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Yujie Wang Geography Department, Boston University

Ph. D. dissertation Defense. Assessment of the MODIS LAI and FPAR Algorithm: Retrieval Quality, Theoretical Basis and Validation. Yujie Wang Geography Department, Boston University. Dissertation committee Ranga B. Myneni Yuri Knyazikhin Mark A. Friedl Curtis E. Woodcock

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Yujie Wang Geography Department, Boston University

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  1. Ph. D. dissertation Defense Assessment of the MODIS LAI and FPAR Algorithm: Retrieval Quality, Theoretical Basis and Validation Yujie Wang Geography Department, Boston University Dissertation committee Ranga B. Myneni Yuri Knyazikhin Mark A. Friedl Curtis E. Woodcock Jeffrey L. Privette 1 of 51

  2. Summary of Presentation • Motivation • Investigation of Retrieval Quality as a function of Input and Model Uncertainty • Parameterization of the Algorithm in Light of the Law of Energy Conservation • Validation of the MODIS LAI Product in Coniferous Forests of Ruokolahti, Finland • Concluding Remarks • Future Directions 2 of 51

  3. Other Works • Dowty, D., Frost, P., Lesolle, P., Midgley, G., Mukelabai, M., Otter, L., Privette, J., Ringrose, S., Scholes, B., Wang, Y., (2000), Summary of the SAFARI 2000 wet season field campaign along the Kalahari transect. The Earth Observer. 12:29-34. • Shabanov, N. V., Wang, Y., Buermann, W., Dong, J., Hoffman, S., Tian, Y. Knyazikhin, Y Gower, S. T. and Myneni, R. B., (2001),Validation of the radiative transfer principles of the MODIS LAI/FPAR algorithm with data from the Harvard forest, Remote Sens. Environ. (in review). • Myneni, R. B., Hoffman, S., Knyazikhin, Y. , Privette, J. L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G. R., Lotsch, A., Friedl, M., Morisette, J. T., Votava, P., Nemani, R. R. and Running, S. W., (2001),Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data, Remote Sens. Environ. (in press). • Tian, Y., Wang, Y., Zhang, Y., Knyazikhin, Y., Bogaert, J., and Myneni, R. B., (2001), Radiative Transfer Based Scaling of LAI/FPAR Retrievals From Reflectance Data of Different Resolutions, Remote Sens. Environ. (in press). 3 of 51

  4. Other Works (cont.) • Privette, J.L., Myneni, R. B., Knyazikhin, Y., Mukufute, M., Robert, G., Tian, Y., Wang, Y. and Leblanc, S.G., (2001), Early Spatial and Temporal Validation of MODIS LAI Product in Africa, (in press). • Buermann, W, Wang, Y., Dong, J., Zhou, L., Zeng, X., Dickinson, R. E., Potter, C. S. and Myneni, R. B., (2001), Analysis of a multi-year global vegetation leaf area index data set, J. Geophys. Res. (in press). • Tian, Y., Woodcock, C. E., Wang, Y., Privette, J. L., Shabanov, N. V., Zhou, L., Buermann, W., Dong, J., Veikkanen, B., Hame, T., Ozdogan M., Knyazikhin Y., and Myneni, R. B., (2001), Multiscale Analysis and Validation of the MODIS LAI Product over Maun, Botswana, I. Uncertainty assessment. Remote Sens. Environ. (Accepted Jan. 2002). • Tian, Y., Woodcock, C. E., Wang, Y., Privette, J. L., Shabanov, N. V., Zhou, L., Buermann, W., Dong, J., Veikkanen, B., Hame, T., Ozdogan M., Knyazikhin Y., and Myneni, R. B., (2001), Multiscale Analysis and Validation of the MODIS LAI Product over Maun, Botswana, II. Sampling strategy. Remote Sens. Environ. (Accepted Jan. 2002). 4 of 51

  5. LAI and FPAR are two key variables for climate and most model studies. They are operationally derived from measurements of the MODIS instrument aboard TERRA . How do uncertainties in input and model influence the performance of the MODIS LAI/FPAR algorithm? Is the parameterization of the law of energy conservation valid in the design of the algorithm? What is the uncertainty of the MODIS LAI product? Motivation 5 of 51

  6. Summary of Presentation • Motivation • Investigation of Retrieval Quality as a Function of Input and Model Uncertainty • Parameterization of the Algorithm in Light of the Law of Energy Conservation • Validation of the MODIS LAI Product in Coniferous Forests of Ruokolahti, Finland • Concluding Remarks • Future Directions Wang et. al. (2001), Investigation of product accuracy as a function of input and model uncertainties: Case study with SeaWiFS and MODIS LAI/FPAR algorithm, Remote Sens. Environ., 78:296-311. 6 of 51

