Satellite Microwave Detection of Phenological Start of Season for North America using AMSR-E Matthew O. Jones 1,2, John S. Kimball 1,2, Lucas A. Jones.

Slides:



Advertisements
Similar presentations
Changes in the seasonal activity of temperate and boreal vegetation The critical role of Autumn temperatures. Shilong Piao, Philippe Ciais, Pierre Friedlingstein,
Advertisements

Satellite data products to support climate modelling: Phenology & Snow Cover Kristin Böttcher, Sari Metsämäki, Olli-Pekka Mattila, Mikko Kervinen, Mika.
Scaling Biomass Measurements for Examining MODIS Derived Vegetation Products Matthew C. Reeves and Maosheng Zhao Numerical Terradynamic Simulation Group.
Extension and application of an AMSR global land parameter data record for ecosystem studies Jinyang Du, John S. Kimball, Lucas A. Jones, Youngwook Kim,
Linking In situ Measurements, Remote Sensing, and Models to Validate MODIS Products Related to the Terrestrial Carbon Cycle Peter B. Reich, University.
Ecoregion level climate constraints on vegetation NPP (Nemani et al., Science 2003) VOD SOS offset by ecoregion relative to NDVI Greenup date. VOD SOS.
Landscape Temperature and Frozen/Thawed Condition over Alaska with Infrared and Passive Microwave Remote Sensing Determination of Thermal Controls on Land-Atmosphere.
Monitoring Effects of Interannual Variation in Climate and Fire Regime on Regional Net Ecosystem Production with Remote Sensing and Modeling D.P. Turner.
Scaling GPP at Flux Tower Sites in the Context of EOS/MODIS Validation
Soil CO 2 Efflux from a Subalpine Catchment Diego A. Riveros-Iregui 1, Brian L. McGlynn 1, Vincent J. Pacific 1, Howard E. Epstein 2, Daniel L. Welsch,
Sensing Winter Soil Respiration Dynamics in Near-Real Time Alexandra Contosta 1, Elizabeth Burakowski 1,2, Ruth Varner 1, and Serita Frey 3 1 University.
School Research Conference, March 2009 Jennifer Wright Supervisors: M.Williams, G. Starr, R.Mitchell, M.Mencuccini Fire and Forest Ecosystems in the Southeastern.
The Role of Models in Remotely Sensed Primary Production Estimates
Questions How do different methods of calculating LAI compare? Does varying Leaf mass per area (LMA) with height affect LAI estimates? LAI can be calculated.
A Study on Vegetation OpticalDepth Parameterization and its Impact on Passive Microwave Soil Moisture Retrievals A Study on Vegetation Optical Depth Parameterization.
O AK R IDGE N ATIONAL L ABORATORY U. S. D EPARTMENT OF E NERGY 1 Carbon Cycle Modeling Terrestrial Ecosystem Models W.M. Post, ORNL Atmospheric Measurements.
Trends in Terrestrial Carbon Sinks Driven by Hydroclimatic Change since 1948: Data-Driven Analysis using FLUXNET Trends in Terrestrial Carbon Sinks Driven.
Findings from a decade-plus study of comparative carbon, water and energy fluxes from an oak savanna and an annual grassland in the Mediterranean climate.
ABSTRACT Advances in remote sensing techniques may have the potential for monitoring freeze/thaw processes, such as changes in permafrost extent, in high-latitudes.
Nara Luísa Reis de Andrade, Luciana Sanches, Peter Zeilhofer, Segundo Durval Rezende Pereira, José de Souza Nogueira Environmental Physics Research Group.
Plant Ecology - Chapter 14 Ecosystem Processes. Ecosystem Ecology Focus on what regulates pools (quantities stored) and fluxes (flows) of materials and.
Paul R. Moorcroft David Medvigy, Stephen Wofsy, J. William Munger, M. Dietze Harvard University Developing a predictive science of the biosphere.
Real-time integration of remote sensing, surface meteorology, and ecological models.
Assessment of Regional Vegetation Productivity: Using NDVI Temporal Profile Metrics Background NOAA satellite AVHRR data archive NDVI temporal profile.
