Antarctic Sea Ice Results from the AMSR-E Bootstrap (ABA) and NASA Team (NT2) Algorithms: Similar but Not Identical Claire L. Parkinson and Josefino C. Comiso, Code 614.1, NASA GSFC Fig. 3 Ice extents and ice areas Fig. 2 Average ice concentrations, for the Southern Ocean as a whole Fig. 4 Extent and area anomalies Fig. 1 (a and b) Sample ABA and NT2 sea ice concentrations, November 2003. (c) November 2003 ice concentration difference (ABA - NT2). - - - - NT2 —— ABA Average Ice Concentration (%) Monitoring polar sea ice is one of the prime objectives of Aqua’s AMSR-E instrument, adding valuable information about this highly variable component of global climate. Results from satellite observations of sea ice have played an important role in recent discussions of climate change. However, NASA has two different sea ice algorithms being applied to the AMSR-E data: the AMSR-E Bootstrap (ABA) and the NASA Team (NT2). Here we compare the Antarctic results from the two algorithms, showing that although there are quantifiable differences, the basic picture revealed of the Antarctic sea ice cover is the same from each of the algorithms. drospheric and Biospheric Sciences Laboratory
Transcript
Antarctic Sea Ice Results from the AMSR-E Bootstrap (ABA) and NASA Team (NT2) Algorithms: Similar but Not Identical
Claire L. Parkinson and Josefino C. Comiso, Code 614.1, NASA GSFC
Fig. 3 Ice extents and ice areas
Fig. 2 Average ice concentrations, for the Southern Ocean as a whole
Fig. 4 Extent and area anomalies
Fig. 1 (a and b) Sample ABA and NT2 sea ice concentrations, November 2003. (c) November 2003 ice concentration difference (ABA - NT2).
- - - - NT2—— ABAA
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Monitoring polar sea ice is one of the primeobjectives of Aqua’s AMSR-E instrument,adding valuable information about this highly variable component of global climate.Results from satellite observations of seaice have played an important role in recentdiscussions of climate change. However,NASA has two different sea ice algorithmsbeing applied to the AMSR-E data: the AMSR-E Bootstrap (ABA) and the NASA Team (NT2). Here we compare the Antarctic results from the two algorithms, showing that although there are quantifiable differences, the basic picture revealed of theAntarctic sea ice cover is the same from each of the algorithms.
Hydrospheric and Biospheric Sciences Laboratory
Name: Claire L. Parkinson, Code 614.1, NASA/GSFCEmail: [email protected]: 301-614-5715
Algorithm Ice Extent Trend
ABA - 65,000 46,000 km2/yr
SBA - 68,000 46,000 km2/yr
NT2 - 67,000 45,000 km2/yr
Reference: Parkinson, C. L., and J. C. Comiso, 2008: Antarctic sea ice parameters from AMSR-E data using two techniques and comparisons with sea ice from SSM/I, J. Geophys. Res., 113, C02S06, doi:10.1029/2007JC004253.
Data Sources: The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) is a Japanese instrument on NASA’s Aqua satellite, launched on May 4, 2002. NASA has funded AMSR-E sea ice calculations using two different algorithms, the AMSR-E Bootstrap Algorithm (ABA) and the NASA Team (NT2) algorithm. We have compared the results from the two algorithms in both the Antarctic (shown here) and the Arctic (separate paper), in each case finding the ABA and NT2 results to be close but not identical.
Technical Description of Images Ice concentration maps appear quite similar from the two algorithms (e.g., Fig. 1a and 1b), although difference maps reveal subtle contrasts (Fig. 1c) and time series reveal that the average ice concentrations for the Southern Ocean as a whole are consistently slightly higher for the NT2 results (Fig. 2). This results in slightly higher ice areas from NT2 (Fig. 3c and 3d) even though the ice extent (ocean area with ice concentrations of at least 15%) is typically slightly higher from the ABA (Fig. 3a and 3b). Although the AMSR-E data set is too short for determining long-term trends, Fig. 4 shows the ABA and NT2 anomalies and their trend lines for July 2002 - December 2006. Both algorithms show negative trends without statistical significance (table).
Scientifc Significance/Comparisons with SSM/I Results: When the AMSR-E Bootstrap (ABA) results were compared with corresponding results from the Special Sensor Microwave/Imager (SSM/I), i.e., the SSM/I Bootstrap (SBA) results, we found that for the ice concentrations and ice areas the Bootstrap results from the two instruments match even more closely than the ABA and NT2 results. This is likely due to considerable effort to minimize the AMSR-E versus SSM/I Bootstrap differences and is encouraging when looking toward the possibility of extending the data record from one instrument with that from another.