  7. Data • Atmospherically corrected and monthly composited multispectral Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) surface reflectance data. • Spatial resolution: 8-km. • Spectral bands: Blue (443 nm), green (555 nm), red (670 nm) and NIR (865 nm). • Global biome classification map derived from AVHRR Pathfinder data (Myneni et. al., 1997). • 6 biome types: Grasses and cereal crops, shrubs, broadleaf crops, savannas, broadleaf forests and needle forests. 7 of 51

  8. Two Types of Uncertainties • Model Uncertainty • Determined by the range of natural variation in biophysical parameters not accounted by the model. • Uncertainty in the land surface reflectance • Determined by the in-orbit data errors and data processing to correct for atmospheric and other environmental effects. 8 of 51

  9. Inverse Problem || Model(LAItrue)-Observation || =  • Ideal Condition: if uncertainties  are known, true LAI can be solved accurately. || Model(LAItrue)-Observation ||   • In the algorithm, the uncertainty information is not available. Therefore, the above inequality is solved. 9 of 51

  10. MODIS LAI/FPAR Algorithm Formulation Input The algorithm retrieves distribution functions of all possible solutions that satisfy the above inequality. The mean values and their dispersions are taken as final solution. Output LAI=0.1 LAI=1 LAI=2 LAI=3 LAI=5 10 of 51

  11. Retrieval Index (RI): 3. Saturation Index (SI): Retrieval Quality • Dispersion: • The root mean square deviation of the solution distribution function. It indicates the reliability of the retrieved LAI/FPAR fields. 11 of 51

  12. Band Independent Uncertainty, Dispersion 12 of 51

  13. Band Independent Uncertainty, Retrieval Index 13 of 51

  14. Band Independent Uncertainty, Saturation Index 14 of 51

  15. Band Dependent Uncertainty and Overall Uncertainty Theoretical estimation of relative uncertainties in atmospherically corrected surface reflectances (Vermote, 2000) An overall uncertainty is defined as: 15 of 51

  16. Band Dependent Uncertainty, Dispersion 16 of 51

  17. Band Dependent Uncertainty, Retrieval Index 17 of 51

  18. Band Dependent Uncertainty, Saturation Index 18 of 51

  19. Test of Physics 19 of 51

  20. SeaWiFS Global LAI in January, April, July and October 20 of 51

  21. Conclusions • Uncertainties in land surface reflectances and models used in the algorithm determine the quality of retrieved LAI and FPAR fields. • Accurate information about uncertainty in surface reflectance and model can improve the retrieval quality. • The more the measured information and the more accurate this information, the more reliable and accurate is the algorithm output. 21 of 51

  22. Summary of Presentation • Motivation • Investigation of Retrieval Quality as a function of Input and Model Uncertainty • Parameterization of the Algorithm in Light of the Law of Energy Conservation • Validation of the MODIS LAI Product in Coniferous Forests of Ruokolahti, Finland • Concluding Remarks • Future Directions Wang et. al. (2002), Hyperspectral remote sensing of vegetation canopy leaf area index and foliage optical properties, Remote Sens. Environ., (submitted). 22 of 51

  23. rbs+tbs+abs=1 Black Soil Problem S Problem rs+ts+as=1 Radiative Transfer Equation Decomposition 23 of 51 *credit: Chandrasekhar, 1950.

  24. Canopy Structure Parameters r() Wavelength, nm () Wavelength, nm t() Wavelength, nm i() : interception 24 of 51

  25. 25 25 20 20 15 15 10 10 5 5 0 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Spectral Invariance of pt and pi Pi Pt f(p)=dF(p)/dp f(p)=dF(p)/dp pi pt p_values depend on canopy structure and illumination geometry *credit: Panferov et. al. 2001 25 of 51

  26. Uncollided and Collided Radiation • Canopy transmittance is the sum of collided and uncollided radiation arriving at the canopy bottom. • Uncollided radiation qt is radiation arriving at the canopy bottom without experiencing any collisions. It equals canopy transmittance t () when single scattering albedo is zero. • Collided radiation is the radiation which experienced at least one collision (t() – qt). • ()pt is the collided portion of canopy transmittance. • ()pi is the multi-collided portion of canopy interception. *Credit: Shabanov et. Al, 2002. 26 of 51

  27. Information Content of Hyperspectral Data *Parameters used in MODIS LAI/FPAR algorithm 27 of 51

  28. Data • The hyperspectral canopy transmittance and reflectance data measured in a 100x150m needle leaf forest plot in Ruokolahti, Finland will be used. 28 of 51

  29. Retrieval of Uncollided Radiation qt 29 of 51

  30. Biophysical Parameters related to qt • Fraction of beam radiation (fdir). • Leaf area index. • Retrieved LAI = 2.02 • Measured LAI =1.95 • Ground cover. 30 of 51

  31. Retrieval of Single Scattering Albedo Bivariate distribution function of solution of single scattering albedo Regression curve of the bivariate distribution function 31 of 51

