Soil Moisture Retrieval Using “Effective Vegetation” Soil Moisture Retrieval Using “Effective Vegetation” with Simulated HYDROS Brightness Temperatureswith Simulated HYDROS Brightness Temperatures

• Hydros mission objective is to collect global scale measurements of the Earth’s soil moisture and land surface freeze/thaw conditions, using a combined L band radiometer and radar system operating at 1.41 and 1.26 GHz

• An observing system simulation experiment (OSSE) was conducted in order to test Hydros soil moisture retrieval algorithms and examine how the retrieval accuracy will be impacted by vegetation water content (VWC) (an ancillary parameter needed in the retrieval)

-- modeled geophysical domain in the south-central United States centered on the Arkansas- Red River basin for a one-month period in 1994

-- OSSE was run both for realistic vegetation conditions and for the artificial case of tripling the vegetation

• Nonlinear scaling of higher resolution ancillary vegetation data (~ at 1 km) to satellite resolutions can adversely affect algorithm retrieval accuracies at the Hydros radiometer scale (~ 40 km). Methods for “effectively” aggregating high resolution vegetation data to improve soil moisture retrieval algorithms for satellite microwave missions are currently under study.

• A simple empirically-derived effective vegetation water content based on a wet & dry day response over a site with known soil moisture partially compensates for the vegetation over-correction due to aggregation and improves soil moisture retrieval accuracy.

Terms:

Ancillary- serving as a supplement or addition

Aggregation- collection into a mass or sum; a collection of particulars; an aggregate

P. O’Neill*, W. Crow, E. Njoku, and JC ShiP. O’Neill*, W. Crow, E. Njoku, and JC Shi

*Hydrological Sciences Branch, Hydrospheric & Biospheric Sciences Laboratory*Hydrological Sciences Branch, Hydrospheric & Biospheric Sciences Laboratory

HYDRS

Mixed Forest/ Grassland

Grassland/ Crops

Shrubland/ Semi-arid

Forest

WaterLand Cover Classification

36 km

9 km

Land Cover Heterogeneity (1-km pixels)

OSSE Domain CharacteristicsOSSE Domain Characteristics

Topography575,000 km2 basin

OSSE one-month test period from May 26 – June 28, 1994

HYDROS Effective VWC and SM Retrieval AccuracyEffective VWC and SM Retrieval Accuracy

(a) (b)

(a) soil moisture retrieval algorithms are adversely affected by aggregation of 1 km ancillary VWC data to 36 km (to match HYDROS TB scale) – use of linearly averaged VWC produces an overcorrection of the vegetation effect and biases the soil moisture retrieved from simulated Hydros TB

(b) a simple empirical modification to the linearly averaged VWC based on a wet day & dry day response to create an “effective” VWC for the 36-km scale results in lower error in retrieved soil moisture, especially as vegetation amount increases.

Simulated Hydros Soil Moisture Retrieval Error

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Day of Year 1994

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1x eff VWC

1x lin avg VWC

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Improvement due to use of "effective VWC"

Comparison of "True" vs Lin-Avg VWC for Wet Day 162 & Dry Day 179

y = -0.0166x2 + 0.8665x + 0.1861

R2 = 0.9737

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Linearly Averaged 36-km VWC (kg/m2)

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Effective VWC value

Amount of veg overcorrection

VWC=Vegetation Water Content

Hydros LogoHydros Logo

Logo designedBy Brian A. Campbell, SAICNASA GSFC, Code 614

Häkkinen, S. and Cavalieri, D. J. , Sea ice drift and its relationship to altimetry-derived ocean currents in the Labrador Sea, GRL, 2005

Sirpa Hakkinen, Code 614.2/Ocean Sciences Branch, Hydrospheric & Biospheric Sciences Laboratory

• Altimetry-derived currents for the North Atlantic show declining current magnitudes in the subpolar gyre during the 1990’s. However, observational current meter records indicate this finding from altimetry may be limited temporally.• Sea ice drift can be derived from remote sensing data. The only driving forces for sea ice drift are wind and ocean currents. Thus, there is a possibility to extract ocean current information from ice drift for a longer time period when ice drift data is available, 1978-2003.• The association of the second ice drift mode to ocean forcing is based on comparison between ice drift Mode 2 and ocean current Mode 1:

Spin-down of Sub-polarNorth Atlantic Gyre

Black lines - Mode-2 AmplitudeRed line - Mode-1 AmplitudePink line = Ocean current at 60N from altimetry

MODE 1:Atmospheredriven mode

MODE 2:Ocean drivenmode

The two time series contain similar broad features through the last two decades confirming the results from the altimetric velocity record of Hakkinen and Rhines (2004) i.e., an intense subpolar gyre in the early 1990s, and a major weakening by the end of 1990s.

