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MODIS Snow and Sea Ice Data Products at the EOSDIS NSIDC DAAC

MODIS Snow and Sea Ice Data Products at the EOSDIS NSIDC DAAC. Dorothy K. Hall Cryospheric Sciences Branch, NASA/GSFC and George A. Riggs SSAI

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MODIS Snow and Sea Ice Data Products at the EOSDIS NSIDC DAAC

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  1. MODIS Snow and Sea Ice Data Products at the EOSDIS NSIDC DAAC Dorothy K. HallCryospheric Sciences Branch, NASA/GSFC and George A. RiggsSSAI Kimberly A. Casey/SSAI Nicolo E. DiGirolamo/SSAI Vincent V. Salomonson/Earth Sciences Directorate Emeritus NASA HQ Data Products Review January 11 – 12, 2006

  2. Why are Global Snow Cover and Sea Ice Important? • Snow cover with its large surface area, up to 46 X 106 km2 in the Northern Hemisphere winter, and its high albedo, is a critical parameter in the Earth’s global energy balance; springtime decreases in N.H. snow cover have been measured • Snow cover supplies >75% of the water resources for the western United States, and is a key component of the water resources in many mountainous areas of the world • The large surface area of sea ice and its high albedo are critical to the Earth’s energy balance, and research shows that the N.H. sea ice extent has been decreasing • Changes in the Earth’s cryosphere must be measured by a variety of sensors and at a variety of different resolutions for observation and modeling studies • It is also critical to develop products that are accurate and well characterized for creation of climate-data records (CDRs)

  3. Snow Products Data Dependencies • All higher-level products depend on the swath products • All climate-modeling grid (CMG) global products depend on the tile products • Other MODIS products use MODIS snow cover product as input • Land-surface temperature, MOD11 • Albedo / BRDF, MCD43

  4. Title (ESDT): MOD10_L2 & MYD10_L2 Description: Terra and Aqua swath snow cover includes fractional (sub pixel) snow cover (FSC) for Collection 5; 500-m resolution Product algorithm theoretical basis: NDSI and criteria tests for snow detection, regression for FSC, cloud masking from MOD35_L2; heritage: Crane and Anderson, 1984; Tucker et al., 1985; Dozier, 1989 Science need (justification): snow-cover monitoring; improved snow cover in terms of temporal and spatial resolution relative to operational products; improved snow/cloud discrimination; automated algorithm (removes possible inconsistencies introduced by analyst interpretation); critical for creation of higher-level snow products Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): very mature, very stable, absolute uncertainty 2-10% (depending on land cover & season); relative uncertainty 5-20% (compared with operational products with lower temporal and spatial resolution); the FSC within a pixel can be provided with a mean absolute error of < 0.1 over the entire range of FSC 0.0 - 1.0 Intended or appropriate product use (including any limitations on use): for a detailed look at regional snow cover and fractional snow cover, cloud-cover permitting; often multiple swaths a day available Science value (use of product for science, papers written, breakthroughs, multidisciplinary use):snow-cover monitoring and creation of higher-level products; see references listed on later slides

  5. MODIS binary snow map (left) and fractional-snow-cover product (right) - 500-m resolution December 14, 2000 MT ND SD WY NE CO KS cloud After Salomonson & Appel (2003)

  6. Title (ESDT): MOD10_L2 & MYD10_L2 Description: Terra and Aqua swath snow cover includes fractional (sub pixel) snow cover (FSC) for Collection 5; 500-m resolution Product algorithm theoretical basis: NDSI and criteria tests for snow detection, NDVI for improved mapping in forests; regression for FSC, cloud masking from MOD35_L2; heritage: Crane and Anderson, 1984; Tucker et al., 1985; Dozier, 1989 Science need (justification): snow-cover monitoring; improved snow cover in terms of temporal and spatial resolution relative to operational products; improved snow/cloud discrimination; automated algorithm (removes possible inconsistencies introduced by analyst interpretation); critical for creation of higher-level snow products Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): very mature, very stable, absolute uncertainty 2-10% (depending on land cover & season); relative uncertainty 5-20% (compared with operational products with lower temporal and spatial resolution); often multiple swaths a day available the FSC within a pixel can be provided with a mean absolute error of <10% over the entire range of FSC from 0.0 - 1.0; Collection 4 cloud shadows may appear as false snow especially during the summer (fixed in Collection 5) causing errors of commission Intended or appropriate product use (including any limitations on use): for a detailed look, often several times daily, at regional snow cover and fractional snow cover, cloud-cover permitting Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): snow-cover monitoring and creation of higher-level products; see references listed on later slides

