Sea ice surface temperature product from MODIS

Hall, Dorothy K.; Key, Jeffrey R.; Casey, Kimberly A.; Riggs, George A.; Cavalieri, Donald J.

doi:10.1109/tgrs.2004.825587

Cited by 191 publications

(196 citation statements)

References 42 publications

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“…In Algorithm II T s and d ice are retrieved simultaneously with T a as a boundary condition. The SMOS-derived T s is in good agreement with that from MODIS (Hall et al, 2004) (Fig. 17).…”

Section: Daily Comparisonsupporting

confidence: 67%

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Kaleschke²,

et al. 2014

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Abstract. Following the launch of ESA's Soil Moisture andOcean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).

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“…In Algorithm II T s and d ice are retrieved simultaneously with T a as a boundary condition. The SMOS-derived T s is in good agreement with that from MODIS (Hall et al, 2004) (Fig. 17).…”

Section: Daily Comparisonsupporting

confidence: 67%

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Kaleschke²,

et al. 2014

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“…The re-calibration of the cloud flag 11 data against OBS ARCTIC data showed only negligible improvements of STDE, indicating that the errors caused by calibration inaccuracy are small compared to other errors. A product similar to MIST, the MODIS 1 km IST product, performs similarly with STDE of 3.7 • C and an associated bias of −2.1 • C (Hall et al, 2004b). This comparable performance indicates the level of quality that can be expected from fully automated satellite IST products.…”

Section: Discussionmentioning

confidence: 78%

“…The 6 GHz microwave radiometer data have elsewhere been used for IST estimation during all sky conditions, but these data provide an integrated snow pack temperature rather than the surface temperature, because of the microwave's penetration in to the snow and ice Hwang and Barber, 2008). There are other satellite infra-red IST products like those based on MODIS data (Hall, 2004b) and the AVHRR Polar Pathfinder data by Fowler et al (2012). These products have been validated and described by, for example, Scambos et al (2006) and Hall et al (2004b) and used for climate and case studies.…”

Section: Introductionmentioning

confidence: 99%

“…There are other satellite infra-red IST products like those based on MODIS data (Hall, 2004b) and the AVHRR Polar Pathfinder data by Fowler et al (2012). These products have been validated and described by, for example, Scambos et al (2006) and Hall et al (2004b) and used for climate and case studies. The Pathfinder dataset is well suited for climatologically studies, but can not be used for recent or real-time ice surface temperature analysis, due to irregular dataset updates.…”

Section: Introductionmentioning

confidence: 99%

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Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

2012

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Abstract. The ice surface temperature (IST) is an important boundary condition for both atmospheric and ocean and sea ice models and for coupled systems. An operational ice surface temperature product using satellite Metop AVHRR infra-red data was developed for MyOcean. The IST can be mapped in clear sky regions using a split window algorithm specially tuned for sea ice. Clear sky conditions prevail during spring in the Arctic, while persistent cloud cover limits data coverage during summer. The cloud covered regions are detected using the EUMETSAT cloud mask. The Metop IST compares to 2 m temperature at the Greenland ice cap Summit within STD error of 3.14 • C and to Arctic drifting buoy temperature data within STD error of 3.69 • C. A case study reveals that the in situ radiometer data versus satellite IST STD error can be much lower (0.73 • C) and that the different in situ measurements complicate the validation. Differences and variability between Metop IST and in situ data are analysed and discussed. An inter-comparison of Metop IST, numerical weather prediction temperatures and in situ observation indicates large biases between the different quantities. Because of the scarcity of conventional surface temperature or surface air temperature data in the Arctic, the satellite IST data with its relatively good coverage can potentially add valuable information to model analysis for the Arctic atmosphere.

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“…MODIS provides several sea ice products, including sea ice extent and surface temperature (Table l).More information and an initial evaluation of the sea ice surface temperature product used in the example below are avail able in Hall et al [2004]; further validation of the MODIS sea ice products is ongoing.…”

Section: Background On Sensorsmentioning

confidence: 99%

NASA EOS sensors demonstrate potential for multiparameter studies of Arctic sea ice

et al. 2004

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Remote sensing of sea ice is a difficult task due to the discontinuous, complex surface (ice, water, snow) and the frequent presence of clouds. Only through combinations of data from a variety of sensor types can a thorough and complete characterization of sea ice be obtained. The sensors on the NASA Earth Observing System (EOS) Aqua,Terra, and ICESat satellites represent a substantial step forward in the ability to observe sea ice.The sensors are a significant advancement over their predecessors in terms of spatial and radiometric resolution. The increased variety of geophysical data products from the sensors provides a wealth of new information about sea ice. Combinations of products from the different sensors have the potential to provide additional insights.

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Sea ice surface temperature product from MODIS

Cited by 191 publications

References 42 publications

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data

NASA EOS sensors demonstrate potential for multiparameter studies of Arctic sea ice

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