Identifying Soil Types using Soil moisture data

CVEN 689

BY 

Uday Sant  

April 26, 2004 

 

• INTRODUCTION

• CONCEPTUAL BASIS / background

• OBJECTIVE OF THIS PROJECT

• METHODOLOGY

• RESULTS

• CONCLUSIONS AND INTERPRETATIONS

• FUTURE WORK

• ACKNOWLEDGEMENTS

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.

 

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 ).

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.

 

EXPERIMENT AREAEXPERIMENT AREA

Southern great plainEstar region

• 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

 

 

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

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.

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

BRIGHTNESS TEMPERATURE

PERCENT SAND

Methodology contdMethodology contd

METHODOLOGY contdMETHODOLOGY contd

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

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

No of Days Brightness Temperature

( tb )

11 0.67270.6727

22 0.64860.6486

33 0.68960.6896

44 0.65860.6586

RESULTSCorrelation coefficients

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

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

 

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

 

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)

SYNERGY OF TOOLSSYNERGY OF TOOLS

REMOTESENSING

GIS

GLOBAL CIRCULATION

MODELS

 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

 

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

acknowledgementsacknowledgements

• DR. OLIVERA CIVIL ENGINEERING DEPARTMENT

• DR. CAHILL CIVIL ENGINEERING DEPARTMENT

• ashish agrawal

 

 QUESTIONS ?