Table. Anomaly Trends, 7/02 - 12/06
Hydrospheric and Biospheric Sciences Laboratory
Algorithm Ice Area Trend
ABA - 78,000 41,000 km2/yr
SBA - 75,000 41,000 km2/yr
NT2 - 86,000 42,000 km2/yr
Assessment of ICESat sea ice elevation accuracies Assessment of ICESat sea ice elevation accuracies using airborne laser altimeter datausing airborne laser altimeter data
Mul
tiyea
r ice
Coincident w/ ICESat orbit
Figure 2: AMSR-E validation campaign in March 2006
Sea ice thickness is still missing in monitoring sea ice mass balance.
As part of the Aqua AMSR-E aircraft validation campaign using NASA’s P-3B, one flight was dedicated to ICESat and the flight line was coordinated with the ICESat orbit. High resolution (1 m) airborne laser altimeter data from the Airborne Topographic Mapper (ATM) are using to assess the accuracy of ICESat elevation and freeboard estimates.
The comparison showed excellent agreement. For single ICESat shots the correlation with ATM data was 0.9 with a bias of less than 2 cm. Applying a running mean of about 1.5 km (9 ICESat footprints) led to an increase in correlation to 0.99.
Figure 1: Comparison of ICESat and ATM elevation data
Kurtz, N., T. Markus, D.J. Cavalieri, W. Krabill, J. Sonntag, and J. Miller, Comparison of ICESat data with airborne laser altimeter measurements over Arctic sea ice, IEEE Trans. Geoscience and Remote Sensing, 2008 (in press).
Cavalieri, D.J. and T. Markus, EOS Aqua AMSR-E Arctic sea ice validation program: Arctic 2006 aircraft campaign flight report, NASA/TM-2006-214142, 27pp., 2006.
Data Sources:
The data were taken as part of the EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E) aircraft validation campaign in March 2006. One instrument on NASA’s P-3B aircraft was the Airborne Topographic Mapper (ATM), a laser altimeter developed at Goddard, which is ideally suited to validate data from the spaceborne laser altimeter, the Geoscience Laser Altimeter System (GLAS) on the NASA Ice Cloud and land Elevation Satellite (ICESat).
Technical Description of Image:
Figure 1: Elevation data from ICESat (red) and ATM data (black and blue). The time difference between the outbound and inbound ATM data was used to correct for sea ice motion..
Figure 2: Flight lines of the March 2006 validation campaign out of Fairbanks, AK. The different shades of gray indicate the AMSR-E-derived snow depth with purple tint delineating multiyear from first-year ice. The flight on March 24, 2006, coincided with an ICESat orbit.
Scientific significance:
Large-scale measurements of sea ice thickness are still missing from our understanding of sea ice mass balance. Since only about a tenth of the sea ice is above the water, inferring sea ice thickness from elevation (or freeboard) data requires a centimeter-level precision of ICESat data.
Relevance for future science and relationship to Decadal Survey:
Sea ice thickness is still missing in monitoring sea ice mass balance. ICESat is well-equipped to perform this task but errors and accuracies must be well understood; with ICESat-2 at the horizon this study becomes even more relevant.
Hydrospheric and Biospheric Sciences Laboratory
Using Mycosporine-like Amino Acids as Taxonomic Indicators in Remote Sensing Reflectance
Tiffany A. Moisan, Stanford Hooker, Antonio Mannino, Code 614.2, NASA GSFC
Collaborators: Timothy S. Moore (University of New Hampshire), Carla P. Makinen, Matthew Linkswiler, Ru Morrison
Mycosporine-like aminoacids contribute to significant absorption in the ocean
Mycosporine-like amino acids act as a sunscreen against ultraviolet radiation
Mycosporine-like amino acids are potentialmarkers for some species because they are found some while others not.
Figure 1: Examples of phytoplankton
Figure 2: Absorption spectra for Mycosporine-like Amino Acid Crusie #1
Figure 3: Coastal Transect research area in the Gulf of MaineFigure 4: Surface Countour for Mycosporine-like Amino Acid Crusie #1. Colors indicate
chlorophyll concentrationsHydrospheric and Biospheric Sciences Laboratory
References
Arrigo, KR 1994. Impact of ozone depletion on phytoplankton growth in the Southern Ocean: large-scalespatial and temporal variability. Mar.Ecol-Progr.Ser. 114: 1-12.
Bandaranayake, WM (1998) Mycosporines: are they nature’s sunscreens? Natural Product Reports: 159-172
Moisan, TA, Mitchell, BG (1999) Photophysiological adaptation of Phaeocystis antarctica Karsten under PAR light limitation. Limnol and Oceanogr 44 (2): 247-258
Vernet, M, Whitehead, K (1996) Release of ultraviolet-absorbing compounds by the red-tide dinoflagellate Gonyaulax polyedra. Mar Biol 127: 35-44
T. A. Moisan, B. G. Mitchell. 2001. UV absorption by mycosporine-like amino acids in Phaeocystis antarctica Karsteninduced by photosynthetically available radiation. Ma.r Biol. 138 (1) 217-227.
