Interannual Variability of Sea Ice Dirtiness in the East Siberian Sea Based on Satellite Data

The objective of this research is into detecting sea ice by using PALSAR (Phased-Array type L-band SAR) polarimetric data. It is generally difficult to detect thin sea ice area by using the methods based on the backscattering coefficient. We propose a new method utilizing scattering entropy to detect sea ice. This paper shows the results of extracting sea ice area from PALSAR fully polarimetric data acquired from 2009 to 2010. The MODIS (Moderate Resolution Imaging Spectroradiometer) data are used as sea ice reference. The threshold of PALSAR scattering entropy to discriminate sea ice from open water is determined from the distribution of scattering entropy for both sea ice and open water. We compared PALSAR derived sea ice area with MODIS derived area. Since most of PALSAR detected sea ice area is also detected by MODIS data, we can conclude that our proposed method is reliable to detect thin sea ice area in the Sea of Okhotsk. The high resolution backscattering and scattering entropy images give us an idea that there are some difficulties in detecting thin sea ice only by backscattering coefficient.

Dinocysts as proxy of primary productivity in mid–high latitudes of the Northern Hemisphere

The Russian-American Long-term Census of the Arctic (RUSALCA) project provides a rare opportunity to study the Russian sector of the Pacific Arctic Region (PAR), which includes the Chukchi and East Siberian Seas. RUSALCA data from 2009 and 2012 allow fuller understanding of changes in ocean chemistry across this the region and, in particular, provide perspectives on the ocean carbon cycle, air-sea CO2 gas exchange, and ocean acidification variability. Summertime surface waters of the western Chukchi Sea and East Siberian Sea mostly exhibited low pCO2 (<100 to 400 µatm) and high pH (8.0 to 8.4) conditions during sea ice retreat. As earlier studies of the adjacent eastern Chukchi Sea show, this area of the PAR had a strong potential for ocean uptake of atmospheric CO2 , with saturation states for calcium carbonate (CaCO3) minerals such as calcite and aragonite (?calcite and ?aragonite, respectively) having values generally greater than two, thereby facilitating CaCO3 production. In contrast, fresher surface waters flowing into the Chukchi Sea from the East Siberian Sea and bottom waters on the PAR shelves exhibited high pCO2 and low pH, ?calcite, and ?aragonite conditions. Low ? surface waters near the Russian coast and nearly 70% of waters next to the seafloor were corrosive to CaCO3 minerals such as aragonite, with this change seemingly occurring at a more rapid rate than typical global open-ocean changes in ocean chemistry. The exposure of subsurface benthic communities and nearshore ecosystems near the Russian coast to potentially corrosive water is likely exacerbated by the ocean uptake of anthropogenic CO2 and gradual ocean acidification. The RUSALCA project also highlights the complexities and uncertainties in the physical and biogeochemical drivers of the ocean carbon cycle and ocean chemistry in this region of the Arctic.

Validation of water vapor retrieval from Moderate Resolution Imaging Spectro-radiometer (MODIS) in near infrared channels using GPS data over IAO-Hanle, in the trans-Himalayan region

The distribution features of diatoms in the surface sediments of the Laptev, East Siberian, and Chukchi seas were studied. The sediment diatom content varied widely, being lowest in the East Siberian Sea and highest in the Chukchi Sea. Cluster analysis revealed several diatom assemblages in the surface sediments of the studied region; their distribution depended on the hydrological conditions. A benthic Amphora libyca assemblage, a freshwater Aulacoseira subarctica one, and a brackish-water Thalassiosira hyperborea assemblage were identified in the Laptev Sea. A cold-water Th. antarctica assemblage was distinguished in the eastern part of the East Siberian Sea and in the northern Chukchi Sea; a highly productive Chaetoceros assemblage was noted in the Herald Canyon area; a sublittoral Odontella aurita assemblage occurred in sandy sediments of the Herald Shoal; a Paralia sulcata assemblage, which is an indicator of low salinity waters, was found in the eastern Chukchi Sea near Alaska; and ”sea-ice” Th. nordenskioeldii and Fragillariopsis oceanica assemblages occurred in the southern Chukchi Sea.

