Limitations imposed by cloud cover on multitemporal visible band satellite data sets from polar regions

The study of surface features and processes on glaciers, ice caps and ice sheets often requires multitemporal data in order to take account of the dynamic behaviour of snow and ice. Satellite remote sensing provides an important means of acquiring such data sets from polar regions, but the effectiveness of visible (VIS) and near-infrared (NIR) wavebands is severely limited by the frequent presence of cloud cover. As well as preventing direct imaging of the surface, cloud cover can be difficult to discriminate from snow and ice because of its similarly high reflectivity. This paper describes a detailed quantitative study of the limitations imposed by cloud cover. The number of potentially useful images which could have been acquired from a study area in Svalbard for the April-September period for 1980–89 is determined. Meteorological data from two stations analyzed over this study period show that fewer than 10% of the available satellite overpasses occurred during cloud-free periods. A cloud cover level of three octas or less, used to define high quality imagery, occurred 29% and 32% of the time at the two stations. Markedly seasonal variations are found which can be explained in terms of the regional climate such that during the months August and September the probability of obtaining a cloud-free Landsat image every year is effectively zero. Analysis of several years of Landsat Quick Look images for other regions of Svalbard confirms these findings. However, a similar series for the Scoresby Sund region of East Greenland shows a much higher percentage of low cloud scenes and no marked seasonality.

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Limitations imposed by cloud cover on multitemporal visible band satellite data sets from polar regions

Annals of Glaciology ◽

10.1017/s0260305500012696 ◽

1993 ◽

Vol 17 ◽

pp. 113-120 ◽

Cited By ~ 7

Author(s):

G.J. Marshall ◽

W.G. Rees ◽

J.A. Dowdeswell

Keyword(s):

Cloud Cover ◽

Near Infrared ◽

Meteorological Data ◽

Regional Climate ◽

Data Sets ◽

Polar Regions ◽

Ice Caps ◽

Low Cloud ◽

Multitemporal Data ◽

Snow And Ice

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

The Cryosphere ◽

10.5194/tc-15-1551-2021 ◽

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.

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

10.5194/tc-2020-159 ◽

2020 ◽

Cited By ~ 1

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.

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Streamflow Changes in Mesoscale Lowland Catchment Under Future Climate Conditions

Papers on Global Change IGBP ◽

10.2478/v10190-012-0004-7 ◽

2012 ◽

Vol 19 (1) ◽

Author(s):

Urszula Somorowska ◽

Izabela Piętka

Keyword(s):

Air Temperature ◽

Climate Model ◽

Stream Water ◽

Meteorological Data ◽

Regional Climate ◽

Future Climate ◽

Data Sets ◽

Moderate Increase ◽

Climate Conditions ◽

Daily Time

AbstractThe objective of this study was to investigate the performance of streamflow in a lowland mesoscale catchment in Poland under current and future climate conditions. Simulations of hypothetical streamflow in the future climate were facilitated by meteorological data sets from ensemble simulations from all over Europe with the Regional Climate Model CLM. Projections of precipitation and air temperature for the 21st century under the SRES A1B scenario were used as an input to the hydrological model simulating streamflow at the daily time scale. The combination of relatively moderate increase of annual precipitation sum and mean air temperature might cause lower annual discharges. The possible decrease in stream water resources might be a signal of reduced subsurface recharge and land over drying processes.

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Validation of the HIRHAM-Simulated Indian Summer Monsoon Circulation

Advances in Meteorology ◽

10.1155/2010/415632 ◽

2010 ◽

Vol 2010 ◽

pp. 1-14 ◽

Cited By ~ 15

Author(s):

Stefan Polanski ◽

Annette Rinke ◽

Klaus Dethloff

Keyword(s):

Summer Monsoon ◽

Climate Model ◽

Regional Climate ◽

Horizontal Resolution ◽

Monsoon Circulation ◽

Data Sets ◽

Data Set ◽

Topographic Features ◽

Temperature And Precipitation ◽

Simulated Precipitation

The regional climate model HIRHAM has been applied over the Asian continent to simulate the Indian monsoon circulation under present-day conditions. The model is driven at the lateral and lower boundaries by European reanalysis (ERA40) data for the period from 1958 to 2001. Simulations with a horizontal resolution of 50 km are carried out to analyze the regional monsoon patterns. The focus in this paper is on the validation of the long-term summer monsoon climatology and its variability concerning circulation, temperature, and precipitation. Additionally, the monsoonal behavior in simulations for wet and dry years has been investigated and compared against several observational data sets. The results successfully reproduce the observations due to a realistic reproduction of topographic features. The simulated precipitation shows a better agreement with a high-resolution gridded precipitation data set over the central land areas of India and in the higher elevated Tibetan and Himalayan regions than ERA40.

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Seasonal and interannual variations of top-of-atmosphere irradiance and cloud cover over polar regions derived from the CERES data set

Geophysical Research Letters ◽

10.1029/2006gl026685 ◽

2006 ◽

Vol 33 (19) ◽

Cited By ~ 33

Author(s):

Seiji Kato ◽

Norman G. Loeb ◽

Patrick Minnis ◽

Jennifer A. Francis ◽

Thomas P. Charlock ◽

...

Keyword(s):

Cloud Cover ◽

Polar Regions ◽

Interannual Variations ◽

Data Set ◽

Seasonal And Interannual Variations

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Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

10.5194/egusphere-egu21-5171 ◽

2021 ◽

Author(s):

Georg Wohlfahrt ◽

Albin Hammerle ◽

Barbara Rainer ◽

Florian Haas

Keyword(s):

Chlorophyll Fluorescence ◽

Vegetation Index ◽

Near Infrared ◽

Census Data ◽

Meteorological Data ◽

Vegetation Indices ◽

Wavelength Region ◽

Remote Sensing Data ◽

Warning Signs ◽

Mitigation Measures

Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.