  7. MOD10_L2: 500-m swath product of California and the western U.S., October 31, 2004 cloud MODIS true-color image (left - bands 1, 4, 3) and snow map (right)

  8. Title (ESDT): MOD10A1 & MYD10A1 Description: Terra & Aqua snow cover daily tile map on sinusoidal grid, includes snow albedo in Collection 4 and albedo and FSC in Collection 5; 500-m resolution Product algorithm theoretical basis: see heritage for MOD 10_L2 snow detection; scoring algorithm for observation selection, plus albedo calculation, heritage: Nolin and Stroeve, 1997 & Stroeve and Nolin, 1997 Science need (justification): snow-cover monitoring; snow albedo for energy balance modeling; as compared to operational products, improved snow cover in terms of temporal and spatial resolution & improved snow/cloud discrimination; automated algorithm (removes possible inconsistencies introduced by analyst interpretation) Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): very mature, very stable, absolute uncertainty 2-10% (depending on land cover & season); relative uncertainty 5-10% (compared with operational products with lower temporal and spatial resolution), with the exception of the snow albedo part of the product which is unvalidated; Collection 4 cloud shadows may appear as false snow especially during the summer (fixed in Collection 5) Intended or appropriate product use (including any limitations on use): for a detailed look at regional snow cover and fractional snow cover, cloud-cover permitting; for use in regional-scale models Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): used in hydrological modeling to develop snow-cover depletion curves; model output results show improvement with inclusion on MODIS snow maps; albedo needed for energy-balance modeling; products suitable for creation of a CDR; see references listed on later slides

  9. Title (ESDT): MOD10A2 & MYD10A2 Description:Terra & Aqua snow cover 8-day composite tile map on a sinusoidal grid; 500-m resolution, maximum snow extent for the period Product algorithm theoretical basis: see MOD10_L2, plus compositing algorithm to map maximum snow cover, see heritage for MOD10A1 and MOD10_L2 Science need (justification): see justification for MOD10A1, plus the following: because of cloudcover, composited data are needed to provide clear views, minimize cloud obscuration of snow-covered areas and for use in developing snow-cover depletion curves during cloudy periods Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): very mature, very stable, absolute uncertainty 2-10% (depending on land cover and season); uncertainty may be greater than in daily products due to propagation of errors during compositing Intended or appropriate product use (including any limitations on use): see justification for MOD10A1, plus the following: due to cloudcover, some snow events in the 8-day period may not be mapped Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): used in hydrological modeling to develop depletion curves; model output results show improvement with inclusion on MODIS snow maps; see references listed on later slides

  10. Day Snow error count Percent error in tile 225 860 0.02 226 62 0.0011 227 38 0.0007 228 146 0.003 229 3625 0.064 230 4188 0.074 231 50 0.0009 232 1832 0.032 White is false snow caused by cloud-shadowed land or by cloud-shadowed cloud that occurred on any day in the period Total cumulative snow error is 0.19% for the period. The cloud-shadow screen in Collection 5 will eliminate most of this type of error MOD10A2 8-day snow map product for 12-19 August 2004 Southeastern U.S. false snow

  11. Title (ESDT): MOD10A2 & MYD10A2 Description:Terra & Aqua snow cover 8-day composite tile map on a sinusoidal grid; 500-m resolution, maximum snow extent for the period Product algorithm theoretical basis: see MOD10_L2, plus compositing algorithm to map maximum snow cover, see heritage for MOD10A1 and MOD10_L2 Science need (justification): see justification for MOD10A1, plus the following: because of cloudcover, composited data are needed to provide clear views, minimize cloud obscuration of snow-covered areas and for use in developing snow-cover depletion curves during cloudy periods; composited maps are needed because cloudcover often precludes daily mapping of snow cover Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): very mature, very stable, absolute uncertainty 2-10% (depending on land cover and season); uncertainty may be greater than in daily products due to propagation of errors during compositing Intended or appropriate product use (including any limitations on use): see justification for MOD10A1, plus: due to cloudcover, some snow events in the 8-day period may not be mapped Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): used in hydrological modeling to develop snow-cover depletion curves; model output results show improvement with inclusion on MODIS snow maps; see references listed on later slides

