A MODIS-Derived Photochemical Reflectance Index to Detect Inter-Annual Variations in the Photosynthetic Light-Use Efficiency of a Boreal Deciduous Forest.

Slides:



Advertisements
Similar presentations
Satellite data products to support climate modelling: Phenology & Snow Cover Kristin Böttcher, Sari Metsämäki, Olli-Pekka Mattila, Mikko Kervinen, Mika.
Advertisements

Objective: ●harmonized data sets on snow cover extent (SE), snow water equivalent (SWE), soil freeze and vegetation status from satellite information,
Scaling Biomass Measurements for Examining MODIS Derived Vegetation Products Matthew C. Reeves and Maosheng Zhao Numerical Terradynamic Simulation Group.
Satellite based estimates of surface visibility for state haze rule implementation planning Air Quality Applied Sciences Team 6th Semi-Annual Meeting (Jan.
SKYE INSTRUMENTS LTD Llandrindod Wells, United Kingdom.
A Tutorial on MODIS and VIIRS Aerosol Products from Direct Broadcast Data on IDEA Hai Zhang 1, Shobha Kondragunta 2, Hongqing Liu 1 1.IMSG at NOAA 2.NOAA.
August 5 – 7, 2008NASA Habitats Workshop Optical Properties and Quantitative Remote Sensing of Kelp Forest and Seagrass Habitats Richard C. Zimmerman -
Estimating forest structure in wetlands using multitemporal SAR by Philip A. Townsend Neal Simpson ES 5053 Final Project.
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.
Green Vegetation Fraction (GVF) derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the SNPP satellite Zhangyan Jiang 1,2,
Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,
Princeton University Global Evaluation of a MODIS based Evapotranspiration Product Eric Wood Hongbo Su Matthew McCabe.
Globally distributed evapotranspiration using remote sensing and CEOP data Eric Wood, Matthew McCabe and Hongbo Su Princeton University.
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.
ESTEC July 2000 Estimation of Aerosol Properties from CHRIS-PROBA Data Jeff Settle Environmental Systems Science Centre University of Reading.
CHANGE DETECTION METHODS IN THE BOUNDARY WATERS CANOE AREA Thomas Juntunen.
Published in Remote Sensing of the Environment in May 2008.
CSIRO LAND and WATER Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems 1- Key Research Issues 2- Evapotranspiration.
Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.
Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –
Comparison of Carbon Fluxes Over Three Boreal Black Spruce Forests in Canada O. Bergeron §, H.A. Margolis §, T.A. Black †, C. Coursolle §, A.L. Dunn ф,
BIOPHYS A PHYSICALLY-BASED CONTINUOUS FIELDS ALGORITHM Ecosystem, Climate and Carbon Models FORREST G. HALL, FRED HUEMMRICH Joint Center for Earth Systems.
Forrest G. Hall 1 Thomas Hilker 1 Compton J. Tucker 1 Nicholas C. Coops 2 T. Andrew Black 2 Caroline J. Nichol 3 Piers J. Sellers 1 1 NASA Goddard Space.
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
Differences b etween Red and Green NDVI, What do they predict and what they don’t predict Shambel Maru.
Global NDVI Data for Climate Studies Compton Tucker NASA/Goddard Space Fight Center Greenbelt, Maryland
Real-time integration of remote sensing, surface meteorology, and ecological models.
Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.
Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.
Introduction To describe the dynamics of the global carbon cycle requires an accurate determination of the spatial and temporal distribution of photosynthetic.
Slide #1 Emerging Remote Sensing Data, Systems, and Tools to Support PEM Applications for Resource Management Olaf Niemann Department of Geography University.
Antwerp march A Bottom-up Approach to Characterize Crop Functioning From VEGETATION Time series Toulouse, France Bucharest, Fundulea, Romania.
ASSESSMENT OF ALBEDO CHANGES AND THEIR DRIVING FACTORS OVER THE QINGHAI-TIBETAN PLATEAU B. Zhang, L. Lei, Hao Zhang, L. Zhang and Z. Zen WE4.T Geology.
MODIS Workshop An Introduction to NASA’s Earth Observing System (EOS), Terra, and the MODIS Instrument Michele Thornton
1 CERES Results Norman Loeb and the CERES Science Team NASA Langley Research Center, Hampton, VA Reception NASA GSFC, Greenbelt, MD.
Remote Sensing. Vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including.
Menghua Wang, NOAA/NESDIS/STAR Remote Sensing of Water Properties Using the SWIR- based Atmospheric Correction Algorithm Menghua Wang Wei Shi and SeungHyun.
Page 1 Combine satellite-based geoinformation, ground measurements and statistical methods to assess green-up trends in earth observational studies Doktor,
Satellite observations of terrestrial ecosystems and links to climate and carbon cycle Bases of remote sensing of vegetation canopies The Greening trend.
NOAA/NESDIS Cooperative Research Program Second Annual Science Symposium SATELLITE CALIBRATION & VALIDATION July Barry Gross (CCNY) Brian Cairns.
