571 research outputs found

    Multi-Decadal Variability of Polynya Characteristics and Ice Production in the North Water Polynya by Means of Passive Microwave and Thermal Infrared Satellite Imagery

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    The North Water (NOW) Polynya is a regularly-forming area of open-water and thin-ice, located between northwestern Greenland and Ellesmere Island (Canada) at the northern tip of Baffin Bay. Due to its large spatial extent, it is of high importance for a variety of physical and biological processes, especially in wintertime. Here, we present a long-term remote sensing study for the winter seasons 1978/1979 to 2014/2015. Polynya characteristics are inferred from (1) sea ice concentrations and brightness temperatures from passive microwave satellite sensors (Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager/Sounder (SSM/I-SSMIS)) and (2) thin-ice thickness distributions, which are calculated using MODIS ice-surface temperatures and European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis data in a 1D thermodynamic energy-balance model. Daily ice production rates are retrieved for each winter season from 2002/2003 to 2014/2015, assuming that all heat loss at the ice surface is balanced by ice growth. Two different cloud-cover correction schemes are applied on daily polynya area and ice production values to account for cloud gaps in the MODIS composites. Our results indicate that the NOW polynya experienced significant seasonal changes over the last three decades considering the overall frequency of polynya occurrences, as well as their spatial extent. In the 1980s, there were prolonged periods of a more or less closed ice cover in northern Baffin Bay in winter. This changed towards an average opening on more than 85% of the days between November and March during the last decade. Noticeably, the sea ice cover in the NOW polynya region shows signs of a later-appearing fall freeze-up, starting in the late 1990s. Different methods to obtain daily polynya area using passive microwave AMSR-E/AMSR2 data and SSM/I-SSMIS data were applied. A comparison with MODIS data (thin-ice thickness ≀ 20 cm) shows that the wintertime polynya area estimates derived by MODIS are about 30 to 40% higher than those derived using the polynya signature simulation method (PSSM) with AMSR-E data. In turn, the difference in polynya area between PSSM and a sea ice concentration (SIC) threshold of 70% is fairly low (approximately 10%) when applied to AMSR-E data. For the coarse-resolution SSM/I-SSMIS data, this difference is much larger, particularly in November and December. Instead of a sea ice concentration threshold, the PSSM method should be used for SSM/I-SSMIS data. Depending on the type of cloud-cover correction, the calculated ice production based on MODIS data reaches an average value of 264.4 ± 65.1 km 3 to 275.7 ± 67.4 km 3 (2002/2003 to 2014/2015) and shows a high interannual variability. Our achieved long-term results underline the major importance of the NOW polynya considering its influence on Arctic ice production and associated atmosphere/ocean processes

    Long-term coastal-polynya dynamics in the southern Weddell Sea from MODIS thermal-infrared imagery

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    Based upon thermal-infrared satellite imagery in combination with ERA-Interim atmospheric reanalysis data, we derive long-term polynya characteristics such as polynya area, thin-ice thickness distribution, and ice-production rates for a 13-year investigation period (2002–2014) for the austral winter (1 April to 30 September) in the Antarctic southern Weddell Sea. All polynya parameters are derived from daily cloud-cover corrected thin-ice thickness composites. The focus lies on coastal polynyas which are important hot spots for new-ice formation, bottom-water formation, and heat/moisture release into the atmosphere. MODIS has the capability to resolve even very narrow coastal polynyas. Its major disadvantage is the sensor limitation due to cloud cover. We make use of a newly developed and adapted spatial feature reconstruction scheme to account for cloud-covered areas. We find the sea-ice areas in front of the Ronne and Brunt ice shelves to be the most active with an annual average polynya area of 3018 ± 1298 and 3516 ± 1420 km2 as well as an accumulated volume ice production of 31 ± 13 and 31 ± 12 km3, respectively. For the remaining four regions, estimates amount to 421 ± 294 km2 and 4 ± 3 km3 (Antarctic Peninsula), 1148 ± 432 km2 and 12 ± 5 km3 (iceberg A23A), 901 ± 703 km2 and 10 ± 8 km3 (Filchner Ice Shelf), as well as 499 ± 277 km2 and 5 ± 2 km3 (Coats Land). Our findings are discussed in comparison to recent studies based on coupled sea-ice/ocean models and passive-microwave satellite imagery, each investigating different parts of the southern Weddell Sea

