Novel machine-learning based cloud mask and its application for Antarctic polynya monitoring using MODIS thermal-infrared imagery

2021 ◽  
Author(s):  
Stephan Paul ◽  
Marcus Huntemann
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
False Positive ◽  
Cloud Cover ◽  
Near Infrared ◽  
False Negative ◽  
Open Water ◽  
Thermal Infrared ◽  
Spatial Coverage ◽  
Cloud Mask

<p>The frequent presence of cloud cover in polar regions limits the use of the Moderate-Resolution Imageing Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of i) present clouds as sea ice/open water (false-negative) and ii) open-water/thin-ice areas as clouds (false-positive), which results in an underestimation of actual polynya area and subsequent derived information. Here, we present a novel machine-learning based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water/thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data results in an overall increase of 20% in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44% through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better sub-daily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.</p>

scholarly journals Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

2021 ◽  
Vol 15 (3) ◽  
pp. 1551-1565
Author(s):  
Stephan Paul ◽  
Marcus Huntemann
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
False Positive ◽  
Cloud Cover ◽  
Near Infrared ◽  
False Negative ◽  
Open Water ◽  
Thermal Infrared ◽  
Polar Regions ◽  
Spatial Coverage

Abstract. The frequent presence of cloud cover in polar regions limits the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of (i) present clouds as sea ice or open water (false negative) and (ii) open-water and/or thin-ice areas as clouds (false positive), which results in an underestimation of actual polynya area and subsequently derived information. Here, we present a novel machine-learning-based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water and/or thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data result in an overall increase of 20 % in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44 % through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better subdaily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.

scholarly journals Improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

2020 ◽  
Author(s):  
Stephan Paul ◽  
Marcus Huntemann
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Cloud Cover ◽  
Near Infrared ◽  
Open Water ◽  
Thermal Infrared ◽  
Polar Regions ◽  
Antarctic Sea Ice ◽  
Spatial Coverage ◽  
Thermal Infrared Imagery

Abstract. The frequent presence of cloud cover in polar regions limits the use of the Moderate-Resolution Imageing Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of i) present clouds as sea ice and ii) open-water/thin-ice areas as clouds, which results in an underestimation of polynya area and subsequently derived information. Here, we present a novel machine-learning based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water/thin-ice areas in a given swath solely from thermal-infrared MODIS channels and additionally derived information. Compared to the reference MODIS sea-ice product, our data results in an overall increase of 31 % in annual swath-based coverage, attributed to an improved cloud-cover discrimination. Overall, higher spatial coverage results in a better sub-daily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.

scholarly journals Sea Ice Albedo from MISR and MODIS: Production, Validation, and Trend Analysis

2018 ◽  
Vol 11 (1) ◽  
pp. 9 ◽  
Author(s):  
Said Kharbouche ◽  
Jan-Peter Muller
Keyword(s):  
Sea Ice ◽  
Near Infrared ◽  
Time Window ◽  
Solar Spectrum ◽  
High Accuracy ◽  
Dynamic Surface ◽  
Spectral Bands ◽  
Langley Research Center ◽  
Terra Satellite ◽  
Cloud Mask

The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo of dynamic surface features such as clouds or sea ice. However, MISR’s cloud mask over snow or sea ice is not yet sufficiently robust because MISR’s spectral bands are only located in the visible and the near infrared. To overcome this obstacle, we performed data fusion using a specially processed MISR sea ice albedo product (that was generated at Langley Research Center using Rayleigh correction) combining this with a cloud mask of a sea ice mask product, MOD29, which is derived from the MODerate Resolution Imaging Spectroradiometer (MODIS), which is also, like MISR, onboard the Terra satellite. The accuracy of the MOD29 cloud mask has been assessed as >90% due to the fact that MODIS has a much larger number of spectral bands and covers a much wider range of the solar spectrum. Four daily sea ice products have been created, each with a different averaging time window (24 h, 7 days, 15 days, 31 days). For each time window, the number of samples, mean and standard deviation of MISR cloud-free sea ice albedo is calculated. These products are publicly available on a predefined polar stereographic grid at three spatial resolutions (1 km, 5 km, 25 km). The time span of the generated sea ice albedo covers the months between March and September of each year from 2000 to 2016 inclusive. In addition to data production, an evaluation of the accuracy of sea ice albedo was performed through a comparison with a dataset generated from a tower based albedometer from NOAA/ESRL/GMD/GRAD. This comparison confirms the high accuracy and stability of MISR’s sea ice albedo since its launch in February 2000. We also performed an evaluation of the day-of-year trend of sea ice albedo between 2000 and 2016, which confirm the reduction of sea ice shortwave albedo with an order of 0.4–1%, depending on the day of year and the length of observed time window.

