Snow and ice products from Suomi NPP VIIRS

Key, Jeffrey R.; Mahoney, Robert; Liu, Yinghui; Romanov, Peter; Tschudi, M. A.; Appel, Igor; Maslanik, James A; Baldwin, Dan; Wang, Xuanji; Meade, Paul E.

doi:10.1002/2013jd020459

Cited by 56 publications

(42 citation statements)

References 30 publications

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“…Consequently, changes in sea ice characteristics, temperature inversion strength, This study presents the validation of the VIIRS IST EDR with multiple datasets, including aircraft measurements, 2 m air temperature from Arctic drifting buoys, MODIS IST, and surface air temperatures from an atmospheric reanalysis product. This work significantly extends the preliminary validation study of Key et al [18] by employing additional IST product inter-comparisons, thereby providing a robust evaluation of the operational VIIRS IST product.…”

Section: Introductionmentioning

confidence: 58%

Validation of the Suomi NPP VIIRS Ice Surface Temperature Environmental Data Record

et al. 2015

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Continuous monitoring of the surface temperature is critical to understanding and forecasting Arctic climate change; as surface temperature integrates changes in the surface energy budget. The sea-ice surface temperature (IST) has been measured with optical and thermal infrared sensors for many years. With the IST Environmental Data Record (EDR) available from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) and future Joint Polar Satellite System (JPSS) satellites; we can continue to monitor and investigate Arctic climate change. This work examines the quality of the VIIRS IST EDR. Validation is performed through comparisons with multiple datasets; including NASA IceBridge measurements; air temperature from Arctic drifting ice buoys; Moderate Resolution Imaging Spectroradiometer (MODIS) IST; MODIS IST simultaneous nadir overpass (SNO); and surface air temperature from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. Results show biases of´0.34;´0.12; 0.16; 3.20; and´3.41 K compared to an aircraft-mounted downward-looking pyrometer; MODIS; MODIS SNO; drifting buoy; and NCEP/NCAR reanalysis; respectively; root-mean-square errors of 0.98; 1.02; 0.95; 4.89; and 6.94 K; and root-mean-square errors with the bias removed of 0.92; 1.01; 0.94; 3.70; and 6.04 K. Based on the IceBridge and MODIS results; the VIIRS IST uncertainty (RMSE) meets or exceeds the JPSS system requirement of 1.0 K. The product can therefore be considered useful for meteorological and climatological applications.

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Section: Introductionmentioning

confidence: 58%

Validation of the Suomi NPP VIIRS Ice Surface Temperature Environmental Data Record

et al. 2015

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“…We suggest that these new daily MODIS OWSI and SIT charts can supplement other MODIS sea ice products (sea ice extent, SIC, and SIT) [23,28,29,35,42] in the Arctic sea ice monitoring, and especially, in the validation and development of sea ice products from the microwave sensor data.…”

Section: Discussionmentioning

confidence: 99%

“…The R (daytime) or IST (night-time) data have also been used for the pixel-based SIC estimation [28][29][30][31]. This requires open water and sea ice (100% SIC) tie points.…”

Section: Discussionmentioning

confidence: 99%

“…At the ice edge there may be no pixels at all with 100% SIC. Therefore, we suggest that currently the pixel-based SIC charts from the visible and IST data [28][29][30][31] are not yet accurate enough, especially in the mid-range of SIC (30% to 70%). It would be good to complement them with the SIC charts retrieved from the pixel-based OWSI classification of the visible and/or IST imagery by calculating fraction of sea ice pixels within radiometer footprints or segments of classified SAR images.…”

Section: Recent Previous Studiesmentioning

confidence: 94%

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MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data

Mäkynen

Karvonen

2017

Remote Sensing

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Abstract:We have developed algorithms and procedures for calculating daily sea ice thickness (SIT) and open water-sea ice (OWSI) charts, based on the Moderate Resolution Imaging Spectroradiometer (MODIS), ice surface temperature (IST) (night-time only), and reflectance (R) swath data, respectively. The resolution of the SIT chart is 1 km and that of the OWSI chart is 250 m. The charts are targeted to be used in development and validation of sea ice products from microwave sensor data. We improve the original MODIS cloud masks for the IST and R data, with a focus on identifying larger cloud-free areas in the data. The SIT estimation from the MODIS IST swath data follows previous studies. The daily SIT chart is composed from available swath charts by assigning daily median SIT to a pixel. The OWSI classification is simply conducted by a fixed threshold for the MODIS band 1 R. This was based on manually selected R data for various ice types in late winter, early melt, and advanced melt conditions. The composition procedures for the daily SIT and OWSI charts somewhat compensates for errors due to the undetected clouds. The SIT and OWSI charts were compared against manual ice charts by Arctic and Antarctic Research Institute in Russia and by Norwegian Meteorological Institute, respectively, and on average, a good relationship between the charts was found. Pixel-wise comparison of the SIT and OWSI charts showed very good agreement in open water vs. sea ice classification, which gives further confidence on the reliability of our algorithms. We also demonstrate usage of the MODIS OWSI and SIT charts for validation of sea ice concentration charts based on the SENTINEL-1 SAR and AMSR2 radiometer data and two different algorithms.

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“…Optical sensors can provide high resolution image, however it is only useful for sea ice monitoring under clear sky conditions and during the day [12]. Passive microwave sensors are alternative choice for observing sea ice since microwaves can penetrate heavy clouds and precipitations [13].…”

mentioning

confidence: 99%

A Method to Determine the Margins of High Sea Ice Concentration Using AMSR-E Passive Microwave Imagery

Zhang¹,

Liu²,

Zhang³

et al. 2017

GEP

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The margin of the sea ice with high sea ice concentration is a principal feature in microwave image and a hotspot in image recognition. A method for determining the margins is developed using the feature of dual-polarized brightness temperatures at 36.5 GHz and a new parameter (contrast ratio) is used in this paper. For the microwaves, the ratio of the horizontal-polarized emissivity to the vertical-polarized emissivity is approximately equal to the ratio between horizontal-polarized and vertical-polarized brightness temperatures of sea surface, which called as the dual-polarized emissivity ratio in this study. It is found that the dual-polarized emissivity ratio of sea ice with nearly 100% sea ice concentration in Arctic at 36.5 GHz band has a value ranged between 0.92 and 0.96, as shown by satellite-observed data in figure of horizontal-polarized brightness temperature versus vertical-polarized brightness temperature. From open water to sea ice covered area, the contrast-ratio can show the changing features of the dual-polarized brightness temperature at 36.5 GHz. The contrast ratio rapidly changes at the ice margins and its gradient appears an extreme value when the ratio changes around 0.92. This extreme value is examined by the ice concentration calculated by the MODIS data. And the results indicate that the threshold ratio coincides with the contour line of 96% sea ice concentration. So the parameter of contrast ratio could be used to determine the position of margins in microwave image.

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Snow and ice products from Suomi NPP VIIRS

Cited by 56 publications

References 30 publications

Validation of the Suomi NPP VIIRS Ice Surface Temperature Environmental Data Record

Validation of the Suomi NPP VIIRS Ice Surface Temperature Environmental Data Record

MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data

A Method to Determine the Margins of High Sea Ice Concentration Using AMSR-E Passive Microwave Imagery

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