ◽  
Surface Air Temperature ◽  
Field Studies ◽  
Surface Processes ◽  
The Arctic ◽  
Arctic Amplification ◽  
Arctic Boundary Layer ◽  
Near Surface ◽  
Airborne Measurements

AbstractClouds play an important role in Arctic amplification. This term represents the recently observed enhanced warming of the Arctic relative to the global increase of near-surface air temperature. However, there are still important knowledge gaps regarding the interplay between Arctic clouds and aerosol particles, and surface properties, as well as turbulent and radiative fluxes that inhibit accurate model simulations of clouds in the Arctic climate system. In an attempt to resolve this so-called Arctic cloud puzzle, two comprehensive and closely coordinated field studies were conducted: the Arctic Cloud Observations Using Airborne Measurements during Polar Day (ACLOUD) aircraft campaign and the Physical Feedbacks of Arctic Boundary Layer, Sea Ice, Cloud and Aerosol (PASCAL) ice breaker expedition. Both observational studies were performed in the framework of the German Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC)3 project. They took place in the vicinity of Svalbard, Norway, in May and June 2017. ACLOUD and PASCAL explored four pieces of the Arctic cloud puzzle: cloud properties, aerosol impact on clouds, atmospheric radiation, and turbulent dynamical processes. The two instrumented Polar 5 and Polar 6 aircraft; the icebreaker Research Vessel (R/V) Polarstern; an ice floe camp including an instrumented tethered balloon; and the permanent ground-based measurement station at Ny-Ålesund, Svalbard, were employed to observe Arctic low- and mid-level mixed-phase clouds and to investigate related atmospheric and surface processes. The Polar 5 aircraft served as a remote sensing observatory examining the clouds from above by downward-looking sensors; the Polar 6 aircraft operated as a flying in situ measurement laboratory sampling inside and below the clouds. Most of the collocated Polar 5/6 flights were conducted either above the R/V Polarstern or over the Ny-Ålesund station, both of which monitored the clouds from below using similar but upward-looking remote sensing techniques as the Polar 5 aircraft. Several of the flights were carried out underneath collocated satellite tracks. The paper motivates the scientific objectives of the ACLOUD/PASCAL observations and describes the measured quantities, retrieved parameters, and the applied complementary instrumentation. Furthermore, it discusses selected measurement results and poses critical research questions to be answered in future papers analyzing the data from the two field campaigns.

Landscape-level remote sensing for upscaling of land cover, above ground biomass and above ground carbon fluxes in the Lena River Delta (Northern Yakutia, Russia)

2021 ◽  
Author(s):  
Birgit Heim ◽  
Iuliia Shevtsova ◽  
Stefan Kruse ◽  
Ulrike Herzschuh ◽  
Agata Buchwal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Carbon Fluxes ◽  
The Arctic ◽  
Stand Age ◽  
Data Repository ◽  
Vegetation Height ◽  
Landscape Level ◽  
Lena Delta ◽  
Sentinel 2