  12. Applications of MODIS snow data in Afghanistan for the Famine Early Warning System (FEWS) The map on the top illustrates the current year snow conditions while the map on the bottom shows the snow extent for the same 8-day MODIS period from last year http://edcw2ks21.cr.usgs.gov/Afghan/product.php?image=so Courtesy Michael Budde/USGS

  13. Snow Accumulation/Depletion Curves above 2500m for the Basin Outlined in Red Snow-Cover Depletion Curves are Developed using MODIS 8-day Snow Maps (MOD10A2) for FEWS to provide an indication of amount of seasonal snow cover for irrigation Courtesy Michael Budde/USGS

  14. Merged MODIS Snow Cover Extent and AFWA Snow Depth Daily snow depth values from the Air Force Weather Agency (AFWA) and NOAA are used to create a maximum 8-day snow depth corresponding to the MODIS composite period. These two data sources are then merged to produce a product that identifies snow depth (AFWA) in terms of the snow extent identified using MODIS. The merged product maintains the spatial resolution of MODIS and provides snow depth measurements. Courtesy Michael Budde/USGS

  15. Title (ESDT): MOD10C1 & MYD10C1 Description:Terra & Aqua snow cover daily map on climate-modeling grid (lat/lon) including fractional snow cover; 0.05-deg resolution (~5 km at Equator) Product algorithm theoretical basis:see justification for MOD10A1; heritage: see heritage for MOD 10A1 Science need (justification): see justification for MOD10A1; needed for GCM and other product (e.g., AMSR-E and SeaWinds) validation Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): very mature, very stable, absolute uncertainty 2-10%; relative uncertainty 5-20% Intended or appropriate product use (including any limitations on use): for an assessment of snow cover globally, cloud-cover permitting, in a single image Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): useful in GCM modeling for validation; monitoring global snow extent; see references listed on later slides; suitable for creation of a CDR

  16. MOD10C1: Daily CMG snow map (0.05º resolution) April 25, 2004 cloud Percent Snow Cover

  17. Title (ESDT): MOD10C1 & MYD10C1 Description:Terra & Aqua snow cover daily map on climate-modeling grid (lat/lon) including fractional snow cover; 0.05-deg resolution (~5 km at Equator) Product algorithm theoretical basis:see justification for MOD10A1; see heritage for MOD 10A1 Science need (justification): see justification for MOD10A1; needed for GCM and other product (e.g., AMSR-E and SeaWinds) validation Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): very mature, very stable, absolute uncertainty 2-10%; relative uncertainty 5-20% Intended or appropriate product use (including any limitations on use): for an assessment of snow cover globally, cloud-cover permitting, in a single image Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): useful in GCM modeling for validation; monitoring global snow extent; see references listed on later slides; suitable for creation of a CDR

  18. Title (ESDT): MOD10C2 & MYD10C2 Description: Snow cover 8-day composite tile map on climate-modeling grid (CMG) (lat/lon) including fractional snow cover; 0.05-deg resolution Product algorithm theoretical basis:see justification for MOD10A2, plus 8-day compositing and binning algorithm, heritage: see heritage for MOD10A2 Science need (justification): see justification for MOD10A2, plus: composited maps are needed because cloudcover often precludes daily mapping of snow cover Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): very mature, very stable, absolute uncertainty 2-10%; relative uncertainty 5-20%; uncertainty may be greater than in daily products due to propagation of errors during compositing Intended or appropriate product use (including any limitations on use): for an assessment of snow cover globally, cloud-cover permitting, in a single image; useful for GCM and other product (e.g., AMSR-E) validation Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): useful in GCM modeling for validation; monitoring global snow extent; see references listed on later slides

  19. MOD10C2: 8-Day Composite CMG snow map fractional snow cover from 1 - 100% not shown April 6 - 13, 2000 March 6-13, 2002 The 8-day composite CMG maps maximize snow cover and minimize cloud cover for the compositing period

