1. 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Aihua Li liaihua33413@sina.comliaihua33413@sina.com Yanchen Bo boyc@bnu.edu.cnboyc@bnu.edu.cn Ling Chen chenling8247@126.comchenling8247@126.com Bayesian Maximum Entropy Data Fusion of Field Observed LAI and Landsat ETM+ Derived LAI 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 1. Introduction 2. Methodology 3. Application(Data) 4. Results 5. 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 SensorSpatial resolution Time resolution Time coverageRef. MODIS1KM8 Days2000-nowMyneni et al. 2002 MISR1KM8 Days2000-nowKnyazikhin et al. 1998, Hu et al. 2003 VEGETATION1KM10 Days1998-now Baret et al. 2007, Weiss et al. 2007, Deng et al. 2006 AVHRR0.25°30 Days1981-2001Chen et al. 2002 POLDER6KM10 Days11/1996-06/1997 04/2003-10/2003 Roujean and Lacaze 2002, Lacaze 2005  These moderate resolution LAI products should be validated before application (Justice and Townshend 1994, Cihlar et al. 1997, Liang 2004)

4. 1. Introduction  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 Current Situation MODIS Landsat LAI2000 combine Problems are solved by combining these two types of data Field LAI measurements and high resolution LAI surface maps are two kinds of so-called “true” data

5. 1. Introduction  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 Need MODIS Landsat LAI2000 combine Problem Our work: Integrating the ETM+ derived LAI and field measurements LAI based on BME

6. 2. Methodology 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) Soft data and hard data 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. Study Sites Harvard Forest (HARV) LTER Mixed hardwoods, Eastern hemlock,Red pine, Old-field meadow Bondville Agricultural Farmland (AGRO) Corn, Soybeans, Fallow Konza Prairie Biological Station (KONZ) LTER Tallgrass, Shortgrass, Shrub, Gallery forest; grazing and burning regimes 3. Application

8. Data SitesDatasets usedData obtained time HARV Field measurements2000-06-18,08-04 ETM+ LAI2000-08-04 ETM+ Land cover2000 AGRO Field measurements2000-05,07,08 ETM+ LAI2000-07-14,08-11 ETM+ Land cover2000 KONZ Field measurements 2000-06-07 to 06-08 2000-08-25 to 08-27 2000-10-12 to 10-13 ETM+ LAI 2000-06-06 to 08 2000-08-25 to 27 ETM+ Land cover2000 Specifications of HARV site, AGRO site and KONZ site 3. Application

9. Creating soft data Field measurements based on ETM+ derived LAI Variance of residuals Interval soft data(Upper boundary and lower boundary) Multiple field measurements can be processed as Gaussian probability soft data 1. Multiple measurements 2. Linear regression model The regression model (trend line in red color) for Field LAI and corresponding ETM+ LAI: HARV (left), AGRO (middle), KONZ (right) SiteSlopeInterceptR2R2 HARV0.611.920.41 AGRO0.980.04840.86 KONZ0.590.8350.29 Interval soft data

10. 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) 3. Application

11. The nested covariance models of different vegetation types BiomeNested Covariance ModelsParameters evergreen neddleleaf forest deciduous broadleaf forest mixed forest grassland open shrubland corn soybean Parameters of covariance models covariance models

12. Three cases based on BME methodsInput data BMEintervalMode ETM+ LAI: Interval soft data In-situ LAI : hard data the difference between maximum and mean estimation BMEprobMoments1 ETM+ LAI: Interval soft data In-situ LAI : hard data the difference between hard field measurements and soft field measurements BMEprobMoments1 ETM+ LAI: probability soft data In-situ LAI : probability data 3. Application

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

15. Summary statistics of LAI predictions compared to field measurements Sites Num. of plots MethodsR2R2 RMSEBiasCRVO HARV48 ETM+ LAI 0.570.688-0.0540.7541.290 BMEintervalMode 0.590.518-0.0300.7700.996 BMEprobMoments1 0.590.3440.0140.7660.656 BMEprobMoments2 0.570.351-0.3110.7540.657 AGRO19 ETM+ LAI 0.820.6310.0490.9051.070 BMEintervalMode 0.890.460.0990.9420.988 BMEprobMoments1 0.840.5820.0620.9171.050 BMEprobMoments2 0.820.623-0.2150.9061.060 KONZ19 ETM+ LAI 0.450.436-0.2750.6691.100 BMEintervalMode 0.890.114-0.0430.9450.657 BMEprobMoments1 0.780.157-0.0620.8830.628 BMEprobMoments2 0.450.216-0.3760.6710.548  R 2 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. July 27, 2011 Some Comments….. Thanks for your attention!