Application of satellite rainfall products for estimation of Soil Moisture

1. Application of satellite rainfall products for estimation of Soil Moisture Class project – Environmental Application of remote sensing (CEE – 6900) Course Instructor: Faisal Hossain (Ph.D) Presenter: AbebeGebregiorgis December 2009

2. Outline • Introduction • Objective of study • Study area • Data source • Model • Model result and analysis • Conclusion • Acknowledgment

3. Introduction • Since recent period, remote sensing tools allow us to look at our planet (earth) • They provide us so many information about our earth and the dynamic events happening every second that helps in managing our resources and keeping our environment safe • Rainfall data – main information

4. Introduction … cont’d • Precipitation is the most crucial variable in land surface hydrology • Probably, it is the main moisture inputs on surface of the land • The estimation of soil moisture depends on how the rainfall value is accurate • hence, to promote remote sensing application, it is important to demonstrate the performance and satellite products (precipitation) in hydrological models

5. Objective of the study • To demonstrate the application of satellite rainfall for estimation of soil moisture • To compare the performance of three satellite rainfall products in predicting soil moisture

6. The study area Arkansas-Red Rivers Basin

7. Data Source • Gridded ground rainfall data • Three satellite rainfall products • TRMM rainfall product version 3B41RT • TRMM rainfall product version 3B42RT • CPC MORPHing (CMORPH)

8. Gridded Ground Rainfall Data • is prepared • from the raw data of EarthInfo National Climate Data Center (NCDC) by University of Washington. • the gridding process - SYMAP Interpolation Algorithm (Shepard, D.S., Computer Mapping) • Spatial resolution = 0.1250 • Temporal resolution = daily

9. Satellite Rainfall Products • The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) provides 0.25x0.25° 3-hourly estimates of precipitation • The TMPA depends on input from two different types of satellite sensors, namely microwave and IR. • Precipitation estimates is made from TMI, SSM/I, AMSR-E, AMSU-B, and geosynchronous-orbit IR (geo-IR) data, all inter calibrated to a single TRMM-based standard data

10. 3B41RT • This is a product of microwave-calibrated geo IR sensors • The merged geosynchronous infrared (geo-IR) data are averaged to the same 0.25° grid and calibrated with microwave data • Spatial resolution: 0.250 • Temporal resolution: hourly (mm/hr) • Aggregated to daily time step

11. 3B42RT • This is a merged microwave and IR sensors rainfall product • The microwave-IR combination is implemented as using the geo-IR estimates to fill gaps in the combined microwave coverage. • Spatial resolution: 0.250 • Temporal resolution: 3 hourly • aggregated to daily time step

12. CMORPH • CMORPH uses a different approach • IR data are used only to derive a cloud motion field to propagate raining pixels; • But rainfall estimates that have been derived from PMW data are used in the procedure. • Spatial resolution: 0.250 • Temporal resolution: 3 hourly • Aggregated to daily time step

13. Consistency of satellite rainfall data • Sort of skill assessment by simple observation • Simple comparison at daily time step • rainfall pattern and distribution over the watershed • Rainfall magnitude • Computation of BIAS (mean error), STDE (standard deviation of error) • Error = (Psat – Pgrd)

14. comparison of Daily rainfall at 0.25 degree Ground data 3B41 3B42 CMOPRH 04/09/2004 04/09/2004 04/09/2004 04/09/2004 05/16/2004 05/16/2004 05/16/2004 05/16/2004 06/30/2004 06/30/2004 06/30/2004 06/30/2004 11/23/2004 11/23/2004 11/23/2004 11/23/2004

15. Hydrologic Model • Remote sensing data • has a capability of synoptic viewing and repetitive coverage that provides useful information on land-use dynamics • physically based spatially distributed hydrological model (LSM) – is best model for remote sensing application • VIC (Variable infiltration Capacity) hydrological model is implemented

16. Ground MODEL STRUCTURE 3B41RT Meteorological forcing inputs 3B42RT CMORPH Rainfall Maximum temperature Minimumtemperature Wind speed Vapor pressure Etc … DEM VIC Vegetation (land cover) Soil data Snow band Grid-based VIC outputs ... … SM1 SM2 SM3 ... ... ... ... ... ... ...