Dec 15, 2004 AGUMolly E. Brown, PhD1 Inter-Sensor Validation of NDVI time series from AVHRR, SPOT-Vegetation, SeaWIFS, MODIS, and LandSAT ETM+ Molly E.
7/24/02 MODIS Science Meeting Seasonal Variability Studies Across the Amazon Basin with MODIS Vegetation Indices Alfredo Huete 1, Kamel Didan 1, Piyachat.
Spatial Model-Data Comparison Project Conclusions Forward models are very different and do not agree on timing or spatial distribution of C sources/sinks.
Satellite data, ecosystem models and site data: contributions of the IGBP flux network to carbon cycle science David Schimel, Galina Churkina, Eva Falge,
BIOPHYS: A Physically-based Algorithm for Inferring Continuous Fields of Vegetative Biophysical and Structural Parameters Forrest Hall 1, Fred Huemmrich.
#9 #118 #11 #13 #14 SOC Camera Images & Tree Samples SOC camera 09/05 11:01 am, composited by R-G-B Tag #Family 9 Vochysiaceae 11 Leguminosae-
NASA Snow and Ice Products NASA Remote Sensing Training Geo Latin America and Caribbean Water Cycle capacity Building Workshop Colombia, November 28-December.
BIOPHYS A PHYSICALLY-BASED CONTINUOUS FIELDS ALGORITHM and Climate and Carbon Models FORREST G. HALL, FRED HUEMMRICH Joint Center for Earth Systems Technology.
Assessing the Phenological Suitability of Global Landsat Data Sets for Forest Change Analysis The Global Land Cover Facility What does.
Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:
Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS N. Christina Hsu and S.-C. Tsay, M. D. King, M.-J. Jeong NASA Goddard.
Rutherford Appleton Laboratory CAMELOT Observation Techniques and Mission Concepts for Atmospheric Chemistry Task 4: Assessment of Cloud Contamination.
Figure 1. (A) Evapotranspiration (ET) in the equatorial Santarém forest: observed (mean ± SD across years of eddy fluxes, K67 site, blue shaded.
0 0 Robert Wolfe NASA GSFC, Greenbelt, MD GSFC Hydrospheric and Biospheric Sciences Laboratory, Terrestrial Information System Branch (614.5) Carbon Cycle.
A Remote Sensing Approach for Estimating Regional Scale Surface Moisture Luke J. Marzen Associate Professor of Geography Auburn University Co-Director.
Monitoring land use and land cover changes in oceanic and fragmented lanscapes with reconstructed MODIS time series R. Lecerf, T. Corpetti, L. Hubert-Moy.
A Difficult Region for Remote Sensing Studies Probability of imaging the Brazilian Legal Amazon Once per year… Asner (2001) Int’l J. of Remote Sensing.
Interactions of EMR with the Earth’s Surface
MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.
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.
Arctic RIMS & WALE (Regional, Integrated Hydrological Monitoring System & Western Arctic Linkage Experiment) John Kimball FaithAnn Heinsch Steve Running.
1) Global Coefficients: Derived from all available cloud-free ASTER scenes for a given network SURFRAD: 246 scenesUSCRN: 371 scenes 2) Day/Night-Specific.
Figure 10. Improvement in landscape resolution that the new 250-meter MODIS (Moderate Resolution Imaging Spectroradiometer) measurement of gross primary.
PADMA ALEKHYA V V L, SURAJ REDDY R, RAJASHEKAR G & JHA C S
Comparison of GPP from Terra-MODIS and AmeriFlux Network Towers
DETERMINATION OF PHOTOSYNTHETICALLY ACTIVE RADIATION
NASA alert as Russian and US satellites crash in space
Planning a Remote Sensing Project
Sources of Variability in Canopy Spectra and the Convergent Properties of Plants Funding From: S.V. Ollinger, L. Lepine, H. Wicklein, F. Sullivan, M. Day.
VALIDATION OF FINE RESOLUTION LAND-SURFACE ENERGY FLUXES DERIVED WITH COMBINED SENTINEL-2 AND SENTINEL-3 OBSERVATIONS IGARSS 2018 – Radoslaw.