scholarly journals The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset

2019 ◽  
Vol 13 (7) ◽  
pp. 2051-2073 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen ◽  
Christian Haas ◽  
Larysa Istomina ◽  
Frank Kauker ◽  
...  
Keyword(s):  
Sea Ice ◽  
Growth Yield ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
Pressure System ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Spatial Coverage ◽  
Ice Concentration

Abstract. Observations of sea-ice concentration are available from satellites year-round and almost weather-independently using passive microwave radiometers at resolutions down to 5 km. Thermal infrared radiometers provide data with a resolution of 1 km but only under cloud-free conditions. We use the best of the two satellite measurements and merge thermal infrared and passive microwave sea-ice concentrations. This yields a merged sea-ice concentration product combining the gap-free spatial coverage of the passive microwave sea-ice concentration and the 1 km resolution of the thermal infrared sea-ice concentration. The benefit of the merged product is demonstrated by observations of a polynya which opened north of Greenland in February 2018. We find that the merged sea-ice concentration product resolves leads at sea-ice concentrations between 60 % and 90 %. They are not resolved by the coarser passive microwave sea-ice concentration product. The benefit of the merged product is most pronounced during the formation of the polynya. Next, the environmental conditions during the polynya event are analysed. The polynya was caused by unusual southerly winds during which the sea ice drifted northward instead of southward as usual. The daily displacement was 50 % stronger than normal. The polynya was associated with a warm-air intrusion caused by a high-pressure system over the Eurasian Arctic. Surface air temperatures were slightly below 0 ∘C and thus more than 20 ∘C higher than normal. Two estimates of thermodynamic sea-ice growth yield sea-ice thicknesses of 60 and 65 cm at the end of March in the area opened by the polynya. This differed from airborne sea-ice thickness measurements, indicating that sea-ice growth processes in the polynya are complicated by rafting and ridging. A sea-ice volume of 33 km3 was produced thermodynamically.

scholarly journals Surface Albedo of the Antarctic Sea Ice Zone

2005 ◽  
Vol 18 (17) ◽  
pp. 3606-3622 ◽  
Author(s):  
Richard E. Brandt ◽  
Stephen G. Warren ◽  
Anthony P. Worby ◽  
Thomas C. Grenfell
Keyword(s):  
Sea Ice ◽  
Surface Albedo ◽  
Near Infrared ◽  
Field Experiments ◽  
Climate Models ◽  
Solar Spectrum ◽  
Open Water ◽  
Visual Observation ◽  
Antarctic Sea Ice ◽  
The Antarctic

Abstract In three ship-based field experiments, spectral albedos were measured at ultraviolet, visible, and near-infrared wavelengths for open water, grease ice, nilas, young “grey” ice, young grey-white ice, and first-year ice, both with and without snow cover. From the spectral measurements, broadband albedos are computed for clear and cloudy sky, for the total solar spectrum as well as for visible and near-infrared bands used in climate models, and for Advanced Very High Resolution Radiometer (AVHRR) solar channels. The all-wave albedos vary from 0.07 for open water to 0.87 for thick snow-covered ice under cloud. The frequency distribution of ice types and snow coverage in all seasons is available from the project on Antarctic Sea Ice Processes and Climate (ASPeCt). The ASPeCt dataset contains routine hourly visual observations of sea ice from research and supply ships of several nations using a standard protocol. Ten thousand of these observations, separated by a minimum of 6 nautical miles along voyage tracks, are used together with the measured albedos for each ice type to assign an albedo to each visual observation, resulting in “ice-only” albedos as a function of latitude for each of five longitudinal sectors around Antarctica, for each of the four seasons. These ice albedos are combined with 13 yr of ice concentration estimates from satellite passive microwave measurements to obtain the geographical and seasonal variation of average surface albedo. Most of the Antarctic sea ice is snow covered, even in summer, so the main determinant of area-averaged albedo is the fraction of open water within the pack.

