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: Abebe Gebregiorgis December 2009

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

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

Introduction … contd 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 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 area Arkansas-Red Rivers Basin

Data 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 = 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: 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: 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: Temporal resolution: 3 hourly Aggregated to daily time step

Consistency of satellite rainfall data 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 = (P sat – P grd ) Sort of skill assessment by simple observation

comparison of Daily rainfall at 0.25 degree Ground data3B413B42CMOPRH 04/09/ /16/ /30/ /23/ /30/ /23/2004

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

VIC Meteorological forcing inputs Rainfall Maximum temperature Minimum temperature Wind speed Vapor pressure Etc … Ground 3B41RT 3B42RT CMORPH DEM Vegetation (land cover) Soil data Snow band Grid-based VIC outputs... … SM1SM2SM3... MODEL STRUCTURE

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

Map of rainfall and soil moisture at top layer, mm at resolution of 0.25 degree Ground data3B413B42CMOPRH 04/09/ /30/2004

Map of rainfall and soil moisture at top layer, mm at resolution of 0.25 degree Ground data3B413B42CMOPRH 05/16/ /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 satellite products and soil moisture Min = -1.2 mm Max = 4.1 mm Mean BIAs = 1.1 mm STDE BIAS = 0.9 mm BIAS - 3B41RT (Rainfall) BIAS - 3B41RT (soil moisture) Min = -1.4 mm Max = 7 mm Mean BIAS = 0.6 mm STDE BIAS = 1.1 mm

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

Min = 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 BIAS for the rainfall & soil moisture… contd BIAS - CMORPH (Rainfall)

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 products and soil moisture STDE - 3B41RT (soil moisture) Min = 7.8 mm Max = 87 mm Mean STDE = 17.9 mm STDE - 3B41RT (Rainfall) Min = 39.1 mm Max = mm Mean STDE = mm

STDE for rainfall & soil moisture … contd STDE - 3B42RT (Rainfall) Min = 5.9 mm Max = 85.4 mm Mean STDE = 15.1 mm STDE - 3B42RT (Soil moisture) Min = 36.2 mm Max = mm Mean STDE = mm

STDE for rainfall & soil moisture … contd Min = 15.2 mm Max = mm Mean = mm STDE - CMORPH (Rainfall) STDE - CMORPH (Soil moisture) Min = 4.3 mm Max = 87 mm Mean = 12.8 mm

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.

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

Thank you