&amp;lt;p&amp;gt;Investigating the temporal and spatial changes in Arctic sea ice freeboard, thickness and volume is crucial for climate and environmental research. In this study, the freeboard, thickness and volume of the Arctic sea ice between 2011 and 2020 were acquired from CryoSat-2 data and it was compared with NASA Operation IceBridge and Alfred Wegener Institue AWI datasets. The effect of wind field and temperature and itscontribution on Arctic sea ice were also investigated. The main steps of our research are as follows. 1) Selecting data points above 66 degrees north latitude and filtering out sea ice by flag value and mask; Using OSI SAF sea ice concentration (SIC) data to further constrict the area; Interpolating the latest mean sea surface data DTU21 into CryoSat-2 data; Calculating the sea ice freeboard via spatial altimetry relationship. 2) Combining with snow density, snow depth, multi-year ice density and first-year ice density to estimate the sea ice thickness respectively from freeboard according to the assumption of hydrostatic equilibrium. 3) Using the area and extent provided by NSIDC to interpret the volume. 4) Utilizing Seasonal and Trend decomposition using Loess(STL) to analyse the seasonal and interannual variations of sea ice. 5) Dividing the Arctic Ocean by its marginal sea, coupled with the HY-2B microwave scatterometer data and NCEP/NCAR reanalysis data, the impact of the Beaufort Sea, the Chukchi Sea, the East Siberian Sea, the Laptev Sea, the Kara Sea, the Barents Sea, the Greenland Sea, and the Baffin Bay wind field on the sea ice were studied. 6) Exploring the correlation between the sea surface temperature and the sea ice freeboard, thickness and volume. The result indicated that (1) the freeboard and thickness was decreasing about 9.748% and 8.80% during 2011-2020,respectively; (2) there are interlunar variations in sea ice freeboard and thickness, the freeboard and thickness of sea ice reach the minimum in August to September each year and the maximum appears in March to April; (3) Arctic sea ice is affected by both thermodynamics and dynamics, the reduction of Arctic sea ice was largely due to the sea surface wind field, one of the dynamic factors is the wind field on the sea surface. The sea ice changes in the various sea areas of the Arctic Ocean are related to the wind field to varying degrees. The changes in the sea surface wind field in recent years have further promoted the reduction of sea ice, making it possible for the Arctic to open to navigation in the summer. Fully understanding the evolution of sea ice change in the Arctic Ocean is helpful for humans to better protect the earth.&amp;lt;/p&amp;gt;

The moderate resolution imaging spectroradiometer (MODIS) is one of the widely used sensors to address environmental and climate change subjects with a daily global coverage. MODIS Collection 6 aerosol products at 10-km resolution are used in this study to monitor aerosol variability and assess its uncertainty using ground-based measurements. The aerosol optical depth (AOD) is retrieved by different algorithms based on the pixel surface, determining between land and ocean. Using data collected from Sidi Salem Aerosol Robotic Network (AERONET) station, we computed the accuracy for aerosol optical depth (AOD) retrieved from MODIS aboard the AQUA satellite using two validation methods. The results show a good agreement between MODIS and AERONET data for the study period using both the algorithms. We obtained high values of the correlation coefficient. These findings indicate that MODIS data perform well over Ben Salem AERONET station and are recommended for air quality monitoring over Tunisia. The conducted validation throughout the AERONET leads to a degree of confidence that allows a deep investigation of the AOD spatial variability over Tunisia. Then, MODIS data shows high performance with good certainty to identify the principal dust sources and typical transport paths occurring on the study region.

