High-resolution maps of the sea-ice concentration from MODIS satellite data (original) (raw)
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Accuracy assessment of sea-ice concentrations from MODIS using in-situ measurements
Remote Sensing of Environment, 2005
The Moderate Resolution Imaging Spectroradiometer (MODIS) potential open water algorithm (MPA) uses infrared satellite images to retrieve sea-ice concentration. From these data, composite maps of the sea-ice concentration are generated at up to 1-km resolution, in order to serve as input for high-resolution atmospheric models. In the present study, aircraft and helicopter-based measurements from the field experiment bAtmospheric Boundary layer and Sea-ice Interaction StudyQ (ABSIS) are used to validate the MPA-based sea-ice concentration maps with in-situ measurements: Aircraft measurements of upwelling longwave radiation are used to derive reference values of the sea-ice concentration. MPA-based maps are checked for accuracy of sea-ice concentration values as well as for the preservation of the sea-ice concentration distribution function. This error analysis yields that sea-ice concentration can be determined from MODIS data with approximately F10% error. The compilation of composite maps from multiple overpasses leads to an overall uncertainty of F11.5%. Helicopter-based measurements of the sea-ice thickness are used to study the dependence of MPA-based sea-ice concentration with respect to sea-ice thickness. The dependence turns out to be minimal. Finally, MPA sea-ice concentration is compared to the popular Special Sensor Microwave Imager (SSM/I) sea-ice concentration data. Both data sets are found to agree within F7%.
Sea Ice Surface Temperature Product from the Moderate Resolution Imaging Spectroradiometer (MODIS)
IEEE Transactions on Geoscience and Remote Sensing
Global sea ice products are produced from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) onboard both the Terra and Aqua satellites. Daily sea ice extent and ice surface temperature (IST) products are available at 1-and 4-km resolution. Validation activities during the "cold period" (when meltwater is generally not present) in the Northern Hemisphere, defined here as October through May, have been undertaken to assess the accuracy of the 1-km resolution MODIS IST algorithm and product. Validation was also done at the South Pole station in Antarctica. In the Arctic Ocean, near-surface air temperatures from the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) Alaska tide stations and from drifting buoys from the North Pole Environmental Observatory (NPEO) buoy program were compared with MODIS-derived ISTs. Using the standard MODIS sea ice product, which utilizes the MODIS cloud mask, results show a bias (mean error) of 2.1 K and a root mean square (RMS) error of 3.7 K. The negative bias means that the satellite retrieval is less than the air temperature. With the bias removed, the RMS error is 3.0 K. When additional visual cloud screening is performed to eliminate MODIS pixels thought to be contaminated by fog, results improved, with a subset of the larger dataset showing a bias of 0.9 K and an RMS error of 1.6 K. Uncertainties would be reduced in the Arctic Ocean dataset if the skin temperature of the sea ice were reported instead of the near-surface air temperatures. With the bias removed, the RMS error for the Arctic Ocean dataset is 1.3 K. Results from the South Pole station in Antarctica show that under clear skies as determined using lidar measurements, the MODIS ISTs are also very close to those of the near-surface air temperatures with a bias of 1.2 K and an RMS error of 1.7 K. With the bias removed, the RMS error for the South Pole dataset is 1.2 K. Thus, the accuracy (RMS error) of the IST measurement is 1.2-1.3 K. It is not possible to obtain an accurate IST from MODIS in the presence of even very thin clouds or fog, and this is the main limitation of the MODIS ice surface temperature product. MODIS sea ice products may be ordered from the National Snow and Ice Data Center in Boulder, CO.
Atmosphere-Ocean, 2014
Sea ice concentration data from passive microwave sensors are not reliable during summer melt season. In this study passive microwave sea ice concentration estimates are improved upon through the assimilation of sea ice surface temperature data from the Moderate-Resolution Imaging Spectroradiometer (MODIS). Ice concentration from the analysis is converted to ice extent and compared with data from the Interactive Multisensor Snow and Ice Mapping System. Sea ice concentration analysis from data assimilation is in better agreement with the IMS data than the original passive microwave data, with the largest improvements during sea ice melt.
IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data
The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3–7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a m...