    Retrieval of Wintertime Sea Ice Production in Arctic Polynyas Using Thermal Infrared and Passive Microwave Remote Sensing Data

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    Precise knowledge of wintertime sea ice production in Arctic polynyas is not only required to enhance our understanding of atmosphere‐sea ice‐ocean interactions but also to verify frequently utilized climate and ocean models. Here, a high‐resolution (2‐km) Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared satellite data set featuring spatial and temporal characteristics of 17 Arctic polynya regions for the winter seasons 2002/2003 to 2017/2018 is directly compared to an akin low‐resolution Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) passive microwave data set for 2002/2003 to 2010/2011. The MODIS data set is purely based on a 1‐D energy‐balance model, where thin‐ice thicknesses (≀ 20 cm) are directly derived from ice‐surface temperature swath data and European Centre for Medium‐Range Weather Forecasts Re‐Analysis‐Interim atmospheric reanalysis data on a quasi‐daily basis. Thin‐ice thicknesses in the AMSR‐E data set are derived empirically. Important polynya properties such as areal extent and potential thermodynamic ice production can be estimated from both pan‐Arctic data sets. Although independently derived, our results show that both data sets feature quite similar spatial and temporal variations of polynya area (POLA) and ice production (IP), which suggests a high reliability. The average POLA (average accumulated IP) for all Arctic polynyas combined derived from both MODIS and AMSR‐E are 1.99×105 km2 (1.34×103 km3) and 2.29×105 km2 (1.31×103 km3), respectively. Narrow polynyas in areas such as the Canadian Arctic Archipelago are notably better resolved by MODIS. Analysis of 16 winter seasons provides an evaluation of long‐term trends in POLA and IP, revealing the significant increase of ice formation in polynyas along the Siberian coast

    A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS

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    Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.</jats:p

    Burnt area mapping in insular Southeast Asia using medium resolution satellite imagery