Data Source - Gulf of Maine
Scientific Significance
Mycosporine-like amino acids act as a sunscreen again ultraviolet radiation. They are a strong UV signalIn the chlorophyll-specific absorption spectrum and can potentially be an important part of remote sensing reflectance. They are an important part of the total absorption of the ocean and may lead to another source of variabilityin remote sensing reflectance. Mycosporine-like amino acids are potential markers for some species becausethey are found some while others not.
Determination of Tree Layer Properties for Improved Determination of Tree Layer Properties for Improved Microwave Soil Moisture Retrieval Microwave Soil Moisture Retrieval
Trunk Layer
Crown Layer dc ~7 m
dt ~8 m
Fig. 1. Test site of deciduous Paulownia trees.
Fig. 2. L band radar time-domain response to Paulownia trees as measured by truck-mounted ComRAD instrument system.
Soil moisture is an important component of the Earth’s water and energy balance, but tree canopies present a challenge to measuring soil moisture from space due to their masking effect on the underlying soil moisture signal.
A field experiment is currently underway tomeasure microwave attenuation and scattering properties of trees using a truck mounted L-band system called the NASACombined Radar/Radiometer (ComRAD)System.
The radar time-domain response is able todistinguish between different layers in the tree canopy, offering a unique solution to measurement of tree properties needed forimproved soil moisture retrieval.
Hydrospheric and Biospheric Sciences Laboratory
Peggy O’Neill, Code 614.3, NASA GSFC
Name: Peggy E. O’Neill, Code 614.3, NASA GSFCEmail: [email protected]: 301-614-5773
References:
O’Neill, P., R. Lang, M. Kurum, A. Joseph, T. Jackson, M. Cosh, R. Nelson, and M. Spicknall, “ComRAD Active/Passive Microwave Measurements of Tree Canopies,” Proc. of IGARSS’07, Barcelona, Spain, July 23-27, 2007.
Kurum, M., R. Lang, P. O’Neill, A. Joseph, T. Jackson, and M. Cosh, “Transient Response from a Vegetation Canopy to Stepped Frequency Radar,” National Radio Science Meeting, URSI, Boulder, CO, January 3-6, 2008.
Kurum, M., R. Lang, P. O’Neill, A. Joseph, T. Jackson, and M. Cosh, “Estimation of Canopy Attenuation for Active/Passive Microwave Soil Moisture Retrievals,” 10th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad’08), Florence, Italy, March 11-14, 2008.
O’Neill, P., R. Lang, M. Kurum, A. Joseph, T. Jackson, and M. Cosh, “Microwave Soil Moisture Retrieval Under Trees,” Proc. of IGARSS’08, IEEE, Boston, MA, July 6-11, 2008.
Kurum, M., R. Lang, and P. O’Neill, “Estimation of Canopy Attenuation at L-Band by a Time-Domain Analysis of Radar Backscatter Response,” URSI General Assembly, Chicago, Illinois, August, 2008 [full paper being submitted to IEEE Transactions on Geoscience and Remote Sensing]. Data Sources: NASA’s Terrestrial Hydrology Program is funding a three-year field experiment to measure the L band microwave response to soil moisture under different types of small to medium tree canopies. The project is a collaboration between GSFC, George Washington University, and USDA. The truck-mounted ComRAD radar / radiometer is used to obtain microwave data over the trees coincident with measurements of soil and vegetation properties.
Technical Description of Image:
Figure 1: The test site for the first two years of this field project was an experimental farm run by the University of Maryland’s Central Maryland Research and Education Center (CMREC) near Upper Marlboro, MD. The site consisted of three plots of different densities of paulownia trees, a fast-growing deciduous tree with broad leaves and distinct seed pods. Crown height was variable, on the order of 11-15 m.
Figure 2: This plot shows the transient or time-domain response of the ComRAD 1.25 GHz radar to the paulownia trees. The internal system response is gated out, and the remaining signal shows discrete contributions from different parts of the tree layer. This information can be used to determine the attenuation and scattering properties of the trees. These parameters are used in turn in passive microwave retrieval algorithms for improved soil moisture estimation.
Scientific significance: While advances in L band microwave technology have led to the upcoming SMOS and SMAP missions, current baseline soil moisture retrieval algorithms for these missions have been developed and validated only over grasslands, agricultural crops, and generally light to moderate vegetation. Tree areas have generally been excluded from operational microwave soil moisture retrieval plans due to the large expected impact of trees on masking the microwave response to the underlying soil moisture. Once completed, this project should provide quantitative assessments of tree scattering and attenuation, leading to improved soil moisture retrievals for tree areas.
