The University of Mississippi Geoinformatics Center NASA MRC RPC: 14-15 April 2008 Greg Easson, Ph.D.- (PI) Robert Holt, Ph.D.- (Co-PI) A. K. M. Azad Hossain.

Slides:

Advertisements

Similar presentations

Environmental Application of Remote Sensing: CE 6900 Tennessee Technological University Department of Civil and Environmental Engineering Course Instructor:

Advertisements

 nm)  nm) PurposeSpatial Resolution (km) Ozone, SO 2, UV8 3251Ozone8 3403Aerosols, UV, and Volcanic Ash8 3883Aerosols, Clouds, UV and Volcanic.

Extension and application of an AMSR global land parameter data record for ecosystem studies Jinyang Du, John S. Kimball, Lucas A. Jones, Youngwook Kim,

1 CODATA 2006 October 23-25, 2006, Beijing Cryospheric Data Assimilation An Integrated Approach for Generating Consistent Cryosphere Data Set Xin Li World.

SMAP and Agricultural Productivity Applications

MODIS satellite image of Sierra Nevada snowcover Big data and mountain water supplies Roger Bales SNRI, UC Merced & CITRIS.

AGU 2004 Fall Meeting, May 19, 2015Slide 1 Jointly Retrieving Surface Soil Moisture from Active and Passive Microwave Observations using Cubist Data-Mining.

 Floods and droughts are the most important hydrological disturbances in intermittent streams.  The concept of hydrological disturbance is strongly.

MODIS Science Team Meeting - 18 – 20 May Routine Mapping of Land-surface Carbon, Water and Energy Fluxes at Field to Regional Scales by Fusing Multi-scale.

The University of Mississippi Geoinformatics Center NASA MRC RPC review meeting, April 2008 Use of NASA Assets for Predicting Wildfire Potential.

ATS 351 Lecture 8 Satellites

SMOS – The Science Perspective Matthias Drusch Hamburg, Germany 30/10/2009.

Combining satellite and surface observations to determine the radiative divergence across the atmosphere Tony Slingo Environmental Systems Science Centre.

CSIRO LAND and WATER Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems 1- Key Research Issues 2- Evapotranspiration.

DROUGHT MONITORING THROUGH THE USE OF MODIS SATELLITE Amy Anderson, Curt Johnson, Dave Prevedel, & Russ Reading.

Satellite Imagery ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Introduction to Remote Sensing and Air Quality Applications.

Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –

Satellite Data Access – Giovanni, LAADS, and NEO Training Workshop in Partnership with BAAQMD Santa Clara, CA September 10 – 12, 2013 Applied Remote SEnsing.

Remote Sensing Technology for Scalable Information Networks Douglas G. Goodin Kansas State University Geoffrey M. Henebry University of Nebraska - Lincoln.

Microwave Remote Sensing Group 1 P. Pampaloni Microwave Remote Sensing Group (MRSG) Institute of Applied Physics -CNR, Florence, Italy Microwave remote.

Experiments with the microwave emissivity model concerning the brightness temperature observation error & SSM/I evaluation Henning Wilker, MIUB Matthias.

The University of Mississippi Geoinformatics Center NASA MRC RPC Review Meeting: April, 2008 Evaluation for the Integration of a Virtual Evapotranspiration.

SMOS STORM KO meeting 30/01/2012 ESRIN Ocean Surface Remote Sensing at High Winds with SMOS.

The University of Mississippi Geoinformatics Center NASA RPC – March, Evaluation for the Integration of a Virtual Evapotranspiration Sensor Based.

Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.

The University of Mississippi Geoinformatics Center NASA MRC RPC Review Meeting: April 2008 Integration of NASA Global Precipitation Measurement.

Assessment of Regional Vegetation Productivity: Using NDVI Temporal Profile Metrics Background NOAA satellite AVHRR data archive NDVI temporal profile.

Operational Agriculture Monitoring System Using Remote Sensing Pei Zhiyuan Center for Remote Sensing Applacation, Ministry of Agriculture, China.

Recent advances in remote sensing in hydrology

Slide #1 Emerging Remote Sensing Data, Systems, and Tools to Support PEM Applications for Resource Management Olaf Niemann Department of Geography University.

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 Greg Easson, Ph.D. Robert Holt, Ph.D. A. K. M. Azad Hossain University.

Michigan Tech Research Institute (MTRI)  Michigan Technological University 3600 Green Court, Suite 100  Ann Arbor, MI (734) – Phone 

PASSIVE MICROWAVE TECHNIQUES FOR HYDROLOGICAL APPLICATIONS by : P. Ferrazzoli Tor Vergata University Roma, Italy

IGARSS 2011, Jul. 26, Vancouver 1 Improving Land Surface Energy and Water Fluxes Simulation over the Tibetan Plateau with Using a Land Data Assimilation.

Preparations for Assimilating Land Surface Observations from GOES-R, NPP/VIIRS and AMSR2 in NCEP NWP Models from GOES-R, NPP/VIIRS and AMSR2 in NCEP NWP.

Remote Sensing of Vegetation. Vegetation and Photosynthesis About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal.

