Global Satellite Products & Services for Agricultural and Vegetation Health

Transcription

1 Global Satellite Products & Services for Agricultural and Vegetation Health Presented at the WMO Expert Meeting on National Early Warning System for Agricultural Weather Management George Mason University Fairfax, VA July 2011 Dr. Alfred M. Powell, Jr Director Center for Satellite Applications & Research NOAA/NESDIS 18 July 2011

2 Agenda Center for Satellite Applications & Research Satellite Constellation and Calibration Precipitation Soil Moisture Vegetation (Thermal and Moist Components) Plant Phenology Impacts Summary

3 Center for SaTellite Applications and Research (STAR) STAR s Mission: To transfer satellite observations from research to operations A holistic understanding of the Earth system through research Accurate and reliable data from sustained and integrated Earth observations An integrated environmental modeling system and provide data, products, and services to end users STAR is the Science Arm of NESDIS 3 3

4 Satellite Constellations STAR is Involved with NOAA, Domestic, and International Missions NOAA Operational LEO GEO GOES East GOES R GOES West DMSP Metop NOAA JPSS Example Domestic and International Systems LEO GEO 4 MSG Europe INSAT India METEOSAT Japan FengYun China Jason 2/3 France CLARREO NASA DESDnyl NASA SMAP NASA ICESat II NASA COSMIC Taiwan Satellite intercalibration is essential for understanding global, regional and local environmental change & climate trends 4

5 Precipitation Automated, Real Time Satellite Based Rainfall Estimates from NESDIS Cold (black) temperatures on infrared satellite imagery indicate deep, cold thunderstorm clouds with heavy rain. The Hydro- Estimator uses this information to estimate rainfall and thus alert forecasters to potential flooding. Flooding near Presidio, TX. GOES-12 infrared image from 10:45 PM EDT Sept. 11, 2008 Courtesy of Bob Kuligowski Satellite-based total rain from 4 PM CDT Sep. 11 to 10 AM Sept. 12, 2008

6 Hydro-Estimator Availability Hourly rainfall estimates for UTC 5 January 2005 Produced worldwide between 60 N N and 60 S S in real time using the following satellites: GOES-11/12 (Western Hemisphere) MTSAT (Western Pacific) METEOSAT-8 8 (Europe and Africa) METEOSAT-5 5 (Central Asia) Produced every 15 minutes over the continental United States using GOES at 4 km resolution and in other regions throughout the globe whenever IR imagery are available (with 15 min latency) Courtesy of Bob Kuligowski

7 Hydro-Estimator Availability Hourly rainfall estimates for UTC 5 January 2005 Produced worldwide between 60 N N and 60 S S in real time using the following satellites: GOES-11/12 (Western Hemisphere) MTSAT (Western Pacific) METEOSAT-8 8 (Europe and Africa) METEOSAT-5 5 (Central Asia) Produced every 15 minutes over the continental United States using GOES at 4 km resolution and in other regions throughout the globe whenever IR imagery are available (with 15 min latency) Courtesy of Bob Kuligowski

8 Soil Moisture Soil Moisture Remote Sensing Research at STAR Observations from microwave satellite sensors are found to have significant calibration differences with the Simultaneous Conical scan Overpass (SCO) method Single Channel Retrieval (SCR) algorithm is less sensitive to calibration differences while the Multi Channel Inversion (MCI) algorithm may fail for large calibration errors A new algorithm combining the SCR and MCI algorithms is being tested and will be used in the NESDIS Soil Moisture Operational Product System (SMOPS) Courtesy of Xiwu Zhan, Jicheng Liu & Chris Hain 8

9 Soil Moisture Operational Product System (SMOPS) Generates global soil moisture data sets from the best available microwave satellite sensors (AMSR E, ASCAT, SMOS, etc) based on the sensor cross calibration technique and the combined algorithm developed at STAR Serves as a friendly provider of global climatologically consistent soil moisture data provider for NCEP NWP soil moisture data needs Disseminates data to users via NOAA s Comprehensive Large Array data Stewardship System (CLASS) Courtesy of Xiwu Zhan, Jicheng Liu & Chris Hain 9

10 NOAA Global Soil Moisture Data Portal: 10 Courtesy of Xiwu Zhan, Jicheng Liu & Chris Hain

11 Vegetation and Climate VEGETATION HEALTH July 2, 2011 Courtesy of Felix Kogan

12 Vegetation and Climate Moisture & Thermal Conditions, Fire Risk, 2 JULY 2011 Courtesy of Felix Kogan

13 R U S S I A M O D E L E D Vegetation and Climate: Agriculture VH modeled Crop Yield MOROCCO Wheat Modeled Yield Observed Yield A R G E N T I N A Y I E L D Courtesy of Felix Kogan MONGOLIA Pasture Kansas, USA 13

14 Vegetation and Climate Assessing Vector borne Disease Malaria Risk from AVHRR Vegetation Health (VH) for July 2 Courtesy of Felix Kogan NOAA 18 & 19

15 Courtesy of Felix Kogan Vegetation and Climate Change in Drought Area & Intensity over Time

16 Advanced CLIMATE SERVICES: Prediction ENSO & Vegetation Health, December Predict regional Drought/NO Drought 2 4 months in Courtesy of Felix Kogan 16

17 CLIMATE FORCING: Sensitivity of Ecosystems to ENSO Correlation of VHI with SST anomaly in the 3.4 Tropical Pacific 3.4 ENSO area Teleconnection (correlation coeff.) between VHI & 3.4 ENSO Courtesy of Felix Kogan The benefit areas from improved ENSO prediction 17

18 Satellite Vegetation Phenology Satellite data can characterize vegetation impacts Vegetation phenology: vegetation growth cycles & date of greenup onset dates of maturity onset & senescence onset date of dormancy onset, & growing season greenness These have broad applications: Environmental Changes: climate changes, numerical weather predications, ecological forecast models, and carbon sequestration Human Health: Timing and prediction of allergy (hay fever) and pulmonary (asthma) problems Agriculture: Monitoring crop planting, germination, harvesting, and drought stress Natural Resource Managements: Forest pest and disease outbreaks, freeze damage, fire, and invasive species Courtesy of X. Zhang, D. Tarpley, and J. T. Sullivan Greenup Onset Mar 21, 2001 Dormancy Onset Sept 7, 2001

19 Responses of Spring Vegetation Greenup to a Warming Climate Early later Vegetation phenology derived from AVHRR NDVI reveals that spring warming temperatures advanced vegetation greenup from 40 N northwards. However, the shortened winter chilling days are insufficient for fulfilling vegetation chilling requirement, so that the plant greenup onset was delayed from 35 N southwards. With the continuous increase of temperature and the reduction of chilling days, seed chilling requirement will not be fulfilled. Thus, plant growth will be significantly impacted. (Zhang et al., GRL, 2007) (Corr. w/lilac) Courtesy of X. Zhang, D. Tarpley, and J. T. Sullivan

20 Summary New and improved satellite products will support the agricultural, plant, and vegetation and related communities Satellite data allows a global understanding of regional changes and provides the ability to address/adapt to environmental change