scholarly journals Extraction of Sea Ice Cover by Sentinel-1 SAR Based on SVM with Unsupervised Generation of Training Data

2020 ◽  
Author(s):  
Xiaoming Li ◽  
Yan Sun ◽  
Qiang Zhang
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Learning Algorithm ◽  
Texture Features ◽  
Open Water ◽  
Ice Cover ◽  
Training Data ◽  
Support Vector ◽  
Training Samples

In this paper, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization (vertical-horizontal, VH or horizontal-vertical, HV) data in extra wide (EW) swath mode based on the machine learning algorithm support vector machine (SVM). The classification basis includes the S1 radar backscatter coefficients and texture features that are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e. entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparison shows good agreement between the SAR-derived sea ice cover using the proposed method and a visual inspection, of which the accuracy reaches approximately 90% - 95% based on a few cases. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of extracted sea ice cover by using S1 data is more than 80%.

scholarly journals Semi-automated open water iceberg detection from Landsat applied to Disko Bay, West Greenland

2019 ◽  
Vol 65 (251) ◽  
pp. 468-480 ◽  
Author(s):  
JESSICA SCHEICK ◽  
ELLYN M. ENDERLIN ◽  
GORDON HAMILTON
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Power Law ◽  
Coastal Waters ◽  
Landsat Imagery ◽  
Open Water ◽  
Size Distributions ◽  
Negative Power ◽  
West Greenland ◽  
Cloud Mask

ABSTRACTChanges in Greenland's marine-terminating outlet glaciers have led to changes in the flux of icebergs into Greenland's coastal waters, yet icebergs remain a relatively understudied component of the ice-ocean system. We developed a simple iceberg delineation algorithm for Landsat imagery. A machine learning-based cloud mask incorporated into the algorithm enables us to extract iceberg size distributions from open water even in partially cloudy scenes. We applied the algorithm to the Landsat archive covering Disko Bay, West Greenland, to derive a time series of iceberg size distributions from 2000–02 and 2013–15. The time series captures a change in iceberg size distributions, which we interpret as a result of changes in the calving regime of the parent glacier, Sermeq Kujalleq (Jakobshavn Isbræ). The change in calving style associated with the disintegration and disappearance of Sermeq Kujalleq's floating ice tongue resulted in the production of more small icebergs. The increased number of small icebergs resulted in increasingly negative power law slopes fit to iceberg size distributions in Disko Bay, suggesting that iceberg size distribution time series provide useful insights into changes in calving dynamics.

scholarly journals Radiometrie measurements of sea-ice surface temperature in East Antarctica

1998 ◽  
Vol 27 ◽  
pp. 466-470
Author(s):  
Kelvin J. Michael ◽  
Clemente S. Hungria ◽  
R. A. Massom
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Snow Cover ◽  
Open Water ◽  
Thermal Infrared ◽  
Near Surface ◽  
Video Footage ◽  
Antarctic Sea Ice ◽  
Ice Surface ◽  
Ice Conditions

This paper presents surface temperature data collected over East Antarctic sea ice by two thermal infrared radiometers mounted on the RSV Aurora Australis in March-May 1993. Operating at wavelengths equivalent to those utilised by channels 4 and 5 of AVHRR and similar channels of ATSR, the radiometers provided high-reso-lution data on surface (skin) temperature along the ship track. Additional information on the sea-ice conditions was obtained from hourly observations made from The ship's bridge, video footage and direct measurements made at ice stations. Following calibration, time series of temperatures from each of the radiometers were compared wi th ice-surface and near-surface air temperatures. Observed changes in the surface temperature are related to different snow and ice conditions. For a given air temperature, the surface temperature depends upon the thickness of ice and its snow cover. While open water areas (leads) have temperatures near -2.0°C, thick ice is characterised by surface temperatures which approximate those of the air. Taken as a whole, the along-track profile of surface temperature provides a proxy estimate of The proportion of open water and thin ice with in the pack. The presence of a snow cover has a significant effect on the surface temperature. It is anticipated that the results will be of use in the validation of sea-ice models and satellite thermal infrared data.