  20. Title (ESDT): MOD10C2 & MYD10C2 Description: Snow cover 8-day composite tile map on climate-modeling grid (CMG) (lat/lon) including fractional snow cover; 0.05-deg resolution Product algorithm theoretical basis:see justification for MOD10A2, plus 8-day compositing and binning algorithm; see heritage for MOD10A2 Science need (justification): see justification for MOD10A2, plus: composited maps are needed because cloudcover often precludes daily mapping of snow cover Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): very mature, very stable, absolute uncertainty 2-10%; relative uncertainty 5-20%; uncertainty may be greater than in daily products due to propagation of errors during compositing Intended or appropriate product use (including any limitations on use): for an assessment of snow cover globally, cloud-cover permitting, in a single image; useful for GCM and other product (e.g., AMSR-E) validation Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): useful in GCM modeling for validation; monitoring global snow extent; see references listed on later slides

  21. Peer-Reviewed Publications – Snow (MOD10 & MYD10) (in press or 2005) • Shamir, E. and K.P. Georgakakos, in press: Distributed snow accumulation and ablation modeling in the American River Basin, Remote Sensing of Environment. • Hall, D.K., G.A. Riggs and V.V. Salomonson, in press : MODIS Snow and Sea Ice Products, Earth Science Satellite Remote Sensing - Volume I: Science and Instruments, J. Qu (ed.), Springer-Verlag Press. • Mcguire, M., A. Wood, A. Hamlet and D. Lettenmaier, 2005: Use of satellite data for streamflow and reservoir storage forecasts in the Snake River Basin, ID, Journal of Water Resources Planning and Management. • Tekeli, A.E., Z. Akyurek, A.A. Sorman, A. Sensoy and A.U. Sorman, 2005: Using MODIS snow-cover maps in modeling snowmelt runoff processes in the eastern part of Turkey, Remote Sensing of Environment, 97:216-230. • Brubaker, K., R. Pinker and E. Deviatova, 2005: Evaluation and comparison of MODIS and IMS snow-cover estimates for the continental U.S. using station data, Journal of Hydrometeorology. • Zhou, X., H. Xie and J.M.H. Hendrickx, 2005: Statistical evaluation of remotely sensed snow-cover products with constraints from streamflow and SNOTEL measurements, Remote Sensing of Environment, 94:214-231. • Déry, S., V.V. Salomonson, M. Stieglitz, D.K. Hall and I. Appel, 2005: An approach to using snow areal depletion curves inferred from MODIS and its application to land surface modeling in Alaska, Hydrological Processes, 19:2755-2774. • Rodell, M. and P. Houser, 2005: Updating a land surface model with MODIS-derived snow cover, Journal of Hydrometeorology, 5:1064-1075. • Lee, S., A.G. Klein and T.M. Over, 2005: A comparison of MODIS and NOHRSC snow-cover products for simulating streamflow using the Snowmelt Runoff Model, Hydrological Processes, DOI10.1002/hyp.5810.

  22. Peer-Reviewed Publications – Snow (MOD10 & MYD10) (2002-2004) • Salomonson, V.V. and I. Appel, 2004: Estimating fractional snow cover from MODIS using the normalized difference snow index, Remote Sensing of Environment, 89:351-360. • Simic, A., R. Fernandes, R. Brown, P. Romanov and W. Park, 2004: Validation of Vegetation, MODIS and GOES+SSM/I snow-cover products over Canada based on surface snow depth observations, Hydrological Processes, 18:1089-1104. • Dankers, R., and S. De Jong. 2004. Monitoring snow-cover dynamics in Northern Fennoscandia with SPOT VEGETATION images. International Journal of Remote Sensing 25 (15): 2933-2949. • Drusch, M., D. Vasiljevic, and P. Viterbo. 2004: ECMWF's global snow analysis: Assessment and revision based on satellite observations. Journal of Applied Meteorology 43 (9): 1282-1294. • Dankers, R. and S. M. De Jong, 2004: Monitoring snow-cover dynamics in Northern Fennoscandia with SPOT VEGETATION images, International Journal of Remote Sensing, 25(15):2933-2949. • Klein A, Barnett A C (2003) Validation of daily MODIS snow maps of the Upper Rio Grande River Basin for the 2000-2001 snow year. Remote Sensing of Environment 86:162-176. • Maurer E P, Rhoads J D, Dubayah R O and Lettenmaier D P (2003) Evaluation of the snow-covered area data product from MODIS. Hydrological Processes 17:59-71. • Klein A G, Stroeve J (2002) Development and validation of a snow albedo algorithm for the MODIS instrument. Annals of Glaciology 34: 45-52. • Hall, D.K., G.A. Riggs, V.V. Salomonson, N.E. DiGirolamo and K.J. Bayr, 2002: MODIS snow-cover products, Remote Sensing of Environment, 83:181-194.