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    Burnt area mapping in humid tropical insular Southeast Asia using medium resolution (250-500m) satellite imagery is characterized by persisting cloud cover, wide range of land cover types, vast amount of wetland areas and highly varying fire regimes. The objective of this study was to deepen understanding of three major aspects affecting the implementation and limits of medium resolution burnt area mapping in insular Southeast Asia: 1) fire-induced spectral changes, 2) most suitable multitemporal compositing methods and 3) burn scars patterns and size distribution. The results revealed a high variation in fire-induced spectral changes depending on the pre-fire greenness of burnt area. It was concluded that this variation needs to be taken into account in change detection based burnt area mapping algorithms in order to maximize the potential of medium resolution satellite data. Minimum near infrared (MODIS band 2, 0.86ÎŒm) compositing method was found to be the most suitable for burnt area mapping purposes using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In general, medium resolution burnt area mapping was found to be usable in the wetlands of insular Southeast Asia, whereas in other areas the usability was seriously jeopardized by the small size of burn scars. The suitability of medium resolution data for burnt area mapping in wetlands is important since recently Southeast Asian wetlands have become a major point of interest in many fields of science due to yearly occurring wild fires that not only degrade these unique ecosystems but also create regional haze problem and release globally significant amounts of carbon into the atmosphere due to burning peat. Finally, super-resolution MODIS images were tested but the test failed to improve the detection of small scars. Therefore, super-resolution technique was not considered to be applicable to regional level burnt area mapping in insular Southeast Asia.Laaja valikoima erilaisia maankĂ€yttöluokkia, pilvisyys ja kosteikkoalueiden suuri mÀÀrĂ€ luovat erityispiirteet paloalueiden kartoitukselle Kaakkois-Aasian saariston kostean troppisissa olosuhteissa keskiresoluutioisilla (250m-500m) satelliittikuva-aineistoilla. TĂ€mĂ€n tutkimuksen tavoitteena oli syventÀÀ ymmĂ€rrystĂ€ keskiresoluutioisen paloaluekartoituksen toteutukseen ja rajoituksiin Kaakkois-Aasian saaristossa vaikuttavista tekijöistĂ€. Tutkimuksen tulokset paljastivat suurta vaihtelua tulipalojen aiheuttamissa heijastussĂ€teilyn muutoksissa riippuen palaneen alueen vehreydestĂ€ ennen tulipaloa. JohtopÀÀtöksenĂ€ todettiin ettĂ€ keskiresoluutioisten satelliittikuvien koko potentiaalin hyödyntĂ€miseksi paloalueiden kartoituksessa tĂ€mĂ€ vaihtelu tulisi ottaa huomioon paloalueiden havainnointialgoritmeissa jotka perustuvat heijastussĂ€teilyn muutosten seurantaan. TĂ€hĂ€n ajatukseen perustuvaa paloalueiden kartoitusta myös kokeiltiin aineistoilla jotka oli tutkimuksissa todettu parhaiten tarkoitukseen sopiviksi. Paloalueiden muoto- ja kokojakauman analyysiin sekĂ€ kĂ€ytĂ€nnön testeihin perustuen keskiresoluutioinen paloalueiden kartoitus todettiin kĂ€yttökelpoiseksi Kaakkois-Aasian saariston kosteikkoalueilla. Muilla alueilla sen sijaan paloalueiden pieni koko uhkasi vakavasti sen kĂ€yttökelpoisuutta. Keskiresoluutioisten satelliittikuva-aineistojen kĂ€yttökelpoisuus paloalueiden kartoitukseen kosteikkoalueilla on kuitenkin merkittĂ€vÀÀ sillĂ€ viime aikoina Kaakkois-Aasian kosteikkoalueet ovat monilla tieteenaloilla nousseet kiinnostuksen kohteeksi vuosittain esiintyvien tulipalojen takia. Vuosittaiset tulipalot eivĂ€t ainoastaan heikennĂ€ nĂ€itĂ€ ainutlaatuisia ekosysteemejĂ€ vaan lĂ€hinnĂ€ palavan turpeen johdosta myös aiheuttavat pahoja alueellisia savusumuongelmia ja vapauttavat maailmanlaajuisesti merkittĂ€viĂ€ mÀÀriĂ€ hiilidioksidia ilmakehÀÀn. TĂ€mĂ€n tutkimuksen tulokset osaltaan luovat pohjaa yhĂ€ tarkempien alueellisten paloalueiden kartoitusmenetelmien kehittĂ€miselle. NĂ€illĂ€ menetelmillĂ€ kerĂ€ttĂ€vĂ€ tieto paloalueiden laajuudesta ja sijainneista antaa muiden alojen tutkijoille yhĂ€ paremmat mahdollisuudet arvioida Kaakkois-Aasian saariston kosteikkoalueiden tulipalojen paikallisia, alueellisia ja maailmanlaajuisia vaikutuksia

    Land cover mapping of a tropical region by integrating multi-year data into an annual time series

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    Generating annual land cover maps in the tropics based on optical data is challenging because of the large amount of invalid observations resulting from the presence of clouds and haze or high moisture content in the atmosphere. This study proposes a strategy to build an annual time series from multi-year data to fill data gaps. The approach was tested using the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index and spectral bands as input for land cover classification of Colombia. In a second step, selected ancillary variables, such as elevation, L-band Radar, and precipitation were added to improve overall accuracy. Decision-tree classification was used for assigning eleven land cover classes using the International Geosphere-Biosphere Programme (IGBP) legend. Maps were assessed by their spatial confidence derived from the decision tree approach and conventional accuracy measures using reference data and statistics based on the error matrix. The multi-year data integration approach drastically decreased the area covered by invalid pixels. Overall accuracy of land cover maps significantly increased from 58.36% using only optical time series of 2011 filtered for low quality observations, to 68.79% when using data for 2011 ± 2 years. Adding elevation to the feature set resulted in 70.50% accuracy

    A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics

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    Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.Peer reviewe
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