Recession of Glaciers in the Harding Icefield and the Grewingk-Yalik Glacier Complex, Alaska

Dorothy K. Hall*, Bruce A. Giffen** & Janet Y.L. Chien+*Cryospheric Sciences Branch, Code 614.1, **National Park Service, +SAIC

Summary. Dramatic recession of glaciers in the Harding Icefield and the Grewingk-Yalik Glacier Complex (Fig. 1), in southern Alaska, has been found in a comprehensive study of all of the glaciers in those icefields spanning a 29-year period, from 1973 to 2002. GIS shape files were created and measurements were made on all of the glaciers shown in Fig. 1 using Landsat data from 1973, 1986 and 2002. Other indicators of glacier changes were also measured such as the highest elevation of the firn line (close to the equilibrium-line altitude), when possible. Air photos and ASTER data were used in addition to the Landsat data, when available.

In addition, 1986 and 2002 Landsat TM and ETM+ scenes were used to measure the areal extent of both ice masses; there was a reduction in glacier area of 3.62%, or approximately 78 km2 from 1986 to 2002, with most of the changes occurring in the Harding Icefield. The areal extent of the two icefields, as measured using 2002 Landsat data, is 2078 km2.

This work was funded by the National Park Service as part of their program to quantify changes in national parks in Alaska.

Fig. 1. Harding Icefield and Grewingk-Yalik Glacier Complex, showing changes in glacier area from 1986 (red) to 2002 (green).

197319862002

Breakup of the Bear Glacier terminus - the Bear (Fig. 2 ) is one of the major glaciers in the Harding Icefield. The terminus breakup is seen on Landsat (left) and ASTER (right) images. Colored lines (see key) depict terminus positions in different years as measured on different images.Fig. 2. The Bear Glacier, a large glacier in the Harding Icefield, displays a

dramatic terminus breakup from 1973 (yellow line on Landsat image, above left), to 2001 (ASTER image, above right) and 2002 (green line on Landsat image).

Landsat Landsat imageimage ASTER ASTER

imageimage

Bear Glacier

1986

2002

Harding Icefield

Grewingk-Yalik Glacier Complex

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2002

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icebergs

Skilak Glacier

Fig. 3. Yellow box shows the location of the Skilak Glacier in the northern part of the Harding Icefield.

Fig. 4. Extensive breakup of the terminus of the Skilak Glacier terminus, as measured from Landsat imagery. A 3811 136 m recession was measured from 1973 to 2002.

Examples of the recession of some of the glaciers in the study area are shown on this page. Details and measurements are provided in Hall et al. (in press)*.

1973

1986

2002

This color key applies to

figs. 2, 4, 5 & 6.

Fig. 5. Recession of the Petrof Glacier terminus. The glacier receded 1101 136 m from 1973 to 2002.

Petrof Glacier

Fig. 6. Recession of the Dinglestadt Glacier terminus. The glacier receded 1040 136 m from 1973 to 2002.

Dinglestadt Glacier

*Hall, D.K., B.A. Giffen and J.Y.L. Chien, in press: Changes in the Harding Icefield and the Grewingk-Yalik Glacier Complex, Proceedings of the 62nd Eastern Snow Conference, 7-10 June 2005, Waterloo, Ontario, Canada.