  23. Selected Non-Peer-Reviewed Publications – Snow (MOD10 & MYD10) (2002-2004) • McGuire, M., A. Wood, Q. Zeng and D. Lettenmaier, 2005: Use of MODIS snow cover imagery for streamflow and reservoir storage forecasts in the Snake River basin, 85th AMS Annual Meeting (San Diego, Ca.), 19th Conference on Hydrology, January 2005. • Riggs, G.A., N. DiGirolamo, D.K. Hall, 2005: "Comparison of MODIS Daily Global Fractional Snow Cover Maps at 0.05 and 0.25 Degree Resolutions," Proceedings of the 62nd Eastern Snow Conference, 7-10 June 2005, Waterloo, ON, Canada. • Hall, D.K., R. Solberg, K.A. Casey, J-G. Winther, 2004: "Comparison of MODIS and SnowStar Snow Maps in Scandinavia," Proceedings of the 61st Eastern Snow Conference, 9-11 June 2004, Portland, ME. • Riggs, G., and D.K. Hall, 2004: "Snow Mapping with the MODIS Aqua Instrument," Proceedings of the 61st Eastern Snow Conference, 9-11 June 2004, Portland, ME. • Hall, D.K., J.L. Foster, D.A. Robinson and G.A. Riggs, 2004: Merging the MODIS and RUCL monthly snow-cover records, Proceedings of IGARSS 2004, 20-24 September 2004, Anchorage, AK. • Riggs, G.A., D. K. Hall & V. V. Salomonson, 2003: MODIS Sea Ice Products User Guide (with updates posted on website in 2006) • Hall, D.K., V.V. Salomonson, G.A. Riggs and A.G. Klein, 2003: "Snow and ice products from the Moderate Resolution Imaging Spectroradiometer," Proceedings of the ASPRS meeting, 5-9 May 2003, Anchorage, AK. • Riggs, G.A. and D. K. Hall, 2003: "Reduction of Cloud Obscuration in the MODIS Snow Data Product," Proceedings of the 60th Eastern Snow Conference, 4-6 June 2003, Sherbrooke, Quebec, Canada. • Bussiéres, N., D. De Séve, and A. Walker. 2002. Evaluation of MODIS snow-cover products over Canadian regions. Proceedings of IGARSS’02, Toronto, Canada, pp. 2302-2304.

  24. Summary of Selected Results from Users • MODIS snow products (MOD10A1 & A2) are being used extensively as input to refining a daily SWE model for Afghanistan (M. Budde, written communication) • USGS is using MOD10A1 & A2 as inputs to Southwest Alaska Network vital-signs monitoring network • MODIS daily snow products compare well with operational products; MODIS products generally show fewer errors with respect to ground measurements (e.g., SNOTEL) • Cloud mask as applied to the MODIS standard snow products allows more snow to be mapped, in some areas, than does the NOHRSC product (Mauer et al., 2003) • MODIS daily snow products agree well with SNOTEL measurements in the Upper Rio Grande river basin (Klein & Barnett, 2003) • Snow-mapping algorithms that are tuned to a specific area can give better results than the standard MODIS snow-map algorithms which are designed to map snow globally, but results are nearly equal if a regionally-tuned cloud mask and coastline is used (Hall et al., 2004) • MODIS daily snow products improved output of land surface models (Rodell and Houser, 2005) • Streamflow forecasts with a 2-week lead time using the VIC hydrologic model were improved by using MODIS snow maps; error reduction of 71% was reported by McGuire et al. (2005) • Use of MODIS snow products in updating reservoir storage volume forecasts showed that storage forecast errors were reduced (or unchanged) in 74% of the seasonal forecasts as a result of MODIS-updated streamflow forecasts (McGuire et al., 2005)

