Application of satellite rainfall Application of satellite rainfall products for estimation of Soil products for estimation of Soil

Moisture Moisture Class project – Environmental Application of

remote sensing (CEE – 6900)

Course Instructor: Faisal Hossain (Ph.D)Presenter: Abebe Gebregiorgis

December 2009

OutlineOutline• Introduction• Objective of study• Study area• Data source• Model• Model result and analysis• Conclusion• Acknowledgment

IntroductionIntroduction• 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

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

Objective of the studyObjective 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

The study areaThe study areaArkansas-Red Rivers Basin

Legend

rivernetwork

Ark_newbdry

demsatreport

Value

High : 4301

Low : -16Legend

rivernetwork

Ark_newbdry

demsatreport

Value

High : 4301

Low : -16

Data SourceData Source

• Gridded ground rainfall data • Three satellite rainfall products

• TRMM rainfall product version 3B41RT• TRMM rainfall product version 3B42RT• CPC MORPHing (CMORPH)

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

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

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

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

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

Consistency of satellite rainfall Consistency of satellite rainfall datadata

• 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)

• Sort of skill assessment by simple observation

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

11/23/2004

06/30/2004 06/30/2004 06/30/2004

11/23/2004 11/23/2004 11/23/2004

Hydrologic ModelHydrologic 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

VIC

Meteorological forcing inputsRainfall Maximum

temperatureMinimum

temperature

Wind speed

Vapor pressure Etc …

Ground

3B41RT

3B42RT

CMORPH

DEM Vegetation (land cover)

Soil dataSnow band

Grid-based VIC outputs

... … SM1 SM2 SM3 ... ... ... ... ... ... ...

MODEL STRUCTUREMODEL STRUCTURE

Model result and analysisModel 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

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

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

• 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)

remark:

Error Matrices (BIAS) for rainfall Error Matrices (BIAS) for rainfall satellite products and soil moisturesatellite products and soil moisture

Min = -1.2 mmMax = 4.1 mmMean BIAs = 1.1 mm STDE BIAS = 0.9 mm

BIAS - 3B41RT (Rainfall)

BIAS - 3B41RT (soil moisture)

Min = -1.4 mmMax = 7 mmMean BIAS = 0.6 mm STDE BIAS = 1.1 mm

Legend

Ark_newbdry

Aggrega_b41b1

<VALUE>

-1.4 - -0.6

-0.6 - -0.2

-0.2 - 1.6

1.6 - 3

3 - 7

Legend

Ark_newbdry

Aggrega_B41b1

<VALUE>

-1.2 - -0.6

-0.6 - -0.2

-0.2 - 1.6

1.6 - 3

3 - 4.1

BIAS for the rainfall & soil moisture… cont’dBIAS for the rainfall & soil moisture… cont’d

Min = -1.4 mmMax = 4.5 mmMean BIAS = 0.76 mmSTD BIAS = 0.89 mm

BIAS - 3B42RT (Rainfall)

BIAS - 3B42RT (soil moisture)

Min = -1.5 mmMax = 7.1 mmMean BIAS = 0.54 mmSTD BIAS = 1.08 mm

Legend

Ark_newbdry

Aggrega_b42b1

<VALUE>

-1.48 - -0.6

-0.59 - -0.2

-0.19 - 1.6

1.61 - 3

3.01 - 7.2

Legend

Ark_newbdry

Aggrega_B42b1

<VALUE>

-1.39 - -0.6

-0.59 - -0.2

-0.19 - 1.6

1.61 - 3

3.01 - 4.6

Min = -1.62 mmMax = 4.1 mmMean BIAS = 1.06 mmSTD BIAS = 0.75 mm

BIAS - CMORPH (soil moisture)

Min = -1.4 mmMax = 7 mmMean BIAS = 1 mmSTD BIAS = 0.9 mm

BIAS for the rainfall & soil moisture… cont’dBIAS for the rainfall & soil moisture… cont’d

Legend

Ark_newbdry

Aggrega_CMbi2

<VALUE>

-1.4 - -0.6

-0.6 - -0.2

-0.2 - 1.6

1.6 - 3

3.0 - 7

BIAS - CMORPH (Rainfall) Legend

Ark_newbdry

Aggrega_CMOb1

<VALUE>

-1.62 - -0.6

-0.6 - -0.2

-0.2 - 1.6

1.6 - 3

3 - 4.1

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

Error Matrices (STDE) for rainfall satellite Error Matrices (STDE) for rainfall satellite products and soil moistureproducts and soil moisture

STDE - 3B41RT (soil moisture) Legend

Aggrega_b41s1

<VALUE>

7.8 - 14.4

14.5 - 19

19.1 - 25.9

26 - 41.3

41.4 - 87

Min = 7.8 mmMax = 87 mmMean STDE = 17.9 mm

STDE - 3B41RT (Rainfall)

Legend

Ark_newbdry

Aggrega_B41s1

<VALUE>

39.1 - 113.9

114 - 168

168.1 - 235.6

235.7 - 344.8

344.9 - 546.4

Min = 39.1 mmMax = 546.4 mmMean STDE = 175.3 mm

STDE for rainfall & soil moisture … cont’dSTDE for rainfall & soil moisture … cont’d

STDE - 3B42RT (Rainfall)

Min = 5.9 mmMax = 85.4 mmMean STDE = 15.1 mm

Legend

Ark_newbdry

Aggrega_b42s1

<VALUE>

5.9 - 13.1

13.2 - 17.5

17.6 - 24.6

24.7 - 38.3

38.4 - 85.4

STDE - 3B42RT (Soil moisture)

Legend

Ark_newbdry

Aggrega_B42s1

<VALUE>

36.2 - 93.8

93.9 - 135.7

135.8 - 182.8

182.9 - 246.4

246.5 - 388.6

Min = 36.2 mmMax = 388.6 mmMean STDE = 144.5 mm

STDE for rainfall & soil moisture … cont’dSTDE for rainfall & soil moisture … cont’d

Min = 15.2 mmMax = 504.9 mmMean = 102.4 mm

STDE - CMORPH (Rainfall)

STDE - CMORPH (Soil moisture)

Min = 4.3 mmMax = 87 mmMean = 12.8 mm

Legend

Ark_newbdry

Aggrega_CMst1

<VALUE>

4.3 - 9.2

9.3 - 12.6

12.7 - 17.7

17.8 - 25.9

26 - 87

Legend

Ark_newbdry

Aggrega_CMOs1

<VALUE>

15.2 - 69.6

69.7 - 109.3

109.4 - 170.2

170.3 - 294.3

294.4 - 504.9

ConclusionConclusion• 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.

Acknowledgment

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

Thank you