NAFE 3rd Workshop 17-18 Sept 2007 Vegetation Water Status from Optical Remote Sensing ___ Preliminary results from the NAFE05 experiment Philippe Maisongrande,

Slides:



Advertisements
Similar presentations
DEPARTMENT OF LAND INFORMATION – SATELLITE REMOTE SENSING SERVICES CRCSI AC Workshop November 2005 Remote Sensing in Near-Real Time of Atmospheric.
Advertisements

Beyond Spectral and Spatial data: Exploring other domains of information GEOG3010 Remote Sensing and Image Processing Lewis RSU.
Examining Vegetation Recovery Time After a Small Scale Disaster using MODIS Data and the OSDC Zac Flamig (University of Oklahoma) Gilbert Warren Cole (University.
MoistureMap: Multi-sensor Retrieval of Soil Moisture Mahdi Allahmoradi PhD Candidate Supervisor: Jeffrey Walker Contributors: Dongryeol Ryu, Chris Rudiger.
Evaluating Calibration of MODIS Thermal Emissive Bands Using Infrared Atmospheric Sounding Interferometer Measurements Yonghong Li a, Aisheng Wu a, Xiaoxiong.
The ESA CoSMOS study for the validation of the SMOS L2 prototype K Saleh Contell, Y. Kerr, MJ Escorihuela, G. Boulet, P. Maisongrande, P. de Rosnay, JP.
A Land Cover Map of Eurasia’s Boreal Ecosystems S. BARTALEV, A. S. BELWARD Institute for Environment and Sustainability, EC Joint Research Centre, Italy.
Selected results of FoodSat research … Food: what’s where and how much is there? 2 Topics: Exploring a New Approach to Prepare Small-Scale Land Use Maps.
GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) LAND COVER MAP OF.
Correction of bidirectionnal effects and impact on land cover classifications J-L. CHAMPEAUX METEO-FRANCE S. GARRIGUES METEO-FRANCE C. GOUVEIA ICAT, Universidade.
Land Cover Mapping of Iceland and Southern Greenland Global Land Cover 2000 S. Bartalev (JRC EC), V. Egorov (IKI RAN) and E. Bartholomé (JRC EC)
Environmental Remote Sensing GEOG 2021 Spectral information in remote sensing.
SKYE INSTRUMENTS LTD Llandrindod Wells, United Kingdom.
Effects of the Great Salt Lake’s Temperature and Size on the Regional Precipitation in the WRF Model Joe Grim Jason Knievel National Center for Atmospheric.
Study on applying MODIS image into drought indicator analysis in Taiwan Yuh-Lurng Chung, Chaur-Tzuhn Chen Chen-Ni Hsi, Shih-Ming Liu Yuh-Lurng.
Estimating forest structure in wetlands using multitemporal SAR by Philip A. Townsend Neal Simpson ES 5053 Final Project.
ReCover for REDD and sustainable forest management EU ReCover project: Remote sensing services to support REDD and sustainable forest management in Fiji.
Green Vegetation Fraction (GVF) derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the SNPP satellite Zhangyan Jiang 1,2,
Remote Sensing of Hydrological Variables over the Red Arkansas Eric Wood Matthew McCabe Rafal Wojcik Hongbo Su Huilin Gao Justin Sheffield Princeton University.
2 nd International Conference on Water and Flood Management ICWFM-2009 Flood Inundation Map of Bangladesh using MODIS Surface Reflectance Data AKM Saiful.
VEGETATION DATA Viviana Maggioni Dr. Jeffrey Walker.
Agricultural, Water, and Health-Related Satellite Products from NESDIS-STAR Felix Kogan NOAA/NESDIS Center for Satellite Applications and Research October.
LST Validation and Analysis Simon J. Hook et al.
Remote Sensing of Drought Lecture 9. What is drought? Drought is a normal, recurrent feature of climate. It occurs almost everywhere, although its features.
Interannual Deforestation Dynamics in Southern Madagascar Humid Forests 2000 to 2005 Jan Dempewolf (1), Ruth DeFries (1), Sandy Andelman (2), Rasolohery.
Synergy of L-band and optical data for soil moisture monitoring O. Merlin, J. Walker and R. Panciera 3 rd NAFE workshop sept
Remote Sensing in Environmental Research Georgios Aim. Skianis University of Athens, Faculty of Geology and Geo-Environment, Department of Geography and.
Ramesh Gautam, Jean Woods, Simon Eching, Mohammad Mostafavi, Scott Hayes, tom Hawkins, Jeff milliken Division of Statewide Integrated Water Management.
Microwave Remote Sensing Group 1 P. Pampaloni Microwave Remote Sensing Group (MRSG) Institute of Applied Physics -CNR, Florence, Italy Microwave remote.
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Assessment of Regional Vegetation Productivity: Using NDVI Temporal Profile Metrics Background NOAA satellite AVHRR data archive NDVI temporal profile.
The role of remote sensing in Climate Change Mitigation and Adaptation.