1 Remote Sensing and Image Processing: 9 Dr. Hassan J. Eghbali.
Science themes: 1.Improved understanding of the carbon cycle. 2.Constraints and feedbacks imposed by water. 3.Nutrient cycling and coupling with carbon.
Getting Ready for the Future Woody Turner Earth Science Division NASA Headquarters May 7, 2014 Biodiversity and Ecological Forecasting Team Meeting Sheraton.
Introduction To describe the dynamics of the global carbon cycle requires an accurate determination of the spatial and temporal distribution of photosynthetic.
BIOME-BGC estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. Model algorithms.
The Big Picture To assess the Global Carbon Budget we need information that is ‘Everywhere, All of the Time’ Many Complementary Methods exist, Each with.
The approximate 2- year VOD recovery lag, relative to NDVI, is consistent with greater VOD sensitivity to photosynthetic and non-photosynthetic woody biomass.
Integrating Remote Sensing, Flux Measurements and Ecosystem Models Faith Ann Heinsch Numerical Terradynamic Simulation Group (NTSG) University of Montana.
U.S. Department of the Interior U.S. Geological Survey Using Advanced Satellite Products to Better Understand I&M Data within the Context of the Larger.
Translation to the New TCO Panel Beverly Law Prof. Global Change Forest Science Science Chair, AmeriFlux Network Oregon State University.
Development and evaluation of Passive Microwave SWE retrieval equations for mountainous area Naoki Mizukami.
A B C D The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities.
Modeling Modes of Variability in Carbon Exchange Between High Latitude Ecosystems and the Atmosphere Dave McGuire (UAF), Joy Clein (UAF), and Qianlai.
Integration of biosphere and atmosphere observations Yingping Wang 1, Gabriel Abramowitz 1, Rachel Law 1, Bernard Pak 1, Cathy Trudinger 1, Ian Enting.
1 Remote Sensing and Image Processing: 9 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel: (x24290)
Satellite data, ecosystem models and site data: contributions of the IGBP flux network to carbon cycle science David Schimel, Galina Churkina, Eva Falge,
Importance of Recent Shifts in Soil Thermal Dynamics on Growing Season Length, Productivity, and Carbon Sequestration in Terrestrial High-Latitude Ecosystems.
Recent increases in the growing season length at high northern latitudes Nicole Smith-Downey* James T. Randerson Harvard University UC Irvine Sassan S.
Flux observation: Integrating fluxes derived from ground station and satellite remote sensing 王鹤松 Hesong Wang Institute of atmospheric physics, Chinese.
Application of the ORCHIDEE global vegetation model to evaluate biomass and soil carbon stocks of Qinghai-Tibetan grasslands Tan Kun.
Change in vegetation growth and C balance in the Tibetan Plateau
WATER VAPOR RETRIEVAL OVER CLOUD COVER AREA ON LAND Dabin Ji, Jiancheng Shi, Shenglei Zhang Institute for Remote Sensing Applications Chinese Academy of.
Variations in Continental Terrestrial Primary Production, Evapotranspiration and Disturbance Faith Ann Heinsch, Maosheng Zhao, Qiaozhen Mu, David Mildrexler,
1 Engaging Students in the Science of Climate Change: Using Earth Observing Data in the Classroom Project Team: PI: Mary MartinErik Froburg Scott Ollinger.
Scaling Up Above Ground Live Biomass From Plot Data to Amazon Landscape Sassan S. Saatchi NASA/Jet Propulsion Laboratory California Institute of Technology.
Modeling CO 2 emissions in Prairie Pothole Region using DNDC model and remotely sensed data Zhengpeng Li 1, Shuguang Liu 2, Robert Gleason 3, Zhengxi Tan.
Surface conductance and evaporation from 1- km to continental scales using remote sensing Ray Leuning, Yonqiang Zhang, Amelie Rajaud, Helen Cleugh, Francis.
Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:
Terra MODIS Collection 4 / 4.5 and Aqua MODIS Collection 4; Sinusoidal Projection Data from 2000 to present; 8-day, 16-day, or annual composites Sites.
MODIS Net Primary Productivity (NPP)
Observing Laramie Basin Grassland Phenology Using MODIS Josh Reynolds with PROPOSED RESEARCH PROJECT Acknowledgments Steven Prager, Dept. of Geography.