&lt;p&gt;Vegetation biomass is a globally important climate-relevant terrestrial carbon pool. Landsat, Sentinel-2 and Sentinel-1 satellite missions provide a landscape-level opportunity to upscale tundra vegetation communities and biomass in high latitude terrestrial environments.&amp;#160;We assessed the applicability of landscape-level remote sensing for the low Arctic Lena Delta region in Northern Yakutia, Siberia, Russia. The Lena Delta is the largest delta in the Arctic and is located North of the treeline and the 10 &amp;#176;C July isotherm at 72&amp;#176; Northern Latitude in the Laptev Sea region.&amp;#160;We evaluated circum-Arctic harmonized ESA GlobPermafrost land cover and vegetation height remote sensing products covering subarctic to Arctic land cover types for the central Lena Delta. The products are freely available and published in the PANGAEA data repository under https://doi.org/10.1594/PANGAEA.897916, and https://doi.org/10.1594/PANGAEA.897045.&lt;/p&gt;&lt;p&gt;Vegetation and biomass field data (30 m x 30 m plot size) and shrub samples for dendrology were collected during a Russian-German expedition in summer 2018 in the central Lena Delta. We also produced a regionally optimized land cover classification for the central Lena Delta based on the in-situ vegetation data and a summer 2018 Sentinel-2 acquisition that we optimized on the biomass and wetness regimes. We also produced biomass maps derived from Sentinel-2 at a pixel size of 20 m investigating several techniques. The final biomass product for the central Lena Delta shows realistic spatial patterns of biomass distribution, and also showing smaller scale patterns. However, patches of high shrubs in the tundra landscape could not spatially be resolved by all of the landscape-level land cover and biomass remote sensing products.&lt;/p&gt;&lt;p&gt;Biomass is providing the magnitude of the carbon flux, whereas stand age is irreplaceable to provide the cycle rate. We found that high disturbance regimes such as floodplains, valleys, and other areas of thermo-erosion are linked to high and rapid above ground carbon fluxes compared to low disturbance on Yedoma upland tundra and Holocene terraces with decades slower and in magnitude smaller above ground carbon fluxes.&lt;/p&gt;

scholarly journals Interpretation and Utilization of Areal Snow-Cover Data from Satellites

1987 ◽  
Vol 9 ◽  
pp. 166-169 ◽  
Author(s):  
J. Martinec ◽  
A. Rango
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Time Scale ◽  
Graphical Representation ◽  
Snow Melt ◽  
Seasonal Snow ◽  
Starting Date ◽  
Seasonal Snow Cover

Areal snow-cover data provided by remote sensing enable the areal water equivalent at the start of the snow melt season to be evaluated. To this end, the time scale in the graphical representation of the snow coverage curves is replaced by the totalized computed daily melt depths. These refer to the seasonal snow cover at the starting date and disregard subsequent snowfalls.

scholarly journals Monitoring Sea Ice in Liaodong Bay of Bohai Sea during the Freezing Period of 2017/2018 Using Sentinel-2 Remote Sensing Data

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhiyong Wang ◽  
Peilei Sun ◽  
Lihua Wang ◽  
Mengyue Zhang ◽  
Zihao Wang
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Bohai Sea ◽  
Remote Sensing Data ◽  
Liaodong Bay ◽  
Near Surface ◽  
Sensing Data ◽  
Freezing Period ◽  
The Mean ◽  
Sentinel 2

It is of great significance to monitor sea ice for relieving and preventing sea ice disasters. In this paper, the growth and development of sea ice in Liaodong Bay of Bohai Sea in China were monitored using Sentinel-2 remote sensing data during the freezing period from January to March in 2018. Based on the comprehensive analysis of the spectral characteristics of seawater and sea ice in visible bands, supplemented by the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI), we proposed a new method based on decision tree classification for extracting sea ice types in Liaodong Bay of Bohai Sea. Using the remote sensing data of eight satellite overpasses acquired from Sentinel-2A/B satellites, the distribution and area of the different sea ice types in Liaodong Bay during the freezing period of 2017/2018 were obtained. Compared with the maximum likelihood (ML) classification method and the support vector machine (SVM) classification method, the proposed method has higher accuracy when discriminating the sea ice types, which proved the new method proposed in this paper is suitable for extracting sea ice types from Sentinel-2 optical remote sensing data in Liaodong Bay. And its classification accuracy reaches 88.05%. The whole process of evolution such as the growth and development of sea ice in Liaodong Bay during the freezing period from January to March in 2018 was monitored. The maximum area of sea ice was detected on 27 January 2018, about 10,187 km2. At last, the quantitative relationship model between the sea ice area and the mean near-surface temperature derived by MODIS data in Liaodong Bay was established. Through research, we found that the mean near-surface temperature was the most important factor for affecting the formation and melt of sea ice in Liaodong Bay.

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