1 of 26 Characterization of Atmospheric Aerosols using Integrated Multi-Sensor Earth Observations Presented by Ratish Menon (Roll Number ) PhD.

Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry

Remote sensing for surface water hydrology RS applications for assessment of hydrometeorological states and fluxes –Soil moisture, snow cover, snow water.

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 Modeling Non-Point Source Pollution and Erosion into Gulf Coast Bays and.

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 Rapid Prototyping of NASA Next Generation Sensors for the SERVIR System.

The University of Mississippi Geoinformatics Center May 17, 2006 Rapid Prototyping of New NASA Sensor Data into the SERVIR System.

Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences NASA ARSET- AQ – EPA Training September 29,

Session 2: Improved Situational Awareness Sixth Meeting of the Science Advisory Committee 28 February – 1 March 2012 Future Activities National Space Science.

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 Evaluating the Integration of a Virtual ET Sensor into AnnGNPS Model Rapid.

Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:

A Remote Sensing Sampler. Typical reflectance spectra Remote Sensing Applications Consultants -

Use of AMSR-E Land Parameter Modeling and Retrievals for SMAP Algorithm Development Steven Chan Eni Njoku Joint AMSR Science Team Meeting Telluride, Colorado.

SIMULATION OF ALBEDO AT A LANDSCAPE SCALE WITH THE D.A.R.T. MODEL AN EFFICIENT TOOL FOR EVALUATING COARSE SCALE SATELLITE PRODUCTS? Sylvie DUTHOIT*, Valérie.

Recent Improvements and Ecological Applications of the UMT AMSR Land Parameter Record Jinyang Du, Lucas A. Jones, and John S. Kimball Numerical Terradynamic.

Hydrologic Data Assimilation with a Representer-Based Variational Algorithm Dennis McLaughlin, Parsons Lab., Civil & Environmental Engineering, MIT Dara.

A Remote Sensing Approach for Estimating Regional Scale Surface Moisture Luke J. Marzen Associate Professor of Geography Auburn University Co-Director.

Recent SeaWiFS view of the forest fires over Alaska Gene Feldman, NASA GSFC, Laboratory for Hydrospheric Processes, Office for Global Carbon Studies

The University of Mississippi Geoinformatics Center NASA MRC RPC – April 2008 Integration of Global Precipitation Measurement Data Product with the Hydrologic.

Consistent Earth System Data Records for Climate Research: Focus on Shortwave and Longwave Radiative Fluxes Rachel T. Pinker, Yingtao Ma and Eric Nussbaumer.

SCM x330 Ocean Discovery through Technology Area F GE.

The Vegetation Drought Response Index (VegDRI) An Update on Progress and Future Activities Brian Wardlow 1, Jesslyn Brown 2, Tsegaye Tadesse 1, and Yingxin.

NOAA, May 2014 Coordination Group for Meteorological Satellites - CGMS NOAA Activities toward Transitioning Mature R&D Missions to an Operational Status.

Bijay Shrestha NASA RPC Ag-Efficiency, July 10, 2007 DATA FUSION TO PRODUCE CLOUD-FREE TEMPORAL NDVI CROSS PLATFORM COMPOSITES USING TEMPORAL MAP ALGEBRA.

CEOS DRM Flood Thematic

JPL Technical Activities

NASA Drought Project Meeting

Temporal Classification and Change Detection

NASA JPL Drought Project Kickoff Meeting

Ocean Report Australia – Ocean Colour & SST

NASA Drought Project Meeting

Greg Easson – PI Dan Irwin – NASA – Co-PI

DEPT OF CIVIL ENGINEERING, TEXAS A&M UNIVERSITY MAY 03, 2004

NASA Earthdata Login Andrew Mitchell - NASA WGISS-44 Beijing, China

Presentation transcript:

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Greg Easson, Ph.D.- (PI) Robert Holt, Ph.D.- (Co-PI) A. K. M. Azad Hossain Patrick Yamnik University of Mississippi Geoinformatics Center The University of Mississippi Evaluating Next Generation NASA Earth Science Observations for Image Fusion to Enable Mapping Variation in Soil Moisture at High Resolution Rapid Prototyping Capability for Earth-Sun Systems Sciences Robert Ryan, Ph.D. Mr. Kent Hilbert SSAI-SSC Don Atwood, Ph.D. Alaska Satellite Facility Mr. Michael B. Hillesheim Sandia National Laboratories Dennis Powers, Ph.D. Consulting Geologist Collaborators Project Team

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008  Project Overview  Objectives  Study Site  RPC Experiments  Project Progress  Data Collection/Processing  Data Analysis and Results  Upcoming Major Tasks  Schedule/Current Status  Future Prospects Outline 2 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April of 21 Problem Statement  Not possible at both high spatial and temporal resolution due to lack of sensors with these combined capabilities Mapping Soil Moisture

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008  We hypothesize that MODIS can be transformed to virtual soil moisture sensors (VSMS) for mapping soil moisture at high spatial and temporal resolution by:  Fusion with SAR data (VSMS1)  Disaggregation model (VSMS2) Virtual Soil Moisture Sensor (VSMS) 4 of 21 Project Overview - Objectives  We designed a RPC project to evaluate potential of VIIRS to replace MODIS to improve monitoring soil moisture by generating VSMS Rapid Prototyping Capability (RPC) Project