scholarly journals Observation of the 2018 North Greenland polynya with a new merged optical and passive microwave sea ice concentration dataset

2019 ◽  
Author(s):  
Valentin Ludwig ◽  
Gunnar Spreen ◽  
Christian Haas ◽  
Larysa Istomina ◽  
Frank Kauker ◽  
...  
Keyword(s):  
Sea Ice ◽  
Growth Yield ◽  
Thermal Infrared ◽  
Passive Microwave ◽  
Pressure System ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Air Temperatures ◽  
Spatial Coverage ◽  
Ice Concentration

Abstract. Observations of sea ice concentration are available from satellites year-round and almost weather-independently using passive microwave radiometers at resolutions down to 5 km. Thermal infrared radiometers provide data with a resolution of 1 km, but only under cloud-free conditions. We use the best of the two satellite measurements and merge thermal infrared and passive microwave sea ice concentrations. This yields a merged sea ice concentration product which combines the gap-free spatial coverage of the passive microwave sea ice concentrations and the 1 km resolution of the thermal infrared sea ice concentrations. The benefit of the merged product is demonstrated by observations of a polynya which opened north of Greenland in February 2018. We find that the merged sea ice concentration product resolves leads as sea ice concentration between 60 % and 80 %. They are not resolved by the coarser passive microwave sea ice concentration product. Next, the environmental conditions during the polynya event are analysed. The polynya was caused by unusual southerly winds during which the sea ice drifted northward instead of southward as usual. The daily displacement was 50 % stronger than normal. The polynya was associated with a warm-air intrusion caused by a high-pressure system over the Eurasian Arctic. Surface air temperatures were slightly beneath 0 °C and thus more than 20 °C above the average. Two estimates of thermodynamic growth yield accumulated growth of 60 and 65 cm at the end of March. This compares well with airborne sea ice thickness measurements. 33 km3 of sea ice were produced thermodynamically.

scholarly journals Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features

2021 ◽  
Vol 15 ◽  
Author(s):  
Urszula Hohmann ◽  
Faramarz Dehghani ◽  
Tim Hohmann
Keyword(s):  
Machine Learning ◽  
Cell Death ◽  
False Positive ◽  
Neuronal Damage ◽  
False Negative ◽  
Neuronal Cell ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Analysis Tool

Neuronal damage presents a major health issue necessitating extensive research to identify mechanisms of neuronal cell death and potential therapeutic targets. Commonly used models are slice cultures out of different brain regions extracted from mice or rats, excitotoxically, ischemic, or traumatically lesioned and subsequently treated with potential neuroprotective agents. Thereby cell death is regularly assessed by measuring the propidium iodide (PI) uptake or counting of PI-positive nuclei. The applied methods have a limited applicability, either in terms of objectivity and time consumption or regarding its applicability. Consequently, new tools for analysis are needed. Here, we present a framework to mimic manual counting using machine learning algorithms as tools for semantic segmentation of PI-positive dead cells in hippocampal slice cultures. Therefore, we trained a support vector machine (SVM) to classify images into either “high” or “low” neuronal damage and used naïve Bayes, discriminant analysis, random forest, and a multilayer perceptron (MLP) as classifiers for segmentation of dead cells. In our final models, pixel-wise accuracies of up to 0.97 were achieved using the MLP classifier. Furthermore, a SVM-based post-processing step was introduced to differentiate between false-positive and false-negative detections using morphological features. As only very few false-positive objects and thus training data remained when using the final model, this approach only mildly improved the results. A final object splitting step using Hough transformations was used to account for overlap, leading to a recall of up to 97.6% of the manually assigned PI-positive dead cells. Taken together, we present an analysis tool that can help to objectively and reproducibly analyze neuronal damage in brain-derived slice cultures, taking advantage of the morphology of pycnotic cells for segmentation, object splitting, and identification of false positives.

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