identifying soil types using soil moisture data cven 689 by uday sant april 26, 2004
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Identifying Soil Types using Soil moisture data
CVEN 689
BY
Uday Sant
April 26, 2004
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• INTRODUCTION
• CONCEPTUAL BASIS / background
• OBJECTIVE OF THIS PROJECT
• METHODOLOGY
• RESULTS
• CONCLUSIONS AND INTERPRETATIONS
• FUTURE WORK
• ACKNOWLEDGEMENTS
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INTRODUCTION
• Soil moisture is a natural variable of the earth’s surface and the most important data of a watershed
• The temporal and spatial distribution of soil moisture is affected by relations among soil, vegetation, topography and environment
• Remote sensing is capable of measuring soil moisture across a wide area instead of at discrete point locations associated with ground measurements.
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Microwave REMOTE SENSINGMicrowave REMOTE SENSING
• Remote Sensing is defined as the acquisition of information about an object without being in physical
contact with it.
• Electromagnetic radiation at long wavelengths (0.1 to 30 cms) falls into a segment of the spectrum commonly called the microwave region.
• Remote sensing has utilized passive microwaves, emanating from thermally activated bodies ( black bodies ).
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BackgroundThe Southern Great Plains 1997 (SGP97) Hydrology
Experiment
• basis :
deployment of the L band Electronically Scanned Thinned Array Radiometer (ESTAR)
• purpose :
daily mapping of surface soil moisture over an area greater than 10,000 km2 and a period on the order of a month.
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EXPERIMENT AREAEXPERIMENT AREA
Southern great plainEstar region
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• For passive microwave remote sensing of soil moisture, a radiometer measures the intensity of emission from the soil surface.
• This intensity is proportional to the brightness temperature
BRIGHTNESS TEMPERATURE (Tb)=
Surface temperature x surface emissivity
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DATA
FTP Site :/ ftp/data/sgp97/air_remote_sensing/estar/sgpprod
PERIOD :
• for the 16 days of full observations in June and July 1997
PROPERTIES :
• 206 pixels by 621 lines with a pixel resolution of 800 m by 800 m. The data is stored in 1 byte format
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OBJECTIVE OF THIS PROJECT
to confirm that the obtained temporal resolutions which show a different change of surface soil moisture for various days can identify soil types.
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METHODOLOGY
. RAW FORMAT . TIFF FORMAT
ADOBE PHOTOSHOP
ZONAL STATISTICS
VALUERASTER
( BRIGHTNESS TEMPERATURE )
ZONE RASTER
( PERCENT SAND )
RAINJUNE 29
TBJUNE 30
TBJULY 01
TBJULY 02
TBJULY 03
DRAWDOWN PERIOD
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BRIGHTNESS TEMPERATURE
PERCENT SAND
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Methodology contdMethodology contd
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METHODOLOGY contdMETHODOLOGY contd
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RESULTS SPATIAL & TEMPORAL VARIATIONS
SAND & TB JULY 03
0.00
50.00
100.00
150.00
200.00
250.00
0 10 20 30 40 50 60 70 80 90 100
Percent Sand
Me
an
TB
Va
lue
s
y = 0.4704x + 158.9R2 = 0.6586
SAND & TB JULY 01
0.00
50.00
100.00
150.00
200.00
250.00
0 10 20 30 40 50 60 70 80 90 100
Percent sand
Mea
n T
B V
alu
es
y = 0.546x + 138.97R2 = 0.6486
SAND & TB JUNE 30
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
200.00
0 10 20 30 40 50 60 70 80 90 100
Percent Sand
Mea
n T
B V
alu
es
y = 0.5686x + 133.49R2 = 0.6727
SAND & TB JULY 02
0.00
50.00
100.00
150.00
200.00
250.00
0 10 20 30 40 50 60 70 80 90 100
Percent Sand
Mea
n T
B V
alu
es
y = 0.3935x + 157.36R2 = 0.6896
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SAND & TB JULY 03
0.00
50.00
100.00
150.00
200.00
250.00
0 10 20 30 40 50 60 70 80 90 100
Percent Sand
Mea
n TB
Val
ues
y = 0.4704x + 158.9R2 = 0.6586
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No of Days Brightness Temperature
( tb )
11 0.67270.6727
22 0.64860.6486
33 0.68960.6896
44 0.65860.6586
RESULTSCorrelation coefficients
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RESULTS CONTD..REGRESSION EQUATIONS for
% sand
Period of Period of ChangeChange
(Days)(Days)
From Brightness TemperatureFrom Brightness Temperature
(tb)(tb)
1 (Jun 30)1 (Jun 30) 0.5686 tb + 133.490.5686 tb + 133.49
2 (July 01)2 (July 01) 0.546 tb + 138.970.546 tb + 138.97
3 (July 02)3 (July 02) 0.394 tb + 157.360.394 tb + 157.36
4 (July 03)4 (July 03) 0.4704 tb + 158.90.4704 tb + 158.9
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RESULTS CONTD..