  25. Title (ESDT): MOD29 & MYD29 Description: Terra and Aqua swath sea ice cover and ice-surface temperature (IST); 1-km resolution Product algorithm theoretical basis: NDSI-based algorithm for sea ice cover (see MOD10_L2 for heritage), and linear regression for IST, cloud masking from MOD35_L2; heritage: Key and Haefliger, 1992; Key et al., 1997 Science need (justification): provides IST needed by modelers - IST controls snow metamorphosis and melt, rate of sea ice growth and air-sea heat exchange; improved clear-sky sea ice cover in terms of temporal and spatial resolution relative to operational clear-sky products; automated algorithm (removes possible inconsistencies introduced by analyst interpretation); critical for creation of higher-level sea ice products Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): algorithm very stable; IST uncertainty 1.2 - 1.3 K (winter conditions); some error due to ice/cloud confusion (if MOD35 cloud mask fails to map a cloud, it may be mapped as sea ice) Intended or appropriate product use (including any limitations on use): for a detailed and global look at sea ice cover and measurement of IST for use in modeling; often multiple swaths a day available; fog (not mapped by cloud mask) affects the IST; cloud obscuration is also a limitation Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): important for modelers; see reference list on a later slide

  26. Title (ESDT): MOD29P1D & MYD29P1D Description: Terra and Aqua sea ice cover and ice-surface temperature (IST) during daylight on the EASE-Grid; 1-km resolution, daily tile Product algorithm theoretical basis: see theoretical basis for MOD29, plus scoring algorithm for observation selection; see heritage for MOD29 Science need (justification): see justification for MOD29 Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): algorithm very stable; IST uncertainty 1.2 - 1.3 K (winter conditions); some error due to ice/cloud confusion propagated from MOD29 Intended or appropriate product use (including any limitations on use): for sea ice extent monitoring, and measurement of IST for use in models Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): useful for development of an IST CDR; see reference list on a later slide

  27. Title (ESDT): MOD29P1N & MYD29P1N Description: Terra and Aqua ice-surface temperature (IST) during nighttime on the EASE-Grid - 1-km resolution, daily tile Product algorithm theoretical basis: see theoretical basis for MOD29; scoring algorithm for observation selection; see heritage for MOD29 Science need (justification): for exploratory investigation of nighttime IST Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): algorithm very stable; IST uncertainty 1.2 - 1.3 K (winter conditions); some error due to ice/cloud confusion propagated from MOD29; major limitation is that the MOD35 cloud mask does not work very well during the nighttime over sea ice Intended or appropriate product use (including any limitations on use): for measurement of IST for use in modeling; nighttime cloud mask is a major limitation Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): N/A

  28. Title (ESDT): MOD29E1D & MYD29E1D Description: Terra and Aqua sea ice cover and ice-surface temperature (IST) during daylight on the EASE-Grid; 4-km resolution Product algorithm theoretical basis: see theoretical basis for MOD29; daily compositing and binning algorithm; see heritage for MOD29 and MOD29P1D Science need (justification): see justification for MOD29P1D Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): algorithm very stable; IST uncertainty 1.2 - 1.3 K (winter conditions); some error due to ice/cloud confusion propagated from MOD29 Intended or appropriate product use (including any limitations on use): for sea ice monitoring and measurement of IST for use in modeling; for validation of GCMs and other products (e.g., AMSR-E sea ice) Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): for creation of a CDR; see reference list on a later slide

  29. Daily global 4-km resolution ice extent & IST products - composites from May 15-19, 2000 North Polar View South Polar View MOD29E1D MOD29E1D cloud Hall et al. (2004)

  30. Title (ESDT): MOD29E1D & MYD29E1D Description: Terra and Aqua sea ice cover and ice-surface temperature (IST) during daylight on the EASE-Grid; 4-km resolution Product algorithm theoretical basis: see theoretical basis for MOD29; daily compositing and binning algorithm; see heritage for MOD29 and MOD29P1D Science need (justification): see justification for MOD29P1D, plus this product provides a global view of sea ice during daylight hours Quality and accuracy information (cal/val, relative and absolute uncertainty, stability, maturity of algorithm): algorithm very stable; IST uncertainty 1.2 - 1.3 K (winter conditions); some error due to ice/cloud confusion propagated from MOD29 Intended or appropriate product use (including any limitations on use): for sea ice monitoring and measurement of IST for use in modeling; for validation of GCMs and other products (e.g., AMSR-E sea ice) Science value (use of product for science, papers written, breakthroughs, multidisciplinary use): for creation of a CDR; for energy-balance modeling; see reference list on a later slide