Karnieli: Introduction to Remote Sensing
Abstract: Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor.
MODIS Workshop An Introduction to NASA’s Earth Observing System (EOS), Terra, and the MODIS Instrument Michele Thornton
Remote Sensing. Vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including.
Remote Sensing of Vegetation. Vegetation and Photosynthesis About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal.
EG2234: Earth Observation Interactions - Land Dr Mark Cresswell.
Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations G.P.Asner and K.B.Heidebrecht.
2005 ARM Science Team Meeting, March 14-18, Daytona Beach, Florida Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada.
Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences NASA ARSET- AQ – EPA Training September 29,
GEOG2021 Environmental Remote Sensing Lecture 3 Spectral Information in Remote Sensing.
Assessing the Phenological Suitability of Global Landsat Data Sets for Forest Change Analysis The Global Land Cover Facility What does.
Long-term drought assessment of Northern Central African continent using Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST)
Test of forest classification over Bavaria (Germany) using a SPOT-VGT pixel mosaic Erwann FILLOL, Pamela KENNEDY, Sten FOLVING.
Early Detection & Monitoring North America Drought from Space
IGARSS 2011, Jul. 27, Vancouver 1 Monitoring Vegetation Water Content by Using Optical Vegetation Index and Microwave Vegetation Index: Field Experiment.
Beyond Spectral and Spatial data: Exploring other domains of information: 3 GEOG3010 Remote Sensing and Image Processing Lewis RSU.
Detecting Arctic Change Using the Koppen Climate Classification Muyin Wang 1 James E. Overland 2 1 JISAO/UW, 2 PMEL/NOAA, Seattle, WA Photos from the.
H51A-01 Evaluation of Global and National LAI Estimates over Canada METHODOLOGY LAI INTERCOMPARISONS LEAF AREA INDEX JUNE 1997 LEAF AREA INDEX 1993 Baseline.
Evaluating different compositing methods using SPOT-VGT S1 data for land cover mapping the dry season in continental Southeast Asia Hans Jurgen StibigSarah.
A Remote Sensing Approach for Estimating Regional Scale Surface Moisture Luke J. Marzen Associate Professor of Geography Auburn University Co-Director.
GLC 2000 Workshop March 2003 Land cover map of southern hemisphere Africa using SPOT-4 VEGETATION data Ana Cabral 1, Maria J.P. de Vasconcelos 1,2,
Estimating Cotton Defoliation with Remote Sensing Glen Ritchie 1 and Craig Bednarz 2 1 UGA Coastal Plain Experiment Station, Tifton, GA 2 Texas Tech, Lubbock,
Data Processing Flow Chart Start NDVI, EVI2 are calculated and Rank SDS are incorporated Integrity Data Check: Is the data correct? Data: Download a) AVHRR.
REMOTE SENSING INDICATORS FOR CROP GROWTH MONITORING AT DIFFERENT SCALES Zongnan Li 1, 2 and Zhongxin Chen 1, 2* 1 Key Laboratory of Resources Remote Sensing.
Lecture Notes – Vegetation indices Fred Watson, ENVS 436/536, CSUMB, Fall 2010 Many of these slides are from Jianglong Zhang and Cindy Schmidt.
Assessment on Phytoplankton Quantity in Coastal Area by Using Remote Sensing Data RI Songgun Marine Environment Monitoring and Forecasting Division State.
REMOTE SENSING FOR VEGETATION AND LAND DEGRADATION MONITORING AND MAPPING Maurizio Sciortino, Luigi De Cecco, Matteo De Felice, Flavio Borfecchia ENEA.
NDVI during wet and dry periods in Puerto Rico Eric Harmsen Course: Satellite Direct Broadcast in Support of Real-Time Environmental Applications
Using vegetation indices (NDVI) to study vegetation
Term Project Presentation
VEGA-GEOGLAM Web-based GIS for crop monitoring and decision support in agriculture Evgeniya Elkina, Russian Space Research Institute The GEO-XIII Plenary.
Jeremy Fisher & John Mustard Geological Sciences - Brown University
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Satellite Sensors – Historical Perspectives
Image Information Extraction
Planning a Remote Sensing Project
Remote Sensing Landscape Changes Before and After King Fire 2014
The PROBA-V Mission Sindy Sterckx, Wouter Dierckx, Tanja Van Achteren, Stefan Adriaensen and the PROBA-V team GSICS Users' Workshop, 2013.
Presentation transcript:

NAFE 3rd Workshop Sept 2007 Vegetation Water Status from Optical Remote Sensing ___ Preliminary results from the NAFE05 experiment Philippe Maisongrande, Julian Kuhlmann Gilles Boulet, Bertrand Guerrero