Page 2

Comparison of MODIS Daily Global Fractional Snow Cover Maps at 0.05 and 0.25 Degree Resolutions

George A. Riggs*, Nicolo DiGirolamo*,and Dorothy K. Hall***SSAI, **Cryospheric Sciences Branch, Code 614.1

Analysis of FSC in the Sierra Nevada Mountains exhibits the impact of CMG resolution on snow maps in mountainous regions. The 0.05° CMG (Fig. 1) has 439 cells of snow for a total area of 10729 km2, The 0.25° CMG (Fig. 2) has 37 cells of snow for total area of 22566 km2. A transect (single row of CMG cells) of FSC across the Sierra Nevada Mountains (Fig. 3) shows the difference in FSC between the CMGs. In the 0.05° CMG FSC ranges from 0-100 %. In the 0.25° CMG FSC ranges from 0-55% and stretches over a longer distance as compared to the 0.05° CMG. Fractional snow percentage is decreased and the gradient in FSC is diminished to about half the range but is spread over a wider area in the 0.25° CMG. Those differences should be considered in relation to the spatial resolution of a model that may ingest the snow map.

*Riggs G.A., DiGirolamo N., Hall D.K. 2005. Comparison of MODIS Daily Fractional Snow Cover Maps at 0.05 and 0.25 Degree Resolutions. Proceedings of the 62nd Annual Eastern Snow Conference, 7-10 June 2005, Waterloo, Ontario, Canada.

Figure 1. Map of fractional snow cover over the Sierra Nevada Mountains 4 May 2005 from the 0.05° CMG

Figure 2. Map of fractional snow cover over the Sierra Nevada Mountains 4 May 2005 from the 0.25° CMG

Figure 3. Transect of FSC across the Sierra Nevada Mountains 4 May 2005 from the 0.05° and 0.05° CMGs.

In response to climate modelers’ needs, a MODIS daily global fractional snow map at 0.25° resolution was created from the standard 0.05° MODIS daily global fractional snow cover (FSC) climate model grid (CMG) product and is available in flat-binary files rather than the hierarchical-data format (HDF) files that many users dislike. Significant differences in FSC can exist between the two CMG resolutions, as shown in Riggs et al. (in press).* It is important for users to understand the differences between the maps.

• Hurricane Bonnie made landfall on August 26, 1998 in Onslow Bay near Wilmington, NC, with the storm surge expected to peak between Cape Fear and Cape Lookout. But the closest tide gauges were located near Cape Lookout at Beaufort, NC, and 40 km up the Cape Fear River at Wilmington. During the landfall, the NASA Scanning Radar Altimeter flew along Bogue Banks, NC, 45 km west of Cape Lookout, aboard one of the NOAA WP-3D hurricane research aircraft at 2100 m altitude, producing a 37 m footprint with its 1º antenna beamwidth. The following week the NASA Airborne Terrain Mapper scanning lidar made several high precision low altitude survey flights with a few centimeters vertical accuracy and much higher spatial resolution (3 m) than the SRA, but it could not function through clouds or precipitation and its 600 m altitude was much lower than was safe during the landfall flight. The ATM and the NASA Experimental Advanced Airborne Research Lidar (EAARL) have now conducted coastal surveys from New England through the Gulf of Mexico. The almost 2 m water level increase observed during Hurricane Bonnie’s landfall included the effects of both storm surge and any change in the tide between it and the survey flight, but it demonstrates that the SRA has the ability to measure storm surge during hurricane landfalls by registering water levels to the lidar surveys.

Airborne Measurement of Hurricane Storm Surge

Charles Wright and Ed Walsh, Code 614.6/Instrumentation Sciences Branch, Hydrospheric & Biospheric Sciences Laboratory

Airborne Measurement of Hurricane Storm Surge

Heig

ht

(m)

Distance (m)

(1)

SRA measuredStorm Surge

1) SRA topo data of surge, waves and terrain at Bogue Banks, NC, taken from a NOAA aircraft during the Hurricane Bonnie landfall.

2) NASA Airborne Terrain Mapper measurements superimposed on SRA data with low relief area of comparison indicated.

3) Aerial photography of (2).

4) Cross-beach profile of SRA surface elevation (dots) vertically registered to ATM topography (curve) to demonstrate a water level almost 2 m higher during Hurricane Bonnie's landfall than during subsequent ATM flight which required good weather and much lower altitude.

(2) (3)

(4)

NASA Scanning Radar Altimeter can provide targeted storm surge measurements not available from the sparse tide gauge network.

Charles.W.Wright@nasa.gov Edward.Walsh@nasa.gov