1 / 32

Potential Soil Moisture Retrieval from Aquarius Instrument

Potential Soil Moisture Retrieval from Aquarius Instrument. Mississippi State University Geosystems Research Institute. Aquarius Evaluation Team & Collaborators. MSU Team Robert Moorhead Xingang Fan Valentine Anantharaj Georgy Mostovoy Graduate student (MSU GRI and ECE Dept.)

lara
Télécharger la présentation

Potential Soil Moisture Retrieval from Aquarius Instrument

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. Potential Soil Moisture Retrieval from Aquarius Instrument Mississippi State University Geosystems Research Institute

  2. Aquarius Evaluation Team & Collaborators • MSU Team • Robert Moorhead • Xingang Fan • Valentine Anantharaj • GeorgyMostovoy • Graduate student (MSU GRI and ECE Dept.) • External Team • GMU & CREW • Partner Agencies • Garry Schaeffer (USDA NRCS) • Steve Hunter (United States Bureau of Reclamation) NASA RPC Review (3/2/09)

  3. Identified Decision Support Needs • Routine analysis land surface state over the continental needs • water • soils • sun • weather • climate • vegetation • terrain observe, model, assimilate NASA RPC Review (3/2/09)

  4. Anticipated Societal Benefits • provides critical information to support drought monitoring and mitigation • provides essential information for predicting droughts based on weather and climate predictions • supports irrigation water management • supports fire risk assessment • supports water supply forecasting and NWS flood forecasting • supplies a critical missing component to assist with snow, climate and associated hydrometeorological data analysis • supports climate change assessment • enables water quality monitoring • supports a wide variety of natural resource management & research activities such as NASA remote sensing activities of soil moisture and ARS watershed studies. NASA RPC Review (3/2/09)

  5. Purpose of RPC Aquarius Experiments • Evaluate feasibility and usefulness of Aquarius data for decision support in water resources management and other cross-cutting applications. Approach: • Aquarius Evaluation: Test, characterize, and evaluatepotential Aquarius soil moisture data via an OSSE. • Downscaling for land surface modeling: Downscale Aquarius data and evaluate by land surface modeling. • Sensitivity Studies: Different LSM(s), input parameters NASA RPC Review (3/2/09)

  6. Experimental Objectives and Approach • Using an OSSE, retrieve potential space-based soil moisture estimation using Aquarius radiometer and scatterometer. • Generate moisture and energy fluxes at 10x10 km2 using a land surface model to assimilate the retrieved synthetic (simulated) soil moisture product. • Evaluate experimental products for use in water management applications. • Quantify uncertainties. NASA RPC Review (3/2/09)

  7. Identified Tasks • Develop and finalize experimental design. (Otherwise mistake could be expensive to correct). This will particularly include strategies for nature run, emission and backscatter models, orbital and sensor model, selection of retrieval algorithms. • Develop and validate nature run. • Retrieve soil moisture using the methodology outlined later. • Compare with the benchmark data aggregated from the NR. Quantify errors and uncertainties. • Document and publish results NASA RPC Review (3/2/09)

  8. NASA RPC Review (3/2/09)

  9. NASA RPC Review (3/2/09)

  10. NASA RPC Review (3/2/09)

  11. NASA RPC Review (3/2/09)

  12. Aquarius OSSE Design NASA RPC Review (3/2/09)

  13. Generalized OSSE Framework(Courtesy: JCSDA) NASA RPC Review (3/2/09)

  14. HYDROS OSSE Example NASA RPC Review (3/2/09)

  15. Aquarius OSSE: Experimental Design NASA RPC Review (3/2/09)

  16. Remote Sensing ConceptRadiativeTransfer at the surface Tb Vegetation Surface • Tbp can be written as Tbp = T1+T2+T3 • T1,T2,T3 correspond to contributions in the above equation • Ts = Soil temperature • Tc =Vegetationtemperature • τc = function of vegetation water content • espand rsp are functions of soil water content T2 Ts T1 τc T3 Soil Surface Ts NASA RPC Review (3/2/09)

  17. 1/8 deg Nature Run using LIS-CLM 1/8 deg domain size: 144x80 points 30.1875 – 40.0625 N, 107.8125 –89.9375 W If aggregated to 100km: 18x10points NASA RPC Review (3/2/09)

  18. Orbital Senor ModelingNature RunForward ModelingSoil Moisture Retrieval NASA RPC Review (3/2/09)

  19. OSSE Flowchart (simplified) Nature Run LIS model CLM LSM NLDAS Forcing 1/8 deg TOA (Satellite) Emission model Radiometer/ Backscatter 1/8 deg Satellite Obs Orbital model Re-sampling Adding Noise 1 deg Data assimilation LIS model NOAH LSM CDAS + ENKF 1/8 deg Retrieval Inversion model Retrieved soil moisture 1 deg Validation Compare to “truth”

