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2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Bayesian Maximum Entropy Data Fusion of Field Observed LAI and Landsat ETM+ Derived LAI. Aihua Li liaihua33413@sina.com Yanchen Bo boyc@bnu.edu.cn

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2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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  1. 2011 IEEE International Geoscienceand Remote Sensing Symposium (IGARSS) Bayesian Maximum Entropy Data Fusion of Field Observed LAI and Landsat ETM+ Derived LAI Aihua Li liaihua33413@sina.com Yanchen Bo boyc@bnu.edu.cn Ling Chen chenling8247@126.com State Key Laboratory of Remote Sensing Science, Beijing, China Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing, China School of Geography, Beijing Normal University,Beijing, China

  2. Outline Introduction Methodology Application(Data) Results Discussion and Conclusions

  3. 1. Introduction • The leaf area index (LAI) characterizes the condition of vegetation growth and is a key input parameter of land-surface-dynamic-process models. • Several LAI products are accessible from different thermal sensors MODIS • These moderate resolution LAI products should be validated before application (Justice and Townshend 1994, Cihlar et al. 1997, Liang 2004)

  4. 1. Introduction Current Situation • In-situ measurements • Heterogeneity makes pixel scale validation not simply equivalent to field measurements average(Liang et al. 2002) • The accuracy of geostatistics methods to obtain LAI surface maps is limited to the number and the spatial distribution of measurement points. • High resolution LAI surface • Extensive cover regions • Lower accuracy MODIS Field LAI measurements and high resolution LAI surface maps are two kinds of so-called “true” data Landsat combine LAI2000 Problems are solved by combining these two types of data

  5. 1. Introduction Need • Accurate high resolution LAI reference maps are needed for the validation of coarser resolution satellite derived LAI • Regression analysis and Geostatistical methods: do not take account of the uncertainties of measurements and models • The uncertainties of obtained data and information are taken into account in the fusion, the result will be more objective MODIS Landsat Problem combine LAI2000 Our work: Integrating the ETM+ derived LAI and field measurements LAI based on BME

  6. 2. Methodology Soft data and hard data • Soft data: non-accurate ；Hard data: accurate • Soft data can be expressed in terms of interval values and probability statements in mathematical computation (Christakos 2000) BME : Probabilistic method • It can take account of the uncertainties associated with measurements and models. • In BME, the uncertainty is considered when the input data are not accurate.

  7. 3. Application Study Sites Harvard Forest (HARV) LTER Mixed hardwoods, Eastern hemlock,Redpine, Old-field meadow Bondville Agricultural Farmland (AGRO) Corn, Soybeans, Fallow KonzaPrairie Biological Station (KONZ) LTER Tallgrass, Shortgrass, Shrub, Gallery forest; grazing and burning regimes

  8. 3. Application Data Specifications of HARV site, AGRO site and KONZ site

  9. Creating soft data 1. Multiple measurements • Multiple field measurements can be processed as Gaussian probability soft data 2. Linear regression model • Field measurements based on ETM+ derived LAI • Variance of residuals • Interval soft data(Upper boundary and lower boundary) The regression model (trend line in red color) for Field LAI and corresponding ETM+ LAI: HARV (left), AGRO (middle), KONZ (right) Interval soft data

  10. 3. Application Selected Soft data The interval ETM+ LAI data (red and green, solid line is about the mean values) and the Gaussian probability field measurements data (blue): HARV (left), AGRO (middle), KONZ (right)

  11. covariance models The nested covariance models of different vegetation types Parameters of covariance models

  12. 3. Application Three cases based on BME

  13. 4. Results Prediction maps and original ETM+ LAI maps have very similar spatial pattern and distribution trend ETM+ LAI surface, BMEintervalMode,BMEprobMoments1 and BMEprobMoments2 prediction surfaces are shown from left to right respectively: HARV (up), AGRO (middle), KONZ (bottom)

  14. 4. Results Predicted LAI1, Predicted LAI2 and Predicted LAI3 are the results of BMEintervalMode, BMEprobMoments1 and BMEprobMoments2 respectively.

  15. Summary statistics of LAI predictions compared to field measurements • R2 and CR of BME methods are higher than those of ETM+ derived LAI and RMSE of BME is lower than those of ETM+ derived LAI. Bias is reduced by BMEintervalMode • and BMEprobMoments1.VO of BME method is less than that of ETM+ derived LAI.

  16. 5. Discussion and Conclusions • BME can: • Get rid of some extreme data and lower the RMSE and result in small variance. • Take account of the uncertainties associated with measurements and models • Combine data at different scale • However, field measurements for validation should not be used in inversion, but in this work, some field measurements may be applied in both validation and inversion. • Further study can be done in LAI inversion by linking high resolution remotely sensed imagery with field measurements to explore the potential of BME.

  17. Thanks for your attention! Some Comments….. July 27, 2011

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