NAFE 3rd Workshop Sept 2007 Optical Remote Sensing and VWC -Rational (1)- REDNIR SWIR NDVI=(  nir-  red) (  nir+  red) SWVI=(  nir-  swir) (  nir+  swir)

NAFE 3rd Workshop Sept 2007 Optical Remote Sensing and VWC -Rational (1)- REDNIR SWIR NDVI=(  nir-  red) (  nir+  red) SWVI=(  nir-  swir) (  nir+  swir) stressed unstressed

NAFE 3rd Workshop Sept 2007 Precipitation climatology -Australian Bureau of Meteo >2006 average NDVI -Spot/VEGETATION data-

NAFE 3rd Workshop Sept 2007 VEGETATION onboard SPOT 4&5 Operational Mission (available free data on the web) 4 channels: Blue ( µm) Red( µm) Near InfraRed (NIR)( µm) Shortwave InfraRed( µm) Dayly global coverage (at km2 res.) & 10day synthesis corrected for atmospheric effects of consistent archive9 years of consistent archive ( ) A third mission under study MODIS differences (aqua&terra) 1.3 and 2.3 µm 250m (Red&NIR) 500m otherwise No 1.6 µm (aqua)

NAFE 3rd Workshop Sept 2007 El Niño & La Niña Events Maisongrande, Kuhlmann et al. MODSIM 2007

NAFE 3rd Workshop Sept 2007 NDVI & SWVI vs Southern Oscillation Index Maisongrande, Kuhlmann et al. MODSIM 2007 NDVI SWVI

NAFE 3rd Workshop Sept 2007 RATIONAL (2) VWC ~ 1-(EC’/A’C’) -> A*NDVI-B*SWVI+C > NDVI-SWVI (  )

NAFE 3rd Workshop Sept 2007 (NDVI-SWVI) vs Southern Oscillation Index 1.5*D SOI/100 Maisongrande, Kuhlmann et al. MODSIM 2007

NAFE 3rd Workshop Sept 2007 FOCUS on NAFE2005 Database handling and organization (multi-layer consistency) Preparation of the Land Use Land Cover map (LULC) Preliminary analysis -Satellite indices against in situ measurements of Vegetation Water Content (VWC)

NAFE 3rd Workshop Sept 2007 Insitu Ancilary Satellite (or infered from it) DATABASE organization and handling exhaustive sparse

NAFE 3rd Workshop Sept 2007

NDVI & SWVI vs Southern Oscillation Index Maisongrande, Kuhlmann et al. MODSIM 2007

NAFE 3rd Workshop Sept 2007 Open woodland Bare soil forest crop Native grass

NAFE 3rd Workshop Sept 2007 Open woodland Bare soil forest crop Native grass NDVI SOI

NAFE 3rd Workshop Sept 2007 NDVI_VGT vs VWC

NAFE 3rd Workshop Sept 2007 SWVI_VGT vs VWC

NAFE 3rd Workshop Sept 2007  _VGT vs VWC

NAFE 3rd Workshop Sept Regression between VGT VIs and VWC for various classes r2r2

NAFE 3rd Workshop Sept /11 03/11 7/ 11 9 /11 12 of November 17 of November 21 of November SWVI SPOT/VGT2 10day composite images SPOTVGT2 Dates composite MODIS Available and clear dates dates Imgclass.m SPOT/ VEGETATION 10day composite products = Each S10 image is a patchwork of dates S30 S31 S32

NAFE 3rd Workshop Sept 2007 R2 sensitivity to the time lag between field VWC and satellite measurements

NAFE 3rd Workshop Sept 2007 NDVI 12/11 MODIS vs VGT

NAFE 3rd Workshop Sept 2007 NDVI_MODIS vs VWC

NAFE 3rd Workshop Sept 2007 NDVI_VGT vs VWC

NAFE 3rd Workshop Sept Regression between MODIS VIs and VWC for various classes r2r2

NAFE 3rd Workshop Sept Regression between VGT VIs and VWC for various classes r2r2

NAFE 3rd Workshop Sept 2007 R2 sensitivity to the time lag between field VWC and MODIS measurements

NAFE 3rd Workshop Sept 2007 Strong effort on the organization collect of VGT images, consistency Classification labeled thanks to in situ inquiries, but the labeling has to be considered with care. CONCLUSIONS Toward modeling studies involving prepared layers, (optical and thermal data). SMOS L2 ->L4 ; NAFE06 Investigation on the potential of optical sat. data to assess the plant water status: Disappointing with SPOT/VGT (1km2=pixel heterogeneity) Encouraging with MODIS (250m) Relative good score for SWVI (2.1  m) compared to NDVI and . (no way to evaluate 1.6  resolution)