  20. Nature Run using LIS-CLM Nature Run LIS CLM NLDAS Forcing 1/8 deg TOA (Satellite) Emission model Radiometer/ Backscatter 1/8 deg Nature Run Output of Soil moisture at 10 soil layers, hourly output from 2002-09-01 to 2003-10-31 1/8 deg resolution Data assimilation LIS model NOAH LSM + ENKF 1/8 deg Retrieval Inversion model Retrieved soil moisture 1 deg Validation Compare to “truth” Soil moisture of the top layer at 13 UTC 2002-09-01

  21. Forward ModelingMicrowave Emissions and Backscatter Forward Emission Output of Brightness Temperature (Tb)as can be seen from radiometer, and radar echo (Sigma) as from back scatterometer, a total of 15 maps each time point: Three look angles (29,38,45) Five variables: two Tb (h,v) and three simga (hh,hv,vv) TOA (Satellite) Emission model Radiometer/ Backscatter 1/8 deg Nature Run LIS model CLM LSM 1/8 deg Shown below are Tb(h), Tb(v), sigma(hh), sigma(hv), sigma(vv) from look angle 29 at 13 UTC 2002-09-01 Tb(h) Tb(v) Data assimilation LIS model NOAH LSM + ENKF 1/8 deg Retrieval Inversion model Retrieved soil moisture 1 deg Validation Compare to “truth” sigma(hh) sigma(hv) sigma(vv)

  22. Orbital Sensor ModelingSimulation of Aquarius Footprints Experiment Domain 30.1875 – 40.0625 N, 107.8125 –89.9375 W

  23. Simulation of Aquarius Footprints • Satellite Tool Kit (STK 8.0) has been used for simulation of Aquarius Footprints • Generated geodetic Latitude – Longitude contour line reports for re-sampling with the temperature data Scatterometer Footprint Radiometer Footprint

  24. TOA (Satellite) Emission model Radiometer/ Backscatter 1/8 deg Orbital model Satellite Obs Re-sampling Adding Noise 1 deg Nature Run LIS model CLM LSM 1/8 deg Scatterometer Footprint Radiometer Footprint Data assimilation LIS model NOAH LSM + ENKF 1/8 deg Retrieval Inversion model Retrieved soil moisture 1 deg • Satellite Tool Kit (STK 8.0) has been used for simulation of Aquarius Footprints • Generated geodetic Latitude – Longitude contour line reports for re-sampling with the temperature data Validation Compare to “truth” Footprints Inner Beam : 94X76 km Middle Beam: 120X84 km Outer Beam: 156X97 km

  25. Forward emission model output of Tb and sigma are re-sampled for the Aquarius footprints, and then aggregated to 1-degree resolution. The satellite has a 7-day revisit time period, so the following shows only one Tb and one sigma over a 7-day period, from three look angles. TOA (Satellite) Emission model Radiometer/ Backscatter 1/8 deg Orbital model Satellite Obs Re-sampling Adding Noise 1 deg Nature Run LIS model CLM LSM 1/8 deg Look angle 29 Look angle 38 Look angle 45 7-day Tb(h) 7-day Sigma(hh) Noise Gaussian noise of zero-mean and stdev equals 1K and 0.5 dB for Tb and sigma, respectively Data assimilation LIS model NOAH LSM + ENKF 1/8 deg Retrieval Inversion model Retrieved soil moisture 1 deg Validation Compare to “truth” Look angle 29 Look angle 38 Look angle 45

  26. Retrieved soil moisture[from brightness temperature (Tb) & backscatter (sigma)] Look angle 29 38 45 Average of Tb(h) and Tb(v) retrievals Average of sigma(hh), sigma(hv), and sigma(vv) retrievals TOA (Satellite) Emission model Radiometer/ Backscatter 1/8 deg Orbital model Satellite Obs Re-sampling Adding Noise 1 deg Nature Run LIS model CLM LSM 1/8 deg Data assimilation LIS model NOAH LSM + ENKF 1/8 deg Retrieval Inversion model Retrieved soil moisture 1 deg Validation Compare to “truth”

  27. Data Assimilation Experiments(Preliminary Results) NASA RPC Review (3/2/09)

  28. Soil moisture at a lightly-vegetated pixel(34.3125°N, 108.562°W ) Truth Open Loop (No D/A) With Data Assimilation Retrieved SM NASA RPC Review (3/2/09)

  29. Soil moisture at a heavily-vegetated pixel(34.3125°N, 94.5625°W ) Truth Open Loop (No D/A) With Data Assimilation Retrieved SM NASA RPC Review (3/2/09)

  30. Soil Moisture(domain averaged) Truth Open Loop (No D/A) With Data Assimilation NASA RPC Review (3/2/09)

  31. Summary of Progress • Completed Tasks • OSSE experimental design • Nature Run (synthetic truth) • Orbital Sensor Modeling and Simulations • Microwave Emissions and Backscatter Modeling • Soil moisture retrieval • Synthesis of experimental SM product • Assimilation of SM product using LIS EnKF (preliminary) • Final Steps (in progress) • Validation of DA runs • Error analysis • Final evaluation (document and publish) NASA RPC Review (3/2/09)

  32. Contact InformationValentine Anantharaj<vga1@msstate.edu>Tel: (662)325-5135 NASA RPC Review (3/2/09)

More Related