17. Model result and analysis • Soil moisture content in mm at the top layer (layer 1: 100 mm from the surface) • Soil moisture content in mm at layer 2 (500 mm) • Soil moisture content in mm at layer 3 (1600 mm) Total depth of soil layer = 2.2 m

18. Map of rainfall and soil moisture at top layer, mm at resolution of 0.25 degree Ground data 3B41 3B42 CMOPRH 04/09/2004 04/09/2004 04/09/2004 04/09/2004 06/30/2004 06/30/2004 06/30/2004 06/30/2004

19. Map of rainfall and soil moisture at top layer, mm at resolution of 0.25 degree Ground data 3B41 3B42 CMOPRH 05/16/2004 05/16/2004 05/16/2004 05/16/2004 11/23/2004 11/23/2004 11/23/2004 11/23/2004

20. remark: • For high rainfall variation, the soil moisture change is small. This may be explained because of the following facts: • The first process during rainfall event is to satisfy the soil moisture demand • soil moisture is only affected by rainfall but also other meteorological variables (max and min temp)

21. Error Matrices (BIAS) for rainfall satellite products and soil moisture BIAS - 3B41RT (Rainfall) Min = -1.2 mm Max = 4.1 mm Mean BIAs = 1.1 mm STDE BIAS = 0.9 mm BIAS - 3B41RT (soil moisture) Min = -1.4 mm Max = 7 mm Mean BIAS = 0.6 mm STDE BIAS = 1.1 mm

22. BIAS for the rainfall & soil moisture… cont’d BIAS - 3B42RT (Rainfall) Min = -1.4 mm Max = 4.5 mm Mean BIAS = 0.76 mm STD BIAS = 0.89 mm BIAS - 3B42RT (soil moisture) Min = -1.5 mm Max = 7.1 mm Mean BIAS = 0.54 mm STD BIAS = 1.08 mm

23. BIAS for the rainfall & soil moisture… cont’d BIAS - CMORPH (Rainfall) Min = -1.62 mm Max = 4.1 mm Mean BIAS = 1.06 mm STD BIAS = 0.75 mm BIAS - CMORPH (soil moisture) Min = -1.4 mm Max = 7 mm Mean BIAS = 1 mm STD BIAS = 0.9 mm

24. remark: • Positive & negative BIAS propagates from rainfall data to the soil moisture • Mountainous area of the basin has the most positive BIAS for all rainfall satellite products and soil moisture but its magnitude reduces in case of CMORPH • This shows that, it is very difficult for the sensors to capture the true information in mountainous region

25. Error Matrices (STDE) for rainfall satellite products and soil moisture STDE - 3B41RT (Rainfall) Min = 39.1 mm Max = 546.4 mm Mean STDE = 175.3 mm STDE - 3B41RT (soil moisture) Min = 7.8 mm Max = 87 mm Mean STDE = 17.9 mm

26. STDE for rainfall & soil moisture … cont’d STDE - 3B42RT (Rainfall) Min = 36.2 mm Max = 388.6 mm Mean STDE = 144.5 mm STDE - 3B42RT (Soil moisture) Min = 5.9 mm Max = 85.4 mm Mean STDE = 15.1 mm

27. STDE for rainfall & soil moisture … cont’d STDE - CMORPH (Rainfall) Min = 15.2 mm Max = 504.9 mm Mean = 102.4 mm STDE - CMORPH (Soil moisture) Min = 4.3 mm Max = 87 mm Mean = 12.8 mm

28. Conclusion • The mean of STDE is high in 3B41RT and less in case of CMORPH data set. • For this study, CMORPH product works better than the other two satellites in predicting the soil moisture. • This is possibly because, the rainfall estimate fully derived from PMW sensors which can not be affected by clouds and absence of illumination.

29. Acknowledgment • I would like to thank • Dr. Andy Wood • Dr. Faisal Hossain • Ling Tang

30. Thank you