Dipl.-Geogr. Markus TumD Wessling Deutsches Zentrum für Luft- und RaumfahrtGermany Deutsches FernerkundungsdatenzentrumTel LandoberflächeFax.
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.
Recent Improvements and Ecological Applications of the UMT AMSR Land Parameter Record Jinyang Du, Lucas A. Jones, and John S. Kimball Numerical Terradynamic.
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
Whats new with MODIS NPP and GPP MODIS/VIIRS Science Team Meeting May 20, 2015 Steven W. Running Numerical Terradynamic Simulation Group College of Forestry.
Production.
Enabling Ecological Forecasting by integrating surface, satellite, and climate data with ecosystem models Ramakrishna Nemani Petr Votava Andy Michaelis.
References: 1)Ganguly, S., Samanta, A., Schull, M. A., Shabanov, N. V., Milesi, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B., Generating vegetation.
Assessing Annual Forest Ecological Change in Western Canada Using Temporal Mixture Analysis of Regional Scale AVHRR Imagery Over a 14 Year Period Joseph.
Arctic RIMS & WALE (Regional, Integrated Hydrological Monitoring System & Western Arctic Linkage Experiment) John Kimball FaithAnn Heinsch Steve Running.
Figure 10. Improvement in landscape resolution that the new 250-meter MODIS (Moderate Resolution Imaging Spectroradiometer) measurement of gross primary.
Figure 1. Spatial distribution of pinyon-juniper and ponderosa pine forests is shown for the southwestern United States. Red dots indicate location of.
Using Remote Sensing to Monitor Plant Phenology Response to Rain Events in the Santa Catalina Mountains Katheryn Landau Arizona Remote Sensing Center Mentors:
Presentation transcript:

Satellite Microwave Detection of Phenological Start of Season for North America using AMSR-E Matthew O. Jones 1,2, John S. Kimball 1,2, Lucas A. Jones 1,2, Kyle C. McDonald 3,4 1 The University of Montana Flathead Lake Biological Station, Polson, MT 2 Numerical Terradynamic Simulation Group, The University of Montana, Missoula, MT 3 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 4 CUNY Environmental Crossroads Initiative and CREST Institute, City College of New York, New York, NY Contact: Websites: & The carbon cycle and land-atmosphere water and energy exchanges are influenced by the timing, rate and duration of vegetation phenology events. The Vegetation Optical Depth (VOD) parameter from satellite passive microwave remote sensing provides a unique phenology signal responsive to changes in canopy water content and biomass. The VOD signal is insensitive to atmosphere and solar illumination effects, and provides high (4-day or better) temporal fidelity at moderate (25-km resolution) spatial scales. We used VOD retrievals from AMSR-E 10.7 GHz, V and H polarization brightness temperature series with TIMESAT to classify vegetation start of season (SOS) phenology metrics over a four year record ( ) for North America at the Level III Ecoregion scale. The VOD SOS phenology metric at the ecoregion scale was strongly correlated (0.66<R<0.89) with MODIS-for-NACP NDVI and LAI Greenup Date and SOS derived from tower eddy covariance CO 2 flux measurements of gross primary productivity (R=0.80) and ecosystem respiration (R=0.68). In some regions microwave VOD displayed a temporal shift (SOS bias) relative to the optical-IR based Greenup Date that followed geographic patterns of climate constraints (temperature and water) on net primary productivity. The SOS bias is attributed to temporal differences in seasonality between canopy greenness versus canopy water content or biomass changes. The results indicate that the VOD signal captures changes in canopy water content and biomass, independent of NDVI greenness, which can better inform regional to global scale carbon, water and energy cycle models. This work was conducted at the University of Montana and Jet Propulsion Laboratory under contract to NASA (NNH07ZDA001N-TE). Abstract * AMSR-E Global Vegetation Optical Depth (VOD) by Level III North America Ecoregion VOD Start of Season & NDVI Greenup Date Mean We thank the tower site principal investigators for the La Thuile Fluxnet Database used in this study. The MODIS-for-NACP MOD09PHN