Presentation transcript:

A MODIS-Derived Photochemical Reflectance Index to Detect Inter-Annual Variations in the Photosynthetic Light-Use Efficiency of a Boreal Deciduous Forest G.G. Drolet 1, K.F. Huemmrich 2, F.G. Hall 2, E.M. Middleton 2, T.A. Black 3, A.G. Barr 4 & H.A. Margolis 1 1 Departement des sciences du bois et de la forêt, Université Laval, Quebec, QC, Canada, G1K 7P4 2 NASA Goddard Space Flight Center, Code 923, Greenbelt, MD 20771, USA 3 Department of Soil Science, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4 4 Meteorological Service of Canada, Climate Research Branch, 11 Innovation Blvd, Saskatoon, SK, Canada, S7N 3H5 Biochemical changes in leaves during stress events have been shown to result in changes in spectral reflectance at 531 nm. These changes are correlated with variations in canopy light-use efficiency. The Photochemical Reflectance Index (PRI) was developed to measure this phenomenon. We calculated PRI from MODerate resolution Imaging Spectroradiometer (MODIS) reflectance data for cloud-free days between 2001 and 2003 for the Fluxnet-Canada Research Network (FCRN) Old Aspen (OA) flux tower in Saskatchewan. The flux and meteorological data from the tower allowed us to calculate photosynthetic light- use efficiency (LUE) at the time of MODIS overpasses. A linear relationship was found between PRI and LUE only when backscatter spectral data (minimal shadowing) was used. The relationship was stronger for top of the atmosphere reflectance data (R 2 =0.76) than for data that had been atmospherically corrected with MODIS-derived aerosol optical depth values and the 6S atmospheric correction model (R 2 =0.53). While our analysis of MODIS-derived PRI did not capture seasonal variations in LUE, it seemed to detect inter-annual variations. An ability to reliably estimate LUE from satellites would significantly improve large-scale modeling of the carbon cycle. To verify whether the results from the regression analysis were consistent with information from a larger spatial scale, a regional analysis of sPRI was performed over a 9900 km 2 area. Two MODIS images from the backscatter LUE-sPRI relationship (July 6, 2001 and August 14, 2001; see above) were co-registered and overlaid on a 1994 Landsat 5 TM physically based land cover classification. The deciduous and medium-age deciduous regeneration classes were then aggregated into a larger deciduous class. MODIS pixels containing more than 60% deciduous Landsat pixels were identified and sPRI values for these pixels were extracted. For the period between June and August of 2001, 2002 and 2003, 52 clear days were identified using radiation data from the Old Aspen tower. For these days, MODIS data were downloaded and processed (top-of-atmosphere reflectance, aerosol optical thickness (AOT), geolocation, cloud mask, and sun/sensor geometry). 30-minute periods of GEP and PAR, corresponding to the MODIS overpasses over the tower, were also downloaded from the FCRN DIS. An atmospheric correction model (6S) was used to obtain the surface reflectance values for bands 11 ( nm), 12 ( nm), and 13 ( nm). The PRI was then calculated as: where  11 is the reflectance in MODIS band 11, containing the PRI signal at 531 nm, and  ref is the reflectance in a reference band unaffected by stress events, in this case bands 12 or 13. To obtain positive values only, PRI were scaled using: Even though the range of LUE values for cloud-free days is narrow, a positive linear relationship (R 2 =0.76) between LUE and sPRI was found when using only backscatter reflectance and MODIS band 13 as the reference band. Moreover, the LUE-sPRI relationship was weakened when using atmospherically corrected data (R 2 =0.53). This is probably due, in part, to the uncertainty in the MODIS-derived AOT. Frequency histograms of the sPRI values extracted from the regional analysis show that, for MODIS deciduous pixels in the 9900 km 2 area, sPRI values differed between the two dates. For July 6, 2001, the mean sPRI was 0.60 (s.d. = 0.02) while it was 0.55 (s.d. = 0.01) for August 14, The red arrows on the histograms indicate the classes where the sPRIs from the tower data were located, for these respective days. Both tower sPRIs fell inside the mean ± 1 s.d. interval. These results indicate that sPRI calculated for the OA site at a specific date was representative of the sPRI of deciduous stands across the landscape. Conclusions: sPRI calculated from MODIS reflectance in bands 11 and 13 (backscatter), without atmospheric correction, showed a strong relationship with LUE from tower measurements. sPRI calculated with band 12 as a reference band did not show a relationship with LUE. For this site, the sPRI failed to track daily or seasonal variations in LUE, although it captured inter-annual variations. To adequately estimate LUE from space-borne remote sensing platforms and use it for modeling of ecosystem photosynthesis, further research is needed to determine whether the sPRI is responding to changes in canopy physiological function or changes in structural properties and the extent to which it is confounded with NDVI. The degree to which such a relationship holds across the Fluxnet-Canada transect needs to be determined as well. Time series of NDVI for the growing seasons at the OA site showed that once the canopy was fully-leaved, around day 160 (June 9), NDVI stayed relatively stable throughout the growing season. This reflects the absence of major changes in the structural characteristics of the canopy during this period. However, a time series of instantaneous LUE, computed as Gross Ecosystem Photosynthesis (GEP) / Absorbed Photosynthetically Active Radiation (APAR) derived from tower measurements for the same period, showed that LUE was very dynamic and responded to changes in environmental variables. In the most extreme cases, four fold variations in instantaneous LUE were observed. This makes it difficult to adequately estimate ecosystem productivity using LUE models driven by remotely sensed inputs Forward scatterBackscatter This research was supported by: NSERC, CFCAS, Biocap and NASA. R 2 = 0.76R 2 = 0.53 August 14, 2003 July 6, 2001 MODIS sPRI image - July 6, 2001 Landsat 5TM Land Cover Classification Daily LUE at Old Aspen site (12:00 PM local time) NDVI from tower sensors at Old Aspen site (12:00 PM local time) Day of year NDVI Day of year LUE (μmol C μmol-1 APAR) R 2 = 0.02R 2 = 0.08