Clay vs TB July 02
0.0
20.0
40.060.0
80.0
100.0
120.0
140.0160.0
180.0
200.0
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0
% TB
Mea
n TB
Val
ues
Clay vs TB July 02
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CONCLUSIONS AND INTERPRETATIONS
• each soil has a different rate of change of surface soil moisture
• Percentage sand holds a GOOD correlation with changes in Tb (R2 = approx 0.7)
• The same type of relationship could not be observed for percentage clay
• perhaps ratio of sand to clay would give better relations as observed in the study
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CONCLUSIONS CONTD…
• the strong relationships observed do confirm that temporal changes in BRIGHTNESS TEMPERATURE can be used to identify soil types
• the equations can be utilized to observe the spatial variability of soil on larger scales
• can be used as input in Global Circulation Models (GCM’s)
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SYNERGY OF TOOLSSYNERGY OF TOOLS
REMOTESENSING
GIS
GLOBAL CIRCULATION
MODELS
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FUTURE WORK
• Extend the relationships between brightness temperature (Tb), soil MOISTURE and soil texture for SGP97 datasets over an area greater than 10,000 km2
• Develop regression equations between multitemporal Brightness Temperature, Soil moisture and soil texture.
• The relations here need to be validated by using similar drawdown patterns after rainfall for other days observed.
• Validating 800m800m resolution pixel with point measurements needs some upscaling, which is beyond the scope of this work
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REFERENCES
• N.M Mattikali, E.T Engman, L.R.Ahuja and T.J.Jackson “Microwave remote N.M Mattikali, E.T Engman, L.R.Ahuja and T.J.Jackson “Microwave remote sensing of soil moisture for estimation of soil moisture properties.” sensing of soil moisture for estimation of soil moisture properties.” International Journal of Remote Sensing, 1998, Vol 19, No.9, 1751 – 1767International Journal of Remote Sensing, 1998, Vol 19, No.9, 1751 – 1767
• Anna Oldak, Thomas J Jackson and Yakov Pachesky “Using GIS in Anna Oldak, Thomas J Jackson and Yakov Pachesky “Using GIS in microwave soil moisture mapping and geostatistical analysis.”microwave soil moisture mapping and geostatistical analysis.”International Journal of Geographic Information Science, 2002, Vol 16, No.7 – International Journal of Geographic Information Science, 2002, Vol 16, No.7 – 681-698681-698
• E.T.Engman and R.J.Gurney “Remote Sensing in Hydrology”E.T.Engman and R.J.Gurney “Remote Sensing in Hydrology”
WEBSITESWEBSITES
• http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/SGP97/air_rem.html
• http://www.ghcc.msfc.nasa.gov/landprocess/lp_smrs.htmlhttp://www.ghcc.msfc.nasa.gov/landprocess/lp_smrs.html
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acknowledgementsacknowledgements
• DR. OLIVERA CIVIL ENGINEERING DEPARTMENT
• DR. CAHILL CIVIL ENGINEERING DEPARTMENT
• ashish agrawal
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QUESTIONS ?