and MOD15PHN Collection 5 products were acquired via the ftp://ladsweb.nascom.nasa.gov site. We also thank Lars Eklundh and Per Jonsson for providing the free TIMESAT software package and the NASA Terrestrial Ecology Program for funding this research. *Jones, M.O., Kimball, J.S, Jones, L.A., & McDonald, K.C. (2011). Microwave and Optical-IR Vegetation Land Surface Phenology Metrics: Comparisons at the Ecoregion Scale. Submission pending 1 Jones, L.A., Ferguson, C.R., Kimball, J.S., Zhang, K., Chan, S.K., McDonald, K.C., Njoku, E.G., & Wood, E.F. (2010). IEEE J-STARS, 3, Jones, M.O., Jones, L.A., Kimball, J.S., McDonald, K.C. (2011). Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sensing of Environment, 115, Nemani, R.R., Keeling, C.D., Hashimoto, H., Jolly, W.M., Piper, S.C., Tucker, C.J., Myneni, R.B., & Running, S.W. (2003) Science, 300, Acknowledgements & References Daily 25 km resolution global EASE Grid brightness temperatures from AMSR-E were used to derive daily 10.7 GHz frequency VOD retrievals over a global domain; the VOD retrievals were temporally composited into 4-day median values and further scaled to Level III North America Ecoregions (left). The VOD 1 product integrates canopy attenuation related to vegetation canopy biomass and water content, while minimizing effects from sub-grid scale open water variability and soil moisture. The detailed VOD algorithm and analysis is described elsewhere 1,2. The AMSR-E VOD and global land parameter database is available online through the University of Montana ( and the NASA NSIDC DAAC ( We selected 33 North America FLUXNET tower sites covering a range regional biomes. SOS estimates were derived from tower Gross Primary Productivity (GPP) and Ecosystem Respiration (Reco) fluxes using TIMESAT and regressed against VOD SOS for corresponding ecoregions (left). Tower Fluxes & VOD Start of Season Scatter plots and correlations of VOD SOS and MODIS- for-NACP NDVI and LAI Greenup Dates by ecoregion from Points are annotated by VOD SOS bias matching regions on the accompanying map (right). Ecoregion level climate constraints on vegetation NPP 3. VOD SOS bias by ecoregion relative to NDVI Greenup date. Ternary plot of climate constraints on vegetation NPP 1 by ecoregion; annotated by the VOD SOS bias. The VOD SOS bias pattern follows the distribution of low temperature and water constraints to NPP. Cold temperature constrained regions show generally earlier VOD SOS bias, while water constrained regions show later or no VOD SOS bias relative to NDVI spring Greenup Date. The biases are attributed to VOD sensitivity to vegetation water content and canopy biomass versus NDVI sensitivity to vegetation greenness. Seasonal frozen temperatures constrain vegetation growth at higher latitudes and elevations. Spring thaw coincides with increasing plant-available moisture and a rise in canopy (stem, branch and leaf) water content, which precedes the vegetation green-up signal by ~4-6 weeks. In water constrained regions VOD SOS is concurrent with or follows NDVI Greenup Date by up to 7 weeks; seasonal increases in canopy water content and green-up follow large precipitation events, while the VOD SOS temporal lag increases for higher woody biomass levels. For croplands, VOD SOS and NDVI Greenup are influenced by planting and harvest dates, and associated changes in canopy cover and biomass; spring green-up coincides with initial growth stages, whereas VOD SOS occurs later under greater canopy biomass levels. Summary GPP, Reco, VOD and MODIS NDVI pixel-scale series are shown below for selected tower sites. The VOD is significantly correlated (0.12<R<0.75) with both ecosystem carbon uptake and respiration. AMSRE VODNDVI Reco GPP * p<.01 ; **p<.05 TIMESAT software was used to calculate VOD SOS by ecoregion (top). The MODIS-for-NACP NDVI Greenup Date mean was also calculated for each ecoregion (right). VOD SOS earlier VOD SOS later No bias present. The VOD displays a SOS bias (temporal shift) relative to the NDVI Greenup Date. The regional bias pattern coincides with major climate constraints to vegetation NPP (See center panel). VOD Start of Season Bias & Climate Constraints on Net Primary Productivity