1 / 10

Research Aim

MoistureMap: Multi-sensor Retrieval of Soil Moisture Mahdi Allahmoradi PhD Candidate Supervisor: Jeffrey Walker Contributors: Dongryeol Ryu, Chris Rudiger. Research Aim.

blanca
Download Presentation

Research Aim

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MoistureMap: Multi-sensor Retrieval of Soil MoistureMahdi AllahmoradiPhD CandidateSupervisor: Jeffrey WalkerContributors:Dongryeol Ryu, Chris Rudiger

  2. Research Aim • This research will test the hypothesis that more accurate soil moisture information can be derived from SMOS if vegetation and soil temperature information are derived from other coincident remote sensing observations at higher resolution. Ground/RS Data, Models, … MoistureMap SMOS

  3. SMOS overview • SMOS • Approximate launch date: May 2009 • Lifetime: Minimum 3 years • Frequency: L-band (21cm - 1.4 GHz) • Orbit: Sun-synchronous • Overpass time: 6 am - 6 pm • Temporal resolution: 3 Days • Spatial resolution: 40 - 50 km (35 km at centre of Field of View) SOURCE: ESA

  4. Theoretical Aspect • The theory behind microwave remote sensing of soil moisture is based on the large contrast between the dielectric properties of liquid water and dry soil. • - For smooth bare soil (Planck’s law): • - Vegetated soil (Tau - omega model): • equation of Ulaby et al. (1986)

  5. Vegetation Effect • Vegetation Effect: Jackson, 93 Wigneron et al. 07 Estimation of VWC using Vegetation indices Reduced sensitivity to VWC changes in dense vegetation (Jackson, 2004) SWIR 1640 nm suitable for croplands SWIR 2130 nm suitable for native vegetation (Maggioni, et al. 2006) * b, b’ and b” are empirical parameters

  6. Brightness Temperature → Soil Moisture Tb Modis Data Land Cover MTSAT 1R & WindSat Data Soil Temperature Modis Aqua or WindSat Vegetation Water Content or LAI Maybe WindSat Data Surface Roughness NAFE ground data Soil Texture Soil Moisture

  7. Spaceborne Remote Sensors • MODIS • Terra launched December 1999 • Aqua launched May 2002 • Design Lifetime: 6 years • No. of Bands: 36 • Orbit: Sun-synchronous • Overpass time: 10:30 am (Terra) – 13:30 pm (Aqua) • Temporal resolution: 2 days • Spatial resolution of bands: 250 m (1-2) 500 m (3-7) and 1 km (8-36) SOURCE: NASA

  8. Spaceborne Remote Sensors • WindSat • Launched on 6th January 2003 • Lifetime: Minimum 3 years • Frequencies: 6.8, 10.7, 18.7, 23.8, 37.0 GHz • Orbit: Sun-synchronous • Overpass time: 6 am / 6 pm • Spatial resolution: 8*13 km (for 37.0 GHz) SOURCE: US NAVAL RESEARCH LAB

  9. Spaceborne Remote Sensors • MTSAT-1R • Launched February 2005 • Lifetime: 5 years for meteorological function, 10 years for aviation function • Bands: visible, Infrared (1-4) • orbit: Geostationary • Temporal resolution: 30 minutes • Spatial resolution: visible (1 km nadir), IR1-4 (4km nadir) SOURCE: JAPAN METHEOROLOGICAL AGENCY

  10. Algorithm Development NAFE Ground Data Algorithm Remotely Sensed Data Modis Aqua/Terra Soil Temperature WindSat MTSAT 1R Vegetation Water Content SMOS NAFE Airborne Data Soil Moisture Verification

More Related