Relevance for future science and relationship to Decadal Survey: Soil moisture is a critical control on water and energy cycles, as well as weather,climate, hydrological and agricultural prediction. Eventually, the knowledge gained from this study will help to extend accurate soil moisture retrievals from global microwave space missions like SMAP to more areas of the Earth’s surface than are currently feasible.
Hydrospheric and Biospheric Sciences Laboratory
Radiance assimilation shows promise for improving Radiance assimilation shows promise for improving microwave snow retrievalsmicrowave snow retrievals
Estimating snow water equivalent (SWE) accurately remains difficult using current remote sensing techniques or models. Assimilation of microwave radiances is being evaluated as a fundamentally new approach. Here a forward radiance model is evaluated with field data to help quantify the potential improvement. Radiances can be modeled with sufficient accuracy, given detailed input data. This input data will come from a snowpack physical model that will be evaluated in a follow-on study. Figure 2: Snow stratigraphy (drawn to scale), characterized by ice
layers (shaded rectangles) and grain size. The measurement date is given, and for all measurements, the grain shape was described as faceted crystals or a mix of faceted and rounded crystals.
Figure 3: GBMR-7 microwave radiometer at NASA Cold Land Proceses eXperiment (CLPX)
Figure1: The measured (open) and modeled (filled) brightness temperatures compared in this study for the vertical (diamonds) and horizontal (circles) polarizations for (a)18.7 GHz, (b) 36.5 GHz, and (c) 89 GHz channels. The vertical bars show the maximum and minimum value observed on each day and across the range of fractional volume.
References:M. Durand, E.J. Kim, and S.A. Margulis; “Quantifying uncertainty in modeling snow microwave radiance for a mountain snowpack at the point-scale, including stratigraphic effects”; IEEE Trans. Geosci. Remote Sens., 2008, in press.
Data Sources: The NASA Cold-land Processes Experiment (CLPX) was conducted during the winters of both 2002 and 2003 in Colorado, USA. The analyses reported here use February, 2003 (dry snow) data from the Local Scale Observing Site, near the Fraser Experimental Forest Headquarters Facility (39°5004900N, 105°5404000W). Intensive ground observations of snow, soil, and vegetation properties were made in conjunction with stationary, ground-based microwave remote sensing and micrometeorological observations. The Univ. of Tokyo’s GBMR-7 ground-based microwave radiometer made the passive microwave observations used in this study over the 1-week Intensive Observing Period 3.
Technical Description of Images:Figure 1: The measured (open) and modeled (filled) brightness temperatures compared in this study for the vertical (diamonds) and horizontal (circles) polarizations for (a)18.7 GHz, (b) 36.5 GHz, and (c) 89 GHz channels. The vertical bars show the maximum and minimum value observed on each day and across the range of fractional volume.
Figure 2: Snow stratigraphy (drawn to scale), characterized by ice layers (shaded rectangles) and grain size. The measurement date is given, and for all measurements, the grain shape was described as faceted crystals or a mix of faceted and rounded crystals. This kind of very detailed snowpack information is critical initially to evaluate the accuracy of the forward radiative transfer model for a radiance assimilation application. Horizontal polarization is very sensitive to the presence of ice layers. Both polarizations are highly sensitive to snow grain size and snow density.
Figure 3: Univ. of Tokyo’s GBMR-7 ground-based microwave radiometer deployed at CLPX. The angular scans and time series data over the 1-week Intensive Observing Period 3 were used in this and related studies (see references).
Scientific significance: More than half of the runoff for 1/6 the world’s population results from melting snow (Barnett et al, Nature (17), Nov 2005), affecting ~1/4 of the global gross domestic product. The water in snow is essential for drinking water, agriculture, industry, and hydropower. Yet estimating snow water equivalent (SWE) accurately remains difficult using current remote sensing techniques or models. Radiance assimilation is being evaluated as a fundamentally new approach. Here a forward radiance model is evaluated with field data to help quantify the potential improvement. We found that radiances can be modeled with sufficient accuracy, given appropriately detailed input data. This input data will come from a snowpack physical model whose fidelity will be evaluated in a follow-on study.
Relevance for future science and relationship to Decadal Survey: Until the Decadal Survey’s Snow and Cold Land Processes (SCLP) mission is launched, no significantly new snow observations will be available from space (although heritage passive microwave and visible observations will continue from operational satellites). Improving snow retrievals will thus depend on making optimal use of existing sensor capabilities and models---i.e., data assimilation. Assimilation of radiances has led to dramatic improvements in other fields; the work highlighted here is part of the Goddard Snow Radiance Assimilation Project, an attempt to exploit radiance assimilation for snow retrievals. Any improvements can be applied to future satellite microwave observations for snow, enhancing the accuracy of one of the longest global satellite data records available (snow extent and water equivalent).
