1. Identifying Soil Types using Soil moisture data CVEN 689 BY Uday Sant April 26, 2004

2. INTRODUCTION CONCEPTUAL BASIS / background OBJECTIVE OF THIS PROJECT METHODOLOGY RESULTS CONCLUSIONS AND INTERPRETATIONS FUTURE WORK ACKNOWLEDGEMENTS

3. 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.

4. Microwave 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 ).

5. Background The Southern Great Plains 1997 (SGP97) Hydrology Experiment basis : deployment of the L­band Electronically Scanned Thinned Array Radiometer (ESTAR) purpose : daily mapping of surface soil moisture over an area greater than 10,000 km 2 and a period on the order of a month.

6. EXPERIMENT AREA Southern great plain Estar region

7. 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

9. 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

10. 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.

11. METHODOLOGY. RAW FORMAT. TIFF FORMAT ADOBE PHOTOSHOP ZONAL STATISTICS VALUE RASTER ( BRIGHTNESS TEMPERATURE ) ZONE RASTER ( PERCENT SAND ) RAIN JUNE 29 TB JUNE 30 TB JULY 01 TB JULY 02 TB JULY 03 DRAWDOWN PERIOD

12. BRIGHTNESS TEMPERATURE PERCENT SAND

13. Methodology contd

14. METHODOLOGY contd

15. RESULTS SPATIAL & TEMPORAL VARIATIONS

17. No of DaysBrightness Temperature ( tb ) 10.6727 20.6486 30.6896 40.6586 RESULTS Correlation coefficients

18. RESULTS CONTD.. REGRESSION EQUATIONS for % sand Period of Change (Days) From Brightness Temperature (tb) 1 (Jun 30) 0.5686 tb + 133.49 2 (July 01) 0.546 tb + 138.97 3 (July 02) 0.394 tb + 157.36 4 (July 03) 0.4704 tb + 158.9

19. RESULTS CONTD..

20. 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 (R 2 = 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

21. 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)

22. SYNERGY OF TOOLS REMOTE SENSING GIS GLOBAL CIRCULATION MODELS

23. FUTURE WORK Extend the relationships between brightness temperature (Tb), soil MOISTURE and soil texture for SGP97 datasets over an area greater than 10,000 km 2 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 800m  800m resolution pixel with point measurements needs some upscaling, which is beyond the scope of this work

24. REFERENCES 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.” 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.” International Journal of Remote Sensing, 1998, Vol 19, No.9, 1751 – 1767 Anna Oldak, Thomas J Jackson and Yakov Pachesky “Using GIS in microwave soil moisture mapping and geostatistical analysis.” Anna Oldak, Thomas J Jackson and Yakov Pachesky “Using GIS in microwave soil moisture mapping and geostatistical analysis.” International Journal of Geographic Information Science, 2002, Vol 16, No.7 – 681-698 E.T.Engman and R.J.Gurney “Remote Sensing in Hydrology” E.T.Engman and R.J.Gurney “Remote Sensing in Hydrology”WEBSITES http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/SGP97/air_rem.html http://www.ghcc.msfc.nasa.gov/landprocess/lp_smrs.html http://www.ghcc.msfc.nasa.gov/landprocess/lp_smrs.html

25. acknowledgements DR. OLIVERA CIVIL ENGINEERING DEPARTMENT DR. CAHILL CIVIL ENGINEERING DEPARTMENT ashish agrawal

26. QUESTIONS ?