Sea ice surface temperature product from MODIS
IEEE Transactions on Geoscience and Remote Sensing, 2000
Global sea ice products are produced from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) onboard both the Terra and Aqua satellites. Daily sea ice extent and ice surface temperature (IST) products are available at 1-and 4-km resolution. Validation activities during the "cold period" (when meltwater is generally not present) in the Northern Hemisphere, defined here as October through May, have been undertaken to assess the accuracy of the 1-km resolution MODIS IST algorithm and product. Validation was also done at the South Pole station in Antarctica. In the Arctic Ocean, near-surface air temperatures from the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) Alaska tide stations and from drifting buoys from the North Pole Environmental Observatory (NPEO) buoy program were compared with MODIS-derived ISTs. Using the standard MODIS sea ice product, which utilizes the MODIS cloud mask, results show a bias (mean error) of 2.1 K and a root mean square (RMS) error of 3.7 K. The negative bias means that the satellite retrieval is less than the air temperature. With the bias removed, the RMS error is 3.0 K. When additional visual cloud screening is performed to eliminate MODIS pixels thought to be contaminated by fog, results improved, with a subset of the larger dataset showing a bias of 0.9 K and an RMS error of 1.6 K. Uncertainties would be reduced in the Arctic Ocean dataset if the skin temperature of the sea ice were reported instead of the near-surface air temperatures. With the bias removed, the RMS error for the Arctic Ocean dataset is 1.3 K. Results from the South Pole station in Antarctica show that under clear skies as determined using lidar measurements, the MODIS ISTs are also very close to those of the near-surface air temperatures with a bias of 1.2 K and an RMS error of 1.7 K. With the bias removed, the RMS error for the South Pole dataset is 1.2 K. Thus, the accuracy (RMS error) of the IST measurement is 1.2-1.3 K. It is not possible to obtain an accurate IST from MODIS in the presence of even very thin clouds or fog, and this is the main limitation of the MODIS ice surface temperature product. MODIS sea ice products may be ordered from the National Snow and Ice Data Center in Boulder, CO.
Study of polar ice using remote sensing data
2007
This Thesis discusses the analysis of the data obtained over the Polar Oceans region during the period June 1999 — September 2001 through the use of Multi-channel Scanning Microwave Radiometer (MSMR) onboard India's Oceansat-1 satellite. The procedures and the algorithms developed for (i) estimating sea ice extent and concentration over the polar oceans (ii) studying seasonal and long term variability of these parameters over different sectors of the polar oceans, and (iii) studying the secular trend in polar sea ice by combining MSMR data with SMMR and SSM/I data. have been described in detail. As a prelude to using the MSMR data for the above mentioned studies, a detailed exercise was also undertaken to inter-compare the MSMR observed brightness temperatures and the MSMR derived sea ice characteristics with the concurrently available SSM/I values. This was done to validate and establish full confidence in the accuracy of MSMR data. As a part of the study, weekly/monthly color ...
Detailed Validation of AMSR2 Sea Ice Concentration Data Using Modis Data in the Sea of Okhotsk
2020
Abstract. Global warming is one of the most serious problems we are facing in the 21st Century. Sea ice has an important role of reflecting the solar radiation back into space. However, once sea ice started to melt, the ice-free water would absorb the solar radiation and amplify global warming in the Arctic region. Thus, importance of sea ice monitoring is increasing. Since longer wavelength microwave can penetrate clouds, passive microwave radiometers on-board satellites are powerful tools for monitoring the global distribution of sea ice on daily basis. The Advanced Passive Microwave Scanning Radiometer AMSR2 which was launched by JAXA in May 2012 on-board GCOM-W satellite provides brightness temperature data that are used to estimate sea ice concentration, the fundamental parameter that is used to monitor the sea ice cover. JAXA is providing AMSR2 sea ice concentration data, derived using ASMR2 Bootstrap Algorithm as a standard product of AMSR2, as a means to communicate how the ...
The EUMETSAT sea ice concentration climate data record
The Cryosphere, 2016
An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of microwave radiometer data from NASA's Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from October 1978 to April 2015 and updates and further developments are planned for the next phase of the project. The methodology for computing the sea ice concentration uses (1) numerical weather prediction (NWP) data input to a radiative transfer model for reduction of the impact of weather conditions on the measured brightness temperatures; (2) dynamical algorithm tie points to mitigate trends in residual atmospheric, sea ice, and water emission characteristics and inter-sensor differences/biases; and (3) a hybrid...
Remote sensing of sea ice: advances during the DAMOCLES project
The Cryosphere, 2012
In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the European Union's (EU) project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). In order to better understand the seasonal variation of the microwave emission of sea ice observed from space, the monthly variations of the microwave emissivity of first-year and multi-year sea ice have been derived for the frequencies of the microwave imagers like AMSR-E (Advanced Microwave Scanning Radiometer on EOS) and sounding frequencies of AMSU (Advanced Microwave Sounding Unit), and have been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. In addition, a sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emissivities from 6 GHz to 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but is more difficult to observe directly. The size of the snow grains on top of the sea ice influences both its albedo and the microwave emission. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and demonstrated in an application on the Ross ice shelf. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMO-CLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer AS-CAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.