Monitoring of temporal and spatial variability of the East

Monitoring of temporal and spatial variability of the East Antarctic

plateau using passive microwave data

G.Macelloni, M. Brogioni, S.Paloscia, S. Pettinato, P. Pampaloni, E. Santi

Microwave Remote Sensing Group

MICRORAD 08 Firenze 11-14 March 2008

Microrad 2008 – Firenze -11-14 March 2008

Introduction

Antarctica is one of the most interesting and challenging natural laboratories on Earth, and plays a fundamental role in atmospheric and ocean circulation. Snow cover, which is a key component in the global hydrological cycle, strongly influences the overlying atmosphere and hence polar and global climate. In recent years the monitoring of extreme area of the globe (i.e. Arctic, Antarctica, Greenland, ) was widely used in order to understand the global warming effect


Introduction (cont.)

The monitoring of glacial environments requires knowledge of the interaction between snow structure and reflectance properties, and this can be achieved by analyzing a large data set of ground measurements, including spectral, snow and climatic data.

Due to the difficulties related to the extreme environmental conditions, satellite sensors are the most suitable tools for observing the temporal and spatial variations in the extensive snow-covered areas of Antarctica.


Objective of the research

Evaluation on the potential of passive microwave sensors for the monitoring of temporal and spatial variation of the physical properties of the Antarctic ice sheets:–

Retrieval of snow temperature profile

–

Monitoring of air temperature variation –

Monitoring of spatial anisotropy

–

Evaluation of the stability of the area in order to use the Antarctic plateau as external calibration target of low-frequency satellite microwave radiometers


The Data

Advanced Microwave Scanning Radiometer (AMSR-E) data at 6.9 GHz, 10 GHz, 19 GHz and 37 GHz were collected over a large portion of the East-Antarctica Plateau (from 90°to 180° of longitude and from -65° to -90° of latitude) in both 2005 and 2006.(more than 3000 orbits were considered)Snow temperature at different depths down to 10 m and AWS data measured at the Italian-French base of Concordia (which is located at Dome-C in the East Antarctic Plateau) in the same period.


Snow temperature data (Dome-C)

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Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06 Jul-06 Oct-06 Jan-07

Date

Snow

Tem

pera

ture

[°C

]

Tair50 cm100 cm150 cm200 cm250 cm300 cm400 cm500 cm600 cm800 cm1000 cm


Microwave data – (Dome-C Area)

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Date

Brig

htne

ss T

empe

ratu

re [K

]

TvCTvXTvKuTvKa


Direct Relation Microwave-Snow data

Frequency T50 T100 T200 T300 T400 T600 T800 T1000

6.9 GHz 0.63 0.72 0.62 0.38 0.13 0.08 0.62 0.54

10 GHz 0.74 0.87 0.78 0.51 0.19 0.07 0.73 0.68

19 GHz 0.83 0.94 0.80 0.50 0.17 0.10 0.80 0.70

37 GHz 0.98 0.90 0.55 0.19 0.01 0.39 0.83 0.43

Correlation coefficient (R2) between Tb and Snow Temperature at different depths

Negative correlation


Retrieval of snow temperature : first meter

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Tsnow Measured [°C]

Tsno

w R

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[°C]

50 cm

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Tsno

w R

etrie

ved

[°C]

100 cm

Data measured for the year 2006 compared with the retrieved one.

Relationship between Tb and Tsnow for the year 2005 were used for the retrieval

R2=0.98, SE=1.5 °C R2=0.95,SE= 1.9 °C


Retrieval of snow temperature : 2-10 m

•To improve these results to the 2-10 m interval an ANN (multi-layer perceptrons MLP’s), was implemented. The training phase of the ANN was based on the back-

propagation (BP) learning rule.

•Inputs of the ANN were the AMSR-E Tb at 6.9 GHz, 10 GHz, 19 GHz and 37 GHz, vertical polarization, outputs were the snow temperatures at different depths in the 200 cm –

1000 cm range.

•The ANN was trained with the 2005 and tested with 2006 data.

Tb C

Tb Ka

T 200

T 1000


Retrieval of snow temperature : 2 – 10 meters

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Tsno

w R

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[°C]

500 cm

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Tsno

w R

etrie

ved

[°C]

800 cm

Example of measured and Retrieved snow temperature for the year 2006 at 500 cm (left) and 800 cm (right)

R2=0.94, SE=0.6 °C R2=0.9,SE= 0.12 °C


Retrieval of snow temperature : 2 – 10 meters

T200 T300 T400 T500 T600 T800 T1000

ΔT [°C] -17.03 -10.30 -6.74 -4.31 -3.39 -1.60 -0.99

R2 0.89 0.90 0.89 0.94 0.97 0.90 0.88

SE [°C] 1.21 0.74 0.54 0.26 0.17 0.12 0.08

ERR [%] 2.54 1.71 1.06 0.48 0.31 0.33 0.26

ΔT

= Maximum – Minimum Temperature, R2 = Correlation coefficient , SE = Standard Error of Estimate, Err = Mean Percentage Error

Very good results !!


Temperature Monitoring using mw data

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Date

Snow

Tem

pera

ture

[°C

]

Tair50 cm100 cm150 cm200 cm250 cm300 cm400 cm500 cm600 cm800 cm1000 cm

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]

TvCTvXTvKuTvKa

Air and MW Temperature increases : from where the thermal flux comes from?


Temperature Monitoring: Example


Spatial and Temporal Temperature Monitoring

Dome-C



6.8 GHz , V polarization

Tb differences between data collected in the same day (different orbits)

June 24


37 GHz , V polarization


Tb differences between data collected in the same day (different orbits)

June 24


Spatial Anisotropy analysis

1: 109.9213 E 74.1461 S 2: 130.3141 E 75.0341 S3: 120.8700 E 73.8045 S

(dome C) 4: 121.0001 E 70.3945 S

(Wilkes land)


Spatial Anisotropy : Area 1

ΔTb Ka = 1 K ; ΔTb C = 4 K

Ka C


Spatial Anisotropy : Area 3 (Dome-C)

ΔTb Ka

< 1 K ; ΔTb C < 2 K

Ka C


Spatial Anisotropy : Area 4

ΔTb Ka

= 3 K ; ΔTb C = 2 K

Ka C


Summary Spatial Anisotropy

For some areas the Tb azimuth variation increases when frequency increase surface effect (sastrugi, etc.) For others area the Tb azimuth variation is high at the lowest frequency deep effectIn other cases (e.g. Dome-C) the azimuth dependencies is minimum good for calibration of low-frequency radiometers


Conclusions

Tb analysis can provides important information about the temporal and spatial variation of the Antarctic ice sheet characteristics

Using MW data it is possible to retrieve snow temperature profiles up to 10m. This is well demonstrated for Dome-C area but it also possible to extend to others area (if snow data will be available)

High frequency is directly related to temperature variation and can be used for monitoring air circulation in the plateau

Tb spatial anisotropy is founded in different areas and can be used to investigate on surface or sub-surface physical properties of the ice sheets as well as to select the candidate site for low-frequency calibration

Documents

Monitoring of temporal and spatial variability of the East