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3-D visualisation of palaeoseismic trench stratigraphy and trench logging using terrestrial remote sensing and GPR – a multiparametric interpretation

Solid Earth ◽

10.5194/se-7-323-2016 ◽

2016 ◽

Vol 7 (2) ◽

pp. 323-340 ◽

Cited By ~ 7

Author(s):

Sascha Schneiderwind ◽

Jack Mason ◽

Thomas Wiatr ◽

Ioannis Papanikolaou ◽

Klaus Reicherter

Keyword(s):

Seismic Hazard ◽

Near Infrared ◽

Normal Faults ◽

Data Sets ◽

Data Set ◽

Backscatter Signal ◽

Multispectral Data ◽

Sedimentary Architecture ◽

Hazard Assessments ◽

Multispectral Analysis

Abstract. Two normal faults on the island of Crete and mainland Greece were studied to test an innovative workflow with the goal of obtaining a more objective palaeoseismic trench log, and a 3-D view of the sedimentary architecture within the trench walls. Sedimentary feature geometries in palaeoseismic trenches are related to palaeoearthquake magnitudes which are used in seismic hazard assessments. If the geometry of these sedimentary features can be more representatively measured, seismic hazard assessments can be improved. In this study more representative measurements of sedimentary features are achieved by combining classical palaeoseismic trenching techniques with multispectral approaches. A conventional trench log was firstly compared to results of ISO (iterative self-organising) cluster analysis of a true colour photomosaic representing the spectrum of visible light. Photomosaic acquisition disadvantages (e.g. illumination) were addressed by complementing the data set with active near-infrared backscatter signal image from t-LiDAR measurements. The multispectral analysis shows that distinct layers can be identified and it compares well with the conventional trench log. According to this, a distinction of adjacent stratigraphic units was enabled by their particular multispectral composition signature. Based on the trench log, a 3-D interpretation of attached 2-D ground-penetrating radar (GPR) profiles collected on the vertical trench wall was then possible. This is highly beneficial for measuring representative layer thicknesses, displacements, and geometries at depth within the trench wall. Thus, misinterpretation due to cutting effects is minimised. This manuscript combines multiparametric approaches and shows (i) how a 3-D visualisation of palaeoseismic trench stratigraphy and logging can be accomplished by combining t-LiDAR and GPR techniques, and (ii) how a multispectral digital analysis can offer additional advantages to interpret palaeoseismic and stratigraphic data. The multispectral data sets are stored allowing unbiased input for future (re)investigations.

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Interactive coupling of regional atmosphere with biosphere in the new generation regional climate system model REMO-iMOVE

Geoscientific Model Development ◽

10.5194/gmd-7-1093-2014 ◽

2014 ◽

Vol 7 (3) ◽

pp. 1093-1114 ◽

Cited By ~ 7

Author(s):

C. Wilhelm ◽

D. Rechid ◽

D. Jacob

Keyword(s):

Land Surface ◽

Regional Climate ◽

Regional Model ◽

Horizontal Resolution ◽

Soil Conditions ◽

Data Sets ◽

Vegetation Phenology ◽

Land Surface Scheme ◽

New Model ◽

Surface Scheme

Abstract. The main objective of this study is the coupling of the regional climate model REMO with a new land surface scheme including dynamic vegetation phenology, and the evaluation of the new model version called REMO with interactive MOsaic-based VEgetation: REMO-iMOVE. First, we focus on the documentation of the technical aspects of the new model constituents and the coupling mechanism. The representation of vegetation in iMOVE is based on plant functional types (PFTs). Their geographical distribution is prescribed to the model which can be derived from different land surface data sets. Here, the PFT distribution is derived from the GLOBCOVER 2000 data set which is available on 1 km × 1 km horizontal resolution. Plant physiological processes like photosynthesis, respiration and transpiration are incorporated into the model. The vegetation modules are fully coupled to atmosphere and soil. In this way, plant physiological activity is directly driven by atmospheric and soil conditions at the model time step (two minutes to some seconds). In turn, the vegetation processes and properties influence the exchange of substances, energy and momentum between land and atmosphere. With the new coupled regional model system, dynamic feedbacks between vegetation, soil and atmosphere are represented at regional to local scale. In the evaluation part, we compare simulation results of REMO-iMOVE and of the reference version REMO2009 to multiple observation data sets of temperature, precipitation, latent heat flux, leaf area index and net primary production, in order to investigate the sensitivity of the regional model to the new land surface scheme and to evaluate the performance of both model versions. Simulations for the regional model domain Europe on a horizontal resolution of 0.44° had been carried out for the time period 1995–2005, forced with ECMWF ERA-Interim reanalyses data as lateral boundary conditions. REMO-iMOVE is able to simulate the European climate with the same quality as the parent model REMO2009. Differences in near-surface climate parameters can be restricted to some regions and are mainly related to the new representation of vegetation phenology. The seasonal and interannual variations in growth and senescence of vegetation are captured by the model. The net primary productivity lies in the range of observed values for most European regions. This study reveals the need for implementing vertical soil water dynamics in order to differentiate the access of plants to water due to different rooting depths. This gets especially important if the model will be used in dynamic vegetation studies.

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Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR

Atmosphere ◽

10.3390/atmos9070262 ◽

2018 ◽

Vol 9 (7) ◽

pp. 262 ◽

Cited By ~ 4

Author(s):

Coraline Wyard ◽

Sébastien Doutreloup ◽

Alexandre Belleflamme ◽

Martin Wild ◽

Xavier Fettweis

Keyword(s):

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Radiative Flux ◽

Added Value ◽

Historical Trends ◽

The Mean

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.

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