1. Mark Friedl 1, Xiaoyang Zhang 2 1 Department of Geography and Environment, Boston University 2 ERT at NOAA/NESDIS/STAR NASA MEASURES #NNX08AT05A Science Review Panel Meeting Biosphere 2, Tucson, AZ - January 4-5, 2011 Vegetation Phenology and Vegetation Index Products from Multiple Long Term Satellite Data Records Assessment and Validation of MEASURES Global Land Surface Phenology Product

2. Goals Evaluate available data sets –Opportunistic Design & collect new in-situ data sets –Spatial, temporal, geographic, ecological sampling Community participation –AGU, IALE, Pehnoloy 2010, CEOS LPV… MEASURES VIP ESDRs Science Review Panel -2-

3. Motivation What is LSP Measuring? –Pixel to regional to global patterns –Climate sensitivity of ecosystems –Interannual variation, trends, disturbance regimes… Ecologists –Often, but not always, want more precise & refined information than LSP provides: Bud swelling, leaf unfolding, flowering, senescence… Modelers –Timing, magnitude, rate of phenologic events Needs –More precise description of LSP units (DOY of what?) –Improved characterization of precision and accuracy Growing season length from MODIS MEASURES VIP ESDRs Science Review Panel -3-

4. Outline Goal: Assessment of Land Surface Phenology –How? And what information do “assessments” provide? Data sources: –Visual Observations by field (citizen) scientists Hubbard Brook, Harvard Forest LTERs, other sources National Phenology Network –Light Interception Bartlett Forest, Harvard Forest, Fluxnet sites –Webcams: Distributed network Issues - What next? MEASURES VIP ESDRs Science Review Panel -4-

5. Measures Land Surface Phenology Image credit: http://tbrs.arizona.edu/project/MODIS/evi.php Challenge – what are we measuring? MEASURES VIP ESDRs Science Review Panel -5-

6. LTER Data: e.g., Hubbard Brook Provide continuous record of phenology observations starting in 1989 Canopy Development Index (0-4); Weekly; 6 sites: 3 samples for each of 3 trees CDI EVI MEASURES VIP ESDRs Science Review Panel -6-

7. Hubbard Brook CDI vs MODIS EVI Correlation still obvious, but what is specific quantitative metric for assessment? 2001 20022004 Substantial uncertainty, subjectivity and variance in CDI data Note that CDI also captures variability below sensor resolution MEASURES VIP ESDRs Science Review Panel -7-

8. Summary of CDI vs MLCD 2001-2006 Variability in CDI relative to MODIS G x probably reflects combination of sampling frequency, and uncertainty in both MODIS and CDI data Likewise at Harvard Forest, but with a different set of issues MEASURES VIP ESDRs Science Review Panel -8-

9. Light Interception Above & below canopy PAR –IPAR as surrogate for phenology Two sites –Harvard Forest; Bartlett Forest After: Richardson, Jenkins, Braswell…. MEASURES VIP ESDRs Science Review Panel -9-

10. EVI vs Light Interception at Bartlett Forest Continuous, objective, integrates over large canopy area, but Essentially measuring canopy closure or “gap fraction” Not really sensitive to canopy coloration (senescence) –May be associated with top of canopy sensor FOV MEASURES VIP ESDRs Science Review Panel -10-

11. Webcam Phenology Mix of phenocam core network (short, high quality) and affiliated sites from archived data (long, variable quality) ~35 sites; 90 cameras total Images courtesy of Andrew Richardson; http://klima.sr.unh.edu/ MEASURES VIP ESDRs Science Review Panel -11-

12. Phenocam Vegetation Indices Image analysis (RGB channel extraction) to quantify phenological dynamics –Richardson et al., 2007, Oecologia; Richardson et al, 2009, Ecological Applications Images courtesy of Andrew Richardson Current Focus: Extracting high quality vegetation indices What parts of images to extra data from? What vegetation indices? How to filter noise & retain information? MEASURES VIP ESDRs Science Review Panel -12-

13. Phenocam Example: Mammoth Cave, Ky Looks promising: clear phenology, modest noise level, agrees well w/MODIS MEASURES VIP ESDRs Science Review Panel -13-

14. Phenocam Example: Shining Rock, NC Still pretty good but a bit noisier and complicated MEASURES VIP ESDRs Science Review Panel -14-

15. Phenocam Processing 1. Selection of ROI Selection of representative region of interest matters Sky, shadow, foreground, background, conifer, broadleaf… Different ROI’s have different VI’s values and levels of noise MEASURES VIP ESDRs Science Review Panel -15-

16. Phenocam Processing 2. Noise Reduction Illumination, sky conditions can significantly bias VI’s Depending on timing of sky conditions….. Simple filtering based on quantiles works quite well MEASURES VIP ESDRs Science Review Panel -16-

17. Phenocam Processing 3. Camera Saturation Possible strategy to adjust for illumination effects – identify saturated images and correct via HLS MEASURES VIP ESDRs Science Review Panel -17-

18. Conclusions Multiple sources of data, but very few are extensive in time or space or well suited to needs of validating LSP –Field observers: NPN, LTER data: Issues of scale –Light interception: Need to explore availability at Fluxnet sites –Webcams: Probably the best fit, but challenges wrt pre-processing, radiometry of cameras versus satellite sensors Current focus on: –Acquiring and processing more webcam data –Methods to “compare” ground against satellite estimates of transition dates MEASURES VIP ESDRs Science Review Panel -18-

19. Discussion & Conclusions Need to make progress defining LSP –LSP of what? fPAR? LAI? Understory vs Overstory? –Phenology of VI imprecise and therefore challenging to many users; SOS vs budburst, leaf unfolding….? Need data sets that are comparable, quantitative, and objective for comparison to remote sensing –Subjective metrics necessary but not sufficient. –Sample designs for comparison with satellite data –Need formal methods define nature, precision, and accuracy of LSP products to the community? Need to continue to push for broader community involvement (i.e., outside remote sensing) MEASURES VIP ESDRs Science Review Panel -19-