  32. Retrieval of pt Value 32 of 51

  33. Influence of Soil Reflectance 33 of 51

  34. Conclusions • A small set of independent variables seem to suffice to describe their spectral response to incident solar radiation. • The spectra of soil reflectance and single scattering albedo, canopy transmittance and absorptance normalized by single scattering albedo, the portion of uncollided and collided canopy transmittance and normalized interception. • These variables satisfy a simple system of equations and constitute a set that fully describes the law of energy conservation in vegetation canopy at any wavelength of the solar spectrum. • The equation system is a closed system, which means once information on some of the variables is available, the rest can be retrieved through this system. 34 of 51

  35. Summary of Presentation • Motivation • Investigation of Retrieval Quality as a function of Input and Model Uncertainty • Parameterization of the Algorithm in Light of the Law of Energy Conservation • Validation of the MODIS LAI Product in Coniferous Forests of Ruokolahti, Finland • Concluding Remarks • Future Directions Wang et. al. (2002), Validation of the MODIS LAI Product in Coniferous Forest of Ruokolahti, Finland, Remote Sens. Environ., (in preparation). 35 of 51

  36. Strategy Field Measurements Compare with MODIS LAI product Fine resolution LAI map Fine resolution satellite image 36 of 51

  37. Data • Field measured LAI data • Ruokolahti, Finland needle leaf forest site • Air-borne and Satellite image • 2 m resolution air-borne CCD image • ETM+ data • MODIS LAI product 37 of 51

  38. Ruokolahti Field Campaign 38 of 51

  39. 50X50m 25*25m grid 100*150m 25*25m grid 150*100m Dense Young 1000m Regular 25*25m grid 200*200m 1000m Ruokolahti Campaign Sampling Strategy 39 of 51

  40. Pixel-by-pixel vs. Patch-by patch comparison • Pixel-by-pixel comparison • High geolocation error • Non-representative sampling • Patch-by-patch comparison • Reduced geolocation error • Small amount of sampling is sufficient to characterize mean Credit: Tian et. Al., 2002 40 of 51

  41. Image Segmentation ETM+ image over campaign site Segmentation result 41 of 51

  42. Correlation between Simple Ratio (SR) and Field-measured LAI Patch scale Pixel scale 42 of 51

  43. Patch Level Correlation between Field-measured LAI and Reduced Simple Ratio (RSR) • RSR includes Shortwave Infrared (SWIR) band. • RSR can suppress background influence and the difference between land cover types. (Brown et. al., 2000). • There is better correlation between field-measured LAI and RSR. 43 of 51

  44. Fine Resolution LAI Map 10 km area 1 km campaign site 44 of 51

  45. MODIS LAI and FPAR Algorithm at 30 m Resolution • At 30 m resolution, the algorithm retrievals are greater than field measurements in this site. The difference between them is a decreasing function of LAI. • The algorithm assumes no biome mixtures within the 30m resolution pixel. However, this assumption is violated at this site because the mixture of understory vegetation and needle forests. 45 of 51

  46. MODIS QA MODIS QA map in the 10x10 km area (day 177, 2000). Green: LAI value produced by the main algorithm; Red: LAI is produced by the backup algorithm; Blue: cloud contaminated pixel; Black: water or barren. 46 of 51

  47. Validation of MODIS LAI Product Contour plot of LAI aggregated from the fine resolution ETM+ LAI map Patch scale correlation between the MODIS and aggregated LAI map 47 of 51

  48. Conclusions • Patch scale comparison is more reliable than pixel scale comparison. • Improved correlation between field measurements and the reduced simple ratio suggests that shortwave infrared band may provide valuable information for needle leaf forests. • MODIS LAI algorithm can only works well for relatively pure pixels at 30 m resolution for needleleaf forests, improvements are needed. • Comparison of MODIS LAI product with aggregated fine resolution LAI map indicates satisfactory performance of the algorithm at coarse resolution. 48 of 51

  49. Concluding Remarks • Uncertainties in input spectral bands and models are critical for the retrieval of biophysical parameters of highest possible quality. Their use can increase the number of high quality retrievals . • Assessment of the parameterization of the algorithm in light of the law of energy conservation indicates that spectra of soil and single scattering albedo combined with canopy interception, transmittance and their collided portions at a fixed reference wavelength are sufficient to simulate the spectral response of a vegetation canopy to incident solar radiation. They satisfy a closed equation system. • Investigation of the relationship between field data on LAI and 30m ETM+ images indicates that comparisons at the patch level are more reliable than the pixel level. Comparisons indicate the need for improvements in the algorithm for needleleaf forests at fine resolution. The MODIS LAI product agrees with ETM+ derived LAI at coarse resolution in Ruokolahti needle forests site. 49 of 51

  50. Future Directions • It is possible to include soil reflectance in the system of equations I derived. This may result in more accurate solutions and also the possibility of retrieving spectral soil reflectance using hyperspectral data. • Shortwave infrared data should perhaps be included in LAI and FPAR retrievals over boreal forests, as there is now considerable evidence to this effect. • More field LAI data should be collected at different locations and periods, representative of the major vegetation types and their phenology, to comprehensively validate both the algorithm and the products. 50 of 51

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