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008  Part of Nash Draw in southeastern New Mexico  Project site is a part of Chihuahuan Desert. Site extent: approximately 225 sq. km  Semi-arid area  Karst topography Project Overview – Study Site 5 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Project Overview – RPC Experiments  Experiment 1: Soil Moisture Estimation  Evaluate VIIRS to replace MODIS in soil moisture estimation using VI-LST Triangle Model  Experiment 2: Generation of VSMS1  Evaluate VIIRS to replace MODIS in virtual soil moisture generation using Multiple Regression and ANN with SAR  Experiment 3: Generation of VSMS2  Evaluate VIIRS to replace MODIS in virtual soil moisture generation using SHEELS, RTM and DisaggNet Three RPC experiments in the project 6 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Estimation of MODIS/VIIRS Based Soil Moisture AMSR-E MODIS SMR MODIS SM(1 km) NDVI LST Reflectance Data (MOD09) Thermal Data (MOD11) VI- LST Triangle Model Project Overview – Experiment # 1 Simulated Reflectance Simulated Thermal Data VIIRS SM(800 m) NDVI-Normalized Difference Vegetation Index LST-Land Surface Temperature R- Regression VI-Vegetation Index SM-Soil Moisture 7 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Field Data MODIS SM(1 km) R SAR-Synthetic Aperture Radar SM-Soil Moisture ANN-Artificial Neural Network R-Regression VSMS1-Virtual Soil Moisture Sensor M-MODIS V-VIIRS RADARSAT 1 SAR Fine Imagery (10 m) SAR SM(10 m) R ANN VSMS1 M SM(10 m) VIIRS SM (800 m) VSMS1 V SM(10 m) Generation of Virtual Soil Moisture Sensor 1 (VSMS1) Project Overview – Experiment # 2 8 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Generation of Virtual Soil Moisture Sensor 2 (VSMS2) SHEELS - Simulator for Hydrology and Energy Exchange at the Land Surface RTM - Radiative Transfer Model DisaggNet - Neural network based Disaggregation model VSMS2 (M) SM(10 m) DisaggNet RTM SHEELS MODIS SM(1 km) SM Emissivity VIIRS SM (800 m) VSMS2 (V) SM(10 m) SM - Soil Moisture VSMS2 - Virtual Soil Moisture Sensor 2 M - MODIS V - VIIRS Project Overview – Experiment # 3 9 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Data Collection and Processing Project Progress - Data 10 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 R AMSR-E L3 25 km SM MOD11 1km FLST MOD m FNDVI VI-LST Triangle Model 1 km SM MODIS/VIIRS Based Soil Moisture Estimation (Exp. # 1) Project Progress – Analysis/Results 11 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 MODIS/VIIRS Based Soil Moisture Estimation (Exp.#1) AMSR-E Soil Moisture (25 km resolution) Project Progress – Analysis/Results 12 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Project Progress – Analysis/Results Comparison between VIIRS and MODIS NDVI (EXP. # 1) R 2 = 0.47 R 2 = 0.60 R 2 = 0.53 R 2 = 0.17 R 2 = 0.71 R 2 = 0.68 R 2 = 0.71 R 2 = 0.66 R 2 = 0.45 R 2 = of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Estimation of SAR Based Soil Moisture (Exp. # 2)  Empirical model for mapping soil moisture using regression  The model will relate field observed soil water with the backscatter values extracted from the acquired SAR imagery Project Progress – Analysis/Results 14 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Estimation of SAR Based Soil Moisture (Exp. # 2) Project Progress – Analysis/Results 15 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Estimation of SAR Based Soil Moisture (Exp. # 2) Project Progress – Analysis/Results 16 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Multi-temporal Analysis of SAR Data Red: September 19 Green: August 02 Blue: August 26 Estimation of SAR Based Soil Moisture (Exp. # 2) Project Progress – Analysis/Results 17 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Upcoming Major Tasks and Issues Tasks  Completion of VIIRS soil moisture estimation and Comparison with MODIS soil moisture  Completion of SAR soil moisture estimation  Generation of VSMS and comparison of VSMS1 and VSMS2  Evaluate results in the field Issues  Simulation of VIIRS Thermal Data  Acquisition on SAR data (November scene) 18 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Project Schedule – Revised Task in progress Task planned Shows when project officially started and fund distribution started Task completed

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Future Prospects  Input of high resolution soil moisture information in DSS  PECAD, SWAT. AGWA  Example: Performance of PECAD can be enhanced by improving crop stress and growth prediction  Currently using- Palmer two layer soil moisture model  Recently projects funded for the inclusion of AMSR-E 25 km soil moisture product  Evaluation of SMAP Mission Performance using Aquarius  Consists of both active and passive microwave sensors for soil moisture mapping  Current project demonstrates the prospects of fusion of active and passive microwave data for producing high resolution soil moisture maps 20 of 21

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Thank You!