Estimation of surface upward longwave radiation from MODIS and VIIRS clear-sky data in the Tibetan Plateau

A quantitative method is developed for deriving water fraction from coarse- to medium-resolution satellite data with visible to short-wave infrared (SWIR) channels based on the linear mixture theory. The method uses a SWIR channel (1.64 μm) by assuming that the water-surface-leaving radiance in this channel is insignificant and is thus less affected by water types and water depth than near-infrared (NIR) channels for inland water bodies. For a mixed water pixel, a dynamic nearest neighbor searching (DNNS) method is used to find the nearby land pixels to determine the average land reflectance. The nearby pure water pixels with a similar water type to the subpixel water portion of the mixed water pixel are found dynamically to derive the average water reflectance. The average reflectance in the SWIR channel from both pure land pixels and water pixels is used to calculate the water fraction from a linear mixture model. The developed method is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data and shows promising results. High-resolution satellite data from the Thematic Mapper (TM) are used to evaluate the water fraction derived from MODIS. During pixel-to-pixel water fraction evaluation, TM data are spatially aggregated to MODIS resolution. When evaluated against the high-resolution TM observations, water fractions derived from MODIS using the DNNS method with the SWIR channel show a bias of -0.021 with a standard deviation of 0.0338. Comparing lake areas between TM and MODIS data also shows consistent results with the pixel-to-pixel water fraction comparison. The DNNS method is also compared to the traditional histogram method both with SWIR channel and NIR channel. The results show that the DNNS method is more accurate than the histogram method and that the SWIR channel is better than the NIR channel to derive highly accurate water fraction from coarse- to medium-resolution satellite data.

Polar cod (Boreogadus saida) of the Siberian Arctic: Distribution and biology

Bi-scale analysis of multitemporal land cover fractions for wetland vegetation mapping

A surface reflectance correction model to improve the retrieval of MISR aerosol optical depth supported by MODIS data

Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model

Remote sensing has been widely used in vegetation-dynamics monitoring. Many studies have used data acquired by multispectral sensors, such as the Landsat TM sensor, due to their high spatial resolution (30 m). However, during the growing season, the temporal resolution (16 day) cannot capture rapid changes of vegetation. Meanwhile, coarse-spectral-resolution sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have high-frequency temporal information that can catch the details of landscape changes. In this research, we proposed a data-fusion approach that lead us to merge the MODIS and Landsat TM data to create a dataset of vegetation dynamics with both a high spatial resolution and a fine temporal resolution. The Comanche and Faith Ranches, located in west Texas, were chosen for this study. The MODIS product was used as a regionally consistent reference dataset to correct the Landsat imagery. Based on this new dataset, NDVI time-series curves from 2004 to 2011 were calculated with the MODIS 13 Vegetation Dataset. One random sample of red-band images was tested and compared with MODIS data. A high correlation coefficient 0.907 and RMSE 0.0245 was found.

In this paper, we intent to use the remotely sensed MODerate resolution Imaging Spectroradiometer (MODIS) data and China’s Environment Satellite (HJ-1) data for extracting the corn cultivated area over a regional scale. The high resolution HJ-1 data was to extract corn distribution at a small scale class with Support Vector Machine (SVM). The mean Enhanced Vegetation Index (EVI) time series curve of corn from MODIS was derived for the reference area and validated in a larger area. The MODIS-EVI time series curve derived from the reference area instead of the MODIS-EVI time series curve derived from the study area after validation, which was taken as the standard MODIS-EVI time series curve in for generating a standard MODIS-EVI image of corn. The mean absolute distance (MAD) between the standard MODIS-EVI image of corn and the MODIS-EVI time series image was used to detect the maximum possible extent of corn distribution in the study area. The results showed that the overall accuracy of the method was 82.17 %, with commission and omission errors of 16.85 and 15.40 %, respectively; at the county level, the satellite-estimated corn area and statistical data were well correlated (R2 = 0.85, N = 50) for the whole Jilin Province. It indicated that the MODIS data integrated with higher spatial resolution of HJ-1 satellite data could be utilized to enhance the extraction accuracy of corn cultivated area at a larger scale.