  31. Peer-Reviewed Publications – Sea Ice (2004 - present) Scambos, T., T. Haran and R. Massom, submitted: Validation of AVHRR and MODIS ice surface temperature products using in-situ radiometers, Annals of Glaciology. Hall, D.K., G.A. Riggs and V.V. Salomonson, in press : MODIS Snow and Sea Ice Products, Earth Science Satellite Remote Sensing - Volume I: Science and Instruments, J. Qu (ed.), Springer-Verlag Press. Hall, D.K., J. Key, K.A. Casey, G.A. Riggs and D.J. Cavalieri, 2004: Sea ice surface temperature product from MODIS, Transactions on Geoscience and Remote Sensing, 42(5):1076-1087. Non-Peer-Reviewed Publications – Sea Ice G. A. Riggs, D. K. Hall & V. V. Salomonson, 2003: MODIS Sea Ice Products User Guide (with updates posted on website in 2006) G. A. Riggs, D. K. Hall & V. V. Salomonson, 2003: Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms (with updates posted on website in 2006)

  32. Conclusions • Algorithm improvements developed for Collection 5 reduce the snow errors of commission which are generally quite low (<1% in Collection 4) • Data dependencies and error propagation through sequence of products must be understood because the higher-level snow and sea ice products are built from the swath product • MOD11 and MCD43 use MOD10 products as input • MODIS daily snow-cover 500-m (MOD10A1 & MYD10A1) and CMG 0.05-deg (MOD10C1 and MYD10C1) products are well suited for development of CDRs • MODIS daily sea ice IST 1-km (MOD29P1D & MYD29P1D) and daily global (MOD29E1D & MYD29E1D) products are well suited for development of CDRs (Scambos et al., submitted) • Daily and composited products have proven useful for developing snow-cover depletion curves for modeling (Dery et al., 2005) and operational (M. Budde/USGS) uses • USGS uses MODIS snow products in a model to map SWE in Afghanistan in a Famine Early Warning System (M. Budde/USGS) • Streamflow forecasts with two-week lead-time using the VIC hydrologic model were improved through inclusion of MODIS snow maps to update model, in 71% of reported forecasts overall (McGuire et al., 2005) • Note the increase in number of MODIS-snow publications in 2005!

  33. Backup slides

  34. MODIS data-product sequence Input MODIS 1-km resolution geolocation, cloud mask MOD35 and land/water mask MOD03 Input MODIS Level 1B bands 1, 2, 4, 6 (MOD02_HKM ), 31 & 32 ( MOD02_1km) Input data is L2G product in which all swaths for the day have been mapped onto the projection. A scoring algorithm based on solar zenith angle, distance from nadir and observation coverage in a cell selects the most favorable observation for the day. Calculate NDSI, NDVI, grouped-criteria tests, vegetation polygon Apply surface temperature screen ( 283K) Swath snow 500-m product MOD10_L2 (includes FSC) Algorithm Heritage: Kyle et al., 1978 Bunting and d’Entremont, 1982 Crane and Anderson, 1984 Tucker et al., 1985 Dozier, 1989 Romanov et al., 2000 Daily snow tile product 500-m resolution MOD10A1 (includes daily snow albedo) 8-day composite snow tile product 500-m resolution MOD10A2 Daily snow CMG 0.05° resolution MOD10C1 8-day composite snow CMG 0.05° resolution MOD10C2 Monthly snow CMG 0.05° resolution MOD10CM

  35. False snow due to cloud shadows can be eliminated by application of a screen for low reflectance situations; the result of screening in right image shows that the erroneous snow is virtually eliminated, with only four “snow” pixels remaining. 17 August 2004 MOD10_L2 MOD10_L2 Before screen After screen Error of commission in the tile (left) is 0.074% of the land area and tile (right) is 0.003%

  36. Nov. 22, 2003 Nov. 27, 2003 Daily snow albedo product (MOD10A1) 500-m resolution Snow albedo swaths - North America Klein, 2003 Albedo (%)

  37. Maps generally agree well when Norwegian land/water mask and both cloud masks are used in both maps April 12, 2004 SnowStar snow map MOD10_L2 snow map snow 15.6 % snow 17.8 % Hall et al., 2004 SnowSat map courtesy of Rune Solberg/Norwegian Computing Centre

  38. MOD10CM: 0.05 Monthly Climate-Modeling Grid (CMG) Snow